阅读说明：本技术 一种快速的疲劳检测方法 (Rapid fatigue detection method ) 是由 刘志军 尹天露 席爱萍 高晓欢 赵朝贤 许岩丽 丁敏 苏县辉 宋永红 于 2020-12-08 设计创作，主要内容包括：本发明公开了一种快速的疲劳检测方法,属于人体疲劳检测领域,通过测定唾液中的丝氨酸蛋白酶抑制蛋白的含量,根据Bayes改良判别方程得到疲劳值和非疲劳值,当疲劳值小于或等于非疲劳值时判定为非疲劳,当疲劳值大于非疲劳值时判定为疲劳,当疲劳值为非疲劳值2倍及2倍以下时为轻度疲劳,当疲劳值为非疲劳值的2倍以上时为重度疲劳,诊断效能达到80%的水平,本发明能够快速测定人体的疲劳程度,且诊断效能达到80%的水平。(The invention discloses a rapid fatigue detection method, which belongs to the field of human body fatigue detection, wherein the content of serine protease inhibitor protein in saliva is measured, a fatigue value and a non-fatigue value are obtained according to a Bayes improved discriminant equation, the method is judged to be non-fatigue when the fatigue value is less than or equal to the non-fatigue value, the method is judged to be fatigue when the fatigue value is greater than the non-fatigue value, the method is slight fatigue when the fatigue value is 2 times and less than 2 times of the non-fatigue value, the method is severe fatigue when the fatigue value is more than 2 times of the non-fatigue value, and the diagnosis efficiency reaches the level of 80%.)

1. A rapid fatigue detection method is characterized in that: the content of serpin in saliva is measured, a fatigue value and a non-fatigue value are obtained according to a Bayes improved discriminant equation, and the saliva is judged to be non-fatigue when the fatigue value is less than or equal to the non-fatigue value, and judged to be fatigue when the fatigue value is greater than the non-fatigue value, and is slightly fatigue when the fatigue value is 2 times or less the non-fatigue value, and is severely fatigue when the fatigue value is 2 times or more the non-fatigue value, and the diagnosis efficiency reaches a level of 80%.

2. The rapid fatigue detection method according to claim 1, wherein: the Bayes improvement discriminant equation is:

a non-fatigue value = ∑ [ content of serpin × (non-fatigue coefficient +0.5) ] +6.180, and a fatigue value = ∑ [ content of serpin × (fatigue coefficient +0.5) ] + 14.741.

3. The rapid fatigue detection method according to claim 2, wherein: the non-fatigue coefficient and the fatigue coefficient are obtained by introducing a fatigue protein marker into Fisher discriminant analysis.

4. The rapid fatigue detection method according to claim 3, wherein: the non-fatigue coefficient is-8.255, and the fatigue coefficient is 3.158.

5. The rapid fatigue detection method according to claim 1, wherein: after collecting saliva, the saliva was placed in a 37 ℃ incubator for one hour to calm.

6. The rapid fatigue detection method according to claim 5, wherein: the saliva after the tranquilization is stored at the temperature of minus 80 to minus 70 ℃.

7. The rapid fatigue detection method according to claim 5, wherein: the content of the serpin is determined by saliva flight mass spectrometry.

Technical Field

The invention relates to a rapid fatigue detection method, and belongs to the field of human body fatigue detection.

Background

Fatigue (fatigue) is a subjective discomfort feeling, and the understanding of fatigue at present has been extended from simple physical fatigue to mental and cognitive dysfunction, meaning that under objective equivalence, fatigue can result in the loss of the normal activities or working abilities that it originally performs. Fatigue was defined as: "the physiological process of the body cannot maintain its function at a certain specific level or cannot maintain a predetermined exercise intensity". The fatigue injury is recorded in China for a long time, Liutao Wufeng: "do not give up and hit, fatigue and hit". The diagnostic fatigue criteria established by Fukuda et al in 1994 is recognized as gold criteria by the International medical community. Mental fatigue often manifests as lassitude, lethargy and inattention; the fatigue of the body is an important reaction of physical strength consumption, and means that the muscle can send out less strength than the normal level of the body of the user, and the muscle is weak and/or sore. After a human body has physical or mental activities for a long time and a certain intensity, each muscle group excessively contracts, metabolic products such as lactic acid, keto acid and the like are generated in the metabolic process of the muscle, the metabolic products are also called fatigue toxins, and the fatigue toxins enter blood and run the whole body, so that the functions of an immune system are reduced, natural killer cells are reduced, dizziness, memory loss, thought response delay and the like are caused, and fatigue is generated if the fatigue is not effectively controlled. The fatigue effect is similar to that of drunk, so that the operator can often decline from the suitable state to the sub-suitable state and the unsuitable state, and the property safety and the personal safety are seriously threatened.

Many countries are actively developing the research work of occupational fatigue detection, and developed western countries invest huge manpower and material resources, but fatigue has the characteristics of progressiveness, difficulty in objectively obtaining data, difficulty in quantifying measurement methods and evaluation indexes, and the like, and in the existing detection method research, for the detection of fatigue physiological signals, although the sensitivity is high, the detection is invasive, and signals need to be extracted and pasted with electrodes; the PERCLOS method has high measurement accuracy, and visual and clear detection of behavior characteristics, but the rule method for detecting and identifying is complex, the pupil measurement information is difficult to extract, the detection of the sight line direction, the mouth state and the like is greatly influenced by individuals, light rays and physiological conditions, and the reliability and the anti-interference performance are poor. Recently reported chip technology adopted in Japan, American vehicle-mounted module system and the like improve detection efficiency, but the popularization is poor due to the reasons of complex design, cost performance and the like. So far, compared with all fatigue detection methods, no detection method which is convenient, reliable, non-invasive and high in cost performance exists, and if one or a group of objective clinical markers are available to assist diagnosis, the method has extremely important significance, which is a hotspot and difficulty of current domestic and foreign research and is also a major public health problem facing both developing countries and developed countries.

The publication number CN108333362A discloses 29 fatigue protein markers, which have the characteristics of stable components, good specificity, less in-vivo interference factors and easy conversion and popularization of a detection system, and can provide accurate and reliable basis for qualitative or quantitative determination of the human fatigue degree. The fatigue protein markers of apolipoprotein-1, heat shock homologous 71kDa protein, immunoglobulin kV-302, immunoglobulin G3, annexin A1, immunoglobulin kV-312 and peroxidase-5 can also be independently used as fatigue diagnosis indexes, the content and the fatigue degree of any protein show obvious correlation without depending on the content level of other proteins (judgment by substituting Bayes discriminant equation), but the independent diagnosis efficiency can only reach the level of 63-75%.

Disclosure of Invention

The invention aims to solve the technical problem of providing a rapid fatigue detection method, which judges the fatigue degree only by measuring the content of serine protease inhibiting protein in saliva and substituting the content into a Bayes improved discriminant equation, and the efficiency of single diagnosis reaches the level of 80 percent.

In order to solve the technical problems, the technical scheme adopted by the invention is as follows:

a rapid fatigue detection method comprises the steps of measuring the content of serine protease inhibitor protein in saliva, obtaining a fatigue value and a non-fatigue value according to a Bayes improved discriminant equation, judging the saliva to be non-fatigue when the fatigue value is less than or equal to the non-fatigue value, judging the saliva to be fatigue when the fatigue value is greater than the non-fatigue value, judging the saliva to be fatigue, judging the saliva to be light fatigue when the fatigue value is 2 times or less than 2 times of the non-fatigue value, and judging the saliva to be heavy fatigue when the fatigue value is more than 2 times of the non-fatigue value, wherein the diagnosis efficiency reaches the level of 80%.

The technical scheme of the invention is further improved as follows: the Bayes improvement discriminant equation is:

a non-fatigue value ∑ [ serine protease inhibitor protein content × (non-fatigue coefficient +0.5) ] +6.180, and a fatigue value ∑ [ serine protease inhibitor protein content × (fatigue coefficient +0.5) ] + 14.741.

The technical scheme of the invention is further improved as follows: the non-fatigue coefficient and the fatigue coefficient are obtained by introducing a fatigue protein marker into Fisher discriminant analysis.

The technical scheme of the invention is further improved as follows: the non-fatigue coefficient is-8.255, and the fatigue coefficient is 3.158.

The technical scheme of the invention is further improved as follows: after collecting saliva, the saliva was placed in a 37 ℃ incubator for one hour to calm.

The technical scheme of the invention is further improved as follows: the saliva after the tranquilization is stored at the temperature of minus 80 to minus 70 ℃.

The technical scheme of the invention is further improved as follows: the content of the serpin is determined by saliva flight mass spectrometry.

Due to the adoption of the technical scheme, the invention has the technical progress that:

1. the method can obtain the content of the serine protease inhibitory protein in the saliva in a non-invasive mode, the saliva is collected by rinsing with normal saline for three times and rinsing with clear water for three times, and then the saliva can be collected, so that the interference of other impurities in the saliva on a detection result can be greatly reduced, and the collection method is superior to other collection modes such as tooth brushing and the like; after saliva is collected, the content of serine protease inhibitor protein can be obtained from saliva samples through a saliva flight mass spectrometry method and is used as a judgment basis for detecting human fatigue. The collected body fluid is small in collection amount in the detection process, and convenient to obtain, store and preserve, the saliva is transported in a heat preservation box filled with dry ice after being collected, and the activity of the fatigue marker protein in the saliva can be perfectly and effectively preserved after being stored at the temperature of minus 80 to minus 70 ℃, so that the content of the protein in the saliva is not changed, the protein is prevented from being degraded, and the accuracy of a judgment result is prevented from being influenced.

2. The serine protease inhibitor protein aimed at in the invention has the characteristics of stable components, good specificity, few in-vivo interference factors and easy conversion and popularization of a detection system, can provide accurate and reliable basis for qualitative or quantitative determination of human fatigue degree, can be independently used as a fatigue diagnosis index, and can bring the independent diagnosis efficiency to the level of 80% by placing saliva in a constant temperature oven at 37 ℃ for one hour and stabilizing the saliva into a Bayes improvement discriminant equation.

Detailed Description

A rapid fatigue detection method comprises the steps of measuring the content of serine protease inhibitor protein in saliva, obtaining a fatigue value and a non-fatigue value according to a Bayes improved discriminant equation, judging the saliva to be non-fatigue when the fatigue value is less than or equal to the non-fatigue value, judging the saliva to be fatigue when the fatigue value is greater than the non-fatigue value, judging the saliva to be fatigue, judging the saliva to be light fatigue when the fatigue value is 2 times or less than 2 times of the non-fatigue value, and judging the saliva to be heavy fatigue when the fatigue value is more than 2 times of the non-fatigue value, wherein the diagnosis efficiency reaches the level of 80%.

The Bayes improvement discriminant equation is:

a non-fatigue value ∑ [ serine protease inhibitor protein content × (non-fatigue coefficient +0.5) ] +6.180, and a fatigue value ∑ [ serine protease inhibitor protein content × (fatigue coefficient +0.5) ] + 14.741.

Further, the non-fatigue coefficient and the fatigue coefficient were obtained by introducing the fatigue protein markers into Fisher discriminant analysis.

Further, the non-fatigue coefficient was-8.255, and the fatigue coefficient was 3.158.

Further, after collecting saliva, the saliva was placed in a 37 ℃ incubator for one hour to calm.

Further, the saliva after being calmed is stored at-80 to-70 ℃.

Further, the content of serpin was determined by saliva flight mass spectrometry.

The present invention will be described in further detail with reference to the following examples:

the experimental group invited 150 volunteers to participate in the study, including 50 emergency doctors, 50 researchers, and 50 general public. The selected volunteers are healthy and free of organic diseases and chronic fatigue symptoms; and eliminating the queue of people with continuous or repeated fatigue lasting more than 6 months, throat pain, neck or axillary lymph node swelling and pain, muscle pain, multiple non-arthritic pain, headache, sleep disorder, and discomfort lasting more than 24 hours after fatigue.

Collecting saliva samples of 0h, 4h, 8h and 16h after the volunteer is awake in normal sleep, taking 2ml of sublingual saliva and subpackaging 0.5ml per part, wherein two parts are taken as a first group (two parallel experiments, the average value is taken after the content of serine protease inhibitory protein is obtained), and after the two parts are calmed for one hour in a constant temperature box at the temperature of 37 ℃, storing the saliva samples at the low temperature of-80 to-70 ℃; the other two are taken as a second group (two parallel experiments, the average value is taken after the content of the serine protease inhibitory protein is obtained), and the two groups are directly stored at low temperature of-80 to-70 ℃ without being stabilized in a constant temperature box. And carrying out parallel sample collection twice at an interval of more than two weeks, and collecting 7200 parts of specimens in total, wherein 3600 parts are taken as the first group, and 3600 parts are taken as the second group.

When the saliva sample is collected, whether the volunteer is tired or not is detected in an electroencephalogram mode, whether the electroencephalogram is subjected to slow wave increase or not and whether the electroencephalogram is subjected to fast wave decrease or not, namely delta wave and theta wave increase and alpha wave and beta wave number decrease are used as standards for judging whether the person is tired or not, and the electroencephalogram number is correspondingly stored with the collected saliva sample.

After the content of serine protease inhibitor protein is determined by a first group of saliva samples through saliva flight mass spectrometry, a Bayes improved discriminant equation is substituted to obtain a non-fatigue value 1 and a fatigue value 1, which are embodiment data; substituting the content of the serine protease inhibitor protein in the first group of saliva samples into a Bayes discriminant equation to obtain a non-fatigue value 2 and a fatigue value 2, which are data of a comparative example; after the content of the serine protease inhibitor protein in the second group of saliva samples is determined by a saliva flight mass spectrometry method, the second group of saliva samples are brought into a Bayes improved discriminant equation to obtain a non-fatigue value 3 and a fatigue value 4, and the non-fatigue value and the fatigue value are data of a comparative example two; the serine proteinase inhibitor protein content of the second group of saliva samples was substituted into the Bayes discriminant equation to obtain a non-fatigue value of 4 and a fatigue value of 4, which are the data of comparative example three.

Among them, Bayes modified discriminant equation: a non-fatigue value ∑ [ serine protease inhibitor protein content × (non-fatigue coefficient +0.5) ] +6.180, and a fatigue value ∑ [ serine protease inhibitor protein content × (fatigue coefficient +0.5) ] + 14.741.

Bayes discriminant equation: the non-fatigue value ═ Σ (content of serpin × non-fatigue coefficient) +6.180, and the fatigue value ═ Σ (content of serpin × fatigue coefficient) + 14.741.

Calculating the non-fatigue value and the fatigue value obtained in the embodiment and the comparative example, and then comparing the calculated non-fatigue value and the calculated fatigue value with the current electroencephalogram of the volunteer to obtain a table 1;

TABLE 1 comparison of Performance

The diagnostic efficacy (parts consistent with electroencephalogram/experimental parts) x 100%

As can be seen from the above table, the diagnosis efficiency of the invention can reach about 80%, and the diagnosis efficiency can be obviously improved by stabilizing the saliva sample in a constant temperature oven at 37 ℃ for one hour and then storing the saliva sample at low temperature and adopting Bayes improved discriminant equation.

Table 2 shows the results of fatigue tests and electroencephalogram results obtained from some volunteers. In the electroencephalogram results, no fatigue wave indicates no fatigue, less fatigue wave indicates mild fatigue, and more fatigue wave indicates severe fatigue. Only part of the data was compared, and a term not consistent with brain waves was excepted in the comparative examples.

Table 2 partial data comparison table

As can be seen from the data of table 2, when the difference between the fatigue value and the non-fatigue value is less than 0 or equal to 0, the electroencephalogram results in no occurrence of fatigue waves, i.e., the human body is not fatigued; when the difference value between the fatigue value and the non-fatigue value is greater than 0 and the fatigue value is less than or equal to 2 times of the non-fatigue value, the electroencephalogram generates fatigue waves, but the density of the electroencephalogram is not large, and the electroencephalogram is in a light fatigue range; when the difference value between the fatigue value and the non-fatigue value is greater than 0 and the fatigue value is greater than 2 times of the non-fatigue value, the electroencephalogram fatigue wave is obviously increased, the density is higher, and the electroencephalogram fatigue wave is severe fatigue.