Quantitative analysis method and early warning device for gas exhaled by human body

Abstract

The invention relates to novel expiration detection equipment based on the quantitative analysis method, and the novel expiration detection equipment is applied to the fields of human health assessment, disease screening, clinical diagnosis and the like. The expiration diagnosis is an emerging in-vitro diagnosis method, the clinical application range of the expiration diagnosis is gradually expanded, real-time and on-line detection of human expiration is achieved, a quantitative method for markers in expiration is established, reliable endogenous marker experimental data is obtained, a human expiration marker data model is constructed, and the real-time detection of the human expiration is achieved. Powerful support can be provided for exhaled air physiology research, clinical application of exhaled air diagnosis and environmental exposure research. And quantitative analysis is carried out on healthy people and sick people in different areas, and hit rules of different people are set according to weight values, so that the accuracy of quantitative analysis is improved, and the requirement on the computing power of data analysis is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of breath detection, and in particular to a quantitative analysis method and early warning device for human exhaled gas. Background Art

[0002] The detection of human exhaled gas is now a common detection method. However, in the detection process, due to the gas, there are certain difficulties in sampling. It is not known whether the sample has been obtained in the sampling bag, or whether the sample meets the detection requirements, which is quite troublesome. And the collected gas still needs to be qualitatively and quantitatively analyzed. The breath analysis methods reported so far are mostly static, and quantitative determination is difficult. The quantitative method and accurate determination of endogenous volatile organic compounds (VOCs) in human exhaled gas need further exploration. The shortcomings of the current breath diagnosis technology based on gas chromatography are mainly the lack of dynamics and quantitative difficulties. In the current breath detection, the two major steps of breath collection and analysis cannot be completed in a coherent manner, and it takes time for the gas to be collected and sent to the instrument for detection, and it is difficult to achieve real-time detection. Moreover, most of the research related to breath diagnosis focuses on the search for disease-related characteristic VOCs, and there are few reports on VOCs quantitative methods, and quantitative issues have been ignored for a long time. Summary of the invention

[0003] The purpose of the present invention is to provide an online detection method for endogenous markers in human exhaled breath to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0004] A method for quantitative analysis of human exhaled gas, the method comprising the following steps:

[0005] S100: Collect human exhaled gas and detect the concentration of several compounds in the gas;

[0006] S200: Detect the content and relative proportion of common endogenous VOCs in the human body, and establish a data model based on the sample;

[0007] S300: Expand the range of human exhaled gas collection and make comparative analysis;

[0008] S400: Analyze the index coefficients inside the human body through data models;

[0009] S500: By analyzing the index coefficients inside the human body and the exhaled gas of the human body, an early warning is issued for problems with index coefficients exceeding the threshold.

[0010] Furthermore, in step S100, human exhaled gas is collected from healthy people in a city, and a source database is constructed by collecting human exhaled gas collection data from the healthy people. The human exhaled gas collection data of the obviously unhealthy part in the database is screened through group investigation, screening and sample classification. The method for obtaining the human exhaled gas collection data is as follows: human exhaled gas is defined as VOCs, the VOCs are defined as samples, the VOCs molecular ion peaks are qualitatively confirmed by high-resolution mass spectrometry, and the human exhaled gas collection data is obtained by quantitative analysis of VOCs.

[0011] Furthermore, in step S200, a comparative analysis of the human exhaled gas collection data is performed, and a new VOCs quantitative analysis method is established by introducing the collected human exhaled gas and combining it with a specific internal standard to determine the content and proportion of characteristic VOCs in the sample. Combined with the grouping of the research cohort and the detection results of VOCs in the exhaled gas, a data model is established and constructed.

[0012] Furthermore, in step S300, the application scenario is further expanded, a number of sampling points are arranged, the VOCs database of healthy people is expanded, the local climate is obtained and the health quality of the regional population is reflected, the device is deployed in the laboratory and wards of the hospital, and breath samples are collected from patients with different diseases. By comparing the VOCs data of healthy people with the VOCs of patients with specific diseases, the exhaled VOCs data of patients with specific diseases are analyzed and compared with the exhaled VOCs data of healthy people, and the correlation between the characteristic VOCs content and proportion and one or several specific diseases is found.

[0013] Furthermore, in step S400, analyzing the index coefficients inside the human body through the data model includes the following methods:

[0014] S401: Obtain the analysis data of healthy people and diseased people in the data model, screen and construct sequences t and f, where t is the analysis data sequence of healthy people and f is the analysis data sequence of diseased people, count the total amount of analysis data T and L of sequence t and sequence f, and calculate the ratio of data, The K and tt K+1 are the kth and k+1th elements in sequence t respectively, and the f K and f K+1 are the kth and k+1th elements in sequence f, respectively. The indicator weight is calculated based on the given r k and l k Assign a value to obtain the weight value Q of the ratio of healthy people to sick people in adjacent data. Q1 is the weight value of the healthy population analysis data, and Q2 is the weight value of the diseased population analysis data;

[0015] A weight matrix is ​​constructed by weight values ​​of the healthy population analysis data and the diseased population analysis data, and a weight vector of the healthy population in the healthy population analysis data and the diseased population analysis data is obtained by the weight matrix, and the healthy population in the healthy population analysis data is defined as p(x, y), where x represents the weight vector of the healthy population analysis data, and y represents the diseased population keyword weight vector, and according to the weight vector p(x, y) of the healthy population analysis data

[0016] Calculate the sovereign weight value of the healthy population analysis data and calculate the information entropy of the healthy population analysis data. The calculation formula is:

[0017]

[0018] Among them, e i represents the information entropy of the i-th healthy population analysis data, and p represents the number of healthy population analysis data;

[0019] Calculate the entropy weight of the evaluation index, the calculation formula is:

[0020]

[0021] Then we get the weight vector Y=(y 1 ,y 2 ,…,y p ), optimize the fitting of the sovereign weight value and weight vector to obtain the comprehensive weight matrix z i , the calculation formula is:

[0022]

[0023] The comprehensive weight matrix Z is calculated as follows: 1 , z 2 ,…,z p ).

[0024] Further, in step S500, the specific steps of confirming the rules are as follows: the rules are divided into the healthy population hit determination rules and the diseased population hit determination rules, and the comprehensive weight matrix z i The rules determine whether each individual in the local population is in a healthy state by collecting data from human exhaled gas, determine the health status and send out early warning signals to volunteers corresponding to the collected data in an unhealthy state.

[0025] A human exhaled gas quantitative analysis and early warning device, the device comprising: a collection device, an analysis device and an early warning device, the collection device, the analysis device and the early warning device are electrically connected to each other, and the collection device, the analysis device and the early warning device run in a processor, the collection device, the analysis device and the early warning device generate data and store them in a memory, and the collection device, the analysis device and the early warning device, the processor and the memory implement the steps of any one of the above-mentioned methods for quantitative analysis of human exhaled gas when executing the computer program.

[0026] The beneficial effects of the present invention are: by improving the analysis and detection method, the real-time and online detection of human exhaled breath is realized, the quantitative method of markers in exhaled breath is established, reliable endogenous marker experimental data is obtained, and a human exhaled breath marker data model is constructed, which can provide strong support for exhaled breath physiology research, clinical application of exhaled breath diagnosis, and environmental exposure research. And according to the quantitative analysis of different regions of healthy people and sick people, and the setting of hit rules for different populations according to weight values, the accuracy of quantitative analysis is increased, and the computing power requirements for data analysis are reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and other features of the present invention will become more obvious by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present invention represent the same or similar elements. It is obvious that the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0028] In the figure:

[0029] Figure 1 Shown is a flow chart of a method for quantitative analysis of human exhaled gas. DETAILED DESCRIPTION

[0030] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0031] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0032] like Figure 1 As shown, a method for quantitative analysis of human exhaled gas comprises the following steps:

[0033] S100: Collect human exhaled gas and detect the concentration of several compounds in the gas;

[0034] S200: Detect the content and relative proportion of common endogenous VOCs in the human body, and establish a data model based on the sample;

[0035] S300: Expand the range of human exhaled gas collection and make comparative analysis;

[0036] S400: Analyze the index coefficients inside the human body through data models;

[0037] S500: By analyzing the index coefficients inside the human body and the exhaled gas of the human body, an early warning is issued for the index coefficients exceeding the threshold.

[0038] Furthermore, in step S100, human exhaled gas is collected from healthy people in a city, and a source database is constructed by collecting human exhaled gas collection data from the healthy people. The human exhaled gas collection data of the obviously unhealthy part in the database is screened through group investigation, screening and sample classification. The method for obtaining the human exhaled gas collection data is as follows: human exhaled gas is defined as VOCs, the VOCs are defined as samples, the VOCs molecular ion peaks are qualitatively confirmed by high-resolution mass spectrometry, and the human exhaled gas collection data is obtained by quantitative analysis of VOCs.

[0039] Furthermore, in step S200, a comparative analysis of the human exhaled gas collection data is performed, and a new VOCs quantitative analysis method is established by introducing the collected human exhaled gas and combining it with a specific internal standard to determine the content and proportion of characteristic VOCs in the sample. Combined with the grouping of the research cohort and the detection results of VOCs in the exhaled gas, a data model is established and constructed.

[0040] Furthermore, in step S300, the application scenario is further expanded, a number of sampling points are arranged, the VOCs database of healthy people is expanded, the local climate is obtained and the health quality of the regional population is reflected, the device is deployed in the laboratory and wards of the hospital, and breath samples are collected from patients with different diseases. By comparing the VOCs data of healthy people with the VOCs of patients with specific diseases, the exhaled VOCs data of patients with specific diseases are analyzed and compared with the exhaled VOCs data of healthy people, and the correlation between the characteristic VOCs content and proportion and one or several specific diseases is found.

[0041] Furthermore, in step S400, analyzing the index coefficients inside the human body through the data model includes the following methods:

[0042] S401: Obtain the analysis data of healthy people and diseased people in the data model, screen and construct sequences t and f, where t is the analysis data sequence of healthy people and f is the analysis data sequence of diseased people, count the total amount of analysis data T and L of sequence t and sequence f, and calculate the ratio of data, The K and tt K+1 are the kth and k+1th elements in sequence t respectively, and the f K and f K+1 are the kth and k+1th elements in sequence f, respectively. The indicator weight is calculated based on the given r k and l k Assign a value to obtain the weight value Q of the ratio of healthy people to sick people in adjacent data. Q1 is the weight value of the healthy population analysis data, and Q2 is the weight value of the diseased population analysis data;

[0043] A weight matrix is ​​constructed by weight values ​​of the healthy population analysis data and the diseased population analysis data, and a weight vector of the healthy population in the healthy population analysis data and the diseased population analysis data is obtained by the weight matrix, and the healthy population in the healthy population analysis data is defined as p(x, y), where x represents the weight vector of the healthy population analysis data, and y represents the diseased population keyword weight vector, and according to the weight vector p(x, y) of the healthy population analysis data

[0044] Calculate the sovereign weight value of the healthy population analysis data and calculate the information entropy of the healthy population analysis data. The calculation formula is:

[0045]

[0046] Among them, e i represents the information entropy of the i-th healthy population analysis data, and p represents the number of healthy population analysis data;

[0047] Calculate the entropy weight of the evaluation index, the calculation formula is:

[0048]

[0049] Then we get the weight vector Y=(y 1 ,y 2 ,…,y p ), optimize the fitting of the sovereign weight value and weight vector to obtain the comprehensive weight matrix z i , the calculation formula is:

[0050]

[0051] The comprehensive weight matrix Z is calculated as follows: 1 , z 2 ,…,z p ).

[0052] Further, in step S500, the specific steps of confirming the rules are as follows: the rules are divided into the healthy population hit determination rules and the diseased population hit determination rules, and the comprehensive weight matrix z i The rules determine whether each individual in the local population is in a healthy state by collecting data from human exhaled gas, determine the health status and send out early warning signals to volunteers corresponding to the collected data in an unhealthy state.

[0053] A human exhaled gas quantitative analysis and early warning device, the device comprising: a collection device, an analysis device and an early warning device, the collection device, the analysis device and the early warning device are electrically connected to each other, and the collection device, the analysis device and the early warning device run in a processor, the data generated by the collection device, the analysis device and the early warning device are stored in a memory, and the collection device, the analysis device and the early warning device, the processor and the memory implement the steps of any one of the human exhaled gas quantitative analysis methods in claims 1-6 when executing the computer program.

[0054] Preferably, establish a research cohort and collect samples

[0055] After the instrument optimization is completed and the operation is stable, sample collection will be carried out. The target population for sample collection in the first phase of the project is healthy local people in the Guangdong-Hong Kong-Macao Greater Bay Area (without serious underlying diseases and obvious respiratory abnormalities). It is expected that a total of 100 samples will be collected and exhaled data will be collected.

[0056] Inclusion criteria:

[0057] 1) Age: >18 years old, <80 years old;

[0058] 2) No serious underlying diseases: severe liver dysfunction (ALT or AST ≥ 3 times the normal value); severe renal dysfunction (serum creatinine > 433umol / L); chronic congestive heart failure (NYHA III-IV heart function), difficult to cooperate with relevant researchers;

[0059] 3) No obvious respiratory abnormalities (such as cough);

[0060] 4) Not diagnosed with pneumonia, bronchitis, active pulmonary tuberculosis within 1 month before enrollment, and not received antiviral, antifungal or antibacterial treatment;

[0061] 5) Those with a history of chronic alcoholism, drug abuse, or any factors that affect compliance are not allowed to be included;

[0062] 6) Fill out the health status questionnaire;

[0063] 7) Signed consent form.

[0064] Before enrolling, the subjects will receive a detailed explanation of the study and will be enrolled after obtaining the informed consent of the subjects. The study cohort and data are classified according to factors such as gender, age, and smoking. The male-to-female ratio is about 1:1, and there are 20 to 40 samples with a history of smoking. When sampling, the subjects are required to exhale from their mouths for 0.5-1 minute. Two hours before collecting the samples, the subjects are required not to eat or smoke, and only drink water if necessary. Before blowing, the subjects are required to rinse their mouths with water to avoid possible interference from food and other oral sources. Three samples are collected in parallel for each person to ensure sample accuracy and reduce errors caused by differences in blowing. After each blowing is completed, the sample VOCs data is obtained in real time and saved for subsequent data analysis. In addition to collecting breath samples from the subjects, the air bag method will be used to collect atmospheric environment samples around the instrument as a reference, and the sample analysis will be performed by the headspace method to remove the interference of exogenous VOCs.

[0065] 3.1.3 Data analysis, establishment of quantitative method and data model for endogenous VOCs in human exhaled breath

[0066] First, the 100 breath samples collected were classified according to the research cohort, and the molecular ion peaks were identified and identified using the instrument's own analysis software, and quantitative analysis was performed using the standard gas external standard method. The focus was on studying the content and relative proportion of common endogenous VOCs in the human body, and a data array was established using control samples.

[0067] In order to organically combine breath analysis data with actual samples, multivariate statistical methods are an effective means of mathematical statistics, especially suitable for the analysis of complex high-throughput data, and are increasingly used in the study of breath characteristics. At present, the statistical methods involved include principal component analysis, cluster analysis, discriminant analysis, partial least squares analysis, etc., and the statistical analysis software involved includes SIMCA-P, SPSS, R, SAS, MATLAB, etc. This project intends to use SIMCA-P for principal component analysis and partial least squares analysis to find the corresponding characteristic substances of breath, and then use regression analysis and cluster analysis for data verification, and combine multiple statistical methods and statistical software for cross-validation to obtain more accurate and reliable analysis results.

[0068] Through data comparison and analysis, we finally screened out the characteristic endogenous VOCs in human exhaled breath and the relative content range of each compound, established an analysis model, and carried out experimental verification to determine the accuracy and sensitivity of the screening model. We established a dynamic, wide-range quantitative analysis method for exhaled VOCs based on "SOFT IONIZATION+DIRECT MASS".

[0069] 3.1.4 Research and development of miniaturized equipment and expansion of its application

[0070] The soft ionization source is connected in series with a small portable mass spectrometer to reduce the size of the equipment. After modification, the ionization source, mass spectrometer, power supply, etc. are integrated into a device the size of a suitcase. The equipment can be directly connected to a laptop or tablet (with data analysis software installed). After commercialization, the equipment can be portable to various application scenarios. It is expected that the equipment will have the following features and advantages (compared with the COVID-19 breathalyzer InspectIR COVID-19Breathalyzer, which was just verified by the US FDA in April 2022):

[0071] (1) Leading mass spectrometry detection method "SOFT IONIZATION + DIRECT MASS", real-time online analysis of exhaled breath;

[0072] (2) New VOCs quantitative analysis methods and data models;

[0073] (3) The molding equipment is only the size of a suitcase and is suitable for many occasions;

[0074] (4) The implementation method is similar to InspectIR COVID-19 (United States), but has a faster detection time (InspectIR COVID-19 detection time is about 3 minutes), which is expected to be less than 1 minute, and the detection efficiency is expected to increase by 5 times, with better high-throughput data collection and disease screening potential.

[0075] If the device is developed smoothly, that is, the project can successfully complete the target task, it is planned to further expand its application scenarios in the second phase, deploy multiple sampling points (multiple devices need to be built), and expand the VOCs database of healthy people. The database has significant local characteristics of Guangdong, which can better connect with the local climate and reflect the health quality of the regional population; then, the device can be deployed in the laboratory and wards of hospitals such as the University of Science and Technology Hospital to collect breath samples from patients with different diseases. By comparing the VOCs data of healthy people with the differences in VOCs of patients with specific diseases (such as viral pneumonia, fungal pneumonia, bacterial pneumonia, tuberculosis, lung cancer, etc.), the application potential of breath detectors in disease screening and diagnosis is explored. The breath VOCs data of patients with specific diseases are analyzed and compared with the breath VOCs data of healthy people, and the relationship between the characteristic VOCs content and proportion and one or several specific diseases is found to explore the application potential of breath detectors in disease screening and diagnosis. At this stage, the artificial intelligence team will be combined to develop an intelligent system for breath disease diagnosis through algorithm learning, and further promote the application of breath detectors in rapid disease screening and clinical diagnosis.

[0076] Although the description of the present invention has been quite detailed and has been described in particular with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively cover the intended scope of the present invention. In addition, the present invention is described above with the embodiments foreseeable by the inventors, and its purpose is to provide a useful description, and those non-substantial changes to the present invention that are not currently foreseen may still represent equivalent changes of the present invention.

Claims

1. A method for quantitative analysis of human exhaled gas, characterized in that: The method comprises the following steps: S100: Collect human exhaled gas and detect the concentration of several compounds in the gas; S200: Detect the content and relative proportion of common endogenous VOCs in the human body, and establish a data model based on the sample; S300: Expand the range of human exhaled gas collection and make comparative analysis; S400: Analyze the index coefficients inside the human body through data models; S500: By analyzing the index coefficients inside the human body and the exhaled gas of the human body, an early warning is issued for problems with index coefficients exceeding the threshold.

2. A method for quantitative analysis of human exhaled gas according to claim 1, characterized in that: In step S100, human exhaled gas is collected from healthy people in a city, and a source database is constructed by collecting human exhaled gas data from the healthy people. The human exhaled gas data of the obviously unhealthy part in the database is screened through group investigation, screening and sample classification. The method for obtaining the human exhaled gas data is as follows: human exhaled gas is defined as VOCs, the VOCs are defined as samples, the VOCs molecular ion peaks are qualitatively confirmed by high-resolution mass spectrometry, and the human exhaled gas data is obtained by quantitative analysis of VOCs.

3. A method for quantitative analysis of human exhaled gas according to claim 1, characterized in that: In step S200, a comparative analysis is performed on the human exhaled gas collection data. By introducing the collected human exhaled gas and combining it with a specific internal standard, a new VOCs quantitative analysis method is established to determine the content and proportion of characteristic VOCs in the sample. Combined with the grouping of the research cohort and the detection results of VOCs in the exhaled gas, a data model is established and constructed.

4. A method for quantitative analysis of human exhaled gas according to claim 1, characterized in that: In step S300, the application scenario is further expanded, a number of sampling points are arranged, the VOCs database of healthy people is expanded, the local climate is obtained and the health quality of the regional population is reflected, the device is deployed in the laboratory and wards of the hospital, and breath samples are collected from patients with different diseases. By comparing the VOCs data of healthy people with the VOCs of patients with specific diseases, the exhaled VOCs data of patients with specific diseases are analyzed and compared with the exhaled VOCs data of healthy people, and the correlation between the characteristic VOCs content and proportion and one or several specific diseases is found.

5. A method for quantitative analysis of human exhaled gas according to claim 1, characterized in that: In step S400, analyzing the index coefficients inside the human body through the data model includes the following methods: S401: Obtain the analysis data of healthy people and diseased people in the data model, screen and construct sequences t and f, where t is the analysis data sequence of healthy people and f is the analysis data sequence of diseased people, count the total amount of analysis data T and L of sequence t and sequence f, and calculate the ratio of data, The K and tt K+1 are the kth and k+1th elements in sequence t respectively, and the f K and f K+1 are the kth and k+1th elements in sequence f, respectively. The indicator weight is calculated based on the given r k and l k Assign a value to obtain the weight value Q of the ratio of healthy people to sick people in adjacent data. Q1 is the weight value of the healthy population analysis data, and Q2 is the weight value of the diseased population analysis data; A weight matrix is ​​constructed by weight values ​​of the healthy population analysis data and the diseased population analysis data, and a weight vector of the healthy population in the healthy population analysis data and the diseased population analysis data is obtained by the weight matrix, and the healthy population in the healthy population analysis data is defined as p(x, y), where x represents the weight vector of the healthy population analysis data, and y represents the diseased population keyword weight vector, and according to the weight vector p(x, y) of the healthy population analysis data Calculate the sovereign weight value of the healthy population analysis data and calculate the information entropy of the healthy population analysis data. The calculation formula is: Among them, e i represents the information entropy of the i-th healthy population analysis data, and p represents the number of healthy population analysis data; Calculate the entropy weight of the evaluation index, the calculation formula is: Then we can get the weight vector Y=(y1, y2, ..., y p ), optimize the fitting of the sovereign weight value and weight vector to obtain the comprehensive weight matrix z i , the calculation formula is: The comprehensive weight matrix Z is calculated as follows: p ).

6. A method for quantitative analysis of human exhaled gas according to claim 1, characterized in that: In step S500, the specific steps of confirming the rules are as follows: the rules are divided into the healthy population hit determination rules and the diseased population hit determination rules, and the comprehensive weight matrix z i The rules determine whether each individual in the local population is in a healthy state by collecting data from human exhaled gas, determine the health status and send out early warning signals to volunteers corresponding to the collected data in an unhealthy state.

7. A quantitative analysis and early warning device for human exhaled gas, characterized in that: The device includes: a collection device, an analysis device and an early warning device, the collection device, the analysis device and the early warning device are electrically connected to each other, and the collection device, the analysis device and the early warning device run in a processor, the data generated by the collection device, the analysis device and the early warning device are stored in a memory, and the collection device, the analysis device and the early warning device, the processor and the memory implement the steps of any one of the methods for quantitative analysis of human exhaled gas in claims 1-6 when executing the computer program.

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