Method and system for evaluating risk of latent tuberculosis infection based on proportion relationship of multiple immune cell populations, and application thereof

Abstract

The application discloses a method and system for evaluating the risk of latent tuberculosis infection based on the proportion relationship of multiple immune cell groups and application, and the method comprises the following steps: obtaining a sample to be tested, detecting the obtained sample to be tested, obtaining the proportion relationship of at least three immune cell groups, and the three cell groups comprise a first immune cell group, a second immune cell group and a third immune cell group; and based on the obtained proportion relationship of the at least three immune cell groups, calculating the evaluation result of the risk of latent tuberculosis infection corresponding to the sample to be tested. The risk evaluation method can effectively reduce the technical threshold, realize the screening of the risk of latent tuberculosis infection, and is better in execution and has better benefits than the TST test in terms of economic cost, manpower occupation, technical threshold and time cost, thereby providing technical support for controlling the entry of latent tuberculosis infection persons into college students and blocking the prevalence of pulmonary tuberculosis in colleges.

Description

Technical Field

[0001] This invention relates to the field of medical technology, specifically to a method, system, and application for assessing the risk of latent tuberculosis infection based on the proportional relationships of multiple immune cell populations. Background Technology

[0002] Tuberculosis is a respiratory infectious disease with a high rate of disability and a lack of effective vaccines, making it a key area for infectious disease prevention and control among university students. The highest incidence of latent tuberculosis infection is found among high school and university students, who are of school age and enter university without screening, leading to frequent clusters of tuberculosis infections and high rates of disability among university students.

[0003] Currently, there is no unified gold standard for diagnosing latent tuberculosis infection. The tuberculin skin test (TST) and interferon-gamma release assay (IGRA) are widely recognized as the main detection methods. However, existing methods for pulmonary tuberculosis diagnosis all have high equipment requirements, long testing time, and high technical difficulty, making it impossible to quickly screen and detect latent tuberculosis infection. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, and application for assessing the risk of latent tuberculosis infection based on the ratio of multiple immune cell populations, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for assessing the risk of latent tuberculosis infection based on the ratio of multiple immune cell populations includes: obtaining a sample to be tested, testing the obtained sample to be tested, and obtaining the ratio of at least three immune cell populations, wherein the three cell populations include a first immune cell population, a second immune cell population, and a third immune cell population.

[0007] Based on the obtained proportional relationships of at least three immune cell populations, the risk assessment result of latent tuberculosis infection corresponding to the test sample is calculated.

[0008] As a further aspect of the present invention, the proportional relationship of the three immune cell populations includes a first ratio, a second ratio, and / or a third ratio;

[0009] The first ratio is the ratio of the first immune cell population to the second immune cell population as reflected in the test.

[0010] The second ratio is the ratio of the second immune cell population to the third immune cell population as reflected in the test.

[0011] The third ratio is the composite ratio of the first, second, and third immune cell populations as reflected in the test.

[0012] As a further embodiment of the present invention, the first cell population is a lymphocyte population that highly expresses the CD3 marker; the second immune cell population is a monocyte population that highly expresses the CD64 marker; and the third immune cell population is a CD161 cell population that highly expresses the CD161 marker.

[0013] As a further aspect of the present invention, the method for obtaining the proportions of at least three immune cell populations by detecting the sample to be tested includes:

[0014] Based on the immune cell populations involved in the detection, a flow cytometry fluorescent staining agent for detection is prepared; the flow cytometry fluorescent staining agent for detection comprises equal amounts of premixed multiple reagents; the multiple reagents include CD3 reagent, CD45 reagent, CD64 reagent, and CD161 reagent;

[0015] Extract the flow cytometry fluorescent staining agent and the sample to be tested according to the specified ratio, mix them evenly, and carry out the staining reaction in the dark and under controlled temperature.

[0016] After the staining reaction is completed, red blood cell lysis buffer is added in proportion to lyse the red blood cells, followed by the addition of phosphate buffer and flow cytometry to obtain the detection results. The detection results are then analyzed and processed to obtain the proportional relationships of at least three immune cell populations.

[0017] As a further aspect of the present invention, the method for calculating the latent tuberculosis infection risk assessment result corresponding to the test sample based on the obtained proportions of at least three cell populations includes:

[0018] Develop an experimental plan, conduct the experiment, and obtain experimental data;

[0019] The experimental data were processed to construct a dataset for a latent tuberculosis infection risk assessment model, and the dataset was divided into a training set and a test set.

[0020] A latent tuberculosis infection risk assessment model is constructed and trained based on the dataset to obtain a trained latent tuberculosis infection risk assessment model; the input of the latent tuberculosis infection risk assessment model includes the proportional relationship of multiple cell populations, and the output of the latent tuberculosis infection risk assessment model includes a latent tuberculosis infection risk assessment value, which takes values ​​in [0,1].

[0021] Based on the obtained proportions of at least three cell populations, the latent tuberculosis infection risk assessment model is used to calculate and obtain the latent tuberculosis infection risk assessment value corresponding to the test sample. Based on the latent tuberculosis infection risk assessment value, the latent tuberculosis infection risk assessment result corresponding to the test sample is calculated and output.

[0022] The system based on the method for assessing the risk of latent tuberculosis infection based on the ratio of multiple immune cell populations includes: an immune cell population ratio detection unit, used to obtain the ratio of at least three immune cell populations.

[0023] And a latent tuberculosis infection risk assessment unit, used to calculate the latent tuberculosis infection risk assessment result for the test sample based on the proportional relationship of at least three immune cell populations.

[0024] Application of the latent tuberculosis infection risk assessment system based on the ratio of multiple cell marker combinations in latent tuberculosis infection risk screening.

[0025] Compared with the prior art, the beneficial effects of the present invention are:

[0026] 1. The method of the present invention, by analyzing the proportional relationship of at least three immune cell populations, can more comprehensively reflect the immune status of the body, thereby improving the accuracy of latent tuberculosis infection risk assessment;

[0027] 2. Compared with traditional single detection methods, the method of the present invention, which is based on the assessment of the proportion of multiple immune cell populations, can more accurately stratify the risk of patients with latent tuberculosis infection, identify individuals with a higher risk of progressing to active tuberculosis, and thus enable more targeted prevention and treatment interventions, improve treatment effectiveness, and reduce the spread of tuberculosis.

[0028] 3. The risk assessment method of this invention can effectively reduce the technical threshold and achieve the screening of latent tuberculosis infection risk with "one drop of blood and immediate results". Compared with the TST test in terms of economic cost, manpower, technical threshold and time cost, it is easier to implement and has better benefits, providing technical support for controlling the entry of latent tuberculosis infected persons into college students and blocking the spread of pulmonary tuberculosis in colleges. Attached Figure Description

[0029] Figure 1 This is a schematic flowchart of the method of the present invention;

[0030] Figure 2 This is a typical flow cytogram for multi-parameter analysis in the method of this invention;

[0031] Figure 3 This is a schematic diagram of the deep learning network model structure in the method of the present invention;

[0032] Figure 4 This is the ROC curve analysis result in the method of this invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Please see Figures 1-4 This invention provides a technical solution: a method for assessing the risk of latent tuberculosis infection based on the proportional relationship of multiple immune cell populations, comprising:

[0035] S1. Obtain a sample to be tested, which may specifically be a blood sample. Detect the obtained sample to obtain the proportional relationship of at least three immune cell populations. The three cell populations include a first immune cell population, a second immune cell population, and a third immune cell population. The proportional relationship of the three immune cell populations includes a first ratio, a second ratio, and / or a third ratio. The first ratio may specifically be the ratio of the first immune cell population to the second immune cell population as reflected in the detection. The second ratio may specifically be the ratio of the second immune cell population to the third immune cell population as reflected in the detection. The third ratio may specifically be the composite ratio of the first, second, and third immune cell populations as reflected in the detection.

[0036] The first cell population is a lymphocyte population that highly expresses the CD3 marker; the second immune cell population is a monocyte population that highly expresses the CD64 marker; and the third immune cell population is a CD161 cell population that highly expresses the CD161 marker.

[0037] S2. Based on the ratio of at least three immune cell populations obtained, calculate the latent tuberculosis infection risk assessment result corresponding to the test sample.

[0038] As a specific solution, the method for detecting the sample to be tested and obtaining the proportional relationships of at least three immune cell populations in step S1 of the present invention includes:

[0039] Based on the immune cell populations involved in the detection, a flow cytometry fluorescent staining agent for detection is prepared; the flow cytometry fluorescent staining agent for detection comprises equal amounts of premixed multiple reagents; the multiple reagents include CD3 reagent, CD45 reagent, CD64 reagent, and CD161 reagent;

[0040] Extract the flow cytometry fluorescent staining agent and the sample to be tested according to the specified ratio, mix them evenly, and carry out the staining reaction in the dark and under controlled temperature.

[0041] After the staining reaction is completed, red blood cell lysis buffer is added in proportion to lyse the red blood cells, followed by the addition of phosphate buffer and flow cytometry to obtain the detection results. The detection results are then analyzed and processed to obtain the proportional relationships of at least three immune cell populations.

[0042] As a specific solution, in step S2 of the present invention, the method for calculating the latent tuberculosis infection risk assessment result corresponding to the test sample based on the obtained proportions of at least three cell populations includes:

[0043] Develop an experimental plan, conduct the experiment, and obtain experimental data;

[0044] The experimental data were processed to construct a dataset for a latent tuberculosis infection risk assessment model, and the dataset was divided into a training set and a test set.

[0045] A latent tuberculosis infection risk assessment model is constructed and trained based on the dataset to obtain a trained latent tuberculosis infection risk assessment model; the input of the latent tuberculosis infection risk assessment model includes the proportional relationship of multiple cell populations, and the output of the latent tuberculosis infection risk assessment model includes a latent tuberculosis infection risk assessment value, which takes values ​​in [0,1].

[0046] Based on the obtained proportions of at least three cell populations, the latent tuberculosis infection risk assessment model is used to calculate and obtain the latent tuberculosis infection risk assessment value corresponding to the test sample. Based on the latent tuberculosis infection risk assessment value, the latent tuberculosis infection risk assessment result corresponding to the test sample is calculated and output.

[0047] As a specific solution, in step S2 of the present invention, the method for constructing an experimental plan, conducting experiments, and obtaining experimental data includes:

[0048] The experimental protocol was developed, which included selecting a large number of participants, including hospitalized patients with latent tuberculosis infection of different ages and genders, as well as participants to be screened.

[0049] A large number of test samples were extracted from selected experimental personnel, and the corresponding proportions of immune cell populations were obtained through testing and collected into a sample set.

[0050] A large number of selected test subjects were tested for latent tuberculosis infection, and the risk of latent tuberculosis infection was assessed based on the test results. The latent tuberculosis infection risk assessment dataset was obtained and incorporated into the sample set to form the test data.

[0051] As a specific approach, methods for testing a large number of selected participants for latent tuberculosis infection and assessing the risk of latent tuberculosis infection based on these tests include:

[0052] The selected large number of test subjects were tested for latent tuberculosis infection one by one, and the risk assessment value of latent tuberculosis infection was evaluated for each of them based on the test results.

[0053] Testing for latent tuberculosis infection also includes tuberculin skin test, interferon-gamma release test, and / or chest imaging.

[0054] The present invention also proposes a system based on the aforementioned method for assessing the risk of latent tuberculosis infection based on the ratio of multiple immune cell populations, comprising: an immune cell population ratio detection unit for obtaining the ratio of at least three immune cell populations;

[0055] And a latent tuberculosis infection risk assessment unit, used to calculate the latent tuberculosis infection risk assessment result for the test sample based on the proportional relationship of at least three immune cell populations.

[0056] This invention also proposes an application of the latent tuberculosis infection risk assessment system based on the ratio of multiple cell marker combinations in latent tuberculosis infection risk screening.

[0057] Example

[0058] I. Construction and validation of a risk assessment model for latent tuberculosis infection:

[0059] 1.1 Materials and Methods

[0060] 1.1.1 Research Subjects

[0061] The experimental group consisted of 22 blood samples collected from cured university student tuberculosis patients before discharge, who had no other infectious diseases, chronic diseases, or autoimmune diseases. The control group consisted of 88 blood samples from age- and sex-matched university students undergoing health checkups. This study was approved by the hospital's medical ethics committee.

[0062] 1.1.2 Test Methods

[0063] 1.1.2.1 Flow Cytometry Detection Method and Analysis: The instrument used was a Mindray BriCyte E6 flow cytometer. The reagents used were CD3, CD45, CD64, and CD161 fluorescent staining agents for flow cytometry, manufactured by Shenzhen United Medical Technology Co., Ltd. The experiment was conducted according to the reagent instructions. The specific steps were as follows: Equal volumes of premixed CD3, CD45, CD64, and CD161 reagents were added to 100 μL of EDTA-anticoagulated venous blood, mixed thoroughly, and stained at room temperature for 15 minutes in the dark. Red blood cells were then lysed with erythrocyte lysis buffer, followed by phosphate buffer for detection. Kaluza cell analysis software was used for flow cytometry analysis: lymphocytes were identified as CD45+CD3+, monocytes as CD45+CD64+, and CD161 cells as CD45+CD161+.

[0064] 1.1.2.2. Gamma Interferon Release Assay: The detection reagent was the Mycobacterium-Specific Cell Immunoassay Kit (ELISA) from Shanghai Diagnostics. The test was performed according to the reagent instructions. The specific steps were as follows: Collect 4 ml of venous blood in a pyrogen-free heparin-anticoagulated tube; separate mononuclear cells using the accompanying lymphocyte separation medium; perform cell counting; and adjust the cell concentration to 2.5 × 10⁻⁶. 6 / mL, add to microplates coated with anti-human gamma interferon antibody. Add 100 μL of cell suspension to each of the blank control well, test well, and phytohemagglutinin positive control well. Add 50 μL each of culture medium, Mycobacterium tuberculosis-specific mixed polypeptide antigen, and phytohemagglutinin to each well. Incubate in a 5% CO2 incubator at 37°C for 20 hours. Remove from the incubator, wash, add 50 μL of freshly prepared 1:200 labeled working antibody, incubate at 2-8°C for 1 hour, wash, add 50 μL of chromogenic substrate, and react at room temperature in the dark for 7 minutes. Stop the reaction with distilled water, photograph the spots, and count them. A positive result is defined as ≥6 antigen spots minus the number of blank control spots, and a negative result is defined as ≤5 spots.

[0065] 1.1.3 Construction and Validation of a Latent Tuberculosis Infection Risk Assessment Model (Deep Learning Network)

[0066] This study aims to predict the risk of latent pulmonary tuberculosis in healthy individuals by calculating a comprehensive immune score based on three immune function evaluation indicators: CD3, CD64, and CD161. Lymphocytes (CD45+CD3+), monocytes (CD45+CD64+), and CD161 cells (CD45+CD161+) were obtained. A deep learning algorithm was used to train a dataset containing these three immune function evaluation indicators to construct a binary classification model. The output layer of this model is designed as a probability distribution with values ​​ranging from 0 to 1, directly reflecting the probability of a sample being classified as positive.

[0067] The deep learning network is built on Tensorflow Keras and has a 5-layer sequential topology. The input layer contains three features: Mon%, L%, and CD161%. The hidden layers include a normalization layer, a concatenation layer, a fully connected layer (Dense), and a dropout layer. The last layer is the output layer, and its activation function is sigmoid.

[0068] The obtained cell proportion data from the three groups needs to be centered and standardized (using data processing software). Following the training rules of deep learning network models, the preprocessed dataset is randomly divided into training and test sets at a ratio of 4:1. During network model training, 20% of the samples in the training set are randomly selected as the validation set. To compare the impact of different network topologies and parameters on model performance, multiple models are trained in batches using predefined sets of metadata. Because the training data is class-imbalanced, the weights of different classes are calculated for the training data before training the model. These weights are then used as parameters for model training. The trained models are temporarily stored in the file system. Finally, different models are tested one by one, and the model with the best performance is selected.

[0069] 1.1.4 Statistical Analysis

[0070] The baseline characteristics of the experimental and control groups, as well as the comparison of multi-parameter detection data, were compared using t-tests and chi-square tests in the construction of the latent tuberculosis infection risk assessment model. Statistical significance was defined as p < 0.05.

[0071] 1.2 Results

[0072] 1.2.1 Baseline Results of Experimental Personnel

[0073] The experimental group (22 participants) and control group (88 participants) totaled 110 participants in the latent tuberculosis infection risk assessment model, all of whom were male. The mean age in the experimental group was 25.32 years (standard deviation 4.09), with a maximum age of 34 years and a minimum age of 21 years. The mean age in the control group was 24.47 years (standard deviation 3.29), with a maximum age of 36 years and a minimum age of 19 years. There was no statistically significant difference in age between the two groups (p=0.304, t=1.033).

[0074] 1.2.2 Comparison Results of Multiple Parameters of CD161

[0075] The lymphocyte markers used in the latent tuberculosis infection risk assessment model were CD45+CD3+, the monocyte markers were CD45+CD64+, and the CD161 cell markers were CD45+CD161+. Typical flow cytometry plots for multi-parameter analysis are shown below. Figure 2Table 1 shows the comparison of lymphocyte, monocyte markers and CD161 cell percentages between the experimental group and the control group.

[0076]

[0077] 1.2.3 Deep Learning Network Training and Testing Results

[0078] By constructing deep learning networks on the training set and testing them on the test set, multiple models were trained in batches using predefined sets of metadata to compare the impact of different network topologies and parameters on model performance. Because the training data is class-imbaled, weights for different classes were calculated before training the models, and these weights were then used as parameters for model training. The trained models were temporarily stored in the file system, and finally, each model was tested individually, and the model with the best performance was selected (see Table 2).

[0079]

[0080] In training artificial intelligence models, to improve training efficiency, multiple models are trained simultaneously by adjusting parameters such as the number of neurons, inactivation value, and learning rate. Then, by comparing and analyzing the performance of these models on the same dataset, differences in sensitivity and specificity can be identified more quickly and accurately, the performance of different models can be evaluated, and the most suitable model for a specific task can be selected. (See...) Figure 3 This is a schematic diagram of a deep learning network model structure.

[0081] The model's performance was tested using a test set, calculating the probability of each sample belonging to the positive class. The default probability threshold was 0.65; that is, if the output probability value was greater than 0.65, it was considered a positive result for latent tuberculosis, otherwise it was considered negative. The final threshold will be determined based on indicators such as sensitivity and specificity obtained from the analysis of the test results. The test results are shown in Table 3.

[0082]

[0083] 1.3 Conclusion

[0084] This embodiment employs 12 models, selecting the model most suitable for the specific task and research objective. Since the primary objective is risk assessment, risk assessment diagnostic tests should have high sensitivity. Furthermore, the sample size of the test set used for methodology establishment and model construction is limited, and sensitivity and specificity generally decrease in practical applications. Therefore, this embodiment pre-sets the model with the highest specificity and threshold at a sensitivity of 100% during model construction.

[0085] In summary, this embodiment establishes an immune scoring method based on three parameters, CD3, CD64, and CD161, for risk assessment of latent tuberculosis infection. By constructing a deep network learning model, a risk assessment threshold of 0.65 was established in a male population aged 16-35, at which the risk assessment sensitivity was 100% and the specificity was 92%.

[0086] II. Evaluation of the effectiveness of the risk assessment method (CD161 immune scoring method) of this invention in assessing the risk of latent tuberculosis:

[0087] 2.1 Materials and Methods

[0088] 2.1.1 Research Subjects

[0089] The CD161 immune scoring method was used to assess the risk of latent tuberculosis in a unit with the highest tuberculosis incidence rate in the past five years. Cluster sampling was employed to conduct the latent tuberculosis risk assessment in this unit.

[0090] Inclusion criteria: (1) No contraindications to the risk assessment trial, (2) Healthy annual physical examination results, (3) Informed consent. Exclusion criteria: (1) Lung disease or cold symptoms in the annual physical examination, (2) Refusal to participate in the latent tuberculosis risk assessment. This study was approved by the Medical Ethics Committee.

[0091] 2.1.2 Test Methods

[0092] After all participants in the pilot units fully understood the project's content and risks and provided informed consent, all personnel underwent tuberculin skin tests and blood collection. In this study, a positive interferon-gamma release assay and normal chest imaging were defined as latent Mycobacterium tuberculosis infection. The TST test, interferon-gamma release assay, and risk assessment test of this invention were performed separately.

[0093] 2.2 Results

[0094] 2.2.1 Age Distribution: A total of 159 people were screened, all of whom were male. Among them, 55 people were from Unit 1, 41 people were from Unit 2, and 63 people were from Unit 3. The age distribution is shown in Table 4.

[0095]

[0096] 2.2.2 Comparison of Latent Tuberculosis Detection: Latent tuberculosis infection was defined as a positive result in the gamma interferon release assay. All 159 personnel completed the gamma interferon release assay, with 11 positive and 148 negative, resulting in a latent tuberculosis infection rate of 6.9%. Specifically, 1 person in 1 unit was screened for latent tuberculosis (infection rate 1.8%), 5 people in 2 units (infection rate 12.2%), and 5 people in 3 units (infection rate 7.9%). A row × table chi-square test was used to analyze the difference in latent tuberculosis infection rates among the three teams. The chi-square test results showed a Pearson Chi Square of 4.096, with 2 degrees of freedom, and a p-value of 0.129 > 0.05. It can be considered that the latent tuberculosis infection rates of the three teams participating in the screening were similar and there was no statistically significant difference. The results are shown in Table 5.

[0097]

[0098] 2.2.3. Results of CD161 Immunoassay for Screening Latent Tuberculosis Infection Risk: The CD161 immunoassay and deep network learning model 1 were used to calculate the risk value of latent tuberculosis infection. The risk value ranged from a minimum of 30.34 to a maximum of 76.26. ROC curve analysis results are shown below. Figure 4 ROC curve analysis showed that the area under the curve was 0.833, the standard error was 0.717, and the 95% approximate reference confidence interval was (0.717, 0.949). The optimal critical point of the ROC curve was determined to be 60.57, with a sensitivity of 0.818 and a specificity of 0.804.

[0099] 2.2.6 Results of TST testing for screening latent tuberculosis infection risk: 41 individuals had induration at the TST skin test site greater than 5 mm, observed 72 hours after the test. The sensitivity was 90%, and the specificity was 72.3%.

[0100] 2.2.7 Comparison of Sensitivity and Specificity of CD161 Immunoassay and TST Detection for Screening Latent Tuberculosis Infection Risk: The sensitivity and specificity of the CD161 Immunoassay and TST detection were compared using chi-square tests. The chi-square test results for sensitivity comparison showed a Pearson Chi Square of 0.413, with 1 degree of freedom and a P-value of 0.521. The chi-square test results for specificity comparison showed a Pearson Chi Square of 0.014, with 1 degree of freedom and a P-value of 0.905. It can be concluded that there is no difference in sensitivity and specificity between the two methods for screening latent tuberculosis infection.

[0101] 2.3 Conclusion

[0102] ROC curve analysis showed that the optimal cutoff value for the CD161 scoring method was 60.58. At this cutoff value, the sensitivity and specificity were 0.818 and 0.804, respectively, and the area under the curve was 0.88 > 0.7. According to the diagnostic test judgment principle, its diagnostic accuracy is moderate.

[0103] Further comparison of the sensitivity and specificity of the CD161 scoring method and the TST test in screening for latent tuberculosis showed that there was no difference in the sensitivity and specificity of the two methods in screening for latent tuberculosis risk. However, in terms of economic cost, manpower, technical threshold and time cost, the CD161 scoring method is more advantageous, easier to implement and more effective than the TST test.

[0104] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for assessing the risk of latent tuberculosis infection based on the proportion of multiple immune cell populations, characterized in that, include: Obtain a sample to be tested, and test the obtained sample to obtain the proportional relationship of at least three immune cell populations, the three cell populations including a first immune cell population, a second immune cell population and a third immune cell population; Based on the obtained proportional relationships of at least three immune cell populations, the risk assessment result of latent tuberculosis infection corresponding to the test sample is calculated.

2. The method for assessing the risk of latent tuberculosis infection based on the proportional relationship of multiple immune cell populations according to claim 1, characterized in that, The sample to be tested is a blood sample.

3. The method for assessing the risk of latent tuberculosis infection based on the proportion of multiple immune cell populations according to claim 2, characterized in that, The proportional relationships of the three immune cell populations include a first ratio, a second ratio, and / or a third ratio; The first ratio is the ratio of the first immune cell population to the second immune cell population as reflected in the test. The second ratio is the ratio of the second immune cell population to the third immune cell population as reflected in the test. The third ratio is the composite ratio of the first, second, and third immune cell populations as reflected in the test.

4. The method for assessing the risk of latent tuberculosis infection based on the proportional relationship of multiple immune cell populations according to claim 2, characterized in that, The first cell population is a lymphocyte population that highly expresses the CD3 marker; the second immune cell population is a monocyte population that highly expresses the CD64 marker; and the third immune cell population is a CD161 cell population that highly expresses the CD161 marker.

5. The method for assessing the risk of latent tuberculosis infection based on the proportion of multiple immune cell populations according to claim 4, characterized in that, The method for obtaining the proportions of at least three immune cell populations by testing the acquired sample includes: Based on the immune cell populations involved in the detection, a flow cytometry fluorescent staining agent for detection is prepared; the flow cytometry fluorescent staining agent for detection comprises equal amounts of premixed multiple reagents; the multiple reagents include CD3 reagent, CD45 reagent, CD64 reagent, and CD161 reagent; Extract the flow cytometry fluorescent staining agent and the sample to be tested according to the specified ratio, mix them evenly, and carry out the staining reaction in the dark and under controlled temperature. After the staining reaction is completed, red blood cell lysis buffer is added in proportion to lyse the red blood cells, followed by the addition of phosphate buffer and flow cytometry to obtain the detection results. The detection results are then analyzed and processed to obtain the proportional relationships of at least three immune cell populations.

6. The method for assessing the risk of latent tuberculosis infection based on the proportional relationship of multiple immune cell populations according to claim 2, characterized in that, Based on the obtained proportions of at least three cell populations, the method for calculating the latent tuberculosis infection risk assessment result for the test sample includes: Develop an experimental plan, conduct the experiment, and obtain experimental data; The experimental data were processed to construct a dataset for a latent tuberculosis infection risk assessment model, and the dataset was divided into a training set and a test set. A latent tuberculosis infection risk assessment model is constructed and trained based on the dataset to obtain a trained latent tuberculosis infection risk assessment model; the input of the latent tuberculosis infection risk assessment model includes the proportional relationship of multiple cell populations, and the output of the latent tuberculosis infection risk assessment model includes a latent tuberculosis infection risk assessment value, which takes values ​​in [0,1]. Based on the obtained proportions of at least three cell populations, the latent tuberculosis infection risk assessment model is used to calculate and obtain the latent tuberculosis infection risk assessment value corresponding to the test sample. Based on the latent tuberculosis infection risk assessment value, the latent tuberculosis infection risk assessment result corresponding to the test sample is calculated and output.

7. The method for assessing the risk of latent tuberculosis infection based on the proportional relationship of multiple immune cell populations according to claim 6, characterized in that, Methods for constructing experimental plans, conducting experiments, and obtaining experimental data include: The experimental protocol was developed, which included selecting a large number of participants, including hospitalized patients with latent tuberculosis infection of different ages and genders, as well as participants to be screened. A large number of test samples were extracted from selected experimental personnel, and the corresponding proportions of immune cell populations were obtained through testing and collected into a sample set. A large number of selected test subjects were tested for latent tuberculosis infection, and the risk of latent tuberculosis infection was assessed based on the test results. The latent tuberculosis infection risk assessment dataset was obtained and incorporated into the sample set to form the test data.

8. The method for assessing the risk of latent tuberculosis infection based on the proportion of multiple immune cell populations according to claim 7, characterized in that, Methods for testing a large number of selected participants for latent tuberculosis infection and assessing the risk of latent tuberculosis infection based on these tests include: The selected large number of test subjects were tested for latent tuberculosis infection one by one, and the risk assessment value of latent tuberculosis infection was evaluated for each of them based on the test results. Testing for latent tuberculosis infection also includes tuberculin skin test, interferon-gamma release test, and / or chest imaging.

9. A system based on the method for assessing the risk of latent tuberculosis infection based on the proportional relationship of multiple immune cell populations as described in any one of claims 2-8, characterized in that, include: An immune cell population ratio detection unit is used to obtain the ratio of at least three immune cell populations. And a latent tuberculosis infection risk assessment unit, used to calculate the latent tuberculosis infection risk assessment result for the test sample based on the proportional relationship of at least three immune cell populations.

10. The application of the latent tuberculosis infection risk assessment system based on the ratio of multiple cell marker combinations as described in claim 9 in the screening of latent tuberculosis infection risk.

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