阅读说明：本技术 用于表征、诊断并治疗患者的健康状况的系统和微管导电率以及其使用方法 (System and microtubule conductivity for characterizing, diagnosing and treating a health condition of a patient and methods of use thereof ) 是由 弗雷德里科·佩雷戈·科斯塔 达 席尔瓦 弗拉维奥·苏亚雷斯·科雷亚 杰克·亚当·图辛斯基 安东 于 2018-10-12 设计创作，主要内容包括：披露了一种用于通过将由处理系统同步的血液动力学参数(Hdp)监测系统和射频发生器集成来治疗癌症的系统和方法。该系统能够标识患者在暴露于低能量调幅电磁场频率时的健康状况特异性Hdp变化值改变。经调制频率的暴露影响患者的细胞功能(如微管导电率)或功能障碍并且可以为该患者提供治疗效果以及预测和预后效果两者,从而提供对特定小病或疾病(如肝细胞癌)的治疗以及关于该治疗可能具有的有效性的预测结果两者。频率文库的构建可以用于通过具体地针对患者自动调谐治疗方案来更高效且有效地诊断并治疗该患者的健康状况。(A system and method for treating cancer by integrating a hemodynamic parameter (Hdp) monitoring system synchronized by a processing system with a radio frequency generator is disclosed. The system is capable of identifying a change in a health-specific Hdp change value of a patient when exposed to a low-energy amplitude-modulated electromagnetic field frequency. Exposure to the modulated frequency affects cellular function (e.g., microtubule conductivity) or dysfunction in the patient and can provide both a therapeutic effect and a predictive and prognostic effect for the patient, thereby providing both treatment of a particular ailment or disease (e.g., hepatocellular carcinoma) and a predictive outcome as to the effectiveness that the treatment may have. The construction of a frequency library can be used to diagnose and treat the health condition of a patient more efficiently and effectively by automatically tuning the treatment regime specifically for that patient.)

1. A system for diagnosing a health condition of a patient, the system comprising:

an electrocardiographic monitoring system configured to detect, measure and store first values of an R-R interval value exhibited by the patient during a basal or non-exposure period in which the patient is exposed to a low energy electromagnetic carrier output signal and second values of an R-R interval value exhibited by the patient during or after an exposure period;

an electrically driven generator adapted to be actuated to generate the low energy electromagnetic carrier output signals for exposure to or application to the patient during the exposure time period; and

a processing system configured to synchronize the electrocardiography monitoring system and the electrically driven generator.

2. The system of claim 1, wherein the processing system is internal to the electrocardiography monitoring system.

3. The system of claim 1, wherein the processing system is external to the electrocardiography monitoring system.

4. The system of claim 1, wherein the processing system is configured to sense and identify one or more of specific electromagnetic field amplitude modulation frequencies.

5. The system of claim 1, further comprising an interface controller in operable communication with the electrocardiogram monitoring system and the electrically driven generator.

6. The system of claim 1, wherein the electrocardiographic monitoring system is configured to measure one or more R-R interval values, calculate one or more heart rate variability values, record one or more heart rate variability values, identify one or more particular electromagnetic field amplitude modulation frequencies, or a combination thereof.

7. The system of claim 6, wherein the processing system integrates the one or more heart rate variability values with a smart learning library and the electrically driven generator.

8. The system of claim 1, wherein the low energy electromagnetic carrier output signals include an amplitude modulation frequency in a range of about 0.01Hz to about 150 kHz.

9. An electrocardiography monitoring system configured to detect, measure and store first values of R-R interval values exhibited by one or more surrogate patients during a basal or non-exposure period in which the one or more surrogate patients were exposed to low-energy electromagnetic output signals and second values of R-R interval values exhibited by the one or more surrogate patients during or after an exposure period, the system comprising:

an electrically driven generator adapted to be actuated to generate low energy electromagnetic carrier output signals for exposure to or application to the surrogate patients during the exposure time period.

10. The system of claim 9, further comprising a processing system configured to synchronize the electrocardiogram monitoring system and the electrically driven generator.

11. The system of claim 10, wherein the processing system is internal to the electrocardiography monitoring system.

12. The system of claim 10, wherein the processing system is external to the electrocardiography monitoring system.

13. The system of claim 10, wherein the processing system is configured to sense and identify one or more of specific electromagnetic field amplitude modulation frequencies.

14. The system of claim 9, further comprising an interface controller in operable communication with the electrocardiogram monitoring system and the electrically driven generator.

15. The system of claim 9, wherein the electrocardiographic monitoring system is configured to measure one or more R-R interval values, calculate one or more heart rate variability values, record one or more heart rate variability values, identify one or more particular electromagnetic field amplitude modulation frequencies, or a combination thereof.

16. The system of claim 15, wherein the processing system integrates the one or more heart rate variability values with a smart learning library and the electrically driven generator.

17. The system of claim 9, wherein the low energy electromagnetic carrier output signals include an amplitude modulation frequency in a range of about 0.01HZ to about 150 kHz.

18. A system for diagnosing a health condition of a patient, the system comprising:

a hemodynamic parameter monitoring system configured to detect, measure and record first values for each of a plurality of hemodynamic parameters exhibited by a patient during an exposure time period, wherein the exposure time period comprises a time period during which the patient is exposed to one or more of electromagnetic frequency signals;

an electrically driven frequency generator adapted to generate one or more electromagnetic frequency signals during the exposure time period, wherein the one or more electromagnetic frequency signals are configured to affect cell function; and

a processing system configured to:

synchronizing the hemodynamic parameter monitoring system with the frequency generator,

automatically tuning a carrier signal to adjust forward energy delivered to the patient, and

the frequency generator is instructed to expose the patient to each of the one or more electromagnetic frequency signals by modulating an amplitude of the carrier signal to produce an amplitude modulated electromagnetic frequency signal.

19. The system of claim 18, wherein the amplitude modulated electromagnetic signal is selected within a range of 10Hz to 2,000 Hz.

20. The system of claim 18, wherein the cellular function is microtubule conductivity.

21. The system of claim 18, wherein the plurality of hemodynamic parameters includes one or more of: RR interval, heart rate, systolic pressure, diastolic pressure, median blood pressure, pulse pressure, stroke volume, cardiac output, and total peripheral resistance.

22. The system of claim 18, wherein the processing system is further configured to actuate the frequency generator to generate one or more highly specific radio frequency carrier signals based on at least the plurality of first values of each of the plurality of hemodynamic parameters.

23. The system of claim 18, wherein the frequency generator comprises a programmable generator.

24. The system of claim 23, wherein the programmable generator comprises one or more controllable generator circuits, wherein each controllable generator circuit is configured to generate one or more highly specific radio frequency carrier signals.

25. The system of claim 24, wherein each controllable generator circuit includes an amplitude modulation frequency control signal generator configured to control amplitude modulation variation of the one or more highly specific radio frequency carrier signals.

26. The system of claim 18, wherein the carrier signal comprises a 27.12MHz signal.

27. The system of claim 18, further comprising a computing device operatively connected to the processing system and configured to store at least one machine learning algorithm configured to output one or more variables for automatically tuning the carrier signal.

28. A system for treating cancer in a patient, the system comprising:

a hemodynamic parameter monitoring system configured to detect, measure and record first values for each of a plurality of hemodynamic parameters exhibited by a patient during an exposure period, wherein the exposure period comprises a period of time during which the patient is exposed to electromagnetic frequency signals;

an electrically driven frequency generator adapted to generate one or more electromagnetic signals during the exposure time period, wherein the one or more electromagnetic signals are configured to affect microtubule conductivity; and

a processing system configured to:

synchronizing the hemodynamic parameter monitoring system with the frequency generator,

automatically tuning a carrier signal to adjust forward energy delivered to the patient, and

the frequency generator is instructed to expose the patient to each of the plurality of electromagnetic frequency signals by modulating an amplitude of the carrier signal to produce a desired electromagnetic frequency signal, wherein the amplitude modulated electromagnetic signal is selected in a range of 10Hz to 2,000 Hz.

29. A method of diagnosing a health condition of a patient, the method comprising:

measuring, by an electrocardiogram monitoring system, a plurality of first values of R-R interval values;

calculating a heart rate variability value exhibited by the patient during exposure of the patient to a radio frequency carrier signal of a highly specific frequency;

processing the patient's heart rate variability value and exposure to the radio frequency carrier signals at the highly specific frequencies;

recording representative heart rate variability values exhibited by one or more surrogate patients during their exposure to the radio frequency carrier signals of the highly specific frequencies;

storing the plurality of first values and a plurality of second values of the representative heart rate variability value; and

these representative heart rate variability values from pre-diagnosed or diagnosed patients are transferred to a library for further processing.

30. The method of claim 29, wherein the highly specific frequency radio frequency carrier signals comprise amplitude modulated frequencies in a range of about 0.01HZ to about 150 kHz.

31. A method of diagnosing a health condition of a patient, the method comprising:

determining, by a processing system, a plurality of electromagnetic frequency signals to be applied to the patient;

automatically tuning, by the processing system, a carrier to balance forward energy delivered to the patient;

exposing, by a frequency generator operatively connected to the processing system, the patient to each of the plurality of electromagnetic frequency signals by modulating an amplitude of a carrier signal to produce a desired electromagnetic frequency signal configured to affect cellular function of the patient;

measuring, by a hemodynamic parameter monitoring system, first values of hemodynamic parameters exhibited by a patient during exposure of the patient to the plurality of electromagnetic frequency signals; and

the plurality of first values are analyzed to provide a diagnosis of the health condition of the patient.

32. The method of claim 31, wherein the amplitude modulated signal is selected in a range of 10Hz to 2,000 Hz.

33. The method of claim 31, further comprising:

a particular frequency response to a single frequency exposure of an electromagnetic amplitude modulation signal is identified as non-reactive, or post-reactive.

34. The method of claim 31, wherein the plurality of hemodynamic parameters includes one or more of: RR interval, heart rate, systolic pressure, diastolic pressure, median blood pressure, pulse pressure, stroke volume, cardiac output, and total peripheral resistance.

35. The method of claim 31, further comprising:

a highly specific radio frequency carrier signal is determined that results in a significant hemodynamic parameter value change for the patient.

36. The method of claim 31, wherein the carrier signal comprises a 27.12MHz signal.

37. The method of claim 31, wherein automatically tuning comprises adjusting the forward energy of the carrier signal based on an output of a machine learning algorithm.

38. The method of claim 31, wherein the cellular function is microtubule conductivity.

39. A method of treating cancer, the method comprising:

applying one or more high frequency carrier signals and at least one amplitude modulation control signal for controlling an amplitude modulation variation of the one or more high frequency carrier signals, wherein an amplitude modulation frequency is selected in the range of 0.01Hz to 150 kHz.

40. The method of claim 39, wherein the amplitude modulation frequency is selected in the range of 10Hz to 2,000 Hz.

41. The method of claim 39, wherein the at least one amplitude modulation control signal modulates micropipe ion conductivity.

42. A programmable generator capable of being activated by electrical power and structured to affect a cellular function or dysfunction in a warm-blooded mammalian subject, the programmable generator comprising:

at least one controllable low energy electromagnetic energy generator circuit for generating one or more high frequency carrier signals, wherein the at least one generator circuit comprises:

at least one amplitude modulation control signal generator for controlling the amplitude modulation variation of the one or more high frequency carrier signals, an

At least one programmable amplitude modulation frequency control signal generator for controlling the frequency at which amplitude modulations are generated, wherein each programmable amplitude modulation frequency control signal generator is adapted to accurately control the frequencies at which the amplitude modulations are generated to within an accuracy of at least 1000 parts per million with respect to a reference amplitude modulation frequency selected in a range of 0.01Hz to 150 kHz;

at least one data processor constructed and arranged for communication with the at least one generator circuit and for receiving control information from a control information source; and

a connection location configured to connect to a conductive applicator configured to apply one or more amplitude modulated low energy emissions to the warm-blooded mammalian subject at a program controlled frequency, wherein the reference amplitude modulated frequencies are selected according to the health condition of the warm-blooded mammalian subject.

Disclosure of Invention

A health device atlas (Hdp) monitor or any other device capable of recording hemodynamic or cardiac electrical activity may be used to sense and monitor various Hdp values. Various Hdp values may be used to diagnose a cardiovascular condition in a patient. Hdp measurements, typically taken in conjunction with an Electrocardiogram (ECG), may include measurements of Stroke Volume (SV), Stroke Index (SI) and Cardiac Output (CO). Such measurements are indicated for the diagnosis and treatment of patients suffering from cardiac disorders such as heart failure, hypertension, coronary artery disease and pericardial disease, as well as obstructive lung and pleural diseases and renal insufficiency.

Electrocardiography, which involves measuring an indicator of heart rate, may also be used to measure the biopotentials generated by the electrical signals that control the expansion and contraction of the heart chamber.

Heart Rate Variability (HRV) is the variation of physiological phenomena during the time interval between heartbeats. It is measured by the change in the beat-to-beat interval. Other terms have also been used to describe oscillations in successive cardiac cycles, such as heart cycle variability, R-wave variability, and R-wave interval (R-R interval or RRI) blood flow velocity maps. Example processes for identifying Heart Rate Variability (HRV) variables include RRI value recording, computer digitization, artifact identification, HRV data editing, HRV interval exclusion, HRV data sorting and interpolation, and sampling for time-domain heart rate variability and frequency-domain heart rate variability.

A summary of the above techniques can be found, for example, in the European Heart Association and the particular group of the North American Society of Pacing and Electrophysiology (Task Force of the European Society of Cardiology and the North American Society of pacifying and Electrophysiology) the European Heart journal (European Heart journal)17(1996) page 354-381.

Some conventional systems employing one or more of the above techniques may enable a user to diagnose the presence or absence of tumor cell growth or cancer in a patient. In some cases, the identity of the cell growth or tumor may also be identified. However, such systems lack the ability to use measured Hdp values and HRVs to determine a specific type of cancer or any other form of health condition of the patient.

In embodiments, a system for diagnosing a health condition of a patient may include an ECG monitoring system configured to detect, measure and store first values of RRI values exhibited by the patient during a basal or non-exposure period in which the patient is exposed to a low energy electromagnetic carrier output signal and second values of RRI values exhibited by the patient during or after an exposure period. The system may further comprise: an electrically driven generator adapted to be actuated to generate the low energy electromagnetic carrier output signals for exposure to or application to the patient during the exposure time period; and a processing system that can be configured to synchronize the ECG monitoring system and the electrically driven generator. In some embodiments, the processing system may be internal or external to the ECG monitoring system. In some embodiments, the processing system may be configured to sense and identify one or more of the particular electromagnetic field amplitude modulation frequencies. The system may further include an interface controller that may be in operable communication with the ECG monitoring system and the electrically driven generator. In some embodiments, the ECG monitoring system may measure one or more RRI values, calculate one or more HRV values, record one or more HRV change values, identify one or more particular electromagnetic field amplitude modulation frequencies, or a combination thereof. In some embodiments, the processing system may integrate the one or more HRV values with an intelligent learning library and the electrically driven generator. In some embodiments, the low energy electromagnetic carrier output signals of the system may include an amplitude modulation frequency in a range of about 0.01Hz to about 150 kHz.

In some embodiments, an ECG monitoring system may be configured to detect, measure, and store first values of RRI values exhibited by one or more surrogate patients during a basal or non-exposure period during which the one or more surrogate patients were exposed to a low energy electromagnetic output signal and second values of RRI values exhibited by the one or more surrogate patients during or after the exposure period. In some embodiments, the system may comprise an electrically driven generator adapted to be actuated to generate low energy electromagnetic carrier output signals for exposing or applying the low energy electromagnetic carrier output signals to the surrogate patients during said exposure time period. The system may further include a processing system configured to synchronize the ECG monitoring system and the electrically driven generator, wherein the processing system may be internal or external to the ECG monitoring system. In some embodiments, the processing system may be configured to sense and identify one or more of the particular electromagnetic field amplitude modulation frequencies. In further embodiments, the system may include an interface controller in operable communication with the ECG monitoring system and the electrically driven generator. In some embodiments, the ECG monitoring system may be configured to measure one or more RRI values, calculate one or more HRV values, record one or more HRV change values, identify one or more particular electromagnetic field amplitude modulation frequencies, or a combination thereof. The processing system may integrate the one or more HRV values with an intelligent learning library and the electrically driven generator. In some embodiments, these low energy electromagnetic carrier output signals may include an amplitude modulation frequency in the range of about 0.01HZ to about 150 kHz.

In some embodiments, a system for diagnosing a health condition of a patient may include a hemodynamic parameter (Hdp) monitoring system that may be configured to detect, measure, and record first values for each of a plurality of hemodynamic parameters exhibited by a patient during an exposure period, wherein the exposure period may include a period of time during which the patient is exposed to one or more of electromagnetic frequency signals. The system may further comprise an electrically driven frequency generator adapted to generate one or more electromagnetic frequency signals during the exposure time period, wherein the one or more electromagnetic frequency signals may be configured to affect cell function. The system may further include a processing system, which may be configured to: synchronizing the Hdp monitoring system and the frequency generator; automatically tuning a carrier signal to adjust forward energy delivered to the patient; and instructing the frequency generator to expose the patient to each of the one or more electromagnetic frequency signals by modulating an amplitude of the carrier signal to produce an amplitude modulated electromagnetic frequency signal. In some embodiments, the amplitude modulated electromagnetic signal may be selected in a range of 10Hz to 1,000 Hz. In further embodiments, the cellular function may be microtubule conductivity. In further embodiments, the plurality of hemodynamic parameters may include one or more of: RR interval, heart rate, systolic pressure, diastolic pressure, median blood pressure, pulse pressure, stroke volume, cardiac output, and total peripheral resistance. In some embodiments, the processing system may be configured to actuate the frequency generator to generate one or more highly specific frequency Radio Frequency (RF) carrier signals based on at least the plurality of first values for each of the plurality of hemodynamic parameters. In some embodiments, the frequency generator may comprise a programmable generator. In further embodiments, the programmable generator may comprise one or more controllable generator circuits, wherein each controllable generator circuit may be configured to generate one or more highly specific frequency RF carrier signals. In some embodiments, each controllable generator circuit may include an Amplitude Modulation (AM) frequency control signal generator that may be configured to control amplitude modulation variations of the one or more highly specific frequency RF carrier signals. In a further embodiment, the carrier signal may be a 27.12MHz signal. In some embodiments, the system may further include a computing device that may be operably connected to the processing system and may be configured to store at least one machine learning algorithm configured to output one or more variables for automatically tuning the carrier signal.

In some embodiments, a system for treating cancer in a patient may include a hemodynamic parameter (Hdp) monitoring system that may be configured to detect, measure, and record first values for each of a plurality of hemodynamic parameters exhibited by a patient during an exposure period, wherein the exposure period may include a period of time during which the patient is exposed to electromagnetic frequency signals. In some embodiments, the system may further comprise an electrically driven frequency generator adapted to generate one or more electromagnetic signals during the exposure time period, wherein the one or more electromagnetic signals may be configured to affect microtubule conductivity. In further embodiments, a processing system may be included and configured to: synchronizing the Hdp monitoring system and the frequency generator; automatically tuning a carrier signal to adjust forward energy delivered to the patient; and instructing the frequency generator to expose the patient to each of the plurality of electromagnetic frequency signals by modulating an amplitude of the carrier signal to produce a desired electromagnetic frequency signal. In some embodiments, the amplitude modulated electromagnetic signal may be selected in a range of 10Hz to 1,000 Hz.

In some embodiments, a method of diagnosing a health condition of a patient may comprise: measuring, by the ECG monitoring system, a plurality of first values of RRI values; calculating an HRV value exhibited by the patient during exposure of the patient to a radio frequency carrier signal of a highly specific frequency; processing the patient's HRV values and exposure to the radio frequency carrier signals at the highly specific frequencies; recording representative HRV change values exhibited by one or more surrogate patients during exposure of the one or more surrogate patients to the radio frequency carrier signals of the highly specific frequencies; storing the plurality of first values and a plurality of second values of the representative HRV change value; and these representative HRV change values from pre-diagnosed or diagnosed patients are passed to the library for further processing. In some embodiments, these highly specific frequency radio frequency carrier signals may include amplitude modulated frequencies in the range of about 0.01HZ to about 150 kHz.

In some embodiments, a method of diagnosing a health condition of a patient may comprise: determining, by a processing system, a plurality of electromagnetic frequency signals to be applied to the patient; automatically tuning, by the processing system, a carrier to balance forward energy delivered to the patient; exposing, by a frequency generator operatively connected to the processing system, the patient to each of the plurality of electromagnetic frequency signals by modulating an amplitude of a carrier signal to produce a desired electromagnetic frequency signal, which may be configured to affect cellular function of the patient; measuring, by a hemodynamic parameter (Hdp) monitoring system, first values of hemodynamic parameters exhibited by a patient during exposure of the patient to the plurality of electromagnetic frequency signals; and analyzing the plurality of first values to provide a diagnosis of the health condition of the patient. In some embodiments, the amplitude modulated signal may be selected in the range of 10Hz to 1,000 Hz. In some embodiments, the method may further include identifying a particular frequency response to a single frequency exposure of the electromagnetic amplitude modulation signal as non-reactive, or post-reactive. In further embodiments, the plurality of hemodynamic parameters may include one or more of: RR interval, heart rate, systolic pressure, diastolic pressure, median blood pressure, pulse pressure, stroke volume, cardiac output, and total peripheral resistance. In some embodiments, the method may include determining a highly specific frequency RF carrier signal that causes a significant Hdp value change for the patient. In further embodiments, the carrier signal may comprise a 27.12MHz signal. In some embodiments, automatically tuning may include adjusting the forward energy of the carrier signal based on an output of a machine learning algorithm. In further embodiments, the cellular function may be microtubule conductivity.

In some embodiments, a method of treating cancer may include administering one or more high frequency carrier signals and at least one amplitude modulation control signal for controlling an amplitude modulation variation of the one or more high frequency carrier signals, wherein an amplitude modulation frequency may be selected in a range of 0.01Hz to 150 kHz. In some embodiments, the amplitude modulation frequency may be selected in the range of 10Hz to 1,000 Hz. In further embodiments, the at least one amplitude modulated control signal may modulate the micropipe ion conductivity.

In some embodiments, a programmable generator may be activated by electrical power and structured to affect cellular function or dysfunction in a warm-blooded mammalian subject. In some embodiments, the programmable generator may comprise at least one controllable low energy electromagnetic energy generator circuit for generating one or more high frequency carrier signals, wherein the at least one generator circuit may comprise: at least one amplitude modulation control signal generator for controlling amplitude modulation variation of the one or more high frequency carrier signals; and at least one programmable amplitude modulation frequency control signal generator for controlling the frequency at which amplitude modulation is generated. In some embodiments, each programmable amplitude modulation frequency control signal generator may be adapted to accurately control the frequencies at which the amplitude modulations are generated to within an accuracy of at least 1000 parts per million with respect to a reference amplitude modulation frequency selected in the range of 0.01Hz to 150 kHz. In some embodiments, the programmable generator may further comprise: at least one data processor constructed and arranged for communication with the at least one generator circuit and for receiving control information from a control information source; and a connection location configured to connect to a conductive applicator that can be configured to apply one or more amplitude modulated low energy emissions to the warm-blooded mammalian subject at a program controlled frequency, wherein the reference amplitude modulated frequencies are selectable according to a health condition of the warm-blooded mammalian subject.

Drawings

Fig. 1 depicts an example usage scenario of a portable medical device as described herein, according to an embodiment.

Fig. 2 depicts an alternative example usage scenario of a portable medical device according to an embodiment.

Fig. 3 illustrates an example circuit diagram of a portable medical device according to an embodiment.

Fig. 4 illustrates an example circuit diagram of a signal modulator according to an embodiment.

Fig. 5 illustrates an example circuit diagram of an amplifier according to an embodiment.

FIG. 6 illustrates an example circuit diagram of a power monitor, according to an embodiment.

Fig. 7 depicts an illustrative schematic structure of an integrated medical system according to an embodiment.

Fig. 8 depicts an illustrative block diagram of an integrated system according to an embodiment.

Fig. 9 shows a flow diagram of a signal modulator according to an embodiment.

Fig. 10 shows a flow diagram of a signal modulator according to an embodiment.

Fig. 11 shows a flow diagram of a signal modulator according to an embodiment.

Fig. 12 depicts an illustrative patient during an experimental setup for continuous monitoring of hemodynamic parameters before and during Amplitude Modulated (AM) Radio Frequency (RF) electromagnetic field (EMF) exposure, according to an embodiment.

Fig. 13A-13E illustrate Amplitude Modulated (AM) Radio Frequency (RF) electromagnetic fields (EMF) in accordance with various embodiments.

Fig. 14 shows a capacitor as a frequency-specific demodulation system.

Fig. 15 shows phase-locked synchronization of demodulation signal discharge.

Fig. 16A illustrates a signal exposure scheme according to an embodiment.

Fig. 16B illustrates a further signal exposure scheme according to an embodiment.

Fig. 17 illustrates a flow chart representing hemodynamic recording that is performed continuously during a non-exposure period and an exposure period in a double-blind manner, in accordance with an embodiment.

Fig. 18A illustrates a graph representing a signal exposure scheme over the course of one or two days, according to an embodiment.

Fig. 18B shows a graph representing the signal exposure scheme under reflected energy measurement over the course of one or two days.

Fig. 18C illustrates a graph representing a signal exposure scheme over the course of one or two days, according to an embodiment.

Fig. 18D illustrates a graph representing a signal exposure scheme over the course of a day, according to an embodiment.

Fig. 18E illustrates a graph representing a signal exposure scheme over the course of a day according to further embodiments.

Fig. 19 shows a table of eleven illustrative hemodynamic parameters measured simultaneously during each heartbeat, according to an embodiment.

Fig. 20 shows a table of eleven illustrative hemodynamic parameters measured simultaneously during each heartbeat after exposure to a signal frequency, according to an embodiment.

Fig. 21 illustrates hemodynamic parameters and signal decomposition during homeostasis and stress response according to an embodiment.

Fig. 22A illustrates hemodynamic parameters and signal decomposition during homeostasis and stress response according to further embodiments.

Fig. 22B illustrates a time domain analysis of R-R interval and heart rate variability, according to an embodiment.

Fig. 23A shows Poincare plots (Poincare plots) from a ten-minute electrocardiogram, in accordance with an embodiment.

Fig. 23B illustrates poincare plot correlation under fourier transform, in accordance with an embodiment.

Fig. 24A illustrates a hemodynamic parameter response to signal frequency in a subject with hepatocellular carcinoma, according to an embodiment.

Figure 24B illustrates a hemodynamic parameter response to signal frequency in a healthy subject according to further embodiments.

Fig. 24C illustrates a flow chart of hemodynamic parameter data analysis after exposing a subject to a signal frequency, according to an embodiment.

Fig. 25A illustrates alteration of hemodynamic parameters in response to signal exposure in a subject with hepatocellular carcinoma, in accordance with an embodiment.

Fig. 25B illustrates hemodynamic parameter alteration in response to signal exposure in a healthy subject, according to an embodiment.

Fig. 25C illustrates alteration of hemodynamic parameters in response to signal exposure in a subject with hepatocellular carcinoma in accordance with further embodiments.

Figure 25D illustrates alteration of hemodynamic parameters in response to signal exposure in a subject with hepatocellular carcinoma in accordance with further embodiments.

Fig. 25E illustrates alterations in heart rate variability in response to signal exposure in subjects with hepatocellular carcinoma, in accordance with embodiments.

Fig. 25F illustrates hemodynamic parameter alteration in response to signal exposure in a subject, according to an embodiment.

Fig. 26 illustrates hemodynamic parameters in response to signal exposure in a subject over two days, according to an embodiment.

Fig. 27 illustrates hemodynamic parameters in response to signal exposure within two days in a plurality of healthy subjects or subjects with hepatocellular carcinoma, according to an embodiment.

Fig. 28A illustrates in vivo patient hemodynamic parameters in response to signal exposure within two days for a subject with hepatocellular carcinoma, under an embodiment.

Fig. 28B illustrates in vivo patient hemodynamic parameters in response to signal exposure over two days in healthy subjects according to an embodiment.

Fig. 29A-29G illustrate mean hemodynamic parameter values in subjects with different time periods and diagnoses, according to various embodiments.

Fig. 30A-30B illustrate intra-patient variation of hemodynamic parameter values in a subject, in accordance with various embodiments.

Fig. 31A-31C illustrate values of hemodynamic parameters in a plurality of healthy or hepatocellular carcinoma subjects, in accordance with various embodiments.

Fig. 32 illustrates a support vector machine analysis of hemodynamic parameters in a subject, in accordance with various embodiments.

Fig. 33 illustrates diagnostic results obtained by analyzing hemodynamic parameters in a subject after exposure to a signal, according to an embodiment.

Fig. 34 illustrates a phase-locked loop system with synchronous solution and cell-based specificity as a platform for transmitting data signals to specific cell types.

Fig. 35A-35F illustrate microtubules in metastatic and non-metastatic cells according to an embodiment.

Figures 36A-36E illustrate a microtube conductivity system according to an embodiment.

Figures 37A-37C illustrate a microtube conductivity system according to further embodiments.

Fig. 38A to 38B illustrate a microtube as a wire having a nanopore according to an embodiment.

FIG. 39 illustrates pulse transmission as a solitary-like wavelet according to an embodiment.

Fig. 40A illustrates an electrode fabricated on a glass surface to observe a response to an AC voltage according to an embodiment.

Figure 40B illustrates a micropipe response to an AC signal, in accordance with an embodiment.

FIG. 41 illustrates the geometry of a micropipe near an electrode at various field frequencies, in accordance with some embodiments; a, 100 kHz; b, 250 kHz; c, 500 kHz; d, 1 MHz; e, 2.5 MHz.

Fig. 42A illustrates the molecular dynamics of the C-terminus in the microtubule ring, in accordance with an embodiment.

Fig. 42B illustrates the effect of an acidic environment on the C-terminus in the microtubule ring, according to an embodiment.

Figure 42C illustrates the effect of a neutral environment on the C-terminus in the micro-tubulation ring according to an embodiment.

Fig. 43 illustrates the effect of C-terminal oscillation on ion movement through a nanopore into a microtubule lumen according to an embodiment.

Figure 44 demonstrates that the cross-linked conformation of the C-terminus stabilizes the straight orientation of tubulin dimers in accordance with the examples.

FIG. 45 illustrates an ordered water structure between charged surfaces according to an embodiment.

Fig. 46 illustrates an exponential decrease in potential near a charged surface in an ionic solution, according to an embodiment.

Fig. 47A shows the counter ion charge distribution around the microtubes according to an embodiment.

Fig. 47B illustrates a counterion charge distribution around the C-terminal tail, according to an embodiment.

Figure 48 illustrates a calculation of the micropipe capacitance according to an embodiment.

Fig. 49A illustrates an expanded end tail according to an embodiment.

Fig. 49B illustrates a beveled end tail according to an embodiment.

Figure 50 illustrates an ionic ambient cable with a microtube of 13 filaments according to an embodiment. The symbols used are: pr-j: strand 'j', j ═ 1.. 13; r₁: resistance along tubulin; r₂: resistance perpendicular to tubulin; r₃: resistance between the filaments, and partial inductance L₁Are connected in series; c₁: the capacitance of the dimer; l is₁: a portion of a full turn inductor.

Fig. 51 shows an effective circuit diagram of an nth cell with Kirchhoff's laws applied to characteristic elements of a microtube as an ion cable, according to an embodiment.

Figure 52 illustrates ions and ordered water within cells and extracellular matrix according to an embodiment.

Fig. 53 shows condensation and decondensation due to cation charge movement in the cytoplasm.

Figure 54A illustrates an alpha-fetoprotein (AFP) tumor marker measurement in a patient over time, in accordance with an embodiment.

Fig. 54B illustrates a liver CT scan of a patient according to an embodiment.

Fig. 55A illustrates a coronal view of a CT liver image with tumor mass according to an embodiment.

Fig. 55B illustrates a sagittal view of a tumor-infiltrating inferior vena cava (arrow), according to an embodiment.

Fig. 55C shows a large tumor mass according to an embodiment.

Fig. 55D illustrates a tumor invading the right branch of the portal vein according to an embodiment.

Figure 55E illustrates multiple lung metastases (arrows) according to an embodiment.

FIG. 56 shows a subdural tumor mass (left), a Seg II/III liver tumor mass (middle), and a tumor mass in the abdominal cavity (right), according to an example.

Fig. 57A illustrates cavitation of lung metastases as depicted by CT scan of the liver of a patient, according to an embodiment.

Fig. 57B illustrates disappearance of liver tumor mass and residual peritoneal implant as depicted by a CT scan of the patient's liver, according to an embodiment.

Fig. 58 illustrates a liver MRI scan of a patient according to an embodiment.

Fig. 59 illustrates a further liver MRI scan of a patient according to an embodiment.

Fig. 60A illustrates a liver CT scan of a patient two weeks after receiving an exposure protocol, according to an embodiment.

Fig. 60B illustrates a CT scan of the liver of a patient four weeks after receiving an exposure protocol, according to an embodiment.

Fig. 61 illustrates a CT scan of the liver of a patient twelve days after receiving an exposure protocol, according to an embodiment.

FIG. 62A illustrates electromagnetic field absorption, transmission, or reflection, according to an embodiment.

Fig. 62B illustrates electromagnetic field reflection coefficients according to an embodiment.

Fig. 63A-63D illustrate reflected energy differences according to some embodiments.

Fig. 64 illustrates an example process for treating a patient, according to an embodiment.

Fig. 65 illustrates an example flow for training a machine learning algorithm, according to an embodiment.

Fig. 66 illustrates a set of patient response graphs, according to an embodiment.

Fig. 67A illustrates a graph representing a signal exposure scheme over the course of a day, according to an embodiment.

Fig. 67B illustrates a determination of a new activity frequency compared to the first frequency exposure scheme, according to an embodiment.

Fig. 68 illustrates a distribution of activity frequencies according to an embodiment.

Fig. 69 illustrates a distribution of activity frequencies according to further embodiments.

Fig. 70 illustrates a normal probability map construction according to an embodiment.

FIG. 71 illustrates a bar code system according to an embodiment.

FIG. 72 illustrates a bar code system according to further embodiments.

Fig. 73 illustrates a barcode system for diagnosing healthy and hepatocellular carcinoma patients, according to an embodiment.

Fig. 74 illustrates a barcode system for diagnosing healthy and hepatocellular carcinoma patients according to further embodiments.

Fig. 75A illustrates Hdp values recorded during 23 consecutive heartbeats according to an embodiment.

Fig. 75B illustrates new attribute parameter values recorded during 23 consecutive heartbeats according to an embodiment.

Fig. 76 illustrates the correct classification rate using centroids from representative Hdp variation values, according to an embodiment.

Fig. 77 shows the correlation of representative Hdp variation values with biofeedback procedures, according to an embodiment.

Fig. 78 illustrates the difference in representative Hdp change values determined during exposure to different SFq, according to an embodiment.

Fig. 79A shows a distribution of 1,054 cancer-specific frequencies from four cancer types, according to an example.

Fig. 79B shows the distribution of disease-specific Sfq and health-specific Sfq in 21 patients during exposure to different three sets of cancer-specific frequencies.

Fig. 80A-80B illustrate dynamic instantaneous variability calculations by heartbeat, according to an embodiment.

Fig. 81 illustrates the dynamic construction of a cancer therapy program through a series of modulated frequencies, in accordance with an embodiment.

Fig. 82 illustrates cell damage after exposure to electromagnetic frequencies, according to an embodiment.

Detailed Description

The present disclosure is not limited to the particular systems, devices, and methods described, as these may vary. The terminology used in the description is for the purpose of describing the particular versions or embodiments only and is not intended to limit the scope.

As used in this document, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Nothing in this disclosure should be construed as an admission that the embodiments described in this disclosure are not entitled to antedate this disclosure by virtue of prior invention. As used in this document, the term "including" means "including but not limited to".

The embodiments of the present teachings described below are not intended to be exhaustive or to limit the teachings to the precise forms disclosed in the following detailed description. Rather, the embodiments are chosen and described so that others skilled in the art may appreciate and understand the principles and practices of the present teachings.

The present disclosure relates to an electronic portable medical device for characterization, diagnosis, treatment and frequency discovery in a warm-blooded mammalian subject. The present disclosure more particularly relates to an integrated system capable of measuring, monitoring and recording the electrical activity of the heart by RR interval (RRI) values and calculating these values to identify specific Heart Rate Variability (HRV) values resulting from exposure of a warm-blooded mammalian subject to specific frequencies of its amplitude modulated radio frequency electromagnetic field (AM RF EMF) to diagnose and treat a health condition. For the purposes of the present invention, the portable medical device is capable of measuring and recording RRI values during exposure to one or more such AM RF EMF frequencies to diagnose, treat and find frequency in a warm-blooded mammalian subject.

In some embodiments, the health condition may include, but is not limited to, hepatocellular carcinoma, colorectal cancer, breast cancer, ovarian cancer, pancreatic cancer, head and neck cancer, bladder cancer, liver cancer, renal cancer, melanoma, gastrointestinal cancer, prostate cancer, small cell lung cancer, non-small cell lung cancer, sarcoma, glioblastoma, T-cell and B-cell lymphoma, endometrial cancer, or cervical cancer.

It was determined through the course of numerous clinical trials that Hdp values have been recorded for numerous patients that the HRV values of patients vary based on the type of health condition to which the patients are exposed. In particular, different HRV values are associated with different types of cancer. This determination provides a basis for proposing diagnostic procedures for diagnosing a particular form of cancer carried by a patient based on HRV values. These determinations further indicate that a number of health conditions suffered by a patient can be diagnosed based on certain identified measured HRV values in the patient's body, including viral, parasitic, or other pathogenic attacks, organ dysfunction leading to undesirable components (such as toxins present in the patient's blood), drug abuse, poisons, high Low Density Lipoprotein (LDL) cholesterol levels, venom from snake bites, and the like.

A frequency synthesizer may be used to generate a specific precise frequency or a series of such frequencies. The user may select such frequencies using a keyboard or other input device, which in turn may cause the circuit to turn on or off the generated signal for well-defined time intervals.

In an embodiment, when the subject is exposed to one or more highly specific frequency radio frequency carrier signals emitted by the programmable generator, the processing system may calculate HRV values from the measured and recorded RRI values using an ECG monitoring system connected to the warm-blooded mammalian subject. The ECG monitoring system measures and records RRI values for further processing. A processing system, either internal or external to the portable medical device, may perform a series of calculation processes directed to calculating HRV values. The HRV values are organized, synchronized, aggregated, recorded and stored as representative HRV variation values for further mathematical and artificial intelligence calculation procedures performed in the frequency intelligence library (ILf). The processing system provides sensing capabilities of the portable medical device and identifies particular electromagnetic field amplitude modulation frequencies characterized by identifiable patterns of HRV value changes (referred to herein as representative HRV variation values) by using various measured and recorded HRV values of the patient (SFq). SFq are a subset of radio frequency carrier signals of highly specific frequencies that affect cellular function or dysfunction in a warm-blooded mammalian subject (i.e., patient). As part of the programmable generator, the portable medical device uses a probe (more broadly described as a conductive applicator) to expose the patient to a radio frequency carrier signal of a highly specific frequency to apply emissions to the patient at one or more defined locations of the human body through local contact. As part of an ECG monitoring system, a portable medical device measures and records RRI values from a patient using electrodes placed in local contact with one or more determined sites of the human body.

In an embodiment, the portable medical device may calculate and identify representative HRV change values, which may be stored in an internal temporary storage device for asynchronous transmission to ILf. In an alternative embodiment, the portable medical device may simultaneously transmit the representative HRV change value to ILf over a wireless connection to the encrypted network. In an alternative embodiment, the portable medical device may transmit the RRI value and information relating to the patient's exposure to the radio frequency carrier signal at the highly specific frequency to an external device having a processing system to calculate and identify the representative HRV change value prior to transmission to ILf.

In an embodiment, a portable medical device and/or an external device with a processing system may be connected to an encrypted network. Representative HRV change values may be transmitted ILf via a synchronous or asynchronous connection to the encrypted Web platform for further mathematical and artificial intelligence computation procedures.

In an embodiment, a computing processing system may include a device synchronizer, a data aggregator, a storage device and/or storage interface, and an interface controller. The interface controller may be configured to match (synchronize) the calculated HRV value with exposure to one or more highly specific frequency radio frequency carrier signals. Additionally or alternatively, the interface controller may be further configured to merge records (data aggregations) to be stored (stored) for further processing and transmission, such that HRV values are linked to exposure to one or more highly specific frequency radio frequency carrier signals from which such HRV values are measured. The ECG monitoring system and the programmable generator may be connected via an interface controller. The modules corresponding to the synchronization and data aggregation and storage interfaces may be arranged in a portable medical device integrated hardware solution or in an external device with external computing capabilities as an integrated hardware solution.

In another embodiment, the portable medical device of the present invention may be used to treat a patient. The treatment procedure may include exposing the patient to one or more highly specific frequencies of radio frequency carrier signals. The programmable generator may be loaded with instructions configured to produce a health condition-specific SFq set for a warm-blooded mammalian subject having a particular health condition. In an embodiment, SFq sets may be accurately controlled. In an embodiment, the SFq set may have a resolution that is about 0.5Hz different from the expected determined or predetermined modulation frequency. In another embodiment, the SFq set may have a resolution that is about 0.1Hz different from the expected determined or predetermined modulation frequency. In yet another embodiment, the SFq set may have a resolution that is about 0.01Hz different from the intended determined or predetermined modulation frequency. In yet another embodiment, the SFq set may have a resolution that is about 0.001Hz different from the expected determined or predetermined modulation frequency.

In an embodiment, the patient may be exposed to SFq emissions at a relatively low and safe energy level that results in a low absorption level by the patient. It is believed that physiological exchanges or electrical pulse flow within a warm-blooded animal (which will be affected by the application of the disclosed system) are similar at low energy levels. Regardless, in certain embodiments, the Specific Absorption Rate (SAR) should be, and most preferably is, substantially less than 1.6mW/g of living tissue weight in the exposed area (at or near the point of contact or sensing of the conductive applicator with the subject receiving treatment).

In addition, the emission can be stably maintained during emission, so that the stability should preferably be at 10^-5Stage, more preferably 10^-6Stage, and most preferably 10^-7And (4) stages. The stability of the emission may be determined as the relative deviation of the frequency divided by the desired frequency, e.g., 10 for 0.01Hz (deviation)/1,000 Hz (desired frequency)^-5。

In some embodiments, the programmable generator may include a microprocessor (or other similar integrated circuit) configured to operate in accordance with control information loaded, for example, from a processing system. In some examples, the programmable generator may be combined with other medical devices (such as an ECG monitoring system) and other computing servers, all operating together and synchronized by the processing system as a single new medical device. Thus, the new and improved programmable generator can be loaded into a single warm-blooded mammalian subject or an updated series of specific SFq identified from a group of warm-blooded mammalian subjects having the same health condition. In addition, the new and improved programmable generator described herein may support different applications besides treating patients, such as diagnosis, search SFq, and treatment follow-up. The microprocessor (or integrated circuit) can control the function of the programmable generator to produce the desired therapeutic emission. In some examples, the programmable generator may include an impedance transformer connected to a transmitter of low-energy electromagnetic emissions and a probe (e.g., a conductive applicator) that applies the emissions to the patient. The impedance transformer may be configured to substantially match an impedance of the patient sensed by the transmitter circuit to an impedance of an output of the transmitter circuit.

The ECG monitoring system may be configured to measure RRI values, calculate HRV values, and record representative HRV change values for the SFq identification. The identified SFq may be used to diagnose and treat a health condition of a patient. The system described herein may thus include an ECG monitoring system that measures RRI values and records various identified representative HRV change values for a patient. Thus, the system may generally measure each identified RRI value of the patient using electrodes placed in local contact with each determined part of the human body. The ECG monitoring system may further include a subsystem configured to record respective identified RRI values for the patient. The calculated and recorded HRV values may be stored in one or more storage devices.

HRV values can be calculated and recorded according to established procedures. The initial measurement of RRI values of an individual or patient during a radio frequency carrier signal time period of a non-exposed highly specific frequency or RRI values thereof is identified herein as a base measurement or base RRI value. For the purposes of the procedure, the initial measurement of the above parameters is carried out on a warm-blooded mammalian subject after a period of relaxation, such as about 15 minutes, when the patient is lying in a supine position (face and preferably also palm up) or in another comfortable and relaxed position.

After obtaining the initial measurements described above, a selected series of one or more highly specific frequency radio frequency carrier output signals may be applied to the subject, thereby providing exposure measurements or exposure RRI values.

The one or more highly specific frequency radio frequency carrier signals may be AM RF EMF output signals generated in accordance with a control program loaded into a programmable generator that generates AM RF EMF output signals at some predetermined Amplitude Modulation (AM) frequency. In some embodiments, during a determined period of time (most preferably, during a period of at least ten heartbeats or during a period of at least 10 seconds of the patient), the patient is preferably exposed to an AM RF EMF output signal or applies such a signal to the patient. This procedure can typically be performed when the patient is connected to both the synchronized ECG monitoring system and the programmable generator, so that RRI values can be measured and HRV values can be calculated and recorded during the exposure or application period. However, the HRV values may also or alternatively be an initial data source to identify SFq and/or representative HRV change values after software processing as described above.

The above-identified RRI values measured during or after the above-identified exposure or application to the subject or patient are referred to herein as an exposed RRI value and a post-exposure RRI value, respectively.

The above-described procedure, as generally applied to a plurality of patients diagnosed with a health condition, can provide a plurality of base RRI values, a plurality of exposed RRI values, and a plurality of post-exposed RRI values as related to the diagnosed health condition. These multiple RRI values may typically be slightly dispersed values, and after calculation, may yield similarly dispersed HRV values. Accordingly, such dispersed values may be periodically submitted to the processing system for the purpose of defining representative HRV change values, such that the processing system may perform mathematical and artificial intelligence calculations that identify SFq using the representative HRV change values.

Continuing with the above example, the ECG monitoring system may be part of a portable medical device as described herein and provides means for software processing RRI values for SFq identification and health condition diagnosis of a patient. SFq and the identification of representative HRV change values are referred to as representative surrogate markers that are determined by the processing system from the ECG monitoring system that measures and records RRI values during non-exposure and exposure periods for patients pre-diagnosed and diagnosed as healthy or having a known form of poor health condition.

Representative surrogate markers for diagnostic, SFq identification, treatment, and treatment follow-up purposes can be derived from a calculated combination of information from representative basal RRI measurements, representative exposure RRI measurements, and/or representative post-exposure RRI values. These computational combinations may be performed at ILf.

The period of time during which the AM RF EMF frequency output signal is exposed or applied by means of the variable frequency programmable generator device may be within a wide frequency range; for example, an AM RF EMF frequency in the range between about 0.01MHz to about 150MHz may require a short period of time for the RRI value to change at any particular frequency value. Thus, it may be desirable to continuously expose or apply portions of the range of AMRF EMF frequencies to identify AMRF EMF frequency values for which basal, exposure, and post-exposure HRV values actually occur during the heartbeat times at which the RRI values are measured by the ECG monitoring system and processed by the computing processing system to calculate HRV values and determine representative HRV change values.

In addition to being integrated in or coupled to the recording apparatus as described above, the processing system may also be located at the central server and connected through the encrypted Web-based platform to a portable device that can perform analysis based on the recorded representative HRV change values received or transmitted to the central server.

The following discussion of fig. 1-6 illustrates various examples of portable devices as described above and the particular environments in which the devices may be utilized.

Fig. 1 illustrates an example system 100 for monitoring a patient 102. In certain embodiments, a portable medical device 104 (similar to the portable medical devices described above) may be operably connected to a set of electrical sensors (e.g., connected to the patient 102 using a strap such as the electrode strap 106). The medical device 104 may be configured to generate a set of electrical signals (e.g., via the programmable generator device described above) and transmit the signals to the patient 102 via the sensors. The sensors may be configured to measure an electrical response produced by the patient 102. The sensor may return a value indicative of the measured electrical response to the medical device 104. In some implementations, the medical device 104 can transmit this information to an external computing device, such as a smartphone 108, for further processing. Additionally, in some examples, smartphone 108 may be operatively connected to encrypted network 110 for transmission of any processed information for remote storage and/or additional computing through a remote computing device, such as a remote server.

As noted above, the portable medical device 104 may work in conjunction with an external computing device, such as the example shown in FIG. 1. In such an arrangement, internal processing components of the portable medical device 104 may be reduced, thereby reducing the overall size of the portable medical device. However, as also noted above, the portable medical device 104 may be designed such that it can perform an analysis of the electrical response received from the patient 102.

Fig. 2 illustrates an example system 200 for monitoring a patient 202. In certain embodiments, a portable medical device 204 (similar to the portable medical devices described above) may be operably connected to a set of electrical sensors (e.g., connected to the patient 202 using a strap such as the electrode strap 206). The medical device 204 may be configured to generate a set of electrical signals (e.g., via a programmable generator device as described above) and transmit the signals to the patient 202 via the sensors. The sensors may be configured to measure an electrical response produced by the patient 202. The sensor may return a value indicative of the measured electrical response to the medical device 204. However, unlike fig. 1, the medical device 204 may be configured to perform additional analysis on the returned values. Additionally, in some examples, the medical device 204 may be operatively connected to the encrypted network 208 for transmission of any processed information for remote storage and/or additional computation by a remote computing device, such as a remote server.

Fig. 3 illustrates an example circuit diagram of a portable medical device 300 as described herein. The medical device 300 may include a fixed carrier frequency oscillator 302 and a current mode balancing modulator 304. In some embodiments, the frequency oscillator 302 may be configured to generate a 27.12MHz signal. However, it should be noted that this signal frequency is provided by way of example only and may vary based on the intended function of the portable medical device 700. In some embodiments, frequency oscillator 702 may be configured to generate a signal having a frequency between 1.0mHz and 1.0Hz, between 1.0Hz and 1.0kHz, between 1.0kHz and 1.0mHz, between 1.0mHz and 1.0GHz, and a frequency higher than 1.0 GHz.

In some examples, the modulator 304 may be modulated by a sinusoidal current generated by a direct digital synthesizer 306, thereby producing a modulated carrier signal. In some embodiments, the sinusoidal current may have a frequency resolution of 10MHz and a frequency accuracy of 10 ppm.

The modulated carrier signal may be amplified by amplifier 308. In some embodiments, amplifier 308 may be a class AB balanced RF amplifier. The output of amplifier 308 may be filtered by filter 310 to a reduced harmonic content. The filtered signal may be processed by the directional coupler 312 and passed to a power monitor 314 and an adaptive matching component 316. Power monitor 314 may be configured to monitor the current power level of the filtered signal and provide an adjustment signal to microprocessor 318 if needed. The microprocessor 318 may alter the sinusoidal current generated by the synthesizer 306, thereby altering the output power. In some embodiments, the microprocessor 318 may be operably connected to an optical interface 320 of the medical device 300 configured to display information received from the microprocessor. In some examples, optical interface 320 may include an input device such as a touch screen to receive input information from a patient or user of medical device 300.

Medical device 300 may also include a battery 322 and a power management component 324 configured to manage and distribute power generated by the battery. In some examples, battery 322 may be a rechargeable battery coupled to charging component 326 configured to provide a charging signal to the battery when medical device 300 is connected to an external power source.

As noted above, the output of the directional coupler 312 may be passed to the adaptive matching component 316. The adaptive matching block 316 may also be connected to and controlled by a microprocessor 318. Upon receiving instructions from the microprocessor 318, the adaptive matching component 316 may be configured to provide RF output signals to one or more sensors positioned on the patient's body.

In some embodiments, the filtered power may be maintained at a desired level by an automatic level control loop including the power monitor 314 and microprocessor 318 as described above, ensuring that variations in voltage do not significantly affect the treatment as the battery 322 discharges. The reflected power is minimized by adjusting the adaptive matching component 316 to ensure efficient delivery of the therapeutic signal to the patient. In some examples, the medical device 300 may be controlled directly from a computer over an optical link using frequency and duration commands and return to a low power state after a defined period of time.

Fig. 4 shows an example circuit diagram for generating a modulated carrier signal as described above in the description of fig. 3. As noted above, the modulated sinusoidal signal may be generated by a Direct Digital Synthesizer (DDS)402 that includes an integrated current output digital-to-analog converter (DAC). In some embodiments, the modulated sinusoidal signal may be in a frequency range of about 10Hz to about 50 kHz.

The current mirror 404 may be used to drive a current mode balanced amplitude modulator. The input to the current mirror 404 may include two components, a sinusoidal current from the DDS 402 and a DC current that is adjustable to set the modulation depth to a desired range (e.g., 85% to 90% of the original signal). The output from the current mirror 404 may form the tail of a transistor pair that operates as a current of 2I_E＝I_DC+I₀sin (ω t) high impedance current source, current setting the gain g of the transistor amplifier in the differential pair_m. Can make the base of the transistorAt a voltage V_bAt bias, a carrier signal of 27.12MHz may be fed to one transistor and the second transistor may be held at a small signal ground. The carrier signal may be differentially amplified, with gain varying as the modulation signal varies, and the two outputs may be combined using, for example, a center-tapped balun. The combined modulated signal may be up to twice the signal level available from a single-ended configuration for the same battery voltage. The output of the modulator is therefore the desired AM modulated signal, for example a carrier signal of 27.12 MHz.

Fig. 5 illustrates an example circuit diagram of an amplifier 500 (e.g., amplifier 308 in the description of fig. 3 as described above). The signal from the current mode modulator may be amplified using a balanced amplifier 500 to achieve the desired signal level.

The gain, and hence the output level, of the amplifier 500 is somewhat dependent on the battery voltage, and therefore the amplifier may be preceded by a variable attenuator 502 which is adjustable to maintain a constant output power as the battery discharges. Attenuator 502 may use a shunt PIN diode in a T-attenuator configuration, where the greater the current flowing through the diode, the greater the attenuation. As noted above, in some embodiments, amplifier 500 may be a class AB balanced design with parallel RF devices to increase current capacity.

The input transformer may convert an input from a single-ended signal to a balanced signal and may match an input impedance of the device. Each pair of devices may amplify a half-cycle signal. In this way, the peak amplitude of the output signal before clipping can be close to twice the battery voltage. The output transducer may combine the two signals to produce a final output signal. The class AB bias ensures that any cross-over distortion is minimized. The impedance transformation ratio of the output balun may be selected such that the desired output power may be generated given the limited supply (battery) voltage. The balanced amplifier design suppresses even-order distortion products; however, odd-order harmonics may be suppressed using a low-pass filter 502, such as a 5 th order low-pass filter.

Fig. 6 illustrates an example circuit diagram for monitoring the RF forward and reflected power produced by the amplifier. The output from an amplifier (e.g., amplifier 500) may be sampled using a directional coupler 602 and the signal detected using a logarithmic power detector 604 with a dynamic range of >30dB, for example. The output signal may be low-pass filtered to obtain an average envelope power from the amplitude modulated signal. In some embodiments, the average envelope power may be measured by an analog-to-digital converter (ADC)612 to allow the microprocessor to report values to the control computer for use in a control loop that maintains a constant output power as the battery voltage changes.

In some examples, the detected voltage, which is equal to the average output power, may be compared to an expected value using comparator 606. The digital output may be filtered using a passive lead-lag loop filter 608 with an appropriate time constant to maintain stability of the control loop. The output of the loop filter 608 may be buffered and used to drive a PIN diode attenuator to adjust the overall gain to achieve the desired output power.

The directional coupler 602 may also sample the power reflected from the load (in this case the applicator) using a logarithmic power detector 610. The average level may be determined using a low pass filter and may be sampled using the ADC 612. The control computer may compare the forward and reflected power and adjust the applicator tuning unit 614 to find the setting with the lowest reflected power or the best power delivered to the patient. Various algorithms may be used. For example, the control computer may sequentially up or down adjust the values of the three elements and determine whether the reflection is decreasing, iteratively repeating this process until a minimum value is found.

As described herein, patient diagnosis may be performed with the aid of measured Hdp values and recorded Hdp values. Recorded Hdp values can be measured in many patients who are pre-diagnosed as having an identified poor health condition or in a health condition. The Hdp values may be stored at certain times and over certain periods of time, as described in more detail below (fig. 9-11).

In an embodiment, a system includes an Hdp monitoring system for measuring and recording Hdp values and a frequency generator configured to provide one or more highly specific frequencies to a patient via a Radio Frequency (RF) carrier signal.

In an embodiment, the system may identify SFq that is a subset of the RF carrier signals of highly specific frequencies. SFq can be used to affect cellular function or dysfunction in a warm-blooded mammalian subject. Exposure of a warm-blooded mammalian subject to SFq may result in a change in the representative Hdp change value in a manner that is indicative of whether one or more highly specific frequency RF carrier signals have a potential biological effect in the warm-blooded mammalian subject. The specificity of the change in the representative Hdp change value may be a surrogate marker for diagnosing and treating a warm-blooded mammalian subject.

In an embodiment, the system may store one or more sets of identified SFq in a server connected by a protected internet-based platform to form SFq smart libraries (ILf). The stored data can be combined, organized, compared, and characterized for use in diagnosing a patient or individual and treating the health of patients with similar diagnoses.

An integrated frequency generator for emitting or exposing a warm-blooded mammalian subject to one or more highly specific frequency RF carrier signals may be a programmable generator and may be an electronic component activatable by electrical power as part of an integrated system. The programmable generator may be used to affect cellular function or dysfunction in a warm blooded mammalian subject. The programmable generator may include one or more controllable low energy electromagnetic energy generator circuits configured to generate one or more highly specific frequency RF carrier signals. One or more microprocessors or integrated circuits are provided that include or communicate with the one or more generator circuits. In an embodiment, the one or more microprocessors may also be used to control the transmission and reception of control information from the processing system. In an embodiment, the one or more generator circuits may include one or more Amplitude Modulation (AM) frequency control signal generators configured to control amplitude modulation variations of the one or more highly specific frequency RF carrier signals. The one or more generator circuits may further include one or more programmable AM frequency control signal generators configured to control a frequency at which the amplitude modulation is generated.

The system may further include a processing system configured to integrate and synchronize the Hdp monitoring system with the one or more programmable generators. When a warm-blooded mammalian subject is exposed to one or more highly specific frequency RF carrier signals emitted by the one or more programmable generators, the individual Hdp values measured and recorded by the Hdp monitoring system may be processed by the processing system. The information resulting from such processing may be stored, for example, at ILf. The processing system may further control the one or more programmable generators in a particular series SFq, synchronize the one or more programmable generators, and load a control program into the one or more programmable generators. In this way, the processing system may integrate and synchronize the Hdp monitor, ILf and the one or more programmable generators to support an integrated solution.

In an embodiment, processing system ILf may be part of a server connected to the rest of the system through a protected Web platform. ILf may include artificial intelligence capabilities for storing, combining, organizing, comparing, characterizing, and processing SFq and recorded representative Hdp change values. ILf can store and organize a series of SFq and representative Hdp change values identified in a warm-blooded mammalian subject or patient. One or more series SFq may then be loaded into the one or more programmable generators. The one or more programmable generators may accurately control the emission of the amplitude modulated frequency with an accuracy of at least 1000 parts per million (ppm) relative to one or more determined or predetermined reference AM frequencies. In an embodiment, the AM frequency may be in the range of 0.01Hz to 150 kHz. The processing system may further comprise a connection or coupling location. The connection or coupling location may be used to connect or couple the processing system to a conductive applicator that applies one or more amplitude modulated low energy emissions to a warm-blooded mammalian subject at an accurately controlled modulation frequency.

It was determined by the process of conducting numerous clinical trials in which multiple measurements of various Hdp values of a patient were recorded that such Hdp values varied based on the type of health condition to which the patient was exposed. In particular, different Hdp values are identified relative to different types of cancer. This determination provides a basis for proposing a diagnostic procedure for diagnosing a particular form of cancer in a patient based on the measured Hdp values. These determinations further indicate that a number of health conditions experienced by the patient can be diagnosed based on certain identified measured Hdp values in the patient's body, including viral, parasitic or other pathogenic attacks, organ dysfunction that may lead to the presence of toxins in the patient's blood, drug abuse, poisons, high Low Density Lipoprotein (LDL) cholesterol levels, venom from snake bites, and the like.

A frequency synthesizer may be used to generate a specific frequency or a series of precise frequencies. For example, a user may select one or more frequencies using a keyboard or other input device, which in turn may cause the circuit to turn on or off the generated signal within a well-defined time interval.

In an embodiment, the processing system processes Hdp values measured and recorded by an Hdp monitoring system connected to a warm-blooded mammalian subject during exposure to one or more highly specific frequency RF carrier signals emitted by the programmable generator. The Hdp monitoring system may measure and record Hdp values for further processing. The processing system may incorporate one or more algorithms that analyze the recorded Hdp values obtained by the Hdp monitoring system. The processing system uses various measured and recorded Hdp values of the subject and identifies SFq characterized by recognizable patterns of Hdp change value changes (referred to herein as representative Hdp change values). SFq are a subset of RF carrier signals of highly specific frequencies that affect cellular function or dysfunction in a warm-blooded mammalian subject (i.e., patient). As part of the Hdp monitoring system, the processing system typically identifies individual measured and recorded Hdp values for the patient using electrodes placed in local contact with various determined sites of the human body. The Hdp monitoring system further includes a recording component that records various identified measured Hdp values for the patient. In an embodiment, the recording component may store the measured Hdp values in a storage device of the Hdp monitoring system. In an alternative embodiment, the recording component may store the measured Hdp values in any storage device on which various identified measured Hdp values of the patient may be recorded for immediate processing and/or further processing. Hdp values may include, for example, values for one or more of the following hemodynamic parameters:

RR intervals (intervals from R peak to next R peak as shown, for example, on an Electrocardiogram (ECG)) (RRI);

heart Rate (HR);

systolic blood pressure (sBP);

diastolic pressure (dBP);

median blood pressure (mBP);

pulse Pressure (PP);

stroke Volume (SV);

cardiac Output (CO); and

total Peripheral Resistance (TPR).

In an embodiment, a processing system may include a device synchronizer, a data aggregator, a storage device and/or storage interface, and an interface controller. The interface controller may be responsible for matching (synchronizing) Hdp values with exposure to one or more highly specific frequency radio frequency carrier signals. Additionally or alternatively, the interface controller may be responsible for merging the records to be stored (data aggregation) for further processing (interface controller) in such a way that the Hdp value is linked to the exposure to the radio frequency carrier signal of the one or more highly specific frequencies from which such Hdp value is measured. The Hdp monitoring system and the programmable generator may be connected via an interface controller. Modules corresponding to synchronization and data aggregation and storage interfaces may be packaged as a portable integrated hardware solution.

In another embodiment, the processing system may include, inter alia, two components: a statistical mining component and a machine learning/evolutionary game theory component. It should be noted that machine learning, as used herein, refers to various types of machine learning, including, for example, deep machine learning and hierarchical machine learning. The statistical mining component may include a series of mathematical procedures based on discriminant analysis and Support Vector Machines (SVMs). Hdp values may be constant selected index variables and their related new attributes are analyzed based on different well-established statistical methods. Using multivariate discriminant analysis based on correlation component analysis and other coordinate transformations, Hdp values can be expressed as centroids of representative Hdp variation values with well-defined thresholds to optimize common metrics. The machine learning/evolutionary game theory component may include permanently refined clustering analysis and updated mathematical algorithms that are cut-off refined or cut-off refined by new discriminative attributes for (1) identifying patterns of response to health condition-specific frequencies (referred to herein as representative Hdp change values) and (2) storing the representative Hdp change values and corresponding health condition-specific frequencies. These components may be implemented on a central secure server-side system connected to the integrated hardware solution via encrypted communications over a network, such as the internet.

In yet another embodiment, ILf may be located in a central secure server system. In an embodiment, the library may be connected to all instances of the integrated hardware solution via encrypted communications over a network (e.g., the internet). In such embodiments, the network solution may provide a real-time, integrated, and evolved system that combines all working devices. Permanently updated de-identified patient demographic and clinical information data collected from the physician, as well as patient report results combined with records of representative Hdp change values and corresponding health condition-specific frequencies (SFq) and these data, may be stored in ILf. The threshold for the representative Hdp change value may be refined based on the newly added value. This data can be structured and processed into programs that refine diagnosis, treatment, and follow-up for the health condition of the patient. ILf may have computing power to support statistical data mining and machine learning for pattern recognition and to evolve gambling theory for balance point identification that characterizes the best possible match of SFq and/or representative Hdp variation values for each series and correspondence to diagnostic and treatment outcome information. The refinement procedure is implemented as artificial intelligence based meta-procedures that take into account patient segmentation. The programmable generator is connected in the processing system by an interface controller to transfer data between the processing system and the programmable generator. The refined program is then downloaded back to the processing system module of the integrated hardware solution to reprogram the programmable generator.

The interface controller may connect the programmable frequency generator with the processing system to allow data to be transferred. A refined program can be downloaded to the processing system to update the programmable frequency generator before or during a treatment session.

In an embodiment, a diagnosis of the health condition of the patient may be determined based on one or a set of SFq and/or representative Hdp change values identified by the processing system. In an embodiment, a plurality of measured and recorded Hdp values may be submitted to a processing system during exposure of the patient to one or more highly specific frequency radio frequency carrier signals. The processing system may identify SFq and/or representative Hdp change values in patients diagnosed with a health condition. In an embodiment, the identified SFq and representative Hdp change values may be stored in ILf. A warm-blooded mammalian subject may have individual Hdp values measured and recorded by an Hdp monitoring system during exposure to a selected SFq set (i.e., a subset of the highly specific frequency radio frequency carrier signals emitted by the programmable generator) that are processed to identify characteristic hemodynamic response patterns for SFq exposure. The processing system identifies representative Hdp change values associated with the selected SFq set. The processed information may be stored ILf for immediate and/or further database comparison. The diagnostic identifier may be the result of searching for a response pattern that is consistent with the particular health condition of the patient. The processing system may diagnose the health of the patient by incorporating a series of mathematical algorithms that analyze recorded Hdp data obtained by the Hdp monitoring system.

In another embodiment, the user may be enabled to search SFq. The search procedure may be performed during exposure of the patient to one or more radio frequency carrier signals of highly specific frequencies. For example, search SFq may include processes involving: the Hdp monitoring system examines the measured and recorded Hdp stored in the processing system during exposure of the patient to one or more radio frequency carrier signals of highly specific frequencies. SFq may involve the application of a mathematical algorithm to determine a series of specific frequencies to be provided by the programmable generator. In an embodiment, the search process may comprise processing measured and recorded Hdp values in a warm-blooded mammalian subject of unknown health condition or in a patient of known health condition by an Hdp monitoring system during exposure to a series of specific frequencies (e.g. a subset of radio frequency carrier signals of highly specific frequencies) generated by a programmable generator. With respect to exposure of a subject or patient to one or more predetermined sequences of highly specific frequency radio frequency carrier signals, the term "accurately controlled" means modulating modulated low energy electromagnetic emissions to within a resolution of higher frequencies up to about 1Hz (greater than about 1000 Hz). For example, if the determined or predetermined modulation frequency to be applied to a warm-blooded mammalian subject is about 2000Hz, accurate control of such modulated low energy emissions requires the generation of a frequency between about 1999Hz and about 2001 Hz. The processing system identifies SFq and a representative Hdp change value during the search procedure.

In an embodiment, a new SFq may be discovered. The discovery procedure may be performed during exposure of an individual or patient to one or more radio frequency carrier signals of highly specific frequencies. Discovering the new SFq may include having the processing system receive measured and recorded Hdp values from the Hdp monitoring system during exposure to one or more highly specific frequency radio frequency carrier signals. Discovering a new SFq may further involve applying a mathematical algorithm to determine a series of specific frequencies for the programmable generator. In an embodiment, the search process may process Hdp values measured and recorded in a warm-blooded mammalian subject of known health from an Hdp monitoring system during exposure to a series of specific frequencies (being a subset of highly specific frequency radio frequency carrier signals) produced by a programmable generator. In an embodiment, the processing system identifies SFq and representative Hdp change values during the process of discovering a new SFq.

In an embodiment, a diagnosis of the health condition of the patient may be determined based on one or a set of SFq and/or representative Hdp change values identified by the processing system. In an embodiment, a plurality of measured and recorded Hdp values may be submitted to a processing system during exposure of the patient to one or more highly specific frequency radio frequency carrier signals. The processing system may identify SFq and/or representative Hdp change values in patients diagnosed with a health condition. In an embodiment, the identified SFq and representative Hdp change values may be stored in ILf. A warm-blooded mammalian subject may have individual Hdp values measured and recorded by an Hdp monitoring system during exposure to a selected SFq set (i.e., a subset of the highly specific frequency radio frequency carrier signals emitted by the programmable generator) that are processed to identify characteristic hemodynamic response patterns for SFq exposure. The processing system identifies representative Hdp change values associated with the selected SFq set. The processed information may be stored ILf for immediate and/or further database comparison. The diagnostic identifier may be the result of searching for a response pattern that is consistent with the particular health condition of the patient. The processing system may diagnose the health of the patient by incorporating a series of mathematical algorithms that analyze recorded Hdp data obtained by the Hdp monitoring system.

In another embodiment, the user may be enabled to search SFq. The search procedure may be performed during exposure of the patient to one or more radio frequency carrier signals of highly specific frequencies. For example, search SFq may include processes involving: the Hdp monitoring system examines the measured and recorded Hdp stored in the processing system during exposure of the patient to one or more radio frequency carrier signals of highly specific frequencies. SFq may involve the application of a mathematical algorithm to determine a series of specific frequencies to be provided by the programmable generator. In an embodiment, the search process may comprise processing measured and recorded Hdp values in a warm-blooded mammalian subject of unknown health condition or in a patient of known health condition by an Hdp monitoring system during exposure to a series of specific frequencies (e.g. a subset of radio frequency carrier signals of highly specific frequencies) generated by a programmable generator. With respect to exposure of a subject or patient to one or more predetermined sequences of highly specific frequency radio frequency carrier signals, the term "accurately controlled" means modulating modulated low energy electromagnetic emissions to within a resolution of higher frequencies up to about 1Hz (greater than about 1000 Hz). For example, if the determined or predetermined modulation frequency to be applied to a warm-blooded mammalian subject is about 2000Hz, accurate control of such modulated low energy emissions requires the generation of a frequency between about 1999Hz and about 2001 Hz. The processing system identifies SFq and a representative Hdp change value during the search procedure.

In an embodiment, a new SFq may be discovered. The discovery procedure may be performed during exposure of an individual or patient to one or more radio frequency carrier signals of highly specific frequencies. Discovering the new SFq may include having the processing system receive measured and recorded Hdp values from the Hdp monitoring system during exposure to one or more highly specific frequency radio frequency carrier signals. Discovering a new SFq may further involve applying a mathematical algorithm to determine a series of specific frequencies for the programmable generator. In an embodiment, the search process may process Hdp values measured and recorded in a warm-blooded mammalian subject of known health from an Hdp monitoring system during exposure to a series of specific frequencies (being a subset of highly specific frequency radio frequency carrier signals) produced by a programmable generator. In an embodiment, the processing system identifies SFq and representative Hdp change values during the process of discovering a new SFq.

In yet another embodiment, the system may be used to build and update ILf. The process for constructing and updating ILf libraries at frequency may use a processing system to identify SFq and/or representative Hdp change values in a warm-blooded mammalian subject. The processing system may store the identified SFq and the representative Hdp change value in a central server connected by the protected internet platform. ILf can store a newly identified SFq from a warm-blooded mammalian subject of known health status. Stored SFq derived from a warm-blooded mammalian subject of known health condition may be subjected to artificial intelligence processing to allow further diagnostic, identification and therapeutic program generation for the treatment of patients diagnosed with the same health condition. For example, one or more SFq identified in a patient diagnosed with a particular health condition may be used in diagnostic and therapeutic proposals with other warm-blooded mammalian subjects.

In another embodiment, the system of the present invention may be used to treat a patient. The treatment procedure may include exposing the patient to one or more highly specific frequencies of radio frequency carrier signals. The programmable generator may be loaded with program controls to produce a selected set of health condition-specific SFq to be provided to a warm-blooded mammalian subject having a particular health condition. ILf can store and update multiple selected SFq groups identified in warm-blooded mammalian subjects having the same health status. The processing system may load the programmable generator with program controls to expose warm-blooded mammalian subjects having a selected health condition to a particular SFq group. In an embodiment, SFq sets may be accurately controlled. In an embodiment, the SFq set may have a resolution that is about 0.5Hz different from the expected determined or predetermined modulation frequency. In another embodiment, the SFq set may have a resolution that is about 0.1Hz different from the expected determined or predetermined modulation frequency. In yet another embodiment, the SFq set may have a resolution that is about 0.01Hz different from the intended determined or predetermined modulation frequency. In yet another embodiment, the SFq set may have a resolution that is about 0.001Hz different from the expected determined or predetermined modulation frequency.

In an embodiment, the system may be used to provide follow-up therapy to a patient. The follow-up procedure may include a test procedure for the patient in treatment of the health condition during a determined period of time, or may be conducted in real-time during exposure to one or more highly specific frequency radio frequency carrier signals within the patient during a treatment cycle. The follow-up test procedure may be performed in an individual or patient during exposure to one or more radio frequency carrier signals of highly specific frequencies. Follow-up testing of patients with known health conditions may involve applying a mathematical algorithm to determine a series of specific frequencies to be loaded into one or more programmable generators. The follow-up test may include providing the measured and recorded Hdp from the Hdp monitoring system to the processing system for follow-up comparison during exposure to one or more highly specific frequency radio frequency carrier signals. The processing system may identify SFq and representative Hdp change values that may or may not be modified as a result of treatment of the patient during the follow-up testing procedure. The follow-up test procedure results may be able to identify response patterns consistent with a non-invasive prediction of a therapeutic response to a health condition. The processing system may incorporate a series of mathematical algorithms that analyze recorded Hdp data obtained by the Hdp monitoring system.

Importantly, the exposure of the SFq emission is at a very low and safe energy level and results in a low level of absorption, believed to be due to physiological exchanges or electrical pulse flow (which will be affected by the application of the emission of the present invention) in the warm-blooded animal similarly being at a very low energy level. In any event, the Specific Absorption Rate (SAR) should be, and most preferably is, substantially less than 1.6mW/g of the weight of living tissue in the region (at or near the location where the conductive applicator is in contact with or proximate to the subject being treated).

To achieve the desired biological therapeutic effect, it is also important to maintain stability of the emission during the emission, and this stability should preferably be about 10^-5Stage, more preferably 10^-6And most preferably 10^-7Stability is determined as the relative deviation of the frequency divided by the desired frequency, e.g., 10 for 0.01Hz (deviation)/1,000 Hz (desired frequency)^-5。

The present invention integrates the exposure of one or more highly specific frequency radio frequency carrier signals by a programmable generator. Programmable generators are electronic components that have significant improvements over proprietary medical devices that include microprocessors (which may more recently be replaced by integrated circuits). The programmable generator in the present invention uses control information loaded from the processing system. A further improvement is that the programmable generator of the present invention is an integrated part of the present invention that combines other medical devices (such as the Hdp monitoring system) and other computing servers, all operating together and synchronized by the processing system as a single new medical device. Thus, the new and improved programmable generator can be immediately loaded with an updated series of specific SFq identified in a single warm-blooded mammalian subject or identified in a group of warm-blooded mammalian subjects having the same health condition. In addition, the new and improved programmable generator of the present invention supports different applications, such as diagnosis, search SFq and treatment follow-up, in addition to treating patients. The microprocessor (or now alternatively an integrated circuit) then controls the function of the programmable generator to produce the desired therapeutic emission. It is also described to provide an impedance transformer in the programmable generator connected intermediate the transmitter of the low energy electromagnetic emission and the probe (more broadly described herein as a conductive applicator) for applying the emission to the patient. The impedance transformer substantially matches the impedance of the patient as seen from the transmitter circuit to the impedance of the output of the transmitter circuit.

Hdp monitoring systems are currently available proprietary medical devices of different brands and used in different applications, which are integrated in the present invention. The Hdp monitoring system is necessary to measure and record the Hdp values that the processing system uses for SFq identification. The identified SFq is used to diagnose and treat the health condition of the patient. The system integrates an Hdp monitoring system that measures and records various identified Hdp values for the patient. The system typically measures various identified Hdp values of the patient using electrodes placed in local contact with various defined parts of the body. The Hdp monitoring system further includes recording means for recording various identified Hdp values for the patient. The recording means may utilize any memory device on which the various identified measured Hdp values of the patient may be recorded. The storage of measured and recorded Hdp according to the invention requires the measurement and recording of various Hdp values, including at least the following nine Hdp values:

RR intervals (intervals from the R peak to the next as shown on the Electrocardiogram (ECG)) (RRI);

heart Rate (HR);

systolic blood pressure (sBP);

diastolic pressure (dBP);

median blood pressure (mBP);

pulse Pressure (PP);

stroke Volume (SV);

cardiac Output (CO); and

total Peripheral Resistance (TPR).

Hdp values were measured and recorded according to established procedures. The initial measurement of an individual or patient during a period of time of the radio frequency carrier signal that is not exposing a highly specific frequency or its Hdp value is herein designated as the base measurement or base Hdp value. For the purposes of the procedure, initial measurements of the above parameters are taken on a warm-blooded mammalian subject after a period of relaxation (e.g., about 15 minutes) when the patient is lying in a supine position (face and preferably also palm up) or in other comfortable and relaxed positions.

After the initial measurement described above has been made, the diagnosed or pre-diagnosed warm-blooded mammalian subject is exposed to, or subjected to, the procedure described above, i.e., diagnosis, search SFq, discovery of new SFq, therapy follow-up (involving exposure to or application of a selected series of one or more highly specific frequency radio frequency carrier output signals) is designated herein as exposure measurement or exposure Hdp value.

The one or more highly specific frequency radio frequency carrier signals are electromagnetic field frequency (EMF) output signals that may be generated by a control program loaded into a programmable generator capable of generating EMF output signals at certain predetermined Amplitude Modulation (AM) frequencies. During the time of a heartbeat within a determined period of time (most preferably, within the time of at least ten heartbeats of the patient or within a period of at least 10 seconds), the subject or patient is most preferably exposed to or an EMF output signal is applied to the patient. In some embodiments, the subject or patient is exposed to or applied to the EMF output signal for a period of at least three heartbeats or for a period of at least 3 seconds of the patient. This procedure is part of the integrated solution of the invention and will typically be performed while the patient remains connected or reconnected to both the synchronized Hdp monitoring system and the programmable generator of the system of the invention, so that Hdp values can be measured and recorded during the exposure or application period. However, the Hdp values may also or alternatively be data sources to identify SFq and/or representative Hdp variation values after the above-described software processing.

The aforementioned Hdp values measured during or after the aforementioned exposure or application to a subject or patient are referred to herein as exposed or exposed Hdp values and post-exposed or post-exposed Hdp values, respectively.

The general application to a plurality of patients pre-diagnosed with or diagnosed with a known health condition, which patients have an identified form of an adverse health condition, provides a plurality of basal Hdp values, a plurality of exposed Hdp values, and a plurality of post-exposed Hdp values as related to the identified pre-diagnosed or diagnosed form of the adverse health condition. These multiple Hdp values (e.g., for most, if not all, of the nine Hdp parameters listed above) may typically be slightly dispersed values. Thus, for the purpose of defining representative Hdp values, such dispersed values will be submitted periodically to a processing system that integrates multiple mathematical calculations for identifying SFq and representative Hdp variation values.

Consistent with the foregoing, the Hdp monitoring system is only part of the present invention, providing means for software processing of Hdp values for identification of SFq and diagnosis of a patient's health condition. SFq and an identification of representative Hdp change values are referred to as representative surrogate markers that are determined by the processing system from an Hdp monitoring system that measures and records Hdp values during non-exposure and exposure periods for patients pre-diagnosed and diagnosed as healthy or having a known form of poor health condition.

For purposes of the present invention, representative surrogate markers for diagnostic, search SFq, treatment, and treatment follow-up purposes are derived from a calculated combination of information from representative basal Hdp measurements, representative exposure Hdp measurements, and representative post-exposure Hdp values. Since the frequency of the exposed EMF used to affect the Hdp value is different for each health condition and the post-exposure Hdp value is similarly different, the combination of calculations for obtaining a representative surrogate marker for a given health condition requires a different calculation for the ideal threshold determination using discriminant analysis and well-established statistical methods.

The reliability of the representative surrogate marker value will of course depend on the number of pre-diagnoses and surrogates that are included for each type of poor health condition examined. Thus, the incidence of poor health conditions in the population, especially the high incidence of poor health conditions (such as hepatocellular carcinoma (HCC) or related liver diseases) that are difficult to diagnose, has received particular attention. Similarly, breast cancer, which has been reported to date to have a relatively high incidence, has received particular attention as well, as reported below.

For purposes of the present invention, post-exposure Hdp measurements, which may be reflected in situations after exposure to or application of a low-energy EMF carrier signal, can be compared to Hdp values that occur after exposure or application to the patient of a predetermined EMF frequency value predetermined to mitigate the cause of a specified poor health condition in the patient. Matching the base Hdp value, the exposed Hdp value and the post-exposure Hdp value, either alone or after use of the processing system, may support treatment efficacy by applying the predetermined EMF carrier signal and provide a preliminary indication of a diagnosis of the patient's health condition. Reference is made below to further scientific details, for example, relating specifically to two different forms of cancer diagnosis. Here, the patient diagnosis after a basal non-exposure period is mentioned as well as the relevance of the patient diagnosis in the hemodynamic pattern of HCC in men and breast cancer in women compared to healthy controls. Similarly, tumor specific hemodynamic response patterns during exposure periods are mentioned.

A period of time during which the EMF frequency output signal is exposed or applied by means of a variable frequency programmable generator device over a wide frequency range; for example, an EMF frequency in the range between about 0.01MHz to about 150MHz may require a short period of time for the Hdp value to change at any particular frequency value. Thus, it may be desirable to continuously expose or apply portions of the range of EMF frequencies to identify EMF frequency values at which basal, exposed and post-exposure Hdp values actually occur during the heartbeat times at which the Hdp values are measured and recorded by the Hdp monitoring system and processed by the computational processing system.

In addition to the programmable generator of EMF frequency output signals, the processing system and ILf central server, the system of the present invention also includes output signal frequency measuring and recording means for measuring and recording frequency values at which a frequency Hdp difference (herein a threshold value) of at least some of the Hdp values is exhibited. Similarly, Hdp value recording means for measuring and recording each of the measured values for each of the identified Hdp values before, during or after (preferably separately from each other) the period of exposure to or application of the output signal to the patient.

In addition to those components described above, additional components of the present integrated invention are processing system components that may be integrated with or coupled to a recording device for recording Hdp values before, during, or after performing or exposing a patient to a cell stimulation procedure. The processing system components may include program controlled computing means for performing a series of mathematical analyses on the various recorded Hdp values to obtain representative surrogate values (e.g., identifying SFq and representative Hdp change values for each of the different recorded Hdp values), optionally determining a ratio between the different representative Hdp values, and comparing either or both of such representative values or ratios between different values to predetermined representative values or ratios (thresholds) that are characteristic of SFq and/or representative Hdp change values that change while exposing the patient to a predetermined cell stimulation procedure in a patient known to be healthy or known to have or likely to develop an identified poor health condition. Comparing calculated representative surrogate values (such as recorded Hdp values or ratios in patients diagnosed with the same health condition and identified SFq and/or representative Hdp change values that match predetermined representative Hdp values or ratios and/or identified SFq and/or representative Hdp change values) results in providing an indication of a diagnosis of a health condition of a patient.

Alternatively or in addition to integration or coupling to the recording device as described above, the processing system components may also be located at a central server connected to the invention through a protected web platform, which may perform analysis based on recorded Hdp information received or transmitted to the center.

Exposure to the marker SFq has demonstrated biological activity and supports its use as a novel therapeutic modality.

Fig. 7 depicts an illustrative schematic structure of an integrated medical system according to an embodiment. As shown in fig. 7, the system for diagnosing the health condition of a patient integrates an Hdp monitoring system with a frequency generator. In an embodiment, the system may further comprise a processing system.

The Hdp monitoring system determines the cardiovascular performance reserve of each individual patient. In an embodiment, the Hdp monitoring system may receive input physiological data from a patient. The input physiological data may be used to obtain a parameter Z that is or approximates the product of the patient's Stroke Volume (SV) and the patient's Systemic Vascular Resistance (SVR). The Hdp monitoring system may further provide a value representative of a Respiratory Rate (RR) of the patient. The RR value may be determined by one or more of: measurements made using a dedicated device, calculations made using input physiological data, or manually determined by using a best estimate (e.g., an estimate based on the heart rate of the patient).

The modulated low energy electromagnetic emission application system generator may be used to emit low energy Radio Frequency (RF) electromagnetic waves to a warm-blooded mammalian subject. Low energy RF electromagnetic waves can be used to treat warm-blooded mammalian subjects suffering from a limited number of described health conditions. The system described herein integrates and synchronizes the Hdp monitoring system and the generator via a processing system that may further be connected to a central server through a protected Web-based platform.

The system described herein may be an integrated solution with a patient-side component and a server-side component. The patient side components may include an Hdp monitoring system connected to a programmable generator. Both the Hdp monitoring system and the programmable generator may be connected to a processing system to synchronize the devices and allow compatible data aggregation. The central server-side component may connect with the patient-side component via a protected web-based platform and may provide artificial intelligence-based computing and data storage.

In some embodiments, the radio frequency generator may be configured to measure and store reflected power values synchronized with the Hdp monitoring values during exposure for further artificial intelligence based calculations and data storage, as described in additional detail below.

As described above, the two components of the system may implement bidirectional data transfer in real time. For example, once the programmable generator is loaded with a selected series SFq of one or more control programs, the programmable generator may be disconnected from the integrated solution for outpatient use. The programmable generator may be reconnected in the integrated solution to permit bulk upload of updated data and to allow transfer of automated treatment mapping analysis back to the processing system.

Referring to fig. 7, the new medical device related to the present invention is an integration of other medical devices. The Hdp monitor provides a method for determining the cardiovascular performance reserve of each individual patient, the method comprising the steps of: a) receiving input physiological data from a patient for obtaining a parameter Z that is or approximates Stroke Volume (SV) multiplied by Systemic Vascular Resistance (SVR); b) providing a value representative of the patient's Respiratory Rate (RR), wherein the Respiratory Rate (RR) value is provided manually through measurements made using a dedicated device(s), calculations from input physiological data, or through the use of a best estimate (as described in U.S. patent application publication No. 2015/0005647); c) electrocardiography (ECG) and photoplethysmography (PPG), in which ECG measures the biopotentials generated by the electrical activity of the heart, while PPG senses the blood flow rate; and d) Heart Rate Variability (HRV), which is the oscillation in successive cardiac cycles.

Electromagnetic emission application system 11 the generator relates to the practice of emitting low energy Radio Frequency (RF) electromagnetic waves to a warm-blooded mammalian subject for the treatment of a warm-blooded mammalian subject suffering from a limited number of described health conditions, as described in previous U.S. patent nos. 4,649,935 and 4,765,322. The new device related to the present invention integrates and synchronizes the two medical devices through a processing system that connects the new device to a central server through a protected web-based platform.

Referring to fig. 7, the new medical device may be an integrated solution with two components: a patient-side component and a central server-side component. The patient-side component may include an Hdp monitoring system connected to a programmable generator, both connected to a processing system in integrated hardware for synchronization and compatible data aggregation. The central server-side component interfaces with the patient-side component through a protected web-based platform and provides artificial intelligence-based computing and data storage.

Referring to fig. 7, as described above, the two components of the new medical device relating to the present invention are connected to bi-directional data transfer in real time. The programmable generator, once loaded with the selected series SFq of control programs, can be disconnected from the integrated solution of the present invention for outpatient use. The programmable generator may be reconnected in the integrated solution of the present invention for bulk upload of updated data and transfer of automated treatment mapping back to the processing system.

The system 11 includes conductive applicators 12, 13 for applying one or more electromagnetic emissions to a warm-blooded mammalian subject. Many different forms of applicators may consist of a conductive probe 13 in intimate contact with the subject undergoing treatment. The probe 13 is connected to an electromagnetic energy transmitter (see also fig. 8) by a coaxial cable 12 and an impedance matching transformer 14.

The electronic system 11 also includes a connector or coupler for connecting to a programmable device such as a computer or an interface or receiver 16 adapted to receive an application storage device 52 (such as, for example, magnetic, semiconductor, optical, or mechanically encoded media) or programmed transmissions programmed with control information for controlling the operation of the system 11 such that a desired type of low energy transmission therapy is applied to the patient.

The application program storage device 52 may be provided with a microprocessor that, when applied to the interface 16, operates to control the functions of the system 11 to apply the desired low energy transmission therapy. The application storage device 52 is provided with a microprocessor for use in combination with the microprocessor 21 within the system 11. In this case, a microprocessor-assisted storage device 52 within the device 52 interfaces with the system 11 and other central servers.

The system 11 may also include a display 17 that may display various indications of the operation of the system 11. Additionally, the system 11 may include an on power button 18 and an off power button 19, optionally replaced by a user interface 21A (see fig. 8).

Fig. 8 depicts an illustrative block diagram of an Hdp monitoring system according to an embodiment. The system includes a computing device 600. Computing device may include various additional components, such as basic configuration 601, bus/interface controller 640, storage 650, output devices 660, peripheral devices 670, communication interfaces 680, and/or other computing devices 690. One or more electrical buses may be configured to operatively connect the components identified above. For example, a memory interface bus 641 may be configured to operatively connect memory device 650 and bus/interface controller 640. Additionally, interface bus 642 may be configured to operatively connect bus/interface controller 640 with output interface 660, peripheral interface 670, and communication interface 680.

The basic configuration 601 may include a processor 610, a system memory 620, and a memory bus 630 configured to operatively connect the processor and the system memory. In some examples, the processor 610 may include a level 1 cache 611, a level 2 cache 612, a processor core 613, one or more registers 614, and a memory controller 615. In some implementations, the system memory 620 can include various software or operating modules, such as an operating system 621, one or more application programs 622, and program data 624.

In some examples, storage 650 may include a removable storage 651 comprising, for example, a USB storage device or other similar removable media. The storage 650 may also include non-removable storage 652, such as a hard disk drive. In some implementations, the output interfaces 660 can include a graphics processing unit 661, an audio processing unit 662, one or more a/V ports 663 operatively connected to the graphics processing unit and the audio processing unit. In some examples, external output devices (such as a monitor or other similar display and/or speakers or other similar audio output devices) may be operatively connected to the a/V port 663.

In some examples, the peripheral interface 670 may include a series interface controller 671, a parallel interface controller 672, and one or more I/O ports 673 operatively connected to the series interface controller 671 and the parallel interface controller 672. In some examples, an external device, such as a printing device, can be operatively connected to computing device 600 via the one or more I/O ports 673. In some implementations, the communication interface 680 may include a network controller 681 configured to facilitate communication with other communication devices 690. In some examples, the network controller 681 can be operably connected to one or more communication ports 682 for establishing communication with other communication devices 690. For example, the established communication may be via a wired or wireless data communication link.

In some embodiments, a system as illustrated in fig. 8 may be configured to measure Hdp values before, during, and/or after application of the electromagnetic output signal to the patient. In an embodiment, the circuitry may be provided with a connector configured to connect with an Hdp monitoring system. Alternatively, the circuitry may be integrated into the Hdp monitoring system. Descriptions of each of the blocks of the block diagrams or their functions are included to facilitate an understanding thereof.

A block diagram of the electronic circuitry of the Hdp monitoring system applies the AM RF output signal to the patient at a predetermined selected AM frequency. The predetermined selected frequency is controlled by an AM frequency value stored in storage device 52 and/or other server. Various predetermined selected AM frequencies applied to the patient are indicated for treating the patient with an adverse health condition for which the patient has been diagnosed.

In embodiments, the integrated or combined device may enable sensing of the patient's Hdp value and reflected power energy before, during, or after application of AM RF electromagnetic signals or other such signals. Of particular interest in this regard, the measured and recorded Hdp and reflected power energy values may vary depending on the patient condition. For example, the measured and recorded Hdp and reflected power energy values may be different in patients with different forms of cancer. In addition, the measured and recorded Hdp and reflected power energy values may be different for patients with some form of cancer and healthy patients. However, such Hdp and reflected power energy values may be substantially similar for patients with the same or closely related poor health conditions. The measured and recorded representative Hdp and reflected power energy change values and identification of SFq thus provides diagnostic and therapeutic opportunities for various forms of poor health conditions. In addition, such Hdp variation values may permit diagnosis of the condition of a healthy patient.

Referring back to fig. 7 (only the programmed generator portion of the invention is described here), microprocessor 21 operates as a controller for the application system and is connected via address bus 22, data bus 23 and input/output (I/O) lines 25 to control the various components of the system. The microprocessor 21 preferably includes internal storage for operating code, control programs and temporary data. In addition, the microprocessor 21 includes an input/output (I/0) port and an internal timer. The microprocessor 21 may be, for example, an 8-bit single chip microcontroller 8048 or 8051 available from Intel Corporation of Santa Clara, California. The timing of the microprocessor 21 is provided by a system clock 24 comprising a clock crystal 26 and capacitors 27 and 28. The system clock 24 may run at any clock frequency suitable for the particular type of microprocessor used. According to one embodiment, the system clock 24 operates at a clock frequency of 8.0 MHz.

In general, the microprocessor 21 is used to control the controllable electromagnetic energy generator circuit 29 to produce a desired form of modulated low energy electromagnetic emissions for application to the patient through the probe 13. The controllable generator circuit 29 comprises a modulation frequency generator circuit 31 and a carrier signal oscillator 32. Microprocessor 21 operates to activate or deactivate controllable generator circuit 29 via oscillator disable line 33. The controllable generator circuit 29 also includes an AM modulator and power generator 34 that operates to amplitude modulate a carrier signal generated by the carrier oscillator 32 on a carrier signal line 36, the modulation signal being generated by the modulation signal generator circuit 31 on a modulation signal line 37. Modulator 34 generates an amplitude modulated carrier signal on modulated carrier signal line 38 which is then applied by filter circuit 39. The filter circuit 39 is connected to the probe 13 via the coaxial cable 12 and the impedance transformer 14.

The microprocessor 21 controls a modulation signal generator circuit 31 of a controllable generator circuit 29 via an address bus 22, a data bus 23 and an I/O line 25. Specifically, the microprocessor 21 selects a desired waveform stored in the modulation waveform storage device 43 via the I/O line 25. Microprocessor 21 also controls waveform address generator 41 to generate a sequence of addresses on waveform address bus 42 for application to modulated signal storage device 43 in order to retrieve the selected modulated signal. The desired modulation signal is retrieved from the waveform look-up table 43 and applied to the modulation signal bus 44 in digital form. The modulation signal bus 44 is applied to a digital-to-analog converter (DAC)46 that converts the digital modulation signal to analog form. This analog modulation signal is then applied to selectivity filter 47, which filters the analog modulation signal under control of microprocessor 21 by using a variable filter network including resistor 48 and capacitors 49 and 51 to smooth the waveform produced by DAC 46 on modulation signal line 20.

In an embodiment of the present invention, various modulated signal waveforms are stored in the look-up table 43. In an embodiment, the look-up table 43 may contain up to 8 different modulated signal waveforms, although more or fewer waveforms may be stored in the look-up table. Waveforms that have been successfully employed include square or sinusoidal waveforms. Other possible modulation signal waveforms include rectified sinusoidal waveforms, triangular waveforms, and combinations of all of the above.

In an embodiment, each modulated signal waveform uses 256 bytes of memory and is retrieved from the lookup table 43 by traversing 256 consecutive addresses. It should be noted that more or fewer bytes of memory may be used for each waveform within the scope of the present disclosure, as will be apparent to those of ordinary skill in the art. The frequency of the modulated signal is controlled by the speed at which the waveform is retrieved from the look-up table 43. In an embodiment, this is accomplished by downloading control code from microprocessor 21 into a programmable counter contained in waveform address generator 41. The output of the programmable counter then drives a ripple counter that generates a sequence of addresses on the waveform address bus 42.

The waveform address generator 41 may be, for example, a programmable timer/counter updd 65042C available from NEC. The modulation signal storage device or look-up table 43 may be, for example, a 28C16 type Electrically Erasable Programmable Read Only Memory (EEPROM) programmed with a desired waveform table. The digital-to-analog converter 46 may be, for example, a DAC port such as the AD557JN available from analog devices, usa, and the selectivity filter 47 may be a type 4052 multiplexer available from national Semiconductor, usa or Harris Semiconductor. Additional or alternative components may be used within the scope of the present disclosure.

The specific modulation control information used by microprocessor 21 to control the operation of controllable generator circuit 29 is stored in application storage device 52 in accordance with the present invention, or in the case of the present invention may be a variable AM frequency tuning device adapted to load interface 16 with an AM frequency between a high frequency level and a low frequency level. Application storage device 52 may be any storage device capable of storing information for later retrieval. The application storage device 52 is connected to the processing system through the interface 16 to complete the integration solution in the present invention.

It should be emphasized that although the figures show the microprocessor 21 separate from the application storage device 52, the microprocessor 21 and the application storage device 52 store the control program loaded from the processing system in the programmable generator. As described herein, the control program, once loaded into the system, controls the operation of the system. In this case, an interface 16 would exist between the combination of the microprocessor 21 and the application storage device 52 and the rest of the system.

The interface 16 is configured to suit the particular application storage device 52 being used. The interface 16 converts the control information stored in the application storage device 52 into a usable form for storage within the memory of the microprocessor 21 to enable the microprocessor 21 to control the controllable generator circuit 29 to produce the desired modulated low energy emission. Interface 16 may read information stored on application storage device 52 directly or may read information through a communication link with the processing system. When the application storage device 52 and the microprocessor 21 are incorporated in the same device, the interface 16 is configured to connect the microprocessor 21 to the rest of the system.

The control information stored in the application storage device 52 specifies various controllable parameters of the modulated low energy RF electromagnetic emissions that are applied to the patient by the probe 13. Such controllable parameters include, for example, the frequency and amplitude of the carrier wave, the amplitude and frequency of the modulation of the carrier wave, the length of time of the transmission, the power level of the transmission, the duty cycle of the transmission (i.e., the ratio of on time to off time of the pulsed transmission applied during application), the order of application of the different modulation frequencies for a particular application, as well as the total number of treatments prescribed for a particular patient and the length of time of each treatment.

For example, a carrier signal and a modulation signal may be selected to drive probe 13 with an amplitude modulated signal, wherein the carrier signal includes spectral frequency components below 1GHz, and preferably between 1MHz and 900MHz, and wherein the modulation signal includes spectral frequency components between about 0.1Hz to about 10MHz, between about 1Hz and about 150KHz, between about 0.01Hz and about 1,000Hz, or between about 0.01Hz and about 2,000 Hz. In an embodiment, one or more modulation frequencies may be ordered to form a modulated signal.

As a further feature, an electromagnetic emission sensor 53 may be provided to detect the presence of electromagnetic emissions at the frequency of the carrier oscillator 32. Emission sensor 53 provides microprocessor 21 with an indication of whether there is electromagnetic emission at the desired frequency. The microprocessor 21 then takes appropriate action, such as displaying an error message on the information output display 17, disabling the controllable generator circuit 29, etc.

The system may further include a power sensor 54 that detects the amount of power applied to the patient through the probe 13 as compared to the amount of power returned or reflected from the patient. This ratio indicates the correct use of the system during a therapeutic session. The power sensor 54 applies an indication to the microprocessor 21 via the power sense line 56 of the amount of power applied to the patient via the probe 13 relative to the amount of power reflected from the patient.

The indication provided on the power sense line 56 may be digitized and used by the microprocessor 21, for example, to detect and control the level of applied power and record information on the application storage device 52 relating to the actual treatment applied. Data transfer information to the processing system may include, for example: the number of treatments applied over a given period of time; the actual time and date of each treatment; the number of treatments attempted; treatment compliance (i.e., whether the probe is in place during the course of treatment); and the cumulative dose for a particular modulation frequency.

The applied power level is preferably controlled such that the Specific Absorption Rate (SAR) of the energy absorbed by the patient is between 1 microwatt per kilogram of tissue and 50 watts per kilogram of tissue. Preferably, the power level is controlled so that the SAR ranges from 100 microwatts per kilogram of tissue to 10 watts per kilogram of tissue. Most preferably, the power level is controlled to deliver a whole-body mean SAR in the range of only 0.2mW/kg to 1mW/kg with a 1g peak-space SAR between 150mW/kg and 350 mW/kg. These SAR may be in any tissue of the patient. The system also includes power supply circuitry including battery and charger circuitry 57 and a battery voltage change detector 58.

In an integrated solution, combining or using two medical devices having the properties described above, but performing different but synchronized tasks derived from their initial concept and application, such as measuring and recording Hdp values (at least nine parameter values mentioned) before, during or after exposing or applying the EMF frequency output signal, and identifying representative Hdp variation values and SFq, has provided a scientific and reproducible method to diagnose and treat a patient's health condition, additional scientific details are provided below, for example relating specifically to one of the different forms of cancer diagnosis.

The identification of changes in pulse amplitude in patients diagnosed with cancer when exposed to low and safe levels of 27.12MHz radio frequency electromagnetic fields that are amplitude modulated at a particular frequency has been previously reported. (Bar, A. et al, Amplitude-modulated electronic fields for the discovery of cancer: discovery of tumor-specific frequencies and assessment of novel therapeutic methods via electromagnetic fields of Amplitude modulation ], J.Exp.Clin.cancer Res.28,51, doi: 10.1186/1756-. The observation that changes in pulse amplitude occur at exactly the same frequency in patients with the same type of cancer leads to the following hypothesis: each type of cancer has a specific frequency signature. In vitro experiments (supra) have shown that tumor specific frequencies have antiproliferative effects on cancer cells, modulate the expression of genes involved in cell migration and invasion, and are capable of disrupting the mitotic spindle. (Zimmerman, J.W. et al, Cancer cell promotion is inhibited by specific modulation frequencies, British Journal of Cancer106,307-313 (2012)). Clinical activity of these tumor-specific frequencies was evaluated in two separate studies in which patients were treated with buccal administration of AM RF EMF modulated at tumor-specific frequencies. Anti-tumor activity was observed in patients with metastatic breast Cancer (Barbault, A. et al, (2009)) and advanced hepatocellular carcinoma (Costa, F.P. et al, treatment of advanced hepatocellular carcinoma with high level of electromagnetic field modulated for treatment of advanced hepatocellular carcinoma, British Journal of Cancer 105,640-648(2011)), and stable disease was observed in patients with other tumor types.

This study was designed to test the following assumptions: analysis of Hdp changes in warm-blooded mammalian subjects upon exposure to tumor-specific frequencies is a novel non-invasive diagnostic method. The method can also identify SFq to be used to treat a health condition in the patient.

The experimental procedures described below were reviewed and approved by the Institutional Review Board of Ribayon Hospital (IRB) (Hospital S i rioLiban Es Institutional Review Board) located at Rua Donaadma Jafet,50Conj.41/43, Brazilian Saint Paul SP 01.308-050 (An)Paulo SP 01.308-050 Brazil). All patients and healthy individuals enrolled in the study signed an informed consent form approved by IRB. Protocol registration was performed prior to enrollment of patient 1: gov, accession number NCT 01686412. 87 individuals were screened and expected to be grouped into 82 individuals. Before performing the computational analysis, the diagnosis of the patient and the nature of the AM RF EMF exposure (HCC-specific frequency, breast cancer-specific frequency, and randomly selected frequency) were revealed to build a knowledge base. The validation group included patients with biopsies confirmed to have cancer (advanced HCC and advanced breast cancer) and healthy controls. The final group included patients with potentially resectable HCC.

The AM RF EMF device used in this study has been described in detail previously. (Costa, F.P. et al, Britishjournal of cancer 105,640-648 (2011)). While patients receiving AM RF EMF treatment were exposed to treatment for about 35 minutes to about 1 hour once a day, twice a day, or three times a day, the diagnostic feasibility of AM RF EMF administration was tested during a single exposure of about 10 minutes or about 35 minutes, so that all individuals were exposed to each of 194 tumor-specific frequencies (HCC-specific and breast cancer-specific) at once, each of these tumor-specific frequencies was emitted for about three seconds or about ten seconds. (Barbault, A. et al, J.Exp. Clin. Cancer Res.28,51, doi: 10.1186/1756-. Similarly, 194 of the 236 randomly selected frequencies previously reported (Zimmerman, j.w. et al, British Journal of cancer106,307-313(2012)) were selected to match the number of tumor-specific frequencies and the duration of exposure. Thus, each individual was exposed to all frequencies (HCC-specific frequency, breast cancer-specific frequency, and randomly selected frequency) included in each of the treatment programs. As previously described, each modulation frequency is transmitted for about three seconds from the lowest frequency to the highest frequency. ((Barbault, A. et al, J.Exp. Clin. cancer Res.28,51, doi: 10.1186/1756-.

Various examples of recording and analyzing Hdp values Using the above-described System are described in U.S. patent application No. 15/844,214 entitled "System for Characterization, Diagnosis, and Methods of Using Same" filed on 2017, 12, 15, the contents of which are incorporated herein by reference in their entirety.

In certain applications of the systems and techniques as described herein, the application of a particular frequency signature or set of frequencies can be beneficial in providing both prognostic and predictive benefits to a patient as well as providing treatment to a patient. For example, when treating a particular type of cancer, such as HCC, applying a series of particular electromagnetic frequencies (e.g., a set of 194 specifically selected frequencies as shown in fig. 13E) or a series of electromagnetic frequencies that occur every 3Hz or 10Hz in the range of about 0.01Hz to about 20KHz or in the range of about 0.01Hz to about 2KHz may be therapeutic in nature and may provide prognostic and predictive information (by, for example, measuring the HRV response of a patient to the application of frequencies). In addition, information may also be measured, such as the amount of energy absorbed by the patient, the amount of energy reflected by the patient, and other similar measurable quantities that may be recorded and analyzed to provide additional information, as described below.

As noted above, to administer a selected frequency to a patient, a carrier signal may be used. For example, oscillator 302 may be configured to generate a carrier signal having a particular frequency, such as 27.12 MHz. However, by modulating the amplitude of the carrier wave, the modulated signal may be generated at each of the 194 frequencies, for example as shown in FIG. 13E, or at a series of electromagnetic frequencies that occur every 3Hz or 10Hz in the range of about 0.01Hz to about 20KHz, in the range of about 10Hz to about 1,000Hz, or in the range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated signal may be generated at a series of electromagnetic frequencies that occur every 3Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated signal may be generated at a series of electromagnetic frequencies that occur every 3Hz in the range of about 10Hz to about 1,000Hz, or in the range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated signal may be generated at a series of electromagnetic frequencies that occur every 10Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated signal may be generated at a series of electromagnetic frequencies that occur at about 10Hz to about 1,000Hz, or every 10Hz in the range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated signal may be generated at a range of electromagnetic frequencies as set forth in table 1 below. In still other embodiments, the modulated signal may be generated at a series of electromagnetic frequencies occurring every about 3Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated signal may be generated at a series of electromagnetic frequencies that occur about every 3Hz in the range of about 10Hz to about 1,000Hz, or about every 3Hz in the range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated signal may be generated at a series of electromagnetic frequencies that occur about every 10Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated signal may be generated at a series of electromagnetic frequencies that occur about every 10Hz in the range of about 10Hz to about 1,000Hz, or about every 10Hz in the range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated signal may be generated at a range of electromagnetic frequencies as set forth in table 1 below.

TABLE 1 (in Hz)

10	76	142	208	274	340	406	472	538	604	670	736	802	868	934	1000
																13	79	145	211	277	343	409	475	541	607	673	739	805	871	937
16	82	148	214	280	346	412	478	544	610	676	742	808	874	940
																19	85	151	217	283	349	415	481	547	613	679	745	811	877	943
22	88	154	220	286	352	418	484	550	616	682	748	814	880	946
																25	91	157	223	289	355	421	487	553	619	685	751	817	883	949
28	94	160	226	292	358	424	490	556	622	688	754	820	886	952
																31	97	163	229	295	361	427	493	559	625	691	757	823	889	955
34	100	166	232	298	364	430	496	562	628	694	760	826	892	958
																37	103	169	235	301	367	433	499	565	631	697	763	829	895	961
40	106	172	238	304	370	436	502	568	634	700	766	832	898	964
																43	109	175	241	307	373	439	505	571	637	703	769	835	901	967
46	112	178	244	310	376	442	508	574	640	706	772	838	904	970
																49	115	181	247	313	379	445	511	577	643	709	775	841	907	973
52	118	184	250	316	382	448	514	580	646	712	778	844	910	976
																55	121	187	253	319	385	451	517	583	649	715	781	847	913	979
58	124	190	256	322	388	454	520	586	652	718	784	850	916	982
																61	127	193	259	325	391	457	523	589	655	721	787	853	919	985
64	130	196	262	328	394	460	526	592	658	724	790	856	922	988
																67	133	199	265	331	397	463	529	595	661	727	793	859	925	991
70	136	202	268	334	400	466	532	598	664	730	796	862	928	994
																73	139	205	271	337	403	469	535	601	667	733	799	865	931	997

In some embodiments, the signal shown in FIG. 13A may represent an AMRF EMF signal generated by modulating the amplitude of a carrier signal. As shown in fig. 13A, by modulating the amplitude of the signal, varying modulation depths can be achieved, thereby producing a modulated signal having a particular modulation frequency. However, as further shown in fig. 13A, during amplitude modulation, the frequency of the carrier signal remains unchanged, e.g., 27.12MHz in this example.

For accurate measurement of HRV, to identify biological substitutes within a human body, a highly accurate and integrated system of signals in a time-domain nonlinear system can be identified at very short time intervals. For example, a real-time coordinated low energy electromagnetic exposure (r.o.l.e.x.) system (such as the system 900 shown in fig. 9B) may synchronize the monitoring device 902, the radio frequency generator 904, the database 908 configured to store a dynamic data library for hemodynamic recording and data processing in an accurate, reliable, and reproducible manner, and the computing device configured to store and execute artificial intelligence computing algorithms and processing techniques.

In certain embodiments, and considering that all hemodynamic parameters are recorded for each heartbeat (beat-to-beat) and the frequency modulation exposure time is measured in seconds, the program coordinator 906 may be configured to control the monitor 902 and frequency generator 904 and send an intervention signal to the monitor for each new frequency modulation exposure for later data synchronization. Data filtering, data synchronization, data processing for calculation of time domain parameters, and a.i. data transformation are all performed in accordance with a strict calculation process and automatically using techniques and processes as described below.

To collect data to train and improve a.i. as described herein, initial patient data may be collected to train an initial machine learning algorithm (explained in more detail below in the discussion of fig. 65). For example, fig. 10 illustrates two data flows that may represent data collection for a set of patients. An initial flow 1005 includes collecting general treatment information for a group of patients. These patients may be diagnosed as healthy or suffering from one or more ailments or diseases (such as HCC). Hemodynamic parameters of the patient may be monitored and collected into the initial data set 1007.

Similarly, the second data flow 1010 may also include collecting data of the patient using electromagnetic exposure. For example, a particular set of frequencies may be determined (or a standard set of frequencies may be used), such as the set of frequencies shown in FIG. 13E. The RF generator may generate those frequencies and expose a group of patients to those frequencies. The HD monitor may measure the hemodynamic response of the patient to the exposure and collect and record this data as data set 1012.

In certain embodiments, the processes and techniques as described in fig. 10 may be used to tune the treatment for a particular patient. For example, as shown in fig. 11, a specialized or tuned therapy for a patient 1105 may be automatically determined or calculated based on records collected for other patients, initial test results for the patient 1105, and machine learning/a.i. techniques. For example, as shown in fig. 11, patient records may be collected as described above in the discussion of fig. 10. The patient records may then be analyzed and further processed to transform the records into a data format configured to be input into one or more machine learning algorithms. The output of the machine learning algorithm can then be further analyzed and compared to information (e.g., demographic information, previous test results, initial energy exposure information) for a particular patient to determine a particular treatment regimen for that patient. In certain embodiments, the personalized therapy regime may include an automatically tuned set of frequencies or energy levels 1110 for use during exposure of the patient 1105 to electromagnetic energy as described herein.

As shown in fig. 18A, 18C, and 18D, a specific treatment timeline may be implemented for exposing a patient to a set of modulated frequency signals. As shown in fig. 18A, 18C, and 18D, the patient may relax in a supine position. An initial non-exposure period of about ten minutes may be included to relax the patient and establish baselines for various hemodynamic parameters and HRV. After the initial non-exposure period, the patient may be exposed to the carrier signal (i.e., at a constant amplitude without amplitude modulation) for a period of about ten minutes. After the initial exposure period, the patient may be exposed to the modulated frequency exposure period for about 120 minutes, about 60 minutes, about 35 minutes, or about 10 minutes. During this time, each of a set of frequencies (e.g., 194 frequencies as described herein or a series of electromagnetic frequencies occurring every 3Hz or 10Hz in the range of about 0.01Hz to about 20KHz, in the range of about 10Hz to about 1,000Hz, or in the range of about 10Hz to about 2,000 Hz) may be applied to the patient for a particular period of time. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur about every 3 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur about every 4 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur about every 5 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur about every 6 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur about every 7 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur about every 8 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur about every 9 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur about every 10 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur in the range of about every 3Hz to about every 10 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur in the range of about every 4Hz to about every 10 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur in the range of about every 5Hz to about every 10 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur in the range of about every 6Hz to about every 10 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur in the range of about every 7Hz to about every 10 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur in the range of about every 8Hz to about every 10 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur in the range of about every 9Hz to about every 10 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur in the range of about every 3Hz to about every 9 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur in the range of about every 3Hz to about every 8 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur in the range of about every 3Hz to about every 7 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur in the range of about every 3Hz to about every 6 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur in the range of about every 3Hz to about every 5 Hz. In some embodiments, a series of electromagnetic frequencies in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may occur in the range of about every 3Hz to about every 4 Hz. In some embodiments, the specific time period is about 2 seconds or about 3 seconds per frequency. In further embodiments, the specific time period is about 10 seconds per frequency. In some embodiments, the specific time period is about 2 seconds per frequency. In some embodiments, the specific time period is about 3 seconds per frequency. In some embodiments, the specific time period is about 4 seconds per frequency. In some embodiments, the specific time period is about 5 seconds per frequency. In some embodiments, the specific time period is about 6 seconds per frequency. In some embodiments, the specific time period is about 7 seconds per frequency. In some embodiments, the specific time period is about 8 seconds per frequency. In some embodiments, the specific time period is about 9 seconds per frequency. In some embodiments, the specific time period is about 10 seconds per frequency.

In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 3Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz within a range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 10Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 2,000 Hz. The system may cycle through each of the frequencies repeatedly over the exposure time period. In some embodiments, the exposure period is about 35 minutes (fig. 18A, 18C, 18D). In further embodiments, the exposure period is about 10 minutes (fig. 18C and 18D). In some embodiments, the exposure period is about 120 minutes or about 60 minutes. After the initial exposure period, the system may expose the patient to another carrier signal period for about 10 minutes, during which the patient's body may recover from the initial exposure period of about 120 minutes, about 60 minutes, about 35 minutes, or about 10 minutes (fig. 18A and 18C). At the end of the second carrier signal time period of about ten minutes, the patient may be again exposed to a second modulated frequency signal time period of about 120 minutes, about 60 minutes, about 35 minutes, or about 10 minutes (fig. 18A and 18C). As before, during this time, each of a set of frequencies (e.g., 194 frequencies as described herein or a series of electromagnetic frequencies occurring every 3Hz or 10Hz within a range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000 Hz) may be applied to the patient for a particular period of time (e.g., about 2 seconds to about 3 seconds per frequency or about 10 seconds per frequency). The system may repeatedly cycle through each of the frequencies for an exposure time period, wherein the exposure time period may be about 120 minutes, about 60 minutes, about 35 minutes, or about 10 minutes. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 3Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz within a range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 10Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 2,000 Hz. After this second exposure period, the patient may be re-exposed to the carrier frequency for about ten minutes, after which the treatment session is ended.

The treatment timeline described above may include a treatment session. As shown in fig. 18A and 18C, the treatment session can be repeated for the patient the next day. The treatment session may be performed continuously or discontinuously for one or more days. In some embodiments, the course of treatment is performed for one day. In some embodiments, the treatment session is performed for two consecutive days. In some embodiments, the treatment session is performed on two discrete days. In some embodiments, the course of treatment is performed once per week, twice per week, three times per week, or a combination thereof. In some embodiments, the treatment course is performed 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or a combination thereof per month.

In some embodiments, the treatment course is for any consecutive or non-consecutive number of days described above, or any number of monthly treatment courses described above totaling about 1 year, about 2 years, about 3 years, about 4 years, about 5 years, about 6 years, about 7 years, about 8 years, about 9 years, about 10 years, about 11 years, about 12 years, about 13 years, about 14 years, about 15 years, about 16 years, about 17 years, about 18 years, about 19 years, about 20 years, about 21 years, about 22 years, about 23 years, about 24 years, about 25 years, about 26 years, about 27 years, about 28 years, about 29 years, about 30 years, about 31 years, about 32 years, about 33 years, about 34 years, about 35 years, about 36 years, about 37 years, about 38 years, about 39 years, about 40 years, about 41 years, about 42 years, about 43 years, about 44 years, about 45 years, about 46 years, about 47 years, about 48 years, about 49 years, or a combination thereof. In some embodiments, the treatment session is conducted for any of the consecutive or non-consecutive days described above, or any number of monthly treatment sessions described above totaling about 1 year. In some embodiments, the treatment session is conducted for any of the consecutive or non-consecutive days described above, or any number of monthly treatment sessions described above for a total of about 2 years. In some embodiments, the treatment session is conducted for any of the consecutive or non-consecutive days described above, or any number of monthly treatment sessions described above for a total of about 3 years. In some embodiments, the treatment session is conducted for any number of consecutive or non-consecutive days described above, or any number of monthly treatment sessions described above for a total of about 4 years. In some embodiments, the treatment session is conducted for any number of consecutive or non-consecutive days described above, or any number of monthly treatment sessions described above for a total of about 5 years. In some embodiments, the treatment session is conducted for any of the consecutive or non-consecutive days described above, or any number of monthly treatment sessions described above for a total of about 6 years. In some embodiments, the treatment session is conducted for any of the consecutive or non-consecutive days described above, or any number of monthly treatment sessions described above for a total of about 7 years. In some embodiments, the treatment session is conducted for any of the consecutive or non-consecutive days described above, or any number of monthly treatment sessions described above for a total of about 8 years. In some embodiments, the treatment session is conducted for any number of consecutive or non-consecutive days described above, or any number of monthly treatment sessions described above for a total of about 9 years. In some embodiments, the treatment session is conducted for any of the consecutive or non-consecutive days described above, or any number of monthly treatment sessions described above for a total of about 10 years.

In some embodiments, the treatment session is conducted for any number of consecutive or non-consecutive days described above, or any number of monthly treatment sessions described above for any number of years described above, until tumor progression is reduced. In some embodiments, tumor progression is reduced by about 1% to about 10%, about 1% to about 20%, about 1% to about 30%, about 1% to about 40%, about 1% to about 50%, about 1% to about 60%, about 1% to about 70%, about 1% to about 80%, about 1% to about 90%, about 1% to about 99%. In some embodiments, tumor progression is reduced by about 20% to about 30%, about 20% to about 40%, about 20% to about 50%, about 20% to about 60%, about 20% to about 70%, about 20% to about 80%, about 20% to about 90%, about 20% to about 99%. In some embodiments, tumor progression is reduced by about 40% to about 50%, about 40% to about 60%, about 40% to about 70%, about 40% to about 80%, about 40% to about 90%, about 40% to about 99%. In some embodiments, tumor progression is reduced by about 60% to about 70%, about 60% to about 80%, about 60% to about 90%, about 60% to about 99%.

In some embodiments, the treatment session is conducted for any number of consecutive or non-consecutive days described above, or any number of monthly treatment sessions described above for any number of years described above, until tumor regression is observed. In some embodiments, tumor regression is determined by measuring the percentage of tumor size reduction. In some embodiments, the tumor regression is about 1% to about 10%, about 1% to about 20%, about 1% to about 30%, about 1% to about 40%, about 1% to about 50%, about 1% to about 60%, about 1% to about 70%, about 1% to about 80%, about 1% to about 90%, about 1% to about 99%. In some embodiments, the tumor regression is about 20% to about 30%, about 20% to about 40%, about 20% to about 50%, about 20% to about 60%, about 20% to about 70%, about 20% to about 80%, about 20% to about 90%, about 20% to about 99%. In some embodiments, the tumor regression is about 40% to about 50%, about 40% to about 60%, about 40% to about 70%, about 40% to about 80%, about 40% to about 90%, about 40% to about 99%. In some embodiments, the tumor regression is about 60% to about 70%, about 60% to about 80%, about 60% to about 90%, about 60% to about 99%.

In some embodiments, the treatment session is performed for any number of consecutive or non-consecutive days described above, or any number of monthly treatment sessions described above for any number of years described above, until the tumor is no longer measured.

It should be noted that the treatment schedule shown in fig. 18A is provided by way of example only. In certain embodiments or patient-specific treatment timelines, various details as shown in fig. 18A may be changed or otherwise altered. For example, the number of modulated frequency exposure periods may be changed to be less than or greater than two. Further, the number of carrier frequency exposure periods may be changed to be less than or greater than two. Similarly, the carrier frequency and/or the length of the modulated frequency exposure period may be varied. Further, the modulated frequency may be repeatedly cycled through each of the frequencies for one or more exposure periods.

In some embodiments, the patient may relax in a supine position. An initial non-exposure period of about ten minutes may be included to relax the patient and establish baselines for various hemodynamic parameters and HRV. After the initial non-exposure period, the patient may be exposed to the carrier signal (i.e., at a constant amplitude without amplitude modulation) for a period of about ten minutes. After the initial exposure period, the patient may be exposed to the modulated frequency exposure period for about 35 minutes. During this time, each of a set of frequencies (e.g., 194 frequencies as described herein or a series of electromagnetic frequencies occurring every 3Hz or 10Hz in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000 Hz) may be applied to the patient for a particular period of time (e.g., 10 seconds per frequency or 3 seconds per frequency). In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 3Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz within a range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 10Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 2,000 Hz. The system may cycle through each of the frequencies repeatedly over the exposure time period. After an initial exposure period of about 35 minutes, the system may expose the patient to another carrier signal period of about 10 minutes, during which the patient's body may recover from the initial exposure period of about 35 minutes. At the end of the second carrier signal period of about ten minutes, the patient may be again exposed to a modulated frequency signal period of about 10 minutes. During this time, each of a set of frequencies (e.g., 194 frequencies as described herein or a series of electromagnetic frequencies occurring every 3Hz or 10Hz within a range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000 Hz) may be applied to the patient for a particular period of time (e.g., 2 seconds or 3 seconds per frequency or 10 seconds per frequency). In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 3Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz within a range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 10Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 2,000 Hz. The system may cycle through each of the frequencies repeatedly over the exposure time period. After this second modulated frequency exposure period, the patient may be re-exposed to the carrier frequency for about ten minutes, after which the treatment session is ended. The same course of treatment may be repeated for the patient the next day. The treatment session may be performed continuously or discontinuously for one or more days. In some embodiments, the course of treatment is performed for one day. In some embodiments, the treatment session is performed for two consecutive days. In some embodiments, the treatment session is performed on two discrete days.

In some embodiments, the patient may relax in a supine position. An initial non-exposure period of about ten minutes may be included to relax the patient and establish baselines for various hemodynamic parameters and HRV. After the initial non-exposure period, the patient may be exposed to the carrier signal (i.e., at a constant amplitude without amplitude modulation) for a period of about ten minutes. After the initial exposure period, the patient may be exposed to a modulated frequency exposure period of about 50 minutes, about 60 minutes, or about 120 minutes. During this time, a series of electromagnetic frequencies occurring every 3Hz in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, about 100Hz to about 1,000Hz, or about 10Hz to about 2,000Hz may be applied to the patient about 3 seconds per frequency (FIG. 18E). The system may cycle through each of the frequencies repeatedly over the exposure time period. After this initial exposure period, the system may expose the patient to another carrier signal for about 10 minutes, during which time the patient's body may recover from the initial exposure period, after which the treatment session is ended. The same course of treatment may be repeated for the patient the next day. The treatment session may be performed continuously or discontinuously for one or more days. In some embodiments, the course of treatment is performed for one day. In some embodiments, the treatment session is performed for two consecutive days. In some embodiments, the treatment session is performed on two discrete days.

In some embodiments, the patient may relax in a supine position. An initial non-exposure period of about ten minutes may be included to relax the patient and establish baselines for various hemodynamic parameters and HRV. After the initial non-exposure period, the patient may be exposed to the carrier signal (i.e., at a constant amplitude without amplitude modulation) for a period of about ten minutes. After the initial exposure period, the patient may be exposed to the modulated frequency exposure period for about 50 minutes, about 60 minutes, or about 120 minutes. During this time, a series of electromagnetic frequencies occurring every 3Hz in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, about 100Hz to about 1,000Hz, about 10Hz to about 2,000Hz may be applied to the patient about 3 seconds per frequency (FIG. 18E). The system may cycle through each of the frequencies repeatedly over the exposure time period. After this initial exposure period, the system may expose the patient to another carrier signal period for about 10 minutes, during which time the patient's body may recover from the initial exposure period. In some embodiments, the system may determine whether the patient is to be exposed to the modulated frequency signal for an additional period of time. If the patient is to be exposed for another period of time, the system may expose the patient to another modulated frequency exposure period of time. In some embodiments, the additional modulated frequency exposure includes a modulated frequency that alters a hemodynamic parameter (e.g., heart rate variability) over an initial modulated frequency exposure period. In some embodiments, the additional modulated frequency exposure includes a series of electromagnetic frequencies that occur every 3Hz or 10Hz within a range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000 Hz. After the initial or additional modulated frequency exposure, the system may expose the patient to another carrier exposure period for about ten minutes. The process may end if the patient will not be exposed to another modulated frequency time period. The same course of treatment may be repeated for the patient the next day. The treatment session may be performed continuously or discontinuously for one or more days. In some embodiments, the course of treatment is performed for one day. In some embodiments, the treatment session is performed for two consecutive days. In some embodiments, the treatment session is performed on two discrete days.

As illustrated in fig. 18D, in some embodiments, the patient may relax in a supine position. An initial non-exposure period of about ten minutes may be included to relax the patient and establish baselines for various hemodynamic parameters and HRV. After the initial non-exposure period, the patient may be exposed to the carrier signal (i.e., at a constant amplitude without amplitude modulation) for a period of about ten minutes. After the initial exposure period, the patient may be exposed to the modulated frequency exposure period for about 35 minutes or about 10 minutes. During this time, each of a set of frequencies (e.g., 194 frequencies as described herein or a series of electromagnetic frequencies occurring every 3Hz or 10Hz within a range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000 Hz) may be applied to the patient for a particular period of time (e.g., about 10 seconds per frequency or about 3 seconds per frequency). In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 3Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz within a range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 10Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 2,000 Hz. The system may cycle through each of the frequencies repeatedly over the exposure time period. After an initial exposure period of about 35 minutes or about 10 minutes, the system may expose the patient to another exposure period that includes an initial modulated frequency that alters a hemodynamic parameter (e.g., heart rate variability). In some embodiments, after an initial exposure period of about 35 minutes or about 10 minutes, the system may expose the patient to another exposure period that includes an initial modulated frequency of unaltered hemodynamic parameters (e.g., heart rate variability). In some embodiments, after an initial exposure period of about 35 minutes or about 10 minutes, the system may expose the patient to another exposure period that includes 194 frequencies as described herein or all of a series of electromagnetic frequencies that occur every 3Hz or 10Hz within a range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 3Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz within a range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 10Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 2,000 Hz. After this second modulated frequency exposure period, the patient may be re-exposed to the carrier frequency for about ten minutes, after which the treatment session is ended. The same course of treatment may be repeated for the patient the next day. The treatment session may be performed continuously or discontinuously for one or more days. In some embodiments, the course of treatment is performed for one day. In some embodiments, the treatment session is performed for two consecutive days. In some embodiments, the treatment session is performed on two discrete days.

As noted above, the therapy can be tailored or automatically tuned for a particular patient such that the forward energy (e.g., the energy used in the carrier and modulation frequency signals) is specifically selected for the particular patient. This automatic tuning may be determined for a particular patient based on various factors such as patient information (e.g., health, volume and weight, previous treatment information) such that the forward energy applied to the patient during treatment and the energy absorbed by the patient during treatment are within a particular range or threshold of normal or average energy values for other similar patients. This auto-tuning may be performed before an initial carrier frequency time period of about ten minutes, as shown in fig. 18B.

In addition, as shown in fig. 18B, during the modulated frequency exposure time period, the reflected energy may also be measured. The reflected energy provides an indication of how much forward energy was not absorbed or transmitted by the patient. Since the delivered energy is generally constant for the patient, it can be considered negligible for the patient during treatment. Thus, by measuring the reflected energy, the system can roughly estimate the amount of energy absorbed by the patient during the exposure period.

In addition, by measuring the reflected energy, the system can also determine alternative data points for assessing and diagnosing a particular ailment or disease. For example, in a related study completed during development of the processes and techniques as described herein, the data in fig. 63B was observed. As shown in fig. 63B, the various bands of energy levels can be seen by measuring and plotting the forward versus reflected energy of a patient population. In addition, for HCC patients, the reflected energy falls into a frequency band that is typically lower than that of healthy patients. This condition may be used as another factor when determining whether a patient is (or is likely to be) diagnosed as having HCC. Additionally, since the reflection energy level of HCC patients is lower, this would likely indicate that HCC patients absorb more energy at the cellular level than healthy patients, and thus may provide at least a partial explanation as to why modulated frequency exposure as described herein leads to cellular dysfunction of cancer cells.

As noted above, fig. 18A-18D illustrate various treatment timelines for patient treatment. These timelines are also summarized in the process flow shown in FIG. 64. As shown in fig. 64, the system (e.g., coordinator 906 as shown in fig. 9B) may initially automatically tune 6405 the forward energy for a particular patient. After auto-tuning, the system may expose 6410 the patient to the initial carrier signal for about ten minutes. After about ten minutes of the initial exposure, the system may expose 6415 the patient to the initial modulated frequency signal for about 120 minutes, about 60 minutes, about 35 minutes, or about 10 minutes, as described herein. After the initial modulated frequency exposure, the system may expose 6420 the patient for another carrier exposure period of about ten minutes. The system may then determine 6425 whether the patient is to be exposed to the modulated frequency signal for an additional period of time. If the patient is to be exposed for another period of time, the process may return to step 6415 where the system exposes the patient to another modulated frequency exposure period for about 120 minutes, about 60 minutes, about 35 minutes, or about 10 minutes. If the patient is not to be exposed to another modulated frequency time period, the process as shown in FIG. 64 may end.

In some embodiments, the system may initially automatically tune 6405 the forward energy of a particular patient. After auto-tuning, the system may expose 6410 the patient to the initial carrier signal for about ten minutes. After about ten minutes of the initial exposure, the system may expose 6415 the patient to the initial modulated frequency signal for about 120 minutes, about 60 minutes, about 35 minutes, or about 10 minutes, as described herein. The system may then determine 6425 whether the patient is to be exposed to the modulated frequency signal for an additional period of time. If the patient is to be exposed for another period of time, the process may return to step 6415 where the system exposes the patient to another modulated frequency exposure period for about 120 minutes, about 60 minutes, about 35 minutes, or about 10 minutes. In some embodiments, after the initial modulated frequency exposure, the system may expose 6420 the patient to another modulated frequency exposure. In some embodiments, the additional modulated frequency exposure includes a modulated frequency that alters a hemodynamic parameter (e.g., heart rate variability). In some embodiments, the additional modulated frequency exposure includes modulated frequencies that do not alter hemodynamic parameters (e.g., heart rate variability). In some embodiments, the additional modulated frequency exposure includes 194 amplitude modulated frequencies or a series of electromagnetic frequencies that occur every 3Hz or 10Hz within a range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 3Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz within a range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 10Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 2,000 Hz. After the initial or additional modulated frequency exposure, the system may expose 6420 the patient for another carrier exposure period of about ten minutes. If the patient is not to be exposed to another modulated frequency time period, the process as shown in FIG. 64 may end.

As noted above, the timing and sequence of the exposure periods as shown in fig. 64 are provided by way of example only. Additionally, in certain embodiments, determining whether to perform another exposure period may be determined by another decision-maker (e.g., the patient's physician).

A.i. and related algorithms as described herein may include machine learning or other similar statistical-based modeling techniques. For example, the algorithm used may depend on the expected result of the algorithm. For example, the processing device may be configured to use a first process or algorithm to compute refinements to the derived weights as described above based on a first set of result data, while using a second or different process/algorithm to compute refinements to the derived weights as described above based on a second set of result data. Different methods and algorithms may be used to compute the refined weights consistently or substantially simultaneously. The outputs of each of the different methods and algorithms may then be compared/further analyzed to determine which output is the highest ranked, or the outputs of each method and algorithm may be combined into a combined index.

In some embodiments, a machine learning model as described in further detail below may be trained on a larger population, for example, the population may range from thousands to thousands of patient records including electrophysiology information, demographic information, and medical history information. Machine learning tools may include, but are not limited to, punitive regression/classification techniques such as random forest and gradient boosting (e.g., implemented using R or any other statistical/mathematical programming language), bayesian belief networks, collaborative filters, support vector machines, and other similar machine learning and classification techniques. Any other classification-based machine learning tool may be used, including neural networks (as described in more detail below) and support vector machines. Because the machine learning tools may be computationally intensive, some or all of the processing of the machine learning tools may be performed on a server separate from the medical device.

An overview of how random forest tools are applied to a given data set may illustrate how a classification tool may work in interpreting given input data. A random forest is a collection of decision trees. A decision tree is a structure like a flow chart, where each node represents a test on an index and each branch represents the result of the test. The tree terminates with a classification label, e.g., the decision made last after each of the metrics is calculated. Each tree in the random forest tool gets a "vote" when classifying a given set of metrics. There are two randomness components that participate in the construction of random forests. First, as each tree is created, a random subsample of the total data set is selected to grow the tree. Second, at each node of the tree, a "splitter variable" is selected and the underlying patients are divided into two categories. For example, one type of patient (e.g., responding positively to a particular medication) may be separated from another type of patient (e.g., responding negatively to a particular medication). The tree is grown with additional splitter variables until all the terminal nodes (leaves) of the tree are completely one class or the other. The tree is "tested" for previously set-up patient records. Each patient test record traverses the tree down through one branch or the other, depending on the index included in the record for each shunt variable. Based on where the records fall on the tree (voting), the patient test record is assigned a prediction. The entire process may be repeated with a new random partitioning of the underlying dataset to generate additional trees and ultimately "forests". In each case, a tree can be built and tested for performance using different subsets of patients.

In developing the results described in the example embodiments below, a predetermined number of model changes were trained. For example, each model change is labeled in order (e.g., labeled 1 to 100 for 100 runs). In each run of the model, the software randomly takes a predetermined portion (e.g., 80% of the portion) of the population as a training set and leaves the rest (e.g., 20%) as a validation set.

As noted above, the machine learning tool may train the model on a first portion of the underlying data set and verify the model on a second portion of the data set or on another separate data set. In evaluating the performance of each model, the performance of the underlying decisions within the decision tree in the random forest may be evaluated based on specificity and sensitivity parameters. For example, the sensitivity parameter may be based on a measure of the model's ability to correctly predict whether a patient is at risk for adverse reactions to drug treatment. For example, the sensitivity parameter may correctly predict the proportion of patients that will respond negatively to treatment based on the model. The specificity parameter may be based on the proportion of patients to be treated with a particular drug and predicted by the relevant model as having a positive response to the drug treatment. It may be advantageous to optimally balance various performance variables, such as sensitivity and specificity, at a higher level. For example, by setting the specificity at a relatively high value, e.g., 95%, the underlying threshold within the classifier model may be adjusted to minimize false positives. After the specificity is defined, the sensitivity measure can be considered as a type of performance measure, e.g., typically in the range of 15% to 35% for a given model, however, smaller or larger sensitivity values are also possible.

The predictive performance of the trained model may be verified using, for example, a verification scheme as described below. In an embodiment, the validation phase may be used to determine an appropriate threshold score for classifying future patients (where the outcome is currently unknown and it is desired to predict the outcome) and to determine the predicted performance of each classifier model generated by the machine learning tool. To validate the various models and associated threshold scores, a second group of individuals may be used, e.g., a validation population (or cohort). For example, the validation population used may be a new validation population. As patients in the validation cohort progress through treatment, the outcome of these patients will eventually be known. In an embodiment, the patients in the validation population may be different from the training and test patient groups described above for training the model. For example, the validation population of patients and their associated indicators (validation indicators) may be independent of the training population of patients and their associated indicators (training indicators). In some embodiments, there may be overlap between the validation metrics and the training metrics.

In some embodiments, the validation population may be updated by at least one of: 1) adjusting one or more of the validation metrics, and 2) extending the validation metrics based on appending another one or more subjects to a population of subjects comprising the validation population. The threshold for classifying future patients may be refined based on the updated validation index. For example, the one or more of the validation metrics may be adjusted using the metrics of the patient currently being treated or monitored or otherwise not progressing through treatment, or the metrics of the patient may be added to the validation population as metrics from a new subject. During monitoring or treatment of a patient, validation metrics may be adjusted when determining a new metric for the patient. In some examples, as the monitored patient progresses through the treatment, the patient's metrics may be added to the validation population and/or used to adjust metrics in the validation metrics after the patient has progressed through the treatment.

In some embodiments, the training population may be updated by at least one of: 1) adjust one or more of the training metrics, and 2) extend the training metrics based on appending one or more additional subjects to the first plurality of subjects. The machine learning classifier model may be retrained based on the updated training metrics. For example, when determining additional patient metrics from a current patient and/or determining metrics from a new patient, the machine learning model may be retrained, e.g., over an increased number of metrics or over a new different metric, to provide an updated classifier model. The training population may be updated when new metrics for the current patient and/or metrics for the new patient are determined or after the patient has progressed through treatment.

FIG. 65 illustrates an example flow for training and validating one or more classifier models of a machine learning algorithm as described above. A set or population of known input data 6502, 6504, 6506 may be provided as a data set for training and validating the classifier model. For example, a known patient record data set may include 1000 patients who have been diagnosed with a particular ailment (such as HCC), their medication regimen, and the associated outcome for each patient. A percentage of known patient data records may be used as input data 6502, 6504, 6506. For example, 80% or 800 patient records may be used as inputs 6502, 6504, 6506.

The input data 6502, 6504, 6506 may be fed into a data aggregator 6508. The data aggregator 6508 may be configured to match patient data into a single training input for the machine learning algorithm and configure the training input into a format readable by the machine learning algorithm. The data aggregator 6508 may feed training data into the algorithm 6510. The algorithm 6510 may include one or more untrained data structures, such as a series of data trees (e.g., organized using random forest tools as described above). Using the training input variables and the known results from the input data 6502, 6504, 6506, the algorithm 6510 may iteratively process each data point in the training set, thereby training the data structure to more accurately produce the expected (and known) result.

Once the algorithm 6510 has exhausted the input data 6502, 6504, 6506, the algorithm may generate one or more outputs 6512. The output 6510 may be compared to expected outputs (known from the initial population) to determine the specificity and sensitivity of the now trained algorithm 6510. In certain embodiments, the verification data 6514 may be used to further refine the trained algorithm 6510 using additional patient records. For example, the verification data 6514 may be input into a verification module for verifying one or more trained algorithms 6510. To continue the above example, the verification data 6514 may include 200 patient records. Typically, there is no overlap between the training data set and the validation data set, as there is no advantage to running the same data set twice.

As a verification classifier model for classifying new patients (e.g., generating new outputs for a set of patient indices as described herein), the results produced may be used to better verify the process using a closed-loop feedback system. For example, when classifying and treating a patient, the results of the treatment may be included in the patient record and verified by, for example, the patient's physician. The patient record, now updated to include known outcomes, may then be provided as feedback to the verification module. The verification module may process the feedback to compare the generated output to known results for the patient. Based on this comparison, the validation module may further refine the validated algorithm, thereby providing a closed-loop system in which the model is periodically updated and upgraded.

Once trained, the algorithm 6510 may be implemented into artificial intelligence as described herein. For example, the algorithm 6510 may be implemented into artificial intelligence 6510 as discussed herein. Then, using a trained algorithm, a system such as system 900 can monitor the patient's response to the applied electromagnetic energy (e.g., by measuring the patient's HRV during exposure) to diagnose both whether the patient suffers from, for example, HCC and how the prognosis for treating the patient is. As noted above, an additional benefit of exposure to electromagnetic energy may be the simultaneous treatment of cancer during diagnosis and long-term prognosis.

In some examples, an immediate change in the patient's response to the applied energy may be observed. For example, as shown in fig. 66, the left panel indicates the frequency response of the patient to various applications during an initial approximately 35 minute therapy session (e.g., as described above with respect to fig. 18A, 18C, or 18D). The right graph of fig. 66 shows the patient's response to the same set of frequencies after a rest period of about ten minutes. In some embodiments, the patient's response to the same set of frequencies occurs without a rest period of about ten minutes as depicted in fig. 18D. As shown in fig. 66, while there are several frequencies that elicit similar responses in the patient, there are now a new frequency that elicits a response in the patient (e.g., the patient's HRV has changed significantly) and several previously identified frequencies that the patient did not respond after the rest period. Such results may indicate an indication of immediate cellular changes in the patient caused by the initial approximately 35 minute exposure period.

As illustrated in fig. 67A, in some embodiments, the patient may relax in a supine position. An initial non-exposure period of about ten minutes (i.e., rest) may be included to relax the patient and establish baselines for various hemodynamic parameters and Heart Rate Variability (HRV). After the initial non-exposure period, the patient may be exposed to the carrier frequency (i.e., at a constant amplitude without amplitude modulation) for about ten minutes. After the initial exposure period, the patient may be exposed to the one minute modulated frequency exposure period for about 120 minutes, about 60 minutes, about 35 minutes, or about 10 minutes. During this time, each of a set of frequencies (e.g., 194 frequencies as described herein or a series of electromagnetic frequencies occurring every 3Hz or 10Hz in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000 Hz) may be applied to the patient for a particular period of time (e.g., 10 seconds per frequency or 3 seconds per frequency). In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 3Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz within a range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 10Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 2,000 Hz. The system may cycle through each of the frequencies repeatedly over the exposure time period. After an initial exposure period of 120 minutes, about 60 minutes, about 35 minutes, or about 10 minutes, the system may determine a set of frequencies (i.e., "activity frequencies") that alter the hemodynamic parameter (e.g., heart rate variability). In some embodiments, the system may determine a new set of frequencies with or without the hemodynamic parameter being altered during the first exposure period. In some embodiments, the set of new frequencies ranges from about 0Hz to about 10Hz in the first set of frequencies with or without altered hemodynamic parameters (e.g., heart rate variability), preferably from about 0Hz to about 7Hz in patients with high tumor burden and with low tumor burden as depicted in fig. 67B. In some embodiments, the new set of frequencies can be combined with the original 194 frequencies as described herein or a series of electromagnetic frequencies that occur every 3Hz or 10Hz in the range of about 0.01Hz to about 20KHz, in the range of about 10Hz to about 1,000Hz, or in the range of about 10Hz to about 2,000Hz as described herein to provide a patient-specific treatment regimen. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 3Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz within a range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 10Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 2,000 Hz. In some embodiments, a patient-specific treatment regimen is administered to the patient. In some embodiments, the patient-specific treatment regimen is administered before or after a rest period of about ten minutes. In some embodiments, the patient-specific treatment regimen removes frequencies that do not produce alterations in hemodynamic parameters (e.g., heart rate variability).

In some embodiments, the new activity frequency is determined by a distribution of activity frequencies, as illustrated in fig. 68. In some embodiments, the distribution of active frequencies is transformed by the following equation:

X∩N(μ；σ²) Wherein X is a mean value μ and a variance σ²Is normally distributed random variable. Thus, σ ± + √ σ²Is the standard deviation, the random variable X ∩ N (μ; σ)²) The Gaussian transform to become a standard normal distribution Z ∩ N (0; 1) may be Z_i(X)＝x_i- μ (x)/σ (x) (. pi. 1) (normalization variable). In some embodiments, with this transformation of variables, one can have a mean μ (x) and a variance σ²(x) Has a mean value mu (z) of 0 and a variance sigma²(z) ═ 1 of random variables, whichever μ (x) is sum σ²(x)。

In some embodiments, the central limit theorem may provide an example distribution of averages, wherein,in some embodiments, the example distribution of the mean tends to a normal distribution as it grows:in some embodiments, a particular set of frequencies may be referred to as a form θ_i1, 2, … …,2 μ +1, wherein,

j＝0，1，...，(u-1).

wherein:

f may be the active frequency selected in the previous step. By construction

μ may be either below or above f; (π.4) the number of frequencies added;

may be a constant difference between these frequencies.

Then, the number of frequencies under investigation may be: n is 2u + 1.

By construction, the plotted frequency distribution (π.3) can have an average valueWherein the average value may naturally be the frequency of activity obtained in the previous exposureFurther, this may prove that its exemplary standard deviation may be:

the set of frequencies plotted may be normally distributed:

in some embodiments, the baseline distal variable (dis 1, heart rate variability) may not be orthostatic. In some embodiments, outliers may occur. As illustrated in fig. 69, two types of atypical values may be considered: outliers and impact or leverage points.

In some embodiments, identification of outliers and points of influence may be performed. In some embodiments, the identification of outliers and impact points to detect potential outliers and/or impact points, student Residuals (RS) may be performed according to a graphical standard of normal probability mapping_iAnd Cook's distance (D)_i) To "confirm or not confirm" the point of influence.

In some embodiments, a linear regression model may be considered:

the method is characterized in that:

Z(DIDS1)_i＝β₀+β₁(freq_Hz)_i+e_i

(a) the submitted observations (DISD1) may be considered outliers. Iirs iiii >2.0

(b) The Cockdistance used to analyze the effect of affecting the response order i can be defined by the following equation:

wherein, in a linear regression model of Z (disc 1) as a function of Z (freq):

r_imay be the study residual of the response;

k may be the number of parameters of the model;

h_ii-nth diagonal value of the orthogonal projection operator of the vector of the response over space generated by the columns of the matrix X in the following design: h ═ X (X' X)^-1X', also known as the hat matrix y.

In some embodiments, ifThen (DISD1)_iMay be influential.

As depicted in fig. 70, an example of an impact point constructed according to the normal probability plot of the embodiments disclosed above may have a frequency response of about 3 Hz. In some embodiments, a frequency response of about 3Hz may be independent of the frequency value.

In some embodiments, the patient may relax in a supine position. An initial non-exposure period of about ten minutes (i.e., rest) may be included to relax the patient and establish baselines for various hemodynamic parameters and Heart Rate Variability (HRV). After the initial non-exposure period, the patient may be exposed to the carrier frequency (i.e., at a constant amplitude without amplitude modulation) for about ten minutes. After the initial exposure period, the patient may be exposed to the modulated frequency exposure period for about 120 minutes, about 60 minutes, about 35 minutes, or about 10 minutes. During this time, each of a set of frequencies (e.g., 194 frequencies as described herein or a series of electromagnetic frequencies occurring every 3Hz or 10Hz in the range of about 0.01Hz to about 20KHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000 Hz) may be applied to the patient for a particular period of time (e.g., 10 seconds per frequency or 3 seconds per frequency). In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 3Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz within a range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 10Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 2,000 Hz. The system may cycle through each of the frequencies repeatedly over the exposure time period. After an initial exposure period of about 120 minutes, about 60 minutes, about 35 minutes, or about 10 minutes, the system may determine a set of frequencies (i.e., "activity frequencies") that alter the hemodynamic parameter (e.g., heart rate variability). In some embodiments, the system may determine a new set of frequencies with or without the hemodynamic parameter being altered during the first exposure period. In further embodiments, the system may determine a frequency attribute of the set of frequencies with or without the hemodynamic parameter being modified. In some embodiments, as a non-limiting example, the frequency attribute may be a bar code system as depicted in fig. 71. In some embodiments, the frequency attribute may be attributed to a code, wherein the code may be code 0, code-1, code 2, or a combination thereof. As illustrated in fig. 72, in some embodiments, the frequency attribute may be code 0, wherein code 0 is illustrated as 0 bars in the barcode system, the frequency attribute may be code-1, wherein code-1 is illustrated as 1 bar in the barcode system, the frequency attribute may be code 1, wherein code 1 is illustrated as 2 bars in the barcode system, the frequency attribute may be code 2, wherein code 2 is illustrated as 3 bars in the barcode system (3 sequential bars with no blank between the 3 sequential bars), or a combination thereof. In further embodiments, code 0 may indicate that frequency does not result in a change in a hemodynamic parameter (e.g., heart rate variability), code-1 may indicate that frequency results in a decrease in a hemodynamic parameter (e.g., heart rate variability), code 1 may indicate that frequency results in an increase in a hemodynamic parameter (e.g., heart rate variability), and code 2 may indicate that frequency results in a very high increase or decrease in a hemodynamic parameter (e.g., heart rate variability).

In some embodiments, the barcode system has a frequency spacing. In some embodiments, the frequency spacing of the barcode system is about 1Hz, about 3Hz, about 5Hz, about 10Hz, about 20Hz, about 30Hz, about 40Hz, about 50Hz, about 60Hz, about 70Hz, about 80Hz, about 90Hz, about 100Hz, about 125Hz, about 150Hz, about 175Hz, about 200Hz, or any range between included. In some embodiments, the frequency spacing of the barcode system is about 100 Hz.

A real-time coordinated low energy electromagnetic exposure system (r.o.l.e.x. system) as described herein (e.g., system 900 as shown in fig. 9B) may provide storage of complete raw data from monitoring devices, radio frequency generator command logs, consolidated and transformed data files for each patient exposure, and other relevant data. Data may be encrypted and stored according to an intelligent dynamic library format to facilitate data capture, new data processing, and data retrieval to support a large amount of data analysis for future research activities. For quality control, all data can be organized to support independent data auditing and data validation.

In addition, the biological surrogate can be a biomarker that can be traced in humans using non-invasive techniques. Like any other biomarker, this biological surrogate can provide evidence (or reflect a physiological response to an intervention) that the amplitude-modulated electromagnetic field has significantly interfered with organ function or other aspects of health. Like any other biomarker, this biological surrogate may be associated with the risk or progression of the disease or with the susceptibility of the disease to a given treatment.

By virtue of significant HRV changes during exposure to specific frequency modulations identified by the r.o.l.e.x. system, the biological surrogate may allow for the construction of a separate list of active frequency modulations (capable of causing HRV changes) for use in an accurate and non-invasive systemic therapeutic approach. This technical solution supports the evaluation of a myriad of patients and therefore can generate a large amount of data for data mining and machine learning.

In addition to the instant construction of cancer specific frequencies in different tumor types as described herein, direct potential clinical applications can be utilized to study biological alternatives as potential prognostic and predictive cancer markers.

In some embodiments, the AM RF EMF frequency can be absorbed, transmitted, or reflected within the subject (fig. 62A). In some embodiments, the reflection frequency may have a reflection coefficient (fig. 62B). In some embodiments, the reflected frequency may be measured. In further embodiments, the reflected frequency may be used to determine a diagnosis of the patient. In some embodiments, the reflected frequency may be used to automatically tune the AM RF EMF frequency to which the patient is exposed. In some embodiments, the frequency of reflections can be altered in a subject diagnosed with a disease as compared to a healthy control. In some embodiments, the frequency of reflexes in a subject diagnosed with hepatocellular carcinoma may be reduced compared to healthy controls (fig. 63A-63D).

In some embodiments, cancer cells exposed to a 27.12MHz radio frequency electromagnetic field can replicate in vitro or in vivo conditions. In some embodiments, the cancer cell is a hepatocellular carcinoma (HCC) cell. In further embodiments, the cancer cells may be exposed to tumor-specific modulation frequencies that may have been previously identified in patients diagnosed with cancer by biofeedback methods. In some embodiments, a cancer exposed to a tumor-specific modulation frequency may undergo cell death, alter its proliferative capacity, or a combination thereof. The control modulation frequency may consist of a randomly selected modulation frequency in the range of about 100Hz to about 21kHz, about 0.01Hz to about 20kHz, about 10Hz to about 1,000Hz, or about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 3Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 3Hz within a range of about 10Hz to about 2,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies occurring every 10Hz in the range of about 0.01Hz to about 20 KHz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 1,000 Hz. In some embodiments, the modulated frequency applied to the patient may be a series of electromagnetic frequencies that occur every 10Hz in the range of about 10Hz to about 2,000 Hz. Since the force of the alternating field acts on tubulin dimers that make up microtubules, the alternating field may interfere with microtubule polymerization and depolymerization. Due to the reduced shielding, the dipole moment of tubulin dimers can be affected by these external fields, especially at mitosis. Theoretical calculations may explain how electrostatic forces generated by microtubules act during mitosis and affect chromosome movement, or suggest other effects that may be involved when an alternating field is applied to cells in mitosis.

In some embodiments, the cancer cell may have an energy metabolism rate. In further embodiments, the energy metabolism rate of a cancerous cell may be altered when compared to a non-cancerous cell. In further embodiments, the altered energy metabolism rate of the cancer cell may be indicative of a phase transition. In further embodiments, the phase change may include a transition of the cell from a non-excitable cell to an excitable cell. In some embodiments, the phase change may include a transition of the cell from an excitable cell to a non-excitable cell. In further embodiments, the cancer cell may be an excitable cell that absorbs the electromagnetic field. In further embodiments, the cancer cell may be an excitable cell that absorbs more of the electromagnetic field when compared to a non-cancer cell, a non-excitable cell, or a combination thereof. In some embodiments, the carrier frequency of the electromagnetic field may increase the sensitivity of the micropipes to the amplitude modulated frequency.

In some embodiments, the biopolymer may be composed of various proteins (e.g., actin or tubulin) or nucleic acids (e.g., DNA or RNA) (fig. 35-36). When immersed in water, these structures may have uncompensated charges, but the ionic solution (e.g., cytoplasm) may provide a bath of counterions that can at least partially neutralize the net charge. This may result in an electric field distribution of dipole moments and higher order moments (fig. 37). Biological water can produce structures with ordered dipole moments and complex dynamics at multiple scales. Further, free ions may impart conductive properties to cells along polymer pathways as well as in a diffusive manner. The membrane can support electric fields that can decay exponentially due to shielding, but do not disappear completely when measured inside the cell.

In further embodiments, proteins in a living being may have semiconducting properties. Protein conductivity may depend on the hydration state of the protein.

In some embodiments, the organism may conduct electricity due to the effects of membrane potentials, ionic currents, and endogenous electromagnetic fields while proliferating, morphogenesis, and regenerating. Water can also transmit electrical pulses due to the structure imparted by the hydrophilic surface. The charge carriers associated with protein semiconductivity may be electrons, protons or ions surrounding the protein in physiological solution. This proton conductivity in proteins can be found in collagen, keratin, cytochrome c, and hemoglobin. In some embodiments, Actin Filaments (AF) and Microtubules (MT) may participate in various forms of electrical smelting that involve ionic and electronic conduction. In further embodiments, AF and MT may support dipole and/or ion kink-like soliton waves, which may travel at velocities in the range of about 2m/s to 100m/s (fig. 39).

In some embodiments, MT may be made from polymerized tubulin (fig. 36). MT can maintain the structure of the cell and can provide a track for intracellular transport and the generation of mitotic spindles upon cell division. The building block of the MT may be a tubulin dimer having about 900 amino acid residues and may comprise about 14,000 atoms with a combined mass of about 110 kDa. Tubulin dimers can be composed of two different monomers called alpha tubulin and beta tubulin. Further, each of the tubulin monomers may be expressed by a different gene, resulting in a panel of tubulin isoforms expressed differently by different cells.

In further embodiments, each dimer in the MT may have a length of about 8nm, a width of about 6.5nm, and a radial dimension of about 4.6nm along the MT cylinder axis. The diameter of the inner core of the cylinder (called the lumen) is about 15 nm. The walls of MT are mainly characterized by a so-called B-lattice structure, which is roughly hexagonal, with at least one filament forming a seam, and thus an offset between two adjacent dimers at the seam.

In some embodiments, the charge of tubulin may depend on the isoform used and range from about-5 basic charge to about-35 basic charge. The dipole moment may vary from about 500 debye to about 4000 debye.

In some embodiments, the microtubes may exhibit intrinsic conductivity along their length as well as ionic conductivity. Intrinsic conductivity may include electronic conductivity through the macromolecule itself, electrons traversing the semiconducting nature of tubulin. In further examples, microtubules may have negatively charged outer surfaces and C-Terminal Tails (TT) due to the large charge on tubulin, creating a cloud of counter ions around them (fig. 38). The ion wave may be amplified along the MT. In further embodiments, the MT may form a cylinder with a hollow interior volume (lumen) and may have conductive properties related to the lumen.

Table 2 summarizes the data for measuring various conductivities of microtubes. In some embodiments, the conductivity along the MT may not be length dependent, which may indicate a non-ohmic resistance.

TABLE 2 conductivity data for microtubes

TABLE 2

In some embodiments, MT may increase its ionic conductivity compared to a buffer solution without tubulin. In further embodiments, the conductivity of the MT may be increased by a factor of about 15 compared to the conductivity of the surrounding solution. In some embodiments, using about 135mM KCl, MT can amplify the increase in ionic charge conductivity of about 69% in current transport along the MT in a buffer similar to the buffer of intracellular ion concentration. In further embodiments, the ionic current amplification along the MT may be explained by a highly negative surface charge density along the outside of the microtube that may create a cloud of counterions, which may allow amplification of the axially transmitted signal. In some embodiments, the conductivity of the MT is about 367S/m.

As depicted in fig. 37, in some embodiments, the microtubule conductivity increases in the ionic buffer as the concentration of the microtubules increases.

In some embodiments, microtubule conductivity is measured in solution, and the increased ionic conductivity due to the formation of a cloud of counter ions around the highly negative surface of the MT is measured. In further embodiments, dry protein conductivity is measured. In some embodiments, the conductivity can be attributed to microelectrodes rather than protein filaments. In further embodiments, the MT is adsorbed onto a glass substrate and yields an intrinsic conductivity of less than about 3S/m. In some embodiments, the intrinsic conductivity of the MT is measured in an ultrapure water solution bridging gold electrodes. In some embodiments, the MT ionic conductivity is measured along the periphery of the MT. In further embodiments, the MT is adsorbed onto glass and HOPG substrates, and four probe measurements are made of DC and AC conductivity. In some embodiments, the DC intrinsic conductivity of the MT may be at about 10 from a gap of about 200nm^-1S/m to about 10²S/m range. In some embodiments, the conductivity of the MT over several AC frequency ranges is improved by a factor of about 1000, and the value of the conductivity of the MT is at a value of about 10³S/m to about 10⁵S/m range. In further embodiments, the resonance peak of the solubilized tubulin dimer may be about 37MHz, about 46MHz, about 91MHz, about 137MHz, about 176MHz, about 281MHz, about 430MHz, about 9GHz, about 19GHz, about 78GHz, about 160GHz, about 224GHz, about 28THz, about 88THz, about 127THz, or about 340 THz. In some embodiments, the resonance peak of the MT may be about 120kHz, about 240kHz, about 320kHz, about 12MHz, about 20MHz, about 22MHz, about 30MHz, about 101MHz, about 113MHz, about 185MHz, about 204MHz, about 3GHz, about 7GHz, about 13GHz, about 18 GHz. In some embodiments, the MT with ionic conductance may overlap with the 100KHz range, which may indicate the sensitivity of the MT to this frequency range. In some embodiments, the water channels inside the MT may have a high conductivity of the MT at a particular AC frequency. In further embodiments, the MT has ballistic conductivity in the range of about 12MHz to about 30 MHz. In some embodiments, a thin film of water may be adsorbed onto a solid surface and may have a high conductivity as measured by using a Scanning Tunneling Microscope (STM). In some embodiments, the MT may be in the range of about 1kHz to about 1MHzTo have an AC conductivity and this conductivity was found to be about 20S/m.

In some embodiments, electrical orientation methods can be used to measure the conductivity and dielectric constant of the MT. In further embodiments, the MT exhibits random 'brownian' movement in the absence of an electric field. In some embodiments, in fields with AC frequencies below about 10kHz, MT may exhibit flow motion due to ionic convection. In further embodiments, convection effects may be avoided by applying an electric field having a frequency greater than about 10 kHz. In some embodiments, the MT may be oriented in solution by an electric field having a field strength greater than about 500V/cm and a frequency in the range of about 10kHz to about 5 MHz. In some embodiments, the MT may be aligned within a few seconds at a field of about 90kV/m at about 1 MHz. In further embodiments, the MT ionic conductivity may be about 150 mS/m. In some embodiments, tubulin has a dielectric constant of about 8.41.

In some embodiments, the resistance value is measurable in the microtube, either inherently or as an ion conducting cable. In some embodiments, electrical contacts to individual micro-tubes can be made after dry etching of the substrate containing the gold micro-electrodes. In further embodiments, the intrinsic resistance of a microtube that is about 12 μ M long ranges about 500M Ω, giving a resistivity value of about 40M Ω/μ M in the dry state. In a further embodiment, measurements may be made on gold-coated microtubes, which may be covered with a layer of gold after sputtering at about 30 nm. In further embodiments, the resistance of the configured metallization MT may be below about 50 Ω.

In some embodiments, the conductivity of the MT can be measured by radio frequency reflectance spectroscopy. In further embodiments, the conductivity of the MT may be about 10⁶And (5) S/m. In some embodiments, the conductivity of the MT can be measured by RF reflectance spectroscopy on an MT sample containing: buffer solution, buffer containing free tubulin, buffer containing microtubules and buffer containing microtubules with MAP. The concentration of tubulin may be about 5mg/ml and the concentration of MAP2 and tau may be about 0.3 mg/ml. The average DC resistance may be about 0.999k Ω (buffer),About 0.424k Ω (tubulin), about 0.883k Ω (microtubules), and about 0.836k Ω (MT + MAP). In some embodiments, the resistance of an approximately 10 μ M long micropipe with a base electrical component in such a circuit may be approximately 8M Ω resistance. In some embodiments, a micropipe about 1 μ M long may range from about 200k Ω, and an intrinsic resistance of a micropipe about 10 μ M long may be about 2M.

Table 3 shows various electrical conductivities through the microtubes.

TABLE 3

In some embodiments, MT conductance may occur along the outer edges of the MT, through the intrinsic conductivity of the MT itself, and possibly proton jump conduction and conductivity through the internal MT lumen.

In some embodiments, the electric field may be measured around the MT. In some embodiments, the MT may be ferroelectric. In some embodiments, an electric field may be applied to the suspended microtubes in the range of about 300V/m, which move from the negative electrode to the positive electrode at a pH of about 6.8, indicating a negative net charge. In a further embodiment, the electrophoretic mobility may be determined to be about 2.6X 10-4cm 2/Vs. In some embodiments, the MTs may be aligned in a parallel array due to the application of an electric field. The electric field strength may range from about 20V/m to about 50V/m and may have a pulse shape.

In some embodiments, the MT in solution may be paclitaxel stabilized and exposed to an AC field. In some embodiments, the MT exhibits electroosmotic or electrothermal flow and MT dielectrophoretic effects. In further embodiments, the conductivity of the MT may be about 0.25S/m at about 5 MHz.

Fig. 40A depicts an electrode pair fabricated on a glass surface to observe a response to an AC voltage. The electrode size was about: 15 μm × 12mm, gap: 20 μm and a 40V voltage bias was applied.

FIG. 40B depicts a schematic representation of the response of a microtube to an AC signal.

In some embodiments, below about 500kHz, the microtubes may flow toward the centerline of the electrode. Electroosmotic forces may cause the fluid to move in a vortex-like manner. This represents the coulomb force that the ionic fluid experiences due to the applied voltage.

In some embodiments, the fluid flow rate may be proportional to the electric field amplitude E, the surface charge density σ, inversely proportional to the viscosity η, and inversely proportional to the Debye length (Debye length) k, such that: v ═ E σ/k η. In some embodiments, the flow rate is greater at lower frequencies. In further embodiments, electrothermal forces may cause movement of the microtubes along the length of the electrodes due to the imposed thermal effect of the AC field.

In some embodiments, above about 500kHz, microtubes may flow to the gap between the electrodes due to dielectrophoresis. This is the force that the microtubes experience in the non-uniform electric field and is given by the following equation:

wherein the subscript "m" refers to the medium and "p" refers to the particle. Thus, the process can be performed by the difference between the conductivity and the dielectric constant (σ) of the microtube and the medium, respectively_p-σ_m) And (a)_p-_m) And (5) driving. Lowering the pH of the solution to the isoelectric point of about pH 5 of the MT can reduce this effect, and in addition, lowering the frequency can further reduce this effect since the first term depends on the square of the frequency. The difference in dielectric constant will remain the same (about 7.8 for water and about 8.4 for tubulin).

Fig. 41A-41E depict the geometry of the MT beam near the electrodes at different field frequencies: 100kHz, 250kHz, 500kHz, 1MHz and 2.5 MHz. In some embodiments, at about 5MHz, electroosmotic and electrothermal flows may be in equilibrium with each other, and microtubule flow may now be due to dielectrophoresis. In some embodiments, the geometry of the MT near the electrodes may have a curvature that is opposite to the curvature seen in the mitotic spindle, which may indicate that such high frequency fields may disrupt the formation of the mitotic spindle. Further, fig. 41A to 41E provide independent measurements of MT conductivityQuantity is σ_mIs about 250mS m^-1As depicted in table 2 above.

It is important to compare the dielectrophoretic force with brownian motion in order to be able to assess whether the electric field is strong enough to overcome random motion. The simplest way to make this comparison is to determine whether the dielectric potential exceeds the thermal energy, i.e.:

πr³ _m[(_p-_m)/(_p+2_m)]E²>kT

wherein the content of the first and second substances,_mis the dielectric constant of the medium, and_pis the dielectric constant of the particles. E is the electric field strength and r is the radius of the particle. Taking the corresponding values of tubulin dimer and dielectric constant in solution as an example, E can exceed about 25V/m for the field to efficiently orient polarizable tubulin dimer. Similarly, for microtubes about 10 μm long, with π r²L replacement factor pi r³Where r is the radius of MT (12.5nm) and L is its length, then condition E can be obtained>1V/m. An electric field value of about 100V/m may be sufficient to exert an electrophoretic effect on tubulin and MT. In some embodiments, the longer the microtubes, the more pronounced the dielectrophoretic effect that can be expected to occur.

In some embodiments, the electrical properties of the MT may be composed of an intrinsic protein effect and an ionic effect due to condensed counterion cloud, which may result from electrostatic attraction of positive ions by the MT's negative surface charge in some embodiments, the aligned MT may be assembled in vitro (FIG. 36) at a strength ranging from about 10V/cm to about 300V/m in the presence of an electric field in some embodiments, the MT may be ferroelectric in some embodiments, in other embodiments, an electric field may be applied to the suspended MT and MT. interacting with the kinesin-coated glass surface while suspended, MT without MT-related protein (MAP) may move from negative to positive electrode at a pH of about 6.8, which may indicate a negative net charge and may indicate a determined electrophoretic mobility of about 2.6 × 10^-4cm²/Vs。

In some embodiments, MTs can be labeled with DNA to adjust the amount of net charge and observe the migration of these MT structuresThis is compared to a control, where the MT is fluorescently labeled with only two different tags, in some embodiments, the electrophoretic mobility of the control MT can be about 2 × 10^-8m²V^-1s^-1. For a field strength of about 100V/m, the average speed of MT translocation can be estimated to be about 2 μm/s, where λ_D0.74nm as Debye length, η -8.90 × 10 ⁴kg m^-ls^-lViscosity as buffer and 6.93 × 10^-10C V^-lm^-lAs a buffer dielectric constant. The estimated effective charge for the TAMRA-tagged tubulin dimer and the AlexaFluor 488-tagged tubulin dimer may be about 10e^-And 9.7e^-。

In some embodiments, the ion wave may trigger the C-terminus of the MT to change from an upright conformation to a downward conformation. This model treats MAP2 as acting as an ion waveguide that can transfer conformational changes in the C-terminal state to the adjacent MT. Perturbations applied to the condensed counterions at the end of MAP2 may force them to initiate a traveling wave from equilibrium. This wave can travel as a phase velocity v_{Phase position}2nm/ps "kinked" solitary waves. The time scale of the C-terminal motion may be about 100ps, which may be too fast for the 100kHz frequency range.

In some embodiments, the flexible and charged MT C-terminus (about 40% of the charge of tubulin is localized at the C-terminus) may dynamically respond to an electric field as depicted in fig. 42A-42C, where local changes in pH may be correlated to the polarity of the positive and negative electric fields, respectively. In further embodiments, this effect can cause MT instability and interference with motor protein transport. In some embodiments, the C-terminal may dynamically oscillate in a range between MHz and GHz (fig. 43).

In some embodiments, a stable dimer conformation may have a C-terminus cross-linked between monomers (fig. 44). In further embodiments, the cross-linked conformation at the C-terminus stabilizes the straight orientation of the tubulin dimer.

In some embodiments, the H bond strength in the MT can range from about 11.9k/mol to about 42.2 kJ/mol. In a further embodiment of the present invention,the H bond strength in the MT may amount to about 462kJ/mol for α -tubulin/β -tubulin interaction and about 472kJ/mol for β -tubulin β -tubulin interaction, which translates to between about 0.3 × 10 based on the planckian relationship between frequency and energy¹⁴Hz and about 1.3 × 10¹⁵Frequency value range between Hz. Therefore, hydrogen bond breakage due to carrier waves in the MHz range is not expected to occur.

In some embodiments, the Resonance Recognition Model (RRM) may be based on the energy distribution of delocalized proteins in biological systems and a velocity of about 7.87 × 10 at resonance⁵m/s and can cover a distance of about 3.8A between amino acids, giving a charge transfer of about 10¹³Hz to about 10¹⁵In some embodiments, a charge transfer rate of about v 0.0005m/s may generate an emf in the range of about 108kHz to about 325kHz for telomerase reverse transcriptase (TERT), TERT mRNA, or telomere, which may correspond to the propagation of solitons on the α helix.

In some embodiments, the polyelectrolyte may have condensed ions around it. In further embodiments, if there is a high linear charge density on the surface of the polymer, the counter ions may "condense" along a length of the polymer. In some embodiments, the linear polymer may be surrounded by counterions from the salt solution such that the counterions more tightly surround the surface of the polymer, and the co-ions of the salt solution may be repelled such that a depletion region is created. In further embodiments, the sum of the surface charge and associated counterions can be reduced to a value ofThe value given by the formula depending on the valence of the counterion and the length of the beijerrum (Bjerrum length). The bayeren length is a phenomenological property of the ability of an ion to compensate for surface charges, depending on the solvent and temperature of the solution. A Bayesian length λ_BDistances are described beyond which, for a given temperature T (in kelvin), the thermal fluctuations are stronger than the electrostatic attractive or repulsive forces between charges in a solution whose dielectric constant is e. Where e is the electron charge,₀Permittivity of vacuum and k_BIs Boltzmann's constant. When the average distance b between charges is such that λ_B/b＝S>Assuming, for simplicity, a linear charge distribution around actin filaments, the linear charge density ξ is much greater than 1/z, where z is the valence of the counterion in solution.

Wherein e is the electronic charge, [ epsilon ]]_rIs the dielectric constant of water and b is the average axial spacing of the charges on the polyelectrolyte. In some embodiments, a cylindrical volume depleted of ions outside of the ion cloud surrounding the polymer may be used as an electrical shield. The "cable-like" behavior of such structures may be supported by the polymer itself, while "adsorbed" counterions may be bound to the polymer. In some embodiments, the strength of this interaction may be such that it is even under infinite dilution conditions (i.e., ionic strength)>0) The counter ions may also be attracted to the polymer and may not diffuse out of the vicinity of the polymer.

In some embodiments, the F-actin or microfilament can be about 7nm in diameter, with a helix repeating about every 37 nm. The F-actin or microfilament may have a high electrostatic charge density and may be capable of conducting ionic current across a cloud of counterions. Actin can constitute from about 1% to 5% of cellular proteins in muscle cells and up to about 10% of cellular proteins.

In some embodiments, actin filaments can be one-dimensional polymers with a non-uniform distribution of charge along the length of the polymer, resulting in spatially correlated electric fields that are arranged in peaks and valleys. This can produce a large change in the density of small ions around the polymer, with the ion distribution having a large dielectric discontinuity. This non-uniform ion distribution (average pitch of about 35nm to about 40nm) along a short segment of polymer can be a circuit with non-linear components that can include (a) non-linear capacitors associated with the space charge distribution between ions located in the outer and inner regions of the polymer, (b) inductances, and (c) resistors. F-actin can act as a polyelectrolyte, which can contain a portion of the surrounding counterions around its surface in the form of a condensed cloud. Such clouds may be very insensitive to large changes in the ionic strength of the surrounding salt solution. Due to the presence of a counterion sheath around the actin filaments, these polymers can act as biological "wires" and can be modeled as nonlinear, non-uniform transmission lines propagating nonlinear dispersed soliton waves.

In further embodiments, remote ionic wave propagation along actin filaments (and also the microtubule network) may result in various simultaneous functions, including subcellular control of ion channel activity. Cytoskeletal biopolymers including Actin Filaments (AF) and Microtubules (MT) may constitute the backbone of wave propagation and may in turn interact with membrane components to modulate synaptic connections and membrane channels. In further embodiments, there may be direct interactions between AF and ion channels, as well as regulatory functions associated with actin, indicating that the cytoskeleton may have direct connections to membrane components, particularly channels and synapses. In further embodiments, the condensed counterion cloud may separate the filament core from the rest of the ions in the bulk solution and may act as a dielectric medium between the two. It may have both a resistive component and a capacitive component associated with each monomer that makes up AF. The ion flow may occur at a radial distance from the surface of the filament that is approximately equal to the length of the bayeren. Inductive components may occur due to the double-stranded helical structure of actin, which induces ion flow in a solenoid fashion. Due to the presence of the counterion sheath around AF, these polymers can act as biological "wires" and can be modeled as nonlinear, non-uniform transmission lines propagating nonlinear, dispersive soliton waves. In some embodiments, an input voltage pulse having an amplitude of about 200mV and a duration of about 800ms may be applied to the AF, and an electrical signal measured at the opposite end of the AF may indicate that the AF supports an ionic wave in the form of an axial nonlinear current. In further embodiments, the wave pattern observed in a single AF of an electrical stimulus may be similar to the recorded soliton waveform of a nonlinear transmission line of an electrical stimulus. Given the highly nonlinear complex physical structure of AF and the thermal fluctuations of the counterion cloud, the observation of soliton-like ionic waves can be consistent with the idea that AF acts as a biological transmission line.

Actin filaments can be modeled as electrical transmission lines for ion currents, and the propagation velocity of these currents can be estimated to be between about 1m/s and about 100m/s, which can provide a realistic model of actin that can support solitary ion travel waves. In some embodiments, calcium ion condensation along actin can play a role, inter alia, in intracellular signal transduction. In further embodiments, and similar to the MT described above, actin filaments can be manipulated by an external electric field.

In some embodiments, the water inside the organism may be gelatinous. Proteins with hydrophilic surfaces can produce structured interfacial water. This interface layer may extend beyond the previously contemplated 1 to 2 layers. The cytosol of a cell can be a packed volume of protein, and thus, the water in the cell can be structured to a large extent. This structured water, for example, adsorbed onto collagen, can then support proton jump conduction. Interfacial water along the membrane can also support rapid migration of protons. In some embodiments, a substantial portion of the water may not be blocky. Water may also be necessary for protein electronic conductivity, as hydration may result in an eight order of magnitude increase in albumin conductivity. In further embodiments, the water may support short and long range jumping motions of the protons. The mobility of protons in water may be about Na⁺About five times the mobility of (a). Mobility of protonCan be enhanced by the Grotthus mechanism (proton mechanism), which is the hopping of protons that occurs along the water chain without the need for hydrodynamic diffusion.

Figure 45 shows ordered water structures between charged surfaces.

In some embodiments, NMR evidence may indicate that cellular water has more structure than liquid water, and that much of the Na and K in the cell is not free in aqueous solution, but is associated with charged sites on macromolecules. In further examples, complexed Na and K cations have been compared to valence electrons in solid conductors and free cations to conduction band electrons. In the presence of an activation energy barrier and a solid-liquid interface in a cell, a liquid cation-free model of the cell may not be feasible when a model based on structured water and associated cations is compatible with thermodynamic evidence. A large proportion of the water molecules in the cell are in the form of hydration water bound to individual macromolecules.

In some embodiments, biological cells can produce "living matrices" of semiconducting macromolecules that are capable of transmitting, storing, and processing information involved in regulating physiological processes.

In some embodiments, activation of the Na + pumping mode with an oscillating electric field having a strength of about 20V/cm may occur at a frequency of about 1.0 MHz. In further embodiments, the applied electric field does not stimulate either Rb + efflux or Na + influx at a frequency range of about 1Hz to about 10 MHz. These results may indicate that only those transport modes that require ATP cleavage under physiological conditions are affected by the applied electric field, although field-stimulated Rb + influx and Na + efflux do not depend on cellular ATP concentrations in the range of about 5 μ M to about 800 μ M. Computer simulations of a four-state enzyme electrically conformationally coupled to an alternating electric field can reproduce the main features of the above results.

In some embodiments, the channel density may vary between different neuron phenotypes reflecting different stabilities of resting potentials and signal reliability. In model cell types, such as mammalian medial entorhinal cortical cells (MECs), modeling results and experimental results can be matched to each spiritChannel average of about 5.10⁵Rapid conductance of Na⁺And a delayed rectifier K⁺A channel. In unmyelinated cuttlefish axons, the count per cell can reach up to about 10⁸A channel. In a model channel such as a bacterial KcsA channel, a K is present at a physiological conductance of approximately about 80pS to about 100pS⁺Ions cross the channel every 10ns to 20ns (Roux and Schulten,2004), which may be consistent with the frequencies of external electrical stimulation mentioned above. This may allow a maximum conductivity of about 10⁸Ions/sec. The distance between the center of the channel pore and the membrane surface was estimated to be along 5.10^-9m (5nm) scaling and assuming the simplest model of continuous electrical diffusion of water pores and channels, this can provide an average velocity of about 5.10 per ion^-1m/s (0.5 m/s). Ion transitions may occur due to a stable sequence of multi-ionic configurations across the filter region of the channel, which may allow for rapid and ion-selective conduction. The movement of ions within the filter can be studied by applying Molecular Dynamics (MD) methods and density functional studies. The MD method used in the simulation may solve newton's ion trajectory equations of motion.

In some embodiments, the ion signal may be associated with morphogenesis and cancer. For regeneration, the damage current and associated electrical signal may be both necessary and sufficient. Patterned information during embryogenesis and regenerative repair may be affected by bio-ionic signals. Ion signaling and endogenous voltage gradients affected by ion flux can be involved in proliferation and cell cycle progression, apoptosis, migration and orientation, and differentiation and dedifferentiation. Ions may allow messaging to occur on a time scale that is orders of magnitude faster than molecules.

In some embodiments, a number of effects may be observed that will involve the electric field and/or current in cytoskeletal or cytoplasmic self-organization processes. Electrotherapy and wound healing may occur, which may involve the flow of ionic currents at the cellular level (e.g., cytochrome oxidase granules) under a number of phenomena, the involvement of coherent polarized waves in cell division playing a key role in chromosome alignment and its subsequent segregation. Many examples of plant and animal cells may have a stable electric field in the range of about 1V/m to about 40V/m, which induces significant changes in the rate of regenerative growth (in both directions, as the case may be). In some embodiments, the EM field may affect mitosis and meiosis. In some embodiments, exposure to an electric field of about 60Hz at a strength of about 430V/m and after about 4 hours of exposure, the mitotic index may decrease by about 55%. In further embodiments, an electric field of about 50Hz at a strength of about 50kV/m can have an effect on the mitotic index of cultured human embryonic fibroblast-like cells, and can be measured to decrease by up to about 30% after about 48 hours of exposure. These frequencies may be close to those of the envelope wave, indicating that this may provide additional effects at the cellular and tissue level.

In some embodiments, the calculation of the electric field strength in a spherical cell may indicate that, assuming the same conductivity for the extracellular and intracellular fluids of the cell, the typical electric field strength inside the cell may be about 5 orders of magnitude less than the electric field strength outside the cell due to the smaller conductivity of the membrane compared to these fluids^-10V/m, thereby shielding the intracellular space to a large extent from extracellular electric fields. In further examples, calculations on spherical cells may indicate that the electric field in the protoplasts of the cells is negligibly small (about 5 orders of magnitude smaller) compared to the saline tissue surrounding the cells. In further embodiments, this becomes increasingly the case when the cell is not spherical, such as when the cell enters mitosis. In further embodiments, shielding occurs in elongated cells, such as those found in muscle and long nerve cells. In cells that are sufficiently elongated, shielding may not occur. In bundles of elongated cells, such as those in muscles, shielding may still not occur.

In some embodiments, endogenous current flow can be detected in an animal cell. Under receivingDuring the phase between seminal and first cleavage, a steady current can enter the animal pole and leave the plant pole (equator). (b) In silkworm pupa oocyte-feeding complexes, the cytoplasm of the oocyte may be more positive (by about 10mV) than the cytoplasm of the feeding cell, although they are connected by a broad cytoplasmic bridge (the resistance of which is estimated to be about 10k Ω and allows small currents to pass). At about 510 deg.f^-8This potential difference may be required for macromolecular transport across the bridge under the current of a, (c) a steady current may enter the expected cleavage furrow of the frog and sea urchin eggs during a period of about 10 minutes before the onset of cleavage, and about 8 minutes after the onset, this current reverses its direction and leaves the furrow area.

In some embodiments, the magnitude of the measured current density j in the cell can be about 0.2 μ A/cm²To about 60. mu.A/cm²In a range of (1), which translates to about 0.002A/m²To about 0.6A/m²The range of (1). In further embodiments, the endogenous potential may be present across an metazoan cell in the epithelium, wherein a potential difference of approximately about 30mV to about 100mV or more has been measured. Bioelectric fields can be used to orient various growth-inducing filaments. The magnitude of the electric field measured or estimated to be present in the cell appears to be too weak to be used for e.g. collagen (about 410 is required)⁵V/m) or long DNA strands (about 510 is required⁴V/m) molecules, actin filaments may be exceptional, with as low as about 510³A field of V/m is sufficient.

In some embodiments, metabolic oscillations in the cell may occur over a time period of about 10 seconds to about 12 seconds, which may be two or three orders of magnitude slower than any of the enveloping electromagnetic field effects described above. In further embodiments, the envelope or carrier may not interfere with the metabolic cycle at the cellular or tissue level.

Two aspects related to electrostatic effects occurring during mitosis may involve external electric fields and possibly endogenous electric fields. Electromagnetic (EM) fields are increasingly relevant for understanding life. Electromagnetic (EM) fields can directly modulate the regulation of cell growth and differentiation, including tumor growth. Both static and electrostatic fields can alter the mitotic index and cell cycle progression of many cell types in various species.

In some embodiments, there may be similarities between the pattern of the mitotic spindle device and the field geometry of the electric dipole moment. In further embodiments, G₂->The M-transform may be associated with a ferroelectric phase transition that establishes the oscillation axis of the cell polarization wave. The mitotic spindle device can use microtubules aligned with electric field lines to delineate a polarizing field. The poles may represent the regions of highest field strength and the equatorial plane will provide the node manifold. At G₂->Condensation of chromosomes during M-switching may be caused by static dielectric polarization of chromatin complexes due to ferroelectric phase transitions in cells. This field is very inhomogeneous, resulting in a force acting on the dipole moment d of the molecule according to the following equation: f ═ dx grad E. This further assumes that over the entire S->G₂->During M and mitosis, the magnitude of cellular polarization can increase. The electric field during metaphase is high, thereby inducing rapid movement of chromosomes. The coulomb repulsion force can act to separate the dye monomers and the electric field enhances the tactile force of the spindle.

Modulation of cell growth and differentiation (including growth of tumors) can be directly regulated by EM fields. Electrostatic fields can alter the mitotic index and cell cycle progression of many cell types in various species. EM low frequency fields in the range of about 50Hz to about 75Hz may cause perturbation of the mitotic activity of plant and animal cells, and significant inhibition of mitotic activity occurs early during exposure. The onset of mitosis may be associated with a ferroelectric phase transition that establishes the oscillation axis of the cell's polarization wave. The mitotic spindle device can use microtubules aligned with electric field lines to delineate a polarizing field. The poles may represent the regions of highest field strength and the equatorial plane will provide the node manifold. Condensation of chromosomes during this transformation may be caused by static dielectric polarization of chromatin complexes resulting from ferroelectric phase transitions in the cells.

The effect of an external electric field on a cell may theoretically depend on the shape of the cellCalculation of the electric field strength in spherical cells may indicate that, assuming that the conductivity of the extracellular and intracellular fluids of the cell are the same, the electric field strength inside a typical cell is about 5 orders of magnitude less than the electric field strength outside the cell due to the smaller conductivity of the membrane compared to these fluids^-10V/m, thereby shielding the intracellular space to a large extent from extracellular electric fields. The electric field in the plasma of the cells is negligibly small (about 5 orders of magnitude smaller) compared to the saline tissue surrounding the cells. In some embodiments, the cells are not spherical, as is the case when the cells enter mitosis. By calculating the theoretical electric field inside a cell modeled as a cylinder, the shielding that occurs in elongated cells (such as those found in muscle and long nerve cells) may be as little as no shielding. In bundles of elongated cells, such as those in muscles, shielding may still not occur. Because of the closed spherical cell nuclear membrane, the DNA content in the cell nucleus can be still free from the influence of external field. During mitosis, external electric field effects can be more correlated with cellular processes as cell elongation typically occurs. This is increasingly the case when cells are not spherical, a more interesting picture appears, as when cells enter mitosis.

During mitosis, external electric field effects can be more correlated with cellular processes as cell elongation typically occurs. Low intensity medium frequency (e.g., about 100kHz to about 300kHz) alternating electric fields can produce profound inhibition of the growth rate of various human and rodent tumor cell lines. These effects can include both prevention of cell proliferation due to interference with the proper formation of the mitotic spindle and destruction of dividing cells. This may be effective in improving survival as an adjunct to chemotherapy in glioblastoma multiforme (GBM), and has subsequently been FDA approved. During mitosis, instead of a uniform electric field in the cell, the field may become concentrated in the cleavage furrow. This may have the effect of attracting any dipoles towards the sulcus and causing cell membrane disruption and post-mitotic apoptotic cell death. Since the muscles surrounding the bone marrow act as a shield against the effects of external electrical fields, the dividing cells of the hematopoietic system may be unaffected by these alternating fields. In the presence of an external electric field, the field can align cell division, allowing a large proportion of cells to have their cleavage planes orthogonal to the electric field. During mitosis, electric field effects can be associated with the functioning of cells, particularly when mitotic spindles are produced.

Cells can generate electric fields through microtubules and mitochondria, and possibly other systems. The electromagnetic activity of yeast cells can peak during mitosis, indicating that yeast cells in the M phase can emit the strongest electric field. The electric field strength can usually only be measured inside the membrane with voltage dye and patch clamp techniques, so that the electric field strength inside the cytoplasm is hitherto unknown. Initial attempts to measure endogenous electric fields in cells using nanosensors embedded in the cytoplasm of the cells have shown large field strengths (about 10)⁷V/m) that are dissipated upon addition of a mitochondrial uncoupling agent. This may generate a 3D electric field pattern of the cell, which may allow studying the changes in the electric field pattern that specifically occur during mitosis.

The morphogenetic field of cells (defined as the sum of patterned signals carrying information about the organism's present and future patterns) can be of importance for embryonic development, regeneration after injury and proper aging. The bioelectrical cues in this field may be altered in cancer cells. Cancer cells can have depolarized membranes, and this is associated with cell proliferation. Artificial depolarization can confer neoplastic-like properties to somatic cells. The biophoton emission from cancer cells may be different from normal cells. Cancer may be affected by the loss of the endogenous electrodynamic field of the cell, which may affect the structured water in the cell and may lead to damping of microtubules. A cancer detection device emitting waves at about 465MHz (for detecting prostate cancer) can detect these damped vibrations of microtubules in cancer cells. As a primary method for detecting cancer, nuclear magnetic resonance can detect an increase in the freedom of movement of water molecules of cancer. Compulsive biophysical changes can also inhibit carcinogenesis. Artificial hyperpolarization of somatic cells can initiate differentiation and inhibit proliferation.

For an effect to be of molecular significance, its interaction energy must exceed kT per degree of freedom (i.e., about 4 × 10)^-21J) In that respect Otherwise, the thermal fluctuations may disrupt the effect of the electric field. It cannot generate excessive heat energy to avoid severely increasing the temperature of the cells. In practical comparison, cells can be grown to about 10^-12W power (about 10)^-12J/s), most of which are used to maintain a constant temperature. The minimum amount of useful force on a nanometer scale is about 1pN in terms of subcellular force in operation. The force generated by motor proteins is approximately a few pN. A force of about 1pN applied to the tip of the microtube can be used to bend it by as much as about 1 μm.

In some embodiments, the net charge on the tubulin dimer may depend on the pH and vary from about +5 at a pH of about 4.5 to about 0 at a pH of about 5 and decrease to about-30 at a pH of about 8. These estimates may be changes in vacuum. In e.g. cytoplasmic plasma solutions, the vast majority of the electrostatic charges are screened out over distances greater than the debye length (see fig. 47). The change in debye length is between about 0.6nm and about 1.5nm, depending on the ionic strength.

Fig. 46 depicts the exponential drop in potential near the charged surface in ionic solutions.

In some embodiments, F-q E (q-10 for unscreened charge) is used^-13C) The force on a microtube of about 10 μm in length due to an electric field of about 100V/m was calculated to yield F-10 pN (assuming that the field has not decayed when penetrating the cell). However, with debye electrostatic charge screening, only less than about 5% of the charge may remain exposed to the field, producing a force of up to about 0.5pN, which is likely insufficient to have a significant effect on the cytoskeleton, particularly since the field is oscillating and the net force will cancel out over the period of oscillation (i.e., on a timescale of tens of microseconds).

In some embodiments, microtubule electrostatics may have tubulin and microtubules that may affect dipole moments. The next charge per monomer may vary between about-5 e and about-30 e depending on the isoform of tubulin. The dipole moment of tubulin (excluding the extremely flexible and dynamic C-terminus) can be about 566 debye for alpha-monomers to about 1714 debye for beta-monomers. However, this is also strongly related to isoforms, and thus these numbers vary greatly from about 500 debye to about 4000 debye between the various tubulin isoforms.

With a dipole moment of the free tubulin dimer of p 3000 debye as a representative figure, and an electric field E of 100V/m for the interaction energy, U-p E can be obtained, where p is the dipole moment, and thus U-10^-24J. This may be too small (about 4000 times less than the thermal energy kT) to affect the kinetics of tubulin dimer alone. Microtubules may contain about 1625 dimers per about 1 μm in length, so microtubules will eventually accumulate enough net dipoles to adequately interact with the field.

There may be a small non-canceling effect along the axis, which may amount to less than about 10% of the next dipole moment, the entire microtube may not be aligned in fields below about 1000V/m, the torque between the dipole moment and the electric field may be proportional to the product of the electric field and the electric field strength (F p × E), in order for the force to have a meaningful effect on the microtube, the lever arm, which is about 1 μm, should have a force exceeding about 1pN, giving about 10^-18Nm of torque. In the case of a field of approximately about 100V/m and a dipole moment of about 2,000 Debye per tubulin dimer, even if they are perfectly aligned, this may result in microtubules of about 1 μm experiencing only about 10^-21Nm, which may also be about 1000 times lower.

The force between the charge and the electric field can be given by F q E (x), where e (x) is screened exponentially by debye length. A test charge of about +5e (for a microtube of about 5 μm, the distance from the MT surface is about 5nm) experienced a force of about 6pN in water and about 0.5pN in ionic solution. Positioned about 5nm away from microtubules with a moment of about 200 debye (this pair of proteins is charged with electricity)Charge is typical) may have an interaction energy of about 0.03meV, which is lower than about 25meV thermal energy. Tubulin dimers of about 3000 debye in the vicinity of microtubules can withstand energies of about 3 meV. The MT-MT interaction due to the net charge in the case of debye screening is considered results in a net force of about 9pN when separated by about 40nm, which may result in a net repulsive force between them. At longer distances, the attractive force may dominate, and at about 90nm, the corresponding dipole-dipole attractive force is only about 0.08 pN. The maximum electrostatic force in a mitotic plate can be F ═ 6n²pN/MT, where n is the number of elementary charges on each filament. Since F is estimated to be about 1pN/MT to 74pN/MT, the uncompensated base charge for each strand is estimated to be about 0.4 to 3.5. The range of values of force involved may be within the range of possible forces required for chromosome segregation (about 700pN for chromosomes).

A detailed charge distribution on the surface of tubulin can be calculated and the placement of electrostatic potentials around tubulin in a range extending to MT can be observed. These results show that for each of the 13 filaments, the potential along the MT radius is not uniform, with peaks and valleys that repeat periodically along the MT axis. Based on these observations, MT can be considered to be a "conducting cable" consisting of 13 parallel ion flux currents.

Since MTs have a predominantly negatively charged outer surface (including the C-terminus), these MTs can be considered as typical polyelectrolyte polymers. Therefore, the polyelectrolyte theory of Manning (Manning) can be safely applied in this case. Thus, each MT should be close to its surface and attract the Ion Cloud (IC) of positive counterions along the C-Terminal Tail (TT), while the negative ions of the cytosol are repelled away from the MT surface, so that a generally cylindrical ion depletion region occurs around the MT. The thickness of this depletion region corresponds to the bayesian length. This is the distance from the edge of the IC at which the coulomb electrostatic energy of the screened condensed ionic charge balances the molecular thermal energy such that

₀＝8.85×10^-12F/m.

At physiological temperature (T310K), the basic charge is taken as e 1.6 × l0^-19C. Boltzmann constant k_B＝1.38×l0^-23J/K and a relative permittivity of the cytosol of 80, a corresponding value of l for the Beehren length can be obtained_B＝0.67×10^-9m is 0.67 nm. Tubulin dimers (. lamda.) have been found_TD) And TT (λ)_TT) The corresponding condensate thickness λ of the counterion sheath of (a) is estimated as follows:

λ_TD＝2.5nm；λ_TT＝1.1nm。

these values will be used to calculate the corresponding capacity and resistivity of the ionic layer around the MT.

Fig. 47A-47B depict schematic representations of the counter ion charge distribution around the microtubes (left panel) and the C-terminal tail (right panel).

The detailed Poisson-Boltzmann approach (Poisson-Boltzmann approach) can be used to estimate the capacitance of the fundamental loop (consisting of 13 dimers) of MT as:

wherein l represents the length of the polymer unit and R_IC＝r_TD+λ_TTRepresenting the outer radius of the IC.

Fig. 48 depicts a schematic illustration for calculating MT capacitance. When l is_TD8nm and R_IC＝r_TD+λ_TTWhen 2.5nm +2.5 nm-5 nm, it was found that for tubulin dimers,

similarly, extended TT is a smaller cylinder with radius r_TT0.5nm and IC thickness equal to λ_TT1 nm. It should extend an effective length of This means that its part close to the tubulin surface has been embedded in the IC considered before. Thus, the corresponding capacitance is:

in view of the fact that two TT are present per tubulin dimer, the final result is

2xC_TT＝0.52×l^-16F。

The two capacitance values above can be considered to be in parallel with each other, so that the total maximum capacitance of tubulin dimers, considered as the basic unit in the ion-conducting cable, is easily estimated as:

C₀＝C_TD+2xC_TT＝1.92×10^-16F。

due to the shrinkage of the flexible TT, the TT capacitance must change as the concentration of condensed cations increases. These changes are slightly different due to the different structures of the α and β TT. In addition, this explains the tilting movement of TT under the combined action of thermal fluctuation and voltage change due to the incident ion wave. As shown in fig. 49A to 49B, the contribution to the capacitance due to TT should also change with the inclination of TT.

Effective length of TTIs that the change of_TTThe additional factor of (2).

Fig. 49A to 49B depict a comparison between extended tt (a) and inclination tt (B) due to oscillation.

Regarding ohmic resistance, if the ion current leakage through the depletion layer is neglected, the main current flows parallel to the MT axis that charges the capacitor and partially leaks through the nanopore of the MT surface (fig. 38). The resistance due to such ion current can be theoretically estimated or based on fluxObtained through experimental evidence provided by an electrical orientation method performed in vitro on MT. Taking the value of the MT conductivity as sigma ═ 0.15 +/-0.01) Sm-¹And with the simplified assumption: the resistivity in the IC chip is uniform, i is 8nm in unit length and a pi r in cross-sectional area_DTλ_DT＝3.14×2.5nm×2.5nm＝19.625nm²Is estimated as the resistance of the ion cableThe theoretical estimated external sheath-external sheath resistance can give R completely₁₃＝4.75×10⁶Omega. The resistance of the ring around the dimer-length microtubule segment may be 13 times greater, i.e. R₀＝6.2×10⁷Ω。

Finally, the conductance of the two nanopores was included to account for the leakage of IC cations into the luminal region. Thus, it is obtained

G₀＝σ₁+σ₂＝(2.93+7.8)nS＝10.7nS；

The basic equation for controlling ion flow along the MT is developed below.

Figure 50 depicts a cable representation of the ionic ambient of a microtube consisting of 13 filaments. The symbols used are: pr-j: strand 'j', j ═ 1.. 13; r₁: resistance along tubulin; r₂: resistance perpendicular to tubulin; r₃: resistance between the filaments, and partial inductance L₁Are connected in series; c₁: the capacitance of the dimer; l is₁: a portion of a full turn inductor.

A periodic circuit can be constructed that simulates one filament of a microtube. It may consist of a long stepped network consisting of lumped parts equal to the same EUP, as represented in fig. 51.

The longitudinal ion current may be provided by a series of ohmic resistors R of an ion conducting unit₀And (4) showing. Charge Q at nth site of ladder_nCan be related to the total conductance G of the two TT₀And (4) connecting in parallel. This may be true if a large number of cytosol and MT lumens are considered to be grounded.

This permits kirchhoff's law to be applied as

v_n-1-v_n＝R₀i_n.

These systems of equations can be transformed into

The next step is to establish an auxiliary function u (x, t) of the unity voltage and its accompanying IC current as follows:

u_n＝Z^1/2i_n＝Z^-1/2v_n，

wherein the characteristic impedance of the EUP is generally defined as

Using Taylor series (Taylor series) for a small spatial parameter l, a

To introduce dimensionless variables, the characteristic time and length scales are first estimated. Conveniently, the EUP capacitor C₀To the resistance R₀Is charged (discharged) for a time of T₀＝R₀C₀It is given. This can be found in

T₀＝6.2×10⁷Ω×l.92×l0^-16F＝1.2×l0^-8s.

The reciprocal, i.e. the frequency, can be found to be about 83MHz, which can be in the range of the carrier frequency in the present application. Furthermore, the frequency scale depends to a large extent on the length of the MT and can therefore be tuned up or down depending on the average length of the MTs involved.

The characteristic propagation velocity of the ion wave isThis generates

The characteristic impedance of each ion-conducting cell is then estimated as

Or Z > 1.24 × 10⁸Ω，s＝1.。

For s-1, a cut-off frequency is obtained

ω_{Maximum of}＝4.3×10⁷s^-1Or v_{Maximum of}＝6.8×10⁶Hz。

This indicates that the characteristic frequency matches the order of magnitude of frequency Ω, which describes the oscillation of TT. The compact form of the ion current equation is now established as:

wherein, abbreviations are introduced as follows

The following transformations to the spatio-temporal variables may be further performed:

(ξ，τ)→(ρ，θ)；ρ(ξ，τ)＝(ξ-ξ₀)exp(-γ₀τ)，

u(ξ，τ)＝W(ρ，θ)exp(-2γ₀τ)，

so that

Then obtaining

Z＝1.87×10⁸Omega and omega 2.7 × 10⁷s^-1。

Subsequently, the equalisation ω ═ Ω ═ 2.7 × 10⁷s^-1Estimating the parameters Г₀Is 0.18. Also, the frequency value is similar to that of the carrier wave of the present invention, and this frequency value represents the oscillation of the C-terminal tail.

The spatial non-uniformity parameter can be found to be Г₀Ω＝4.8×10⁶s^-1This is compared to the reciprocal characteristic time T₀ ^-1＝1.83×10⁷s^-1One order of magnitude smaller, usually by introducing a new transformation, so that the function μ (ξ, τ) is of the form

The soliton solution can retain its width, but its amplitude decays rapidly, so that over a length of about 500l, the amplitude becomes negligible. The rate of soliton decomposition drops very slightly. The average speed is estimated as follows:

Δx≈400l＝400×8mm＝3.2μm，

Δt＝1000T₀＝10³×1，2×10^-8s＝1.2×10^-5s.

this soliton ranged from about 3.2 μm, which is roughly the diameter of the cell. Ion pulses with such parameters can be efficiently transported in cells at high propagation speeds.

Ionic conduction along and away from charged protein filaments (such as microtubules) can involve cabling equations generated by the RLC circuitry around each protein unit in the network. Due to the viscosity in the ionic fluid, conduction along the filament may experience electrical resistance. Depending on the bayeren length, the capacitance may be caused by charge separation between the surface and the ions. The inductance may be caused by the helical nature of the microtube surface and, therefore, the ionic fluid flows along and around the microtube in a solenoid-like manner.

The following summarizes key numerical estimates of RLC circuit components. For the single dimer, C-6.610 was found^-16F、R1＝6 10⁶Omega (along MT), R₂＝1.2 10⁶Ω (perpendicular to MT) and L210^-12H. These numbers can be used to estimate the characteristic time scale of oscillations (LC) and exponential decay (RC) occurring in this equivalent circuit. For the decay time (τ ═ RC), the following values were obtained: (a) along the length of MT, τ₁＝10^-8s; and (b) away from the MT surface, x₂＝2×l0^-9And s. However, since the value of the inductance L is low, τ is used_o＝(LC)^1/2Finding the corresponding electromagnetic oscillation time as tau_o＝0.2 10^-12s＝0.2ps。

These calculations for the microtubes were repeated, noting that R1 is proportional to the length of the microtubes, whereas R2 is independent of length. In both cases, the corresponding capacitance is proportional to the length, so τ₁And square of length (l)²) Proportional, and τ 2 is proportional to length. To obtain the actual value, the value of a single ring needs to be multiplied by the number of rings in the microtube. Using the value found for a single ring, i.e. τ₁＝10^-8s and τ₁＝2x10^-9s and scaled accordingly to estimate the length of the microtube along and away from which ion currents along the surface of the microtube may experience resonance effects, a scaling factor was found that results in a characteristic time of approximately 40 ns. Thus, for longitudinal effects, approximately about 2 rings (i.e., microtubule nucleation) or microtubules about 20nm long will respond to a stimulus of about 27 MHz. On the other hand, for ion flow, it would be necessary to pulse radially around a microtube with about 20 rings or a microtube about 160nm long. However, it should be emphasized that these results are very sensitive to the choice of parameter values, in particular resistivity, wherein a wide variety of estimates can be found in the literature. In general, there is a strong overlap between the time scales of ion wave propagation and electric field stimulation. It is conceivable that both effects may work, depending on the geometry of the field relative to the mitotic spindleShape and orientation of the MT forming these mitotic spindles. It appears that a short MT will be more sensitive to longitudinal waves generated by the E-field, while a long MT will be more sensitive to vertical waves generated.

For actin in solution, the key feature is that the positively charged ends assemble faster than the negatively charged ends. This results in asymmetric charge at the ends of the filament and electrical polarization of the F-actin. The actin monomers themselves can be arranged head-to-head to form actin dimers, resulting in an alternating distribution of electric dipole moments along the length of the filament. In some embodiments, there is a helical distribution of ions wound around the filament at about one bayeren length. This may correspond to a solenoid in which a fluctuating current flows due to a voltage gradient between the two terminals. For a filament with n monomers, the following effective resistances, inductances and capacitances are obtained:

wherein R is_1,i＝6.11×10⁶Omega and R_2,i＝0.9×10⁶Ω, such that R_1,i＝7R_2,i. Thus, for actin filaments 1 μm long, R was found_{Is effective}＝1.2×10⁹Ω、L_{Is effective}＝340×10^-12H、C_{Is effective}＝0.02×10^-12F. The electrical model of actin filaments is part of an effective circuit that applies kirchhoff's law to couple to adjacent monomers. Taking the continuous limit, for a large number of monomers along the actin filament, the following equation is derived, which describes the time-space behavior of the potential along the actin filament:

in some embodiments, an input voltage pulse of about 200mV in amplitude and about 800 μ s in duration may be applied to the actin filament, and an electrical signal at the opposite end of the actin filament may be measured. This type of measurement may indicate that actin filaments support an ionic wave in the form of an axial nonlinear current. These ion waves can be described by the solution of the nonlinear partial differential equation described above.

In a similar manner to the analysis of the timescale of microtubules as ion conducting cables with RLC components, it is now possible to estimate similar timescales for actin and actin filaments. In some embodiments, the time scale of LC oscillation can be very fast for a single actin monomer, i.e., τ_o＝(LC)^1/2And τ_o＝6×10^-14And s. Attenuation time of longitudinal ion wave is tau_i＝R_iC＝6×10^-10s and the corresponding time of the radial wave is τ₂＝R₂C＝0.9×10^-10And s. All of the time scales described above are incompatible with interactions involving electric fields in the range of about 27 MHz. However, the situation is greatly changed for actin filaments, with similar scaling as for the length of actin filaments described for microtubules. Tau was found in the case of actin filaments of about 10 μm_o＝10^-10s, this is still too short, but for short filaments with about 100 layers (i.e., about 400nm long), τ_i＝R_iC＝2.4×10^-8s, which is in the correct range to interact with the carrier electromagnetic field in the range of about 27 MHz. It was concluded that actin filaments may also be affected by carrier electromagnetic waves, although less likely than MT.

Disclosed herein are current densities related to those mentioned above for dividing cells, wherein at about 0.002A/m²<j<0.6A/m²Is measured within the range of (1). Since j ═ σ E (where E ═ 100V/m and the reported range of values for σ is large (see table 2), between about 0.1 and about 100), taking a lower limit of about 0.1 may result in an ion current along the microtubeWill overwhelm the inherent flow of ions in dividing cells. These externally stimulated currents may cause a process that greatly disrupts mitosis.

In some embodiments, dissipation of power due to current flowing along the micropipes may occur. Taking a micropipe about 10 μm long, the average power consumption is estimated to be

<P>＝(1/2)V₀ ²[R/(R²+X_c ²)]

Wherein, X_c1/ω C is the capacitive resistance. Substituting the correlation number to obtain a dissipated power of about 10^-11W, which is comparable to the power produced by cells during metabolism (about 100W power produced in vivo/about 3 × 10 in vivo)¹³Individual cells). Thus, the additional heat generated by these processes may be destructive to living cells.

The time scale of the process in the ion channel can be passed through two filter sites separated by about 0.3nm (i.e., about 5 × 10)^-10s and about 5 × 10^-11s) translocation time (t)_{Translocation}) The transition rate (from the potential averaged force (PMF)) graph and the Kramer transition rate model (Kramer transition rate model) may be consistent with these numbers the change between non-conductive and conductive states in KcsA may occur at about 7.1 × 10³s-1, thereby giving a lifetime of about 0.14ms (about 10) for the non-conductive state^-4s). Length of time in the non-conductive state is about 10^-3s to about 10^-4s is scaled and is about 10 in filter translocation time^-11s, about 10 can be expected during the non-conductive state⁷Secondary filter state change and about 10 per second¹⁰Secondary handover (about 10 GHz). Thus, these time scales may not be compatible with those resulting from the effects of electric fields in the MHz range.

In some embodiments, the DNA content in the nucleus may be protected from external fields due to confinement in the spherical nuclear membrane. In addition to the screening effect that both cell membrane and cell nucleus wall screen, the irregular geometry of the DNA strand and its short persistence length may also indicate that, despite the high charge, DNA is unlikely to participate in the ion conduction effect shown in actin filaments or microtubules.

Kinesin (along MT) can be about 10^-6maximum velocity propagation of m/s. This value may depend on the concentration of ATP and the concentration of ions in the medium. In the case of MT, kinesins transport various vital cargo, and for actin filaments, dyneins do the same at similar rates. Each step of motor protein can occur in milliseconds or less, which is much longer than the period of AC field oscillation. Kinesins bind to MT through the C-terminus, which may be sensitive to electric field fluctuations, and transport of kinesins will be very strongly disrupted by these rapid oscillations of the C-terminus. Thus, interruption of motor transport in living cells exposed to electromagnetic fields in the MHz range can be an important downstream effect.

Fig. 52 depicts a schematic representation of ions and ordered water within living cells and in the extracellular matrix.

The cytoplasm can provide a medium in which the basic biophysical processes (e.g., cellular respiration) take place. Most cells maintain a neutral pH (about 7.25 to about 7.35) and their dry matter is composed of at least 50% protein, the pH of cancer cells is about 6.85 (acidic). The remaining dry material consists of nucleic acids, trace ions, lipids and carbohydrates. Most trace ions are positively charged. Some of the metal ions required for incorporation into metalloproteins, e.g., Fe, were found²⁺Usually in nanomolar concentrations. The following is a summary of the composition of the cytoplasm with respect to the most abundant and important components (table 4).

TABLE 4 cytoplasmic composition

Negative protein charge: 1.6mol/kg

Positive protein charge: 1.01mol/kg

Net protein charge: 0.6mol/kg (-)

Potassium ion: 0.5mol/kg (+)

Chloride ion: 0.2mol/kg (-)

Net ionic charge: 0.3mol/kg (+)

Net cytoplasmic charge: 0.3mol/kg (-)

The viscosity of the plasma was about η -0.001 pa.s (Howard,2001), so the coefficient of friction of the ions in solution was estimated to be about γ -6 pi η r, where r is the ionic radius (hydrated layer radius), and γ -210 was found^-12Pa.s.m. In an oscillating electric field with an amplitude of about 100V/m and a frequency of about f-20 MHz, the position of the ions may follow a periodic motion given by the following equation: x (t) 0.1A sin (2 pi ft), i.e. harmonic motion will be performed out of phase with the field with an amplitude of about 10% of the same frequency and radius.

The net force of the total charge in the cytoplasm can be estimated as F qE, q 4 × 10¹¹E and E ═ 100V/m, so the total force is about 6 μ N, which may be sufficient to cause major perturbations inside the cell. This may depend on the ability of the electric field to penetrate the interior of the cell, which is easier in the case of non-spherical cells. The net result of these ion oscillations away from and towards the protein surface that interacts attractively inside the cytoplasm may be a concomitant series of oscillations of the structure affected by the ion cloud, as schematically shown below.

Fig. 53 depicts the condensation/decondensation effect due to cation charge movement in the cytoplasm.

Cytoskeletal microtubules can participate in many interactions with electromagnetic forces due to the complex charge distribution in and around these protein filaments surrounded by polyionic solutions. Tubulin has a large number of net charges that are screened largely, but not completely, by counterions. Some of these charges are localized on the C-terminal, which is extremely flexible, resulting in an oscillating charge configuration. Ions present around proteins can be partially condensed and are prone to collective oscillations. Tubulin and microtubules, whose geometry influences their response to external fields, have large dipole moments. Finally, dipole moments can be induced, especially in the presence of electric field gradients.

Based on a broad analysis of the various possible effects that an AC electric field can have on living cells, the following conclusions are drawn. Electric field gradients, especially when dividing cells, generate a large amount of dielectrophoretic forces on tubulin dimers and microtubules. The longer the microtubes, the more pronounced the effect. In addition, another possible scenario is that ion current flow occurs along and perpendicular to the MT surface (and actin filaments, but with little probability), which may be generated by electromagnetic oscillations in the range of about 27 MHz. The particular frequency selection depends critically on the length of each filament.

Depending on the orientation of the electromagnetic fields relative to the cell axis and particularly to the axis of the microtubule (however, these electromagnetic fields fan out from centrosomes in mitotic cells and will therefore be at different angles to any field), the following three types of ion waves can generally be generated:

a) longitudinal waves propagating along the surface of the microtube. In the case where each wire of the microtube functions like a cable with its intrinsic resistance R, the resistance of the entire microtube will be R/13, since all these cables are parallel to each other,

b) the helical wave that propagates around and along each microtube may have 3 or 5 such waves propagating simultaneously, thereby mimicking the microtube geometry having 3 or 5 origins. The effective resistance of such a cable will be the individual resistance divided by the number of cables connected in parallel

c) Radial waves propagating perpendicular to the surface of the microtube.

If the field is oriented at an angle to the axis of the micro-tube, it is contemplated that all of these wave types may be generated simultaneously. The elongation of dividing cells promotes the penetration of these fields through the cells, while spherical cells will largely shield these fields and prevent them from entering the interior thereof. Once the electromagnetic field generates oscillating ion currents, these ion currents in turn can cause the following downstream effects:

a) interfering with ion flux in the cleavage zone of dividing cells

b) Interfering with motor protein movement and MAP-MT interaction

c) Possibly affecting the ion channel dynamics to a lesser extent

d) The net charge of the cytoplasm may be affected in general.

Some measurable heating effect in the cytoplasm is also expected.

These fields are not expected to affect the permanent dipole of proteins such as tubulin and actin.

In some embodiments, exposure of the patient to the amplitude modulated frequency may result in cell damage. In some embodiments, the cellular injury may be watery degeneration, apoptosis, or a combination thereof. In some embodiments, the cellular injury is a water-like degeneration. Aqueous denaturation can be observed when the cell membrane or cellular integrity is disrupted, resulting in impairment of the mechanisms that maintain cellular homeostasis. In some embodiments, the aqueous denaturation may result in a more permeable cytoplasmic membrane, resulting in cell swelling, cell rupture, or cell death.

In some embodiments, the subject has hepatocellular carcinoma. In some embodiments, the subject has cancer. In some embodiments, the cancer is selected from the group consisting of: colon cancer, breast cancer, pancreatic cancer, ovarian cancer, prostate cancer, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchial carcinoma, renal cell carcinoma, liver carcinoma, bile duct carcinoma, choriocarcinoma, seminoma, embryonic carcinoma, Wilms' tumor, cervical cancer, testicular tumor, lung cancer, small cell lung cancer, bladder cancer, epithelial cancer, glioma, astrocytoma, medulloblastoma, merkel cell carcinoma (merkel carcinoma), craniopharyngeal tumor, ependymoma, angioma, neuroblastoma, and neuroblastoma, Pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, melanoma, neuroblastoma, retinoblastoma; leukemias, e.g., acute lymphocytic leukemia and acute myeloid leukemia, chronic leukemia; polycythemia vera, lymphoma, multiple myeloma, Waldenstrom's macroglobulinemia, heavy chain disease, or a combination thereof.

In some embodiments, the subject receives carrier frequency modulation therapy alone or in combination with anti-cancer therapy. In some embodiments, the subject receives carrier frequency modulation therapy, either alone or in combination with anti-cancer therapy, prior to or in preparation for a tumor resection procedure. In some embodiments, the additional anti-cancer therapy can be Sorafenib (Sorafenib) (Nexavar), Nivolumab (Nivolumab) (addivo (Opdivo)), Regorafenib (Regorafenib) (Stivarga), or a combination thereof. In some embodiments, the additional anti-cancer therapy may be selected from the group consisting of bemaciclib (abemaciclib), abiraterone acetate (abiraterone acetate), abitrexate (methotrexate), abraxane (paclitaxel albumin-stabilized nanoparticle formulation), abdd, ABVE-PC, AC, acartinib (acarabutinib), AC-T, antralene (actarra) (toclizumab), adcetris (bentuximab) and/or adelimumab (brentuximab), ADE, trastuzumab-melem conjugate (ado-trastuzumab emsine), adriamycin (adriamycin) (doxombicin hydrochloride), alfacartib (azadirachalazide), tretinomycin (afinitafine), tretinomycin (vitamin), and/or nilla), interleukin (zearalfate) (e), and/or (e), doxorubicin maleate (e), and/or (e) and/or (e), and (e) or (e, e (e), and (e, e) or (e, alebensia (alemtinib), alemtinib, alemtuzumab (alemtuzumab), alutamide (alimata) (pemetrexed disodium), aliqopa (apanimib hydrochloride), injection lokal (alkeran for injection) (melphalan hydrochloride), lokal (melphalan), aloxii (aloxi) (palonosetron hydrochloride), alubriig (butrybine), ambrochlorin (brottinib), ambochlorosine (chlorambucil), ambocorin (chlorambucil), aluzoluz (aminolevulinic acid), amifostine (amisulindac), amiratonamide (amantamide), amitriptan (amitriptan), amitriptan (amitriptolide), amitriptan (amitriptan), amitriptan (amitriptolide), amitriptolide (amitriptolide), amitriptorepaludimide (amitriptolide), amitriptolide (amitriptolide) (aprepizide), amitriptan (amitriptolide), amitriptolide (amitriptolide) (aprepizide (amitriptolide) (amisole (amitriptolide), amitriptan (amisole (amitriptolide), amitriptolide) (amisole (amitriptonide), amitriptonide (amitriptonide), amitriptonide (amitript, araranon (nelarabine), arsenic trioxide, arzerra (ofatumumab), asparaginase erwinia chlorogenic acid (asparaginase erwinia chrysogene), alexan (atezolizumab), avastin (avastin) (bevacizumab), avilam (aveluumab), sibutramine (acerolosin), sibutramine (axicabobatin cillucidum), axicarb (axicarb ciloleucide), Axitinib (Axitinib), Azacitidine (Azacitidine), bavinpocitomo (bavenuocio) (avilamumab), BEACOPP, Becenum (Carmustine), beleodor (Belinostat)), Belinostat (Bendamustine), Bendamustine (benralin), Bendamustine (Bleomycin), bevacizumab (benezatidine), bevacizumab (bevacizumab), bevacizumab (bevacizine), bevacizumab), bevacizine (bevacizine), bevacizine (bevacizine), bevacizine (bevaciz, Bortezomib (Bortezomib), Bosulif (Bosutinib), Bosutinib, bentuximab rosuvastatin, bujitabine, BuMel, Busulfan (busufan), busufex (Busulfan), Busulfan (Busulfan), Cabazitaxel (Cabazitaxel), Cabozantinib (Cabometyx) (Cabozantinib-S-Malate) Malate, Cabozantinib (acarputinib)), capacamphasin (Campath) (Alemtuzumab), Camptosar (Irinotecan Hydrochloride)), Capecitabine (Capecitabine), capacil (ox), carol (fluoroplex-ox), topical Carboplatin (Carboplatin), bosutin (platinum), Carboplatin (platinum-platinum), Carboplatin (Carboplatin), Carboplatin (Carmustine Hydrochloride), Carmustine (Carmustine), Carboplatin (Carmustine), Carmustine (Carmustine), Carmustine, Shelvirix (recombinant HPV bivalent vaccine), Cetuximab (Cetuximab), CEV, chlorambucil-prednisone, CHOP, Cisplatin (Cisplatin), Cladribine (Cladripine), Clafen (Cyclophosphamide), Clofarabine (Clofarabine), Clofarabine (Clofarabine), Colara (Clolaparin), CMF, Cobimetinib (Cobimetinib), Cometriq (Calvatinib malate), Copalinib (Copaliside Hydrochloride), COPAC, COPP-ABV, Cosmegen (Cosmegen) (actinomycin D (Dactinomycin), Cotellic (Cotemicinib), Cotelic (Cometinib), Cocubetinib (Cratinib), Cytamicin (Cytoxinomycin), Cytoxytamide (Cytoxytamide), Cytoxytamide (Cytoxytamine (Cytoxytamide), Cytoxytamine (Cytoxytine), Cytoxytine (Cytoxytamide), Cytoxytine (Cytamicine), Cytamicine (Cytamicid), Cytamicid-Ab (Cytamicid), Cytoxytine), Cytamicid (Cytamicid), Cytamicid-C (Cytamicid), Cytamicid, Dalafinib (Dabraafinib), Dacarbazine (Dacabazine), dactographen (Dacogen) (Decitabine), actinomycin D, darunavir (Daratumumab), Adadabepotine, Darzalex (Darzalex), Dasatinib (Dasatinib), daunorubicin Hydrochloride and Doxyme liposomes, Decitabine, defibrinoside Sodium (Defibrotide Sodium), Defitelio (Sodium defibroside), Degarelix (Degarelix), Denineleukin (Denileukin Diftotox), Dennoub (Denosumab), Depocyt (cytarabine liposomes), Dexamethasone (Dexamaesone), Dexrazoxane Hydrochloride (Dexrazoxane Hydroxazone), Dituximab (Dituximab), Doxyzine (Doxyme), Doxyzine (Doxyzine), Doxymycin Hydrochloride (Doxymycin Hydrochloride), Doxymycin Hydrochloride (Doxyme), Doxyme (Doxyme) liposome (Doxyme), Doxyme (Doxymine Hydrochloride), Doxymine Hydrochloride) liposome, Doxyme (Do, Efudex (fluorouracil-topical), Eligard (Leuprolide Acetate), erite (Elitek) (labiriase (Rasburicase), elence (Epirubicin Hydrochloride), elituzumab (Elotuzumab), Eloxatin (Oxaliplatin), eltrombopag (eltrompagine), emide (Aprepitant), empferitin (Elotuzumab), empferitin (elasuzumab), isassib Mesylate (elastineb), Enzalutamide (Enzalutamide), Epirubicin Hydrochloride, EPOCH, alfuettin (alfuzumab), epothidin (efuzumab), epothidin (eaf), alfuib (avastine), Epirubicin Hydrochloride (Epirubicin), EPOCH, alfuzin Alfa (Alfa), leotide (Epirubicin), amisole (amitriptylin), amisole (amitriptyline), amisulosin Hydrochloride (amitriptolide), efonidine (amitudinamide), efonidine (efonide (efavirenzamide), efavirenzamide (Erlotinib (amitriptonide), etidine (efavirenzamide), etidinalutamide (efutamide), etidinate (efutamide (eforme), efutamide (efutamide), etoposis (etoposide phosphate ()), etoposide phosphate, Evacet (doxorubicin liposome Hydrochloride), Everolimus (Everolimus), evaluvit (evasta) (Raloxifene Hydrochloride)), evaluene (Evomela) (melphalan Hydrochloride), Exemestane (Exemestane), 5-FU (fluorouracil injection), 5-FU (fluorouracil-topical), fareton (Fareston) (Toremifene (Torrifene)), Farydak (Panobinostat), falodex (Fulvestrant), FEC, Femara (Letrozole)), Filgrastim (Filgrastim), dermagon (Degarelix), fudarada (flarabira) (fludarabine phosphate)), fludarabine (fludarabine), fludarabine (fludarodes), fludarabine (fludarodes), fludarabine (fludarodes), fludarodes (fludarodes), fludarabine (fludarodes), fludarodes (fludarabine (fludarodes), fludarodes (fludarodes, PFS (methotrexate), FOLFIRI-bevacizumab, FOLFIRI-cetuximab, FOLFIRINOX, FOLFOX, Folotyn (Pralatrexate), FU-LV, Fulvestrant (Fulvestrant), Fusilev (calcium folinate (LeucovorinCalcum)), Gardasil (recombinant HPV quadrivalent vaccine), Gardenia 9 (recombinant HPV nonavalent vaccine), Gezywa (Gazyva) (Orbinitumumab (Obtuzumab)), Gefitinib (Gefitinib cisplatin), gemcitabine hydrochloride, gemcitabine-oxaliplatin, Gemtuzumab (Gemtuzumab Oxigosamicin), Gemcitabine hydrochloride (Geotrimaf) (Arctine maleate), glitinib (Gliacetin) (Gluvetine mesylate), carmustine Acetate (carmustine Acetate), carmustine Acetate (Gluconatin), Gloecium Acetate (Gloecium Acetate), and other salts thereof, Halaven (Eribulin Mesylate), Hemangel (Propranolol Hydrochloride (Hydrochlride)), Herceptin (Herceptin) (trastuzumab), HPV bivalent vaccine, HPV nonavalent vaccine, HPV tetravalent vaccine, and Mexicamin (Hydroclin Hydrochloride), Hydroxyurea (Hydroxyurea), Hydroxyurea, super-CVAD, Ibratane (Palbociclib), ibritumomab (Ibritimo Tixetan), Ibrutinib (Ibrutinib), ICE, Icilaria (Ponatinibrosidase), Imamycin (Ididarubicin Hydrochloride), Identinib (Hydrochlorinib), Identin (Identin Hydrochloride), Identin (Identin Mesylate, Identin (Identin 2), Isophorectin (Enfosfamide), Imidanib (Identin Mesylate), Identin (Identin 2), Isophorectin (Identin Hydrochloride), Isophorectin (Albuminib), Isophorocib (Albuminib), Isophorib (Albuminib Hydrochloride), Isophorib (Albuminib), Isophorib (Albuminib Hydrochloride), Isophoretic acid, Identin 2) and Isophoretic acid (Albizumab (Id 2), Imidan Hydrochloride), Isophoretic acid (Id-2) and Ibenicin (Id-2) in (Albizumab) in, Imidarubicin Hydrochloride, Ibenicin, imiquimod, imiqigic (immigomere (tamimogen laherparvec)), Inlyta (axitinib), oximtuzumab, interferon alpha-2 b, interleukin-2 (aldesleukin), intron a (recombinant interferon alpha-2 b), ipipima (Ipilimumab), Iressa (Iressa) (gefitinib), irinotecan Hydrochloride liposome, isotaxax (Romidepsin)), Ixabepilone (Ixabepilone), ixabenzomi Citrate (ixazob Citrate), imixperi (Ixabepilone), jakafa (ruxolinonexene), jekakakakali (ruxolinib Phosphate), rituximab (rilofibrizumab)), rituximab (rivastigmine Hydrochloride (clavicine), rituximab (clavam)), ritamine (clavulan), rituximab (clavulan (e), rituximab (clavulan (rilicumab)), rituximab (clavine (clavulan (clavine)), rituximab (clavine (e (clavulan (e)), rituximab (e), rituximab (e)), jejunipene (e), rituximab (e)), jejuniper (e)), or (e)), jejuniper (e), riteria (e)), or (e, Kymria (Kymriah) (Tisagenlecuelel), Kyprolis (Carfilzomib), Lanreotide Acetate (Lanreotide Acetate), lapatinib ditosylate xylenesulfonate (Lapatinib), Lartruvo (Olaratumab), Lenalidomide (Lenalidomide), Lenvatinib Mesylate (Lenvatinib Mesylate), Lenvima (Letrozole), calcium folinate, Leucohun (Leukeran) (Chlorambucil), leuprolide Acetate, Leustridine (Cladribine), levan (Levulan) (Aminolevulinic acid), Linfolin (Chlorambucil (Leucocerul)), Lipobulin (Lipomdox), Loustine Hydrochloride (Loustine), Trilactitol Acetate (Lorentil), Lucarindine Acetate (Leuprolide-Hydrochloride), Lupulipran Acetate (Lupulan), Lumipristine Acetate (Lumiproline), Lumipristine (Lumipriline Acetate (Lumipriline), Liporubicin (Liporubicin Hydrochloride), Liporubicin (Lumipril) (Liporubine Acetate (Lumipril-Acetate), Lumipril-and Lumipristine (Lumiprin-Acetate (Lumiprin-D-L, Lutetium oxooctreotide (Lutathia) (Lutetium (Lutetium) Lu 177-dottate), Lutetium (Lu 177-dottate), Lynparza (Olaparib), Marqibo (Vincristine sulfate liposome), Procarbazine (matrix) (Procarbazine Hydrochloride), Mechlorethamine Hydrochloride (Mechlorethamine Hydrochloride), Megestrol Acetate (Megestrol Acetate), Meltist (Trametinib), Melphalan Hydrochloride, Mercaptopurine (Mercapterin), Mesna (Mesnara), Mesnex (mestrexone), Methazone (Methazamide), Methazathiopterine (Methazapine Bromide), Methazathiopterine (Methazathiopterine), Methazathiopterine (Methazathixathimide), Methazathiopterine (Methazathixathixate), Methazathiopterine (Methazathixate Hydrochloride), Methazathiopterine (Methazathiuram), Methazathiopterine (Methazathiopterine), Methazathiopterine (Methazathiopterine C) (Methazathiopterine C (Methazathiopterine, Methazathiopterine (Methazathiopterine C), Methazathiopterine (Methazathiopterine, Methazathiopterine (Methazathiopterine, Methaza, MOPP, Mozobil (Plerixafor), Mustargen (mechlorethamine Hydrochloride), mitomycin (mitomycin C), malilan (Myleran) (busulfan), Mylosar (azacitidine), Mylotarg (gemtuzumab), nanoparticulate Paclitaxel (Nanoparticle Paclitaxel) (Paclitaxel albumin-stabilized Nanoparticle formulation), Vinorelbine (navelene) (Vinorelbine Tartrate), toluzumab (necitumomab), Nelarabine (nerabane), neostaurin (cyclophosphamide), Neratinib (Neratinib Maleate), Neratinib (Neratinib)), Neratinib and paleonesoxim Hydrochloride (netosetron), neratin (netrolein), Neratinib (Neratinib), Neratinib (netorubine sulfonate), Neratinib (neviranib (nevirapine), Neratinib (nevirapine Hydrochloride (nevirapine), Neratinib (nevirapine (neferin), Neratinib (nevirapine (neferine (neferin), Neratinib (neferin), nervone (nevirapine (neferine), nervone (neferin), nervone (neferine), nervone (nefirsflate), nervone (neferine (nefirsflate), nervone (neferine (nefirsflate, Nilutamide, ixalamide (Ninlaro) (isazomide Citrate), nilapanib Tosylate Monohydrate (Niraparib Tosylate Monohydrate), naluzumab, Tamoxifen (Nolvadex) (Tamoxifen Citrate), Nplate (romiplosmith), gexiwa, Odomzo (Sonidegib)), OEPA, ofatumumab, OFF, Olaparib (Olaparib), olabrab, homoharringtonine (omapaxile mepericcinate), onaflapari (pemasomax), ondansetrase (pegasparagargase), Ondansetron (danstroleroniron Hydrochloride), deanide (ovapanib Hydrochloride), onponicade (ovipad) (warriotecan), wortisone (wollenital), wollenital (Paclitaxel), Paclitaxel, oxsultap (oxsultap), Paclitaxel Hydrochloride, oxsultap (oxsultap), lox (oxsultap), Paclitaxel Hydrochloride (oxsultap), Paclitaxel (oxsultap), Paclitaxel, and a, Pamidronate Disodium (Pamidronate Disodium), panitumumab, Panobinostat (Panobinostat), Paraplat (carboplatin), beradine (Paraplatin), Pazopanib Hydrochloride (Pazopanib Hydrochloride), PCV, PEB, Pemetrexed, pefegratin, Peginterferon alpha-2 b (Peginterferon Alfa-2b), PEG-intron (Peginterferon alpha-2 b), Pembrolizumab (Pemetrexed), Pemetrexed Disodium (Pemetrexed disodds), Perjeta (Pertuzumab), Pertuzumab (platinumab), platinunol (platinula), palestramur (Plerixafor), Pamidronate (Pomalidone), Polypamidronate (Poquinacrine), hydrastine (Poxiletine), prometrexen (procarbazine), prometrexendin (Prinolide), prometrexendin Hydrochloride (Prinolide (Prinodizonate), prometrexendin (prometrexendin), pemetrexendin (Prinolide), pemetrexendin (Perfectamine), Perfectamine (Perfectamine), Perfect, Proliia (Prolia) (dinosaum (Denosumab)), Promacta (Eltrombopag Oliamine)), Pronapolol Hydrochloride (Propranol Hydrochloride), Provecarb (Provence) (Provence-T (Sipuleucel-T)), Purinethol (mercaptopurine), Purixan (mercaptopurine), Radium 223 dichlorinate (Radium 223 dichlorinate), Raloxifen Hydrochloride, Ramomucirumab, Labridase, R-CHOP, R-CVP, recombinant Human Papilloma Virus (HPV) bivalent vaccine, recombinant Human Papilloma Virus (HPV) nine vaccine, recombinant Human Papilloma Virus (HPV) tetravalent vaccine, recombinant interferon alpha-2 b, Stogolinib, Relir (methylnaltrexone bromide), R-CH, Ractine (EPOTATaPectit) (Rituximab), Rifamid (Rituximab), RevRevrituximab (RvR, Revrituximab), Rermonix (Rnaproxen), Reynamide (Reynaudix, Reynaudix-T (Sipurotuzole), Reynamide (Reynamide, and its salt (Reynamide, rituximab (Rituxan Hycela) (Rituximab and hyaluronidase Human) for humans), Rituximab and hyaluronidase for humans, rolipidem Hydrochloride (Rolapitant hydroxychloride), romidepsin, rubicin (Rubidomycin Hydrochloride), rubiaca (Rucaparib Camsylate), rubiginib phosphate, Rydapt (Midostaurin), Sclerosol (Sclerosol endosol) (Talc)), steruximab (silteximab), cillulosest-T (sipuufel-T), somaturatel (sorafenib Acetate), Talc (sorafenib), sorafenib (sorafenib) Sterile Talc (orlistat), sorafenib (Talc (sorafei), sorafenib (sorafei), Talc (talcite), sorafenib (sorafei), sorafei (lanigenide), Talc (sorafenib sulfate (sorafei), Talc (sorafei-n), sorafei-r (sorafei), Talc (sorafei-r (sorafei-r), r (sorafei-r, Sunitinib malate (sunitinib Malate), sunitinib (Sutent) (sunitinib malate), selatron (Sylaron) (PEG interferon alpha-2 a), Sylvant (cetuximab), Synribo (homoharringtonine Omacetaxine Mepessulinate), Tabloid (Thioguanine), TAC, Tafinar (Dabrafanib), Tarriso (Tagrisso) (Octagenib), Talcum, Tarimogragliflozin Parapervix (Talimogen Laherparepvepvec), Tamoxifen Citrate (Tamoxifen Cite), Tarabine PFS (arabinoside), Tarceva (Tarceva) (erlotinib), Targetrietin (Berothine), Targetrietinib (Targetie), paclitaxel (Taxoit), paclitaxel (Teloxit), paclitaxel (Teloxil), docetaxel (Teloximol), docetaxel (Teloximol), paclitaxel (Teloximol (Texolimumab (Teloxil (Teloximol), Taxol (Teloxi), Talmocine (Teloxi), Talmorigamimir (T), Talmorimod (T-D (T), Talmocine (Taxol (Teloxi) (Telmicin), Talmorine (T), Talmorigamimir (T-D), Talmorigamimid) (T-D) (T, Thioguanine, Thiotepa (Thiotepa), tesarenesul (tisagenlecucel), tolizumab (Tocilizumab), tolarak (Tolak) (fluorouracil-topical), Topotecan Hydrochloride (Topotecan Hydrochloride), Toremifene (Toremifene), Torisel (Torisel) (Temsirolimus), dexrazoxane (Totect) (dexrazoxane Hydrochloride), TPF, Trabectedin (Tramectin), tremelimumab, Trenda (Bendamustine Hydrochloride), Trexathrix (methotrexate), trofloxuridine and dipivefrin Hydrochloride, Trisenox (arsenic trioxide), Tykerb (Tykerb) (dipalmondylacetin (Lapatinib), Vatica (valbutrytin Triacetate), valbivalin (valbivalbivalin (valbivalrubicin), valbivir Hydrochloride (valbivalbivir Hydrochloride), valbivalidamine (valbivalbivalbivir Hydrochloride), valbivalidamine (valbivalbivalbizic), valbivalidamine Hydrochloride), valbizidine (valbizium Hydrochloride), valbizium (valbizium), valbizium Hydrochloride), valbizium (valbizium Hydrochloride), valbizium (valbizium), valbizi, Victoridine (Vectibix) (panitumumab), VeIP, Vinblastine (Velban) (Vinblastine Sulfate)), Velcade (Velcade) (bortezomib), Velsar (Vinblastine Sulfate)), vemofenib (Vemurafenib), venletxa (venoteclax), vinnetitol, Verzenio (Abemaciclib), viduru (leuprolide acetate), vidazane (azacitidine), Vinblastine Sulfate, vincaster PFS (Vinblastine Sulfate (vincristalline Sulfate)), Vincristine Sulfate, Vincristine liposome (vinristine Sulfate), vinristine Tartrate (Vinorelbine Sulfate), vinristine Triacetate, vitamin (VIP, vitamin citrate), Vincristine (Vinblastine hydrochloride), Vincristine hydrochloride (Vinblastine hydrochloride), Vinorelbine Tartrate (Vinblastine hydrochloride), Vinblastine (VIP, vitamin citrate (vitamin), calcium citrate (vitamin D), Vinblastine hydrochloride (vitamin D), and calcium citrate (vitamin D) hydrochloride), vitamin D (vitamin D) and vitamin D (vitamin D) hydrochloride (vitamin D, vitamin D (vitamin D) and vitamin D, vitamin D (vitamin D, xeloda (capecitabine), XELIRI, XELOX, Xgeva (dinosaum), Xofigo (radium 223dichloride), Xtandi (enzalutamide), yrevoy yiprioma), yercata (yescata) (west cappuccino (axinebutagen Ciloleucel)), yondeis (trabectedin), Zaltrap (Ziv-Aflibercept)), Zarxio (Filgrastim), Zejula (nilapanib tosylate monohydrate), Zelboraf (vemurafenib), Zevalin (ibritumomab), Zinecard (dexrazoxane hydrochloride), Aflibercept (Ziv-Aflibercept), pinoxide (Zofran hydrochloride) (zodane), ranitidine (acetominox) (zolamide), Zoledronic Acid (zolamide), zoledronate (zolamide), zolamide (zolamide), zolamide (Zylone (Zylon), or combinations thereof.

In some embodiments, the subject receives carrier frequency modulation therapy alone or in combination with anti-cancer therapy. In some embodiments, the dose of the anti-cancer therapy can range from about 10 mg/day to about 1000 mg/day. In some embodiments, the dose of the anti-cancer therapy can range from about 20 mg/day to about 1000 mg/day, about 30 mg/day to about 1000 mg/day, about 40 mg/day to about 1000 mg/day, about 50 mg/day to about 1000 mg/day, about 60 mg/day to about 1000 mg/day, about 70 mg/day to about 1000 mg/day, about 80 mg/day to about 1000 mg/day, about 90 mg/day to about 1000 mg/day, about 100 mg/day to about 1000 mg/day, about 200 mg/day to about 1000 mg/day, about 300 mg/day to about 1000 mg/day, about 400 mg/day to about 1000 mg/day, about 500 mg/day to about 1000 mg/day, about 600 mg/day to about 1000 mg/day, From about 700 mg/day to about 1000 mg/day, from about 800 mg/day to about 1000 mg/day, from about 900 mg/day to about 1000 mg/day, from about 100 mg/day to about 200 mg/day, from about 200 mg/day to about 800 mg/day, from about 200 mg/day to about 600 mg/day, or from about 200 mg/day to about 500 mg/day.

The following examples are provided for illustrative and exemplary purposes only and are not intended to limit the present invention in any way.

Example 1

An example of Hdp values recorded during 23 consecutive heartbeats is set forth in fig. 75A. The measured and recorded Hdp values for each of the nine hemodynamic parameters are examples of such values exhibited by a single patient.

An example of relevant new attribute parameter values recorded during 23 consecutive heartbeats is set forth in fig. 75B. The relevant new attribute parameter values can be used as representative Hbp change values exhibited by individual patients.

Example 2

Fig. 76 demonstrates that the correct classification rate of centroids using representative Hdp change values during exposure Hdp values (dark) and basal Hdp values (white) in four patients diagnosed with hepatocellular carcinoma during systemic treatment is high. Patients A, B and C did not respond to cancer treatment, while patient D responded well to cancer treatment. Using four well-established statistical methods, centroids from representative Hdp variation patterns were significantly different between exposure and non-exposure periods, and between responding to treatment and not responding to treatment (p < 0.0001).

Example 3

Fig. 77 demonstrates the high correlation rate of representative Hdp change values and biofeedback programs, which involves a very large number of observations and measurements of the physiological response (at certain well-defined AM frequencies) of reactive pulses (e.g., shown as blue in the correlation plot) and non-reactive pulses (e.g., shown as red in the correlation plot) of four patients diagnosed with hepatocellular carcinoma. Using different well-established statistical methods, centroids from representative Hdp variation patterns were significantly different between reactive and non-reactive pulse alterations (p < 0.0001).

Example 4

Fig. 78 illustrates the difference in representative Hdp change values determined during exposure to different SFq. Applying mathematical algorithms and artificial intelligence processing identifies EMF frequencies that may result in a change in Hdp change value consistent with the patient's health condition. The EMF frequency cause demonstrated in this example is named SFq.

Example 5

Fig. 79A shows that the health condition specificity SFq is distributed in a significantly chaotic way among the different health conditions according to the amplitude of the modulation from 100Hz to 40,000Hz the recombination of the different health condition specificities SFq, obtained by Hz ═ a + β x (R) was obtained²99.9%) of the linear equation defined. The artificial intelligence algorithm allows for the construction of a series SFq for diagnosing and treating patients. The example shows the distribution of 1,054 cancer-specific frequencies from four cancer types.

Fig. 79B shows that the health condition specificity SFq is distributed in a deterministic manner among different health conditions according to the amplitude of the modulation from 100Hz to 40,000 Hz. The artificial intelligence algorithm may allow for the identification of a series SFq of patterns for diagnosing a patient. The examples show the distribution of disease-specific Sfq (red) and health-specific Sfq (blue) in 21 patients during exposure to different three sets of cancer-specific frequencies.

Example 6

In vitro studies have shown that low levels of amplitude modulated electromagnetic fields can modify cell growth. Specific frequencies have been identified that can block cancer cell growth. Portable programmable devices have been developed that are capable of delivering low levels of amplitude modulated electromagnetic fields. The device emits a 27.12MHz radio frequency signal amplitude modulated with high accuracy at cancer specific frequencies ranging from 0.2Hz to 23,000 Hz. The device is connected to a scoop coupler that is placed in the patient's mouth during treatment.

The method comprises the following steps:

a phase I study consisting of three 40 minute treatments per day was performed. From 3 months 2004 to 9 months 2006, 24 patients with advanced solid tumors were enrolled. Median age was 57.0 years +/-12.2 years. The 16 patients were females. By 1 month 2007, 5 patients were still receiving treatment, 13 patients died due to tumor progression, two patients were lost visit and one patient withdrawn consent. The most common tumor types are breast (7), ovarian (5) and pancreatic (3) cancer. 22 patients had previously received systemic therapy and 16 patients had documented tumor progression prior to study addition.

As a result:

the median duration of treatment was 15.7 weeks +/-19.9 weeks (range: 0.4 to 72.0 weeks). There was no NCI grade 2, 3 or 4 toxicity. Three patients experienced grade 1 fatigue during and immediately after treatment. Severe pain was reported in 12 patients prior to study addition. Two of the patients reported significant relief of pain under treatment. Objective responses can be assessed in 13 patients, six of whom also have elevated tumor markers. Six additional patients could be evaluated by tumor markers only. Of the patients with progressive disease at the time of study enrollment, one patient had a partial response >14.4 weeks, which correlates with a > 50% reduction in CEA, CA 125, and CA 15-3 (previously untreated metastatic breast cancer); one patient had stable disease for 34.6 weeks (add information); one patient had a 50% reduction in CA19-9 (recurrent pancreatic cancer) within 12.4 weeks. Of the patients with stable disease at enrollment, four patients maintained stable disease for 17.0 weeks, >19.4 weeks, 30.4 weeks, and >63.4 weeks.

And (4) conclusion:

this treatment is a safe and promising new therapeutic modality for advanced cancers. Phase II studies and molecular studies were continued to confirm these results.

Example 7

Federico P Costa, Andre Cosme de Oliveira, Robert Meirells Jr., Rodrigo Surjan, Tatiana Zanesco, Maria Cristina Chammas, Alexandre Barbalt, Boris Pasche for phase II study of therapeutic, amplitude modulated electromagnetic fields in the treatment of advanced hepatocellular carcinoma (HCC).

Background:

phase I data indicate that low-level electromagnetic fields, amplitude modulated at specific frequencies applied intrabuccally with the apparatus of example a, are safe and potentially effective treatments for advanced cancer. The device emits a 27.12MHz RF signal amplitude modulated with high accuracy at cancer specific frequencies ranging from 0.2Hz to 23,000 Hz. The device is connected to a scoop-like coupler that is placed in the patient's mouth during treatment. Patients with advanced hepatocellular carcinoma HCC and limited therapeutic options are provided with treatment in combination with HCC-specific frequency.

The method comprises the following steps:

from 10 months 2005 to 7 months 2007, 43 patients with advanced HCC were enrolled in the phase II study. Two patients were considered to have failed the screening. Patients received 1 hour treatment three times a day until disease progression or death. Median age was 64.0 years +/-14.2 years. 17 patients were Child-Pugh status A5-6, and 24 patients were Child-Pugh B7-9. Prior to study enrollment, 75.6% of patients had documented disease Progression (POD).

As a result:

overall objective response rates as defined by Partial Response (PR) or Stable Disease (SD) in patients with POD recorded at the time of study enrollment; 4 patients had PR (1 patient had almost complete response for 58 months) and 16 patients had SD. Median survival was 6.7 months (95% CI from 3.0 to 10.2) and median progression-free survival was 4.4 months (95% CI from 2.1 to 5.3). 14 patients received therapy for more than six months. The estimated survival rates at 12 months, 24 months and 36 months were 27.9%, 15.2% and 10.1%, respectively. Pain was reported by 12 patients at study enrollment: eight of the patients (66%) had reduced pain during treatment. Has no NCI 2/3/4 grade toxicity. One patient developed grade 1 mucositis and grade 1 fatigue.

And (4) conclusion:

in patients with advanced HCC, this treatment is a safe and effective new treatment option, which has an anti-tumor effect and relieves pain for most patients.

It can therefore be seen that the electronic device of the present invention, including the means for accurately controlling the frequency and stability of the amplitude modulation of the high frequency carrier signal, provides a safe and promising new therapeutic modality for treating patients suffering from various types of advanced forms of cancer.

Examples of the above accurately controlled amplitude modulated frequencies that control the frequency of the amplitude modulation of the high frequency carrier signal are set forth below, along with the type of cancer or tumor from which the subject to be treated is suffering.

Referring again to the figures, fig. 12 depicts an illustrative patient during experimental setup for continuous monitoring of hemodynamic parameters before and during AM RF EMF exposure. Using TaskA monitor (CNSystems mediatinechnik GmbH, version 2.2.12.0, Austria Gretz 8020Reininghausstra β e 13(Reininghausstra β e 13,8020Graz, Austria)) performs non-invasive hemodynamic measurementsThe monitor is marked 1. The AM RF EMF emitting device is labeled 2. AM RF EMF emitting device connected to a coaxial cable connected to the scoop antenna 3. The right arm digital pressure band is labeled 4. The digital photoplethysmography is marked 5. The left arm digital pressure band is labeled 6. Electrode cables for ECG and impedance cardiography are provided.

Values of heart rate variability, blood pressure, baroreceptor sensitivity and blood pressure were measured by digital photoplethysmography and ECG acquired through three chest affixed electrodes for high resolution RR interval analysis. A digital pressure band is placed on the right arm around the middle phalanx of the third and fourth right fingers, and another digital pressure band is placed on the left arm between the shoulder and elbow. The blood pressure measurements are converted to absolute values for each successive heartbeat.

With reference to fig. 19 to 20, an example of eleven hemodynamic parameters measured simultaneously during each heartbeat is seen: heart Rate (HR), systolic pressure (sBP), median blood pressure (mBP), diastolic pressure (dBP), Total Peripheral Resistance (TPR), Total Peripheral Resistance Index (TPRI), Cardiac Output (CO), Cardiac Index (CI), RR interval (RRI), Stroke Volume (SV), and Systolic Index (SI). Hemodynamic recording was continuously performed in the supine position before and during exposure to AM RF EMF. A total of three million hemodynamic parameters were analyzed in this study.

During the entire experiment, participants contained the scoop antenna in their mouths. Three different devices each programmed with one of the treatment programs (HCC-specific frequency, breast cancer-specific frequency, and randomly selected frequency) were connected before starting each of the AM RF EMF exposure periods. The protocol was performed in a double-blind manner.

Referring to fig. 17-18, hemodynamic recordings were continuously performed in a double-blind manner during the non-exposure period and the exposure period. The non-exposure period is the initial basal interval and rest interval between five minutes of RF EMF exposure. During the exposure period, the patient received AM RF EMF (HCC-specific frequency, breast cancer-specific frequency, randomly selected frequency).

Hemodynamic parameters were analyzed according to three factors: diagnosis (HCC, breast cancer, healthy controls), gender, and recording period (baseline and exposure to HCC-specific modulation frequency, breast cancer-specific modulation frequency, and randomly selected modulation frequency).

The recorded hemodynamic data is only analyzed after the patient has grown completely naturally. The patients are selected to form a machine learning knowledge base. The expected outcome of the knowledgebase set analysis is to create calculations specific to hepatocellular carcinoma patients, breast cancer patients, and healthy controls. Once the calculations are constructed, the data from the validation set is analyzed in a blind manner to validate the calculations.

The analysis of six patients diagnosed with potential resectable HCC was included in the validation set analysis. These patients had the same non-invasive hemodynamic parameter measurements within 24 hours prior to HCC surgical resection and after complete recovery within four to six weeks post-surgery. Pre-operative and post-operative analysis was performed.

Analysis of hemodynamic parameters during the basal non-exposure period was significantly different (p <0.0001) between healthy controls, hepatocellular carcinoma patients, and breast cancer patients in the "discovery group". There were also significant differences in hemodynamic parameters between male and female participants.

Hemodynamic parameter analysis was performed during the basal non-exposure period and the exposure period, respectively. Differences in representative Hdp change values were determined during exposure to baseline and exposure to HCC-specific modulation frequency, breast cancer-specific modulation frequency, and randomly selected modulation frequency, separated at different SFq. A mathematical algorithm and artificial intelligence process was applied to identify the HRV pattern of each different SFq for each individual diagnosed as HCC, breast cancer, healthy controls.

Application of mathematical algorithms and artificial intelligence processes resulting from exposure of healthy controls and patients with cancer (HCC or breast cancer) to modulated frequencies identifies HRV patterns, demonstrating the differences in HRV patterns that can be used to identify a diagnosis of a patient.

Using a previously selected knowledge base set of 10 patients with biopsy confirmed HCC and 10 male healthy controls, artificial intelligence processing combined with 582 modulated frequencies analyzed a 10 minute baseline non-exposure period to identify a particular HRV pattern for use in diagnosing the patient.

A validation group of 40 male subjects was tested. The diagnosis was correctly identified in 37 individuals. There were 2 healthy controls labeled as HCC patients, and one HCC patient was labeled as a healthy individual. The artificial intelligence processing algorithm also indicates the associated modulated frequency or SFq to use in correctly distinguishing HCC from healthy controls.

Using a previously selected knowledge base set of 10 biopsy confirmed patients with breast cancer and 10 female healthy controls, a baseline non-exposure period of 10 minutes was analyzed in combination with 582 modulated frequencies by artificial intelligence processing to identify the particular HRV pattern used in diagnosing the patient.

A validation group of 27 female subjects was tested. The diagnosis of 17 patients was correctly identified among 18 breast cancer patients. The artificial intelligence processing algorithm also indicates the associated modulated frequency or SFq to use in correctly distinguishing HCC from healthy controls.

The particular HRV pattern identified for the modulated frequency significantly different from healthy controls observed primarily in HCC was selected as SFq for use in the treatment procedure of HCC patients. The same rationale applies to breast cancer patients and possibly other health conditions.

According to another aspect of the present invention, a new method is provided to identify and characterize in a blinded manner allowing diagnosis of hepatocellular carcinoma and breast cancer based solely on the identification of HRV patterns during exposure to 27.12MHz RF EMF amplitude modulated at tumor specific frequencies. These findings can have broad clinical implications for the diagnosis of cancer.

U.S. patent application serial No. 12/450,450 lists frequencies known as of application date 2009, 9-25, while U.S. patent No. 8,977,365 adds frequencies known as of application date 2012, 8-22. Since these archives, additional AM frequencies have been determined by biofeedback procedures involving a very large number of observations and measurements of physiological responses (at certain well-defined AM frequencies) of subjects exposed to low-energy electromagnetic emission excitations, effective for characterization, diagnosis, treatment and frequency discovery of the type of cancer or tumor from which the subject to be treated suffers. Based on such a plurality of amplitude modulation frequency values, it has surprisingly been found that there is a relationship between sequential values or groups of sequential values within a range of frequency values.

Considering SFq is linearly related to a series of numbers constituting a superset of a series of prime numbers, a series of common denominators of all SFq constructed to characterize previously patented and determined by biofeedback procedures involving a very large number of observations and measurements of physiological responses (at certain well-defined AM frequencies) of subjects exposed to low-energy electromagnetic emissions excitations in any health condition of a patient are now determined by a new mathematical model described below as an important aspect of the present invention, in order to carry out accurate diagnosis and treatment of warm-blooded mammalian subjects.

Using n-7 x + i (where x-1, 2, … is a natural number and i-0, 1,2, …,6 is a family index or remainder), SFq can be organized into seven infinite groups of congruent element groups (mod 7). Assigning n (where k is 1,2, …,6) in a block b of i × k 42 positions defines the position of n in family i in the corresponding block, resulting in x 6b + k by construction, where a convenient representation of a natural number is obtained: n-42 b +7k + i-42 b + θ.

It is readily verified that all prime numbers (Pn) except 2 and 3 belong to the following two AP ratio r-6: x is 0,1, …: AP (Access Point)₁＝6x+5,AP₂6x + 7. Obviously, these two APs also contain a constituent number (Cn). This superset of natural numbers represents prime number candidates (Cp). In view of the representation of natural numbers in terms of b, k and i, Cp given by a superset of natural numbers is obtained such that i + k is 5 or 7 or 11, n ≧ 5. Since each family of natural numbers is intersected by two AP's, e.g., n 42b +7k + i 42b + θ, each family (i 1,2, …,6) obtains two values k and θ under the constraint of i and k.

Cp may be organized in subdivisions. Limited to natural numbers such as i ≠ 0 and i + k ≠ 5 or 7 or 11, 6 linear equations are obtained, called the natural number family n ≠ 7x + i, with an AP ratio r ═ 7, intersected by a ratio r of 2 APs ≠ 6. Thus, there are 12 sets of Cpn-42 b +7k + i-42 b + θ, which by transformation correspond to 6 groups. x is 6b + k. The Cp family is the basis for building the common denominator of SFq.

A series of common denominators constructed against SFq can be tested and validated in warm-blooded mammalian subjects and patients by exposure to single, series or combination highly specific frequency radio frequency carrier signals predetermined by the new mathematical model described above as an important aspect of the present invention. The verification results obtained by the integrated solution of the present invention, named representative Hdp variation values and correlations SFq, provide a permanent refinement and adjustment to the linear model based on the artificial intelligence approach for pattern recognition described above. The methods described above provide an unlimited series of identification and production of SFq related to the health condition of warm-blooded mammalian subjects.

Example 8

A total of 81 patients were prospectively evaluated in three separate groups of patients and healthy controls: 1) the finding group consisted of 6 patients with advanced hepatocellular carcinoma, 6 patients with advanced breast cancer and 6 healthy controls; 2) the validation group consisted of 25 patients diagnosed with cancer (14 female patients with advanced breast cancer and 11 male patients with advanced HCC) and 31 healthy controls (18 females and 13 males); 3)6 patients with potentially resectable hepatocellular carcinoma (5 men and one woman). All individuals were exposed once to each of the previously reported 194 tumor-specific frequencies (HCC-specific and breast cancer-specific) and 194 randomly selected frequencies, each emitting 3 s.

Values for heart rate variability, blood pressure, baroreceptor sensitivity and blood pressure were measured by digital photoplethysmography and ECG. Hemodynamic recordings were made of patients in supine position before and during exposure to AM RF EMF.

Data analysis began with the discovery group, creating mathematical algorithms specific to hepatocellular carcinoma patients, breast cancer patients, and healthy controls. The data from the validation set was analyzed in a blind test to validate the algorithm.

In the discovery group, 6 of 6 HCC patients (100.0%) were observed to have a similar hemodynamic response pattern to AM RF EMF exposure, which occurred only during exposure to HCC-specific modulation frequencies. Of the patients with breast cancer, 5 of 6 cases (83.3%) identified a similar hemodynamic response pattern during exposure to the breast cancer-specific modulation frequency. No tumor-specific hemodynamic response was observed in healthy controls or during exposure to randomly selected frequencies. In the validation group, 22 of 25 patients (88.0%) identified a tumor-specific hemodynamic response pattern.

Pre-planned post-hoc analysis demonstrates that this phenomenon can be explained by interval oscillations between successive heartbeats and oscillations between successive instantaneous heart rates, known as Heart Rate Variability (HRV), describing changes in both instantaneous heart Rate and RR Interval (RRI). The variation in heart rate can be studied by time domain measurements in a continuous succession of RRI measurements. RRI fluctuations have complex nonlinear behavior. To analyze very short R-R intervals, Poincar é plots (Poincar é plot) are typically used, but this requires accuracy in data collection (data synchronization).

Example 9

Two prospective clinical studies were conducted exposing cancer patients to monotherapy regimens of AM RFEMF selected as a HRV biological surrogate: feasibility studies were performed in 28 patients with various tumor types, and phase I/II studies were performed in 41 patients with advanced hepatocellular carcinoma (HCC). In the feasibility study, two of seven patients with metastatic breast cancer and one patient with recurrent metastatic thyroid cancer had a significant persistent response. Objective responses were observed in four (9.8%) HCC patients. One patient had a near complete response lasting more than 5 years, and 7 patients were observed to have disease control for more than 2 years. Even after more than 7 years of treatment, no patients developed any limiting toxicity.

In vitro experiments in cancer patients using frequency modulation identified by HRV biological surrogate blocked the growth of breast cancer cells and HCC cells under specific cancer frequency modulation. Confocal laser scanning microscopy showed significant disruption of the mitotic spindle. Alterations in gene expression of the IP3/DAG signaling arm and calcium binding proteins of the PI3K pathway in key regulators were identified by RNA-seq and Micro-RNA analysis. Calcium flux alterations through microtubules were identified by calcium indicator analysis. Some anti-tumor effects may be calcium dependent.

To demonstrate whether the biological effect in humans depends on the carrier frequency or on the amplitude modulated signal (see fig. 13), a prospective study was conducted on 14 individuals (7 healthy controls and 7 HCCs) exposed to a 27.12MHz carrier frequency ± 194 amplitude modulated frequencies (10 seconds of sequential exposure, ranging from 400Hz to 20kHz) in 5 alternating exposure sessions of two consecutive days of exposure. Hemodynamic parameters were recorded using a high precision non-invasive hemodynamic monitor (Task Force monitor, CN systems) synchronized with a frequency modulated rf generator (see fig. 14-16). Real-time HRV data analysis was performed for each exposure period using an artificial intelligence calculation process. In addition, using a support vector machine algorithm, a single frequency modulation response for each patient can be identified and used for patient diagnosis. We can demonstrate that human body specific frequency modulation to HCC exposed to a 27.12MHz carrier frequency produces a reproducible autonomous stress response, which is more pronounced in HCC patients (Tukey-HSD test, p <0.05) (fig. 29). The response pattern identification enables differentiation of healthy individuals from HCC patient groups (fig. 28). The hemodynamic response to frequency amplitude modulation is immediate, which supports the idea of a frequency-specific intracellular electrical signal demodulation system, resulting in tissue-specific biological modification (e.g., anti-tumor effects) and diagnosis of patients in a non-invasive and non-toxic manner. It was hypothesized that the cellular level mechanism of action of these fields involved ionic conduction along microtubules and possibly actin filaments consistent with earlier computational simulations.

Fig. 21-33 depict the significant difference in Sd2/Sd1 (poincare semi-axis plot) between the carrier frequency exposure period (B1, B2, and B3) and the carrier frequency modulated at the HCC-specific frequency amplitude modulation exposure period (E1 and E2) over the course of several days.

Example 10

Studies were performed in patients with advanced hepatocellular carcinoma (HCC) with hepatitis b. There was a progressive tumor mass in the right lobe of the liver, with tumor mass size >20 cm. The pathology demonstrated poorly differentiated HCC with portal vein invasion. The tumor marker for alpha-fetoprotein (AFP) was first detected at 3,883 (fig. 54A). Patients began taking sorafenib 800 mg/day and then decreased to 400 mg/day. Subsequent AFP measurements included values of 17,000 and 60,5000 (fig. 54A). The patient was exposed to 27.12MHz carrier frequency ± 194 amplitude modulated frequencies (10 seconds sequential exposure, ranging from 400Hz to 20kHz) for 2 alternating exposure periods of two consecutive days (fig. 18A). Hemodynamic parameters were recorded using a high precision non-invasive hemodynamic monitor (Task Force monitor, CN systems) synchronized with a frequency modulated rf generator (see fig. 14-16). Real-time HRV data analysis was performed for each exposure period using an artificial intelligence calculation process. Additionally, using support vector machine algorithms, a single frequency modulation response for each patient can be identified and used for patient diagnosis/treatment. After the exposure regimen, the AFP level of the patient was reduced to 1,080 (fig. 54A), and the CT scan showed complete clinical response (fig. 54B).

Example 11

Studies were conducted in a 71 year old white male with pulmonary thromboembolism and chronic hepatitis b virus. Initial abdominal CT scans showed large tumor masses in the right lobe, 18.7cm × 14.2cm × 14.5cm in size, with tumor invasion into the inferior vena cava (fig. 55A and 55B). Blood results showed total bilirubin to be 1.12 mg/dL; total protein INR of 1.31; and creatinine was 0.8 mg/dl. The patient then receives a conservative enlarged right liver lobectomy with a positive margin. Tumor invasion into the inferior vena cava and right portal vein and hepatocellular carcinoma poorly differentiated was confirmed by pathology (fig. 55C to fig. 55D). Subsequent CT scans showed bilateral metastatic lung nodules (fig. 55E), and abdominal MRI showed a 5.7cm × 4.5cm × 6.8cm Seg II/III and a 1.5cm Seg IVA progressive tumor mass with multiple subdural and peritoneal metastatic lesions with an average size of 3.0cm (fig. 56). The patient then started 800 mg/day sorafenib anticancer therapy and the dose was reduced to 400 mg/day due to hand-foot syndrome. Patients maintained irregular use of sorafenib 400 mg/day. Blood tests were performed again and showed total bilirubin to be 0.98 mg/dL; total protein 1.27; and creatinine was 0.84. The initial AFP value was shown to be 38,834(nl <7.0) at the beginning of sorafenib therapy and 60,500 after 2 months of sorafenib therapy. The patient was exposed to 27.12MHz carrier frequency ± 194 amplitude modulated frequencies (10 seconds sequential exposure, ranging from 400Hz to 20kHz, followed by 3 seconds sequential exposure, ranging from 400Hz to 20kHz) for 1 exposure period of the day (fig. 18D). Hemodynamic parameters were recorded using a high precision non-invasive hemodynamic monitor (Task Force monitor, CN systems) synchronized with a frequency modulated rf generator (see fig. 14-16). Real-time HRV data analysis was performed for each exposure period using an artificial intelligence calculation process. Additionally, using support vector machine algorithms, a single frequency modulation response for each patient can be identified and used for patient diagnosis/treatment. After the exposure protocol, the AFP level of the patient was reduced to 1,008, and the CT scan showed a significant clinical response, cavitation of lung metastases as shown in fig. 57A and disappearance of liver tumor mass and residual peritoneal implant after the exposure protocol as shown in fig. 57B.

Example 12

Studies were conducted in 60 year old white men diagnosed with chronic hepatitis c virus who subsequently received interferon therapy for hepatitis c and had a complete virological response. Follow-up abdominal ultrasound showed cirrhosis and portal hypertension. Follow-up abdominal CT scans showed a single right hepatic nodule of 4.5cm x 4.22cm x 4.35cm in size. Follow-up abdominal CT scans confirmed 4.9cm SEG VIII nodules, 0.7cm Seg VIII nodules, and 1.4cm SEG VII nodules. Liver MRI confirmed the same hepatic nodules consistent with hepatocellular carcinoma. The patient then receives transarterial chemoembolization (TACE). Six months later, the patient received Seg VIII radiation ablation. The patient then received Seg II, Seg IV, Seg VI, Seg VII, and Seg VIII radiation ablations. At follow-up, liver MRI nodules of Seg VIII showed portal vein progression and invasion (fig. 58). Blood results showed total bilirubin to be 1.66 mg/dL; total protein INR is 1.1; and creatinine was 1.1 mg/dl. Experimental resection of the patient's right lobe was then performed, but without success. Abdominal CT and MRI showed Seg VIII progressive disease compared to the patient's previous liver MRI scan. The patient then showed tumor thrombosis in the portal vein and new nodules in the left lobe (fig. 59). The patient then started anticancer therapy with 800 mg/day of duojimei. Blood tests were performed again and showed total bilirubin to be 0.9 mg/dL; total protein INR is 1.03; and creatinine was 0.95 mg/dL. The patient was exposed to 27.12MHz carrier frequency ± 194 amplitude modulated frequencies for 2 exposure periods of two consecutive days (10 second sequential exposure, ranging from 400Hz to 20 kHz). Hemodynamic parameters were recorded using a high precision non-invasive hemodynamic monitor (Task Force monitor, CN systems) synchronized with a frequency modulated rf generator (see fig. 14-16). Real-time HRV data analysis was performed for each exposure period using an artificial intelligence calculation process. Additionally, using support vector machine algorithms, a single frequency modulation response for each patient can be identified and used for patient diagnosis/treatment. After the exposure protocol, the CT scan of the patient showed complete clinical response with disappearance of the tumor mass after two weeks after the exposure protocol as shown in figure 60A and four weeks after the exposure protocol as shown in figure 60B.

Example 13

A study was conducted in a 75 year old white male diagnosed with alcoholic cirrhosis. Abdominal ultrasonography revealed new nodules in the right lobe. Abdominal MRI showed multiple Seg VIII/V hepatic nodules of 2.3cm and 1.6cm in size, respectively, with hyperplastic nodules. The patient then received transarterial chemoembolization (TACE) and started to undergo duogement anti-cancer therapy at 200 mg/day. Full column MRI showed metastatic lesions in T%, T11, T9, T12 and L3, L4 and L5. The patient is subjected to an external beam of radiation for treating a bone disorder. Blood results showed total bilirubin to be 1.75 mg/dL; total protein INR is 1.2; and creatinine was 0.97mg/dl and AFP was 257.6. The patient was exposed to 27.12MHz carrier frequency ± 194 amplitude modulated frequencies for 2 exposure periods of two consecutive days (10 second sequential exposure, ranging from 400Hz to 20 kHz). Hemodynamic parameters were recorded using a high precision non-invasive hemodynamic monitor (Task Force monitor, CN systems) synchronized with a frequency modulated rf generator (see fig. 14-16). Real-time HRV data analysis was performed for each exposure period using an artificial intelligence calculation process. Additionally, using support vector machine algorithms, a single frequency modulation response for each patient can be identified and used for patient diagnosis/treatment. Twelve days after the exposure regimen, the patient's CT scan showed near complete clinical response, with tumor reduction, as shown in figure 61.

Example 14

The study was conducted in twenty patients diagnosed with hepatocellular carcinoma. The patient was exposed to 27.12MHz carrier frequency ± 194 amplitude modulated frequencies (10 or 3 second sequential exposure, ranging from 400Hz to 20kHz) for 2 consecutive exposure periods of the day. After the first exposure period, hemodynamic parameters were recorded using a high precision non-invasive hemodynamic monitor (Task Force monitor, CN systems) synchronized with a frequency modulated rf generator (see fig. 14-16). Real-time HRV data analysis was performed for each exposure period using an artificial intelligence calculation process. In addition, using a support vector machine algorithm, a single frequency modulation response for each patient can be identified and used for patient therapy identification. After the initial exposure protocol, the patient is exposed to a subsequent exposure protocol, wherein the subsequent exposure protocol includes only the amplitude modulated frequencies from the initial exposure protocol that are determined to alter heart rate variability, as illustrated in fig. 18D. CT scans of patients show a complete clinical response with disappearance of tumor mass. In this way, by exposing the same range of amplitude modulated frequencies to individual patients with the same diagnosis (e.g., hepatocellular carcinoma), we are able to identify the "active" frequencies of the patient (i.e., those modulated frequencies that cause alterations in heart rate variability) and thus be able to construct a personalized treatment regime tailored to the patient.

Example 15

Studies were conducted in patients diagnosed with hepatocellular carcinoma. The patient was exposed to 27.12MHz carrier frequency ± 194 amplitude modulated frequencies (10 or 3 second sequential exposure, ranging from 400Hz to 20kHz) for 2 consecutive exposure periods of the day. After the first exposure period, hemodynamic parameters were recorded using a high precision non-invasive hemodynamic monitor (Task Force monitor, CN systems) synchronized with a frequency modulated rf generator (see fig. 14-16). Real-time HRV data analysis was performed for each exposure period using an artificial intelligence calculation process. In addition, using a support vector machine algorithm, a single frequency modulation response for each patient can be identified and used for patient therapy identification. After the initial exposure protocol, the patient was exposed to a subsequent exposure protocol, wherein the subsequent exposure protocol included a 27.12MHz carrier frequency ± 194 amplitude modulated frequencies (10 or 3 second sequential exposure, ranging from 400Hz to 20kHz), as shown in fig. 18D. CT scans of patients show a complete clinical response with disappearance of tumor mass. In this way, by exposing the same range of amplitude modulated frequencies to each patient with the same diagnosis (e.g., hepatocellular carcinoma), we can build a personalized treatment regime tailored to the patient.

Example 16

Studies were conducted in patients diagnosed with hepatocellular carcinoma. The patient was exposed to 27.12MHz carrier frequency ± 194 amplitude modulated frequencies (10 or 3 second sequential exposure, ranging from 400Hz to 20kHz) for 2 consecutive exposure periods of the day. After the first exposure period, hemodynamic parameters were recorded using a high precision non-invasive hemodynamic monitor (Task Force monitor, CN systems) synchronized with a frequency modulated rf generator (see fig. 14-16). Real-time HRV data analysis was performed for each exposure period using an artificial intelligence calculation process. In addition, using a support vector machine algorithm, a single frequency modulation response for each patient can be identified and used for patient therapy identification. After the initial exposure protocol, the patient is exposed to a subsequent exposure protocol, wherein the subsequent exposure protocol includes only the amplitude modulated frequencies from the initial exposure protocol that are determined to have unaltered heart rate variability, as illustrated in fig. 18D. CT scans of patients show a complete clinical response with disappearance of tumor mass.

Example 17

The study was performed in 72 patients. The patient is exposed to a device that emits a 27.12MHz RF signal that is amplitude modulated with high accuracy at cancer specific frequencies ranging from 0.2Hz to 23,000 Hz. The device is connected to a scoop-like coupler that is placed in the patient's mouth during treatment. Patients with advanced hepatocellular carcinoma HCC and limited therapeutic options are provided with treatment in combination with HCC-specific frequency.

According to the embodiments disclosed in fig. 18A, fig. 18C, fig. 18D and fig. 67A to 67B, 34 patients with advanced HCC and 38 healthy controls received one or both exposure regimens of 35 minutes or 10 minutes (simultaneously or after a 10 minute rest period). Each patient was exposed to a 27.12MHz carrier frequency ± 194 amplitude modulated frequencies or a series of electromagnetic frequencies (10 seconds per frequency or 3 seconds per frequency exposure in the range 50Hz to 20KHz) that occurred in the range 0.01Hz to 20KHz, in the range 10Hz to 1,000Hz, or in the range 10Hz to 2,000Hz every 3Hz or 10 Hz. The system may determine a series of frequencies at which the hemodynamic parameter is altered or unaltered during the first exposure period and provide a frequency attribute to the series of frequencies at which the hemodynamic parameter is altered or unaltered. As depicted in fig. 73, the frequency attribute is a bar code system as depicted in fig. 71. Each frequency is assigned a frequency attribute code 0, code-1, code 2 depending on whether the particular frequency causes a change in a hemodynamic parameter (e.g., heart rate variability). As illustrated in figure 74, the sensitivity of the system in diagnosing the 72 patients studied was 100%, the specificity was 84.2% and the accuracy was 91.6%.

Example 18

As depicted in fig. 63A-63D, the reflected energy data from 58 individuals (27 hepatocellular carcinoma patients) demonstrated pattern differences between the hepatocellular carcinoma patients and healthy controls (Mann-Whitney 2-panel test, p 1.9E-101). In the healthy control group and hepatocellular carcinoma, the reflection energy values were 1051.5V (95% CI: 1050.1 to 1052.89) and 1008.53V (95% CI: 1007.44 to 1009.61), respectively. This analysis may support the following concepts: HCC patients absorb a large amount of high energy during exposure to an electromagnetic field modulated at an HCC-specific frequency compared to healthy controls.

Example 19

To analyze the hemodynamic pattern of heart rate variability, a series of algorithms (fig. 80A-80B) were created and validated for immediate variability analysis based on the next heart rate, studies of the interaction of variables between RRI, dBP and SV determined that RRI had the highest effect in the hemodynamic response elicited by frequency modulation (part η)²0.7973; MANOVA test p ═ 2.54E-06) (fig. 80B).

A cohort of 65 patients (20 hepatocellular carcinoma patients) was exposed to a series of electromagnetic frequencies occurring every 3Hz in the range of 10Hz to 1,000Hz, each frequency exposure lasting 3 seconds. As demonstrated in figure 81, many frequencies caused changes in heart rate variability, and these frequencies correlated with tumor burden (figure 67B). Partial response/remission was observed in 2 patients (11.7%). Stable disease was observed in 9 patients (52.9%). Over a follow-up visit of 3.5 months (range: 0 months to 5.3 months), discharge of 64.7% of patients was observed. The quality of life EORTC-C30 v3.0 questionnaire was analyzed for 41 cancer patients, of which 64% showed an improvement in global health status. No grade 2 toxicity was reported. Hemodynamic variability data can be processed and variability results can be categorized in real-time as consistent with tumor-specific frequencies. The specific tumor frequencies can further be identified for each patient in a dynamic and real-time, continuous program that is capable of determining instantaneous variability values that can indicate new tumor-specific frequencies over any desired frequency interval. This procedure can identify any number of tumor-specific frequencies from different patients with the same disease in order to create and update tumor-specific frequencies for use in treating patients diagnosed with any type of cancer. Tumor-specific frequencies of individual patients can be further identified to support dynamic treatment of cancer in individual patients.

Tumor resection was performed in two patients after exposure to the above-described series of electromagnetic frequencies (fig. 82). Histological examination of tumors revealed watery degeneration marked by cell swelling, intracellular vesicles, cell membrane rupture and nucleolar destruction.

In the foregoing detailed description, reference has been made to the accompanying drawings that form a part hereof. In the drawings, like numerals generally identify like parts, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here. It will be readily understood that the various features of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are explicitly contemplated herein.

The present disclosure is not limited to the particular embodiments described in this application, which are intended as illustrations of individual features. As will be apparent to those skilled in the art, many modifications and variations can be made thereto without departing from the spirit and scope of the invention. From the foregoing description, functionally equivalent methods and apparatuses within the scope of the disclosure, in addition to those enumerated herein, will be apparent to those skilled in the art. Such modifications and variations are intended to fall within the scope of the appended claims. The disclosure is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled. It is to be understood that this disclosure is not limited to particular methods, reagents, compounds, compositions or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. Various singular/plural permutations may be expressly set forth herein for the sake of clarity.

It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.). While various compositions, methods, and devices are described in terms of "comprising" various means or steps (interpreted as meaning "including, but not limited to"), the compositions, methods, and devices may also "consist essentially of" or "consist of" the various compositions and steps, and such terms should be interpreted as defining a substantially closed group of members. It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present.

For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an" (e.g., "a" or "an" should be interpreted to mean "at least one" or "one or more"); the same holds true for the use of definite articles used to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of "two recitations," without other modifiers, means at least two recitations, or two or more recitations). Moreover, in those instances where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems having a alone, B alone, C, A alone and B together, a and C together, B and C together, and/or A, B and C together, etc.). In these examples using conventions similar to "at least one of A, B or C, etc." it is generally intended that such configurations be in the sense one of ordinary skill in the art would understand the conventions (e.g., "a system having at least one of A, B or C" would include, but not be limited to, systems having a alone, B alone, C, A and B together, a and C together, B and C together, and/or A, B and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "a or B" will be understood to include the possibility of "a" or "B" or "a and B".

In addition, where features of the present disclosure are described in terms of Markush groups, those skilled in the art will recognize that the present disclosure is also thereby described in terms of any individual member or subgroup of members of the Markush group.

As will be understood by those skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations of subranges thereof. Any listed range can be easily identified as being fully descriptive and enabling the same range to be broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein may be readily broken down into a lower third, a middle third, an upper third, and so on. As will also be understood by those of skill in the art, all language such as "at most," "at least," and the like includes the recited number and refers to the range that may subsequently be resolved into subranges as discussed above. Finally, as will be understood by those skilled in the art, a range includes each individual member. Thus, for example, a group having 1 to 3 cells refers to a group having 1,2, or 3 cells. Similarly, a group having 1 to 5 elements refers to groups having 1,2, 3, 4, or 5 elements, and so forth.

As used herein, the term "about" refers to a variation of the numerical quantity that may occur, for example, as a result of: due to the measurement and processing procedures in the real world; due to inadvertent errors in these procedures; due to differences in the manufacture, source, or purity of the composition or reagent; and so on. Generally, the term "about" as used herein means greater than or less than the value or range of values represented by 1/10 (e.g., ± 10%) of the stated value. The term "about" also refers to variations that would be understood by one of ordinary skill in the art to be equivalent, provided such variations do not encompass known values of prior art practice. Each value or range of values after the term "about" is also intended to encompass embodiments of the absolute value or range of values. Quantitative values stated in the claims, whether modified by the term "about" or not, include equivalents to the stated values, e.g., numerical variations of such values that may occur but which would be recognized as equivalents by those skilled in the art.

Various of the above-disclosed and other features and functions, or alternatives thereof, may be desirably combined into many other different systems or applications. Various presently unforeseen or unanticipated alternatives, modifications, variations or improvements therein may be subsequently made by those skilled in the art, each of which is also intended to be encompassed by the disclosed embodiments.