CN116908466A - Application of quantitative detection reagent of cytokines related to AIDS combined active tuberculosis - Google Patents

Application of quantitative detection reagent of cytokines related to AIDS combined active tuberculosis Download PDF

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CN116908466A
CN116908466A CN202310710554.XA CN202310710554A CN116908466A CN 116908466 A CN116908466 A CN 116908466A CN 202310710554 A CN202310710554 A CN 202310710554A CN 116908466 A CN116908466 A CN 116908466A
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aids
active tuberculosis
cytokines
quantitative detection
detection reagent
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CN116908466B (en
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代友超
张惠华
刘艳霞
李凌华
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Eighth Affiliated Hospital Of Guangzhou Medical University Guangzhou Eighth People's Hospital Guangzhou Liver Disease Hospital Guangzhou Institute Of Infectious Diseases
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Eighth Affiliated Hospital Of Guangzhou Medical University Guangzhou Eighth People's Hospital Guangzhou Liver Disease Hospital Guangzhou Institute Of Infectious Diseases
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Abstract

The application relates to the technical field of biomedicine, and provides application of a quantitative detection reagent of cytokines related to AIDS-associated active tuberculosis, in particular to application of the quantitative detection reagent of the cytokines related to the AIDS-associated active tuberculosis in preparation of a predicted product of the AIDS-associated active tuberculosis, wherein the cytokines comprise IL6, IL10, IFN-gamma and TNF alpha. The application detects the content of the cytokine in the AIDS-associated active tuberculosis patients and the AIDS patients, performs statistical analysis, and calculates by using the established support vector machine model to predict whether the AIDS patients are associated with active tuberculosis, thus the quantitative detection reagent of the cytokine can prepare a predicted product of the AIDS-associated active tuberculosis.

Description

Application of quantitative detection reagent of cytokines related to AIDS combined active tuberculosis
Technical Field
The application belongs to the technical field of biomedicine, and particularly relates to application of a quantitative detection reagent of cytokines related to AIDS combined active tuberculosis.
Background
The Human Immunodeficiency Virus (HIV), also known as human immunodeficiency virus (Human Immunodeficiency Virus), is a retrovirus responsible for the deficiency of the human immune system. An hiv infected person, also known as an hiv carrier (People Living with Hiv, PLWH), refers to a person who has an hiv in vivo, and typically experiences 3 periods of time in clinic: acute infection stage, asymptomatic stage, and AIDS stage. AIDS (AcquiredImmune Deficiency Syndrome, AIDS) is the final stage of infection by the HIV, and patients can cause various opportunistic infections and tumors and even threaten life due to extremely reduced body resistance after entering the AIDS stage.
Tuberculosis (Tuberculosis) is a chronic infectious disease caused by tubercle bacillus and is a main cause of death of people infected with AIDS virus and patients with AIDS. PLWH/AIDS patients often incorporate various opportunistic infections due to low immune cell function, with M.tuberculosis (Mycobacterium tuberculosis, mtb) infection being the most common, accounting for about 20-50%, and Mtb infection accelerating the disease progression of the PLWH/AIDS patient. Therefore, the prevention and treatment of AIDS complicated with active tuberculosis (HIV-TB) has important significance in the public health field.
Traditional tuberculosis prediction mainly depends on mycobacterium tuberculosis acid-fast staining smear and mycobacterium tuberculosis culture. With the development of molecular biology technology in recent years, the nucleic acid detection technology is successfully applied to the detection of tuberculosis pathogens. Although pathogen-based assays have good specificity, these detection techniques are currently limited by sputum specimens, which are less sensitive even when used in combination.
Disclosure of Invention
The application aims to provide an application of a quantitative detection reagent of cytokines related to AIDS combined active tuberculosis, and aims to solve the problem of how to better predict the risk of the AIDS patients suffering from active tuberculosis.
In order to achieve the purposes of the application, the technical scheme adopted by the application is as follows:
the application provides an application of a quantitative detection reagent of cytokines related to AIDS-associated active tuberculosis, wherein the quantitative detection reagent is used for preparing a predicted product of the AIDS-associated active tuberculosis, and the cytokines comprise IL6, IL10, IFN-gamma and TNF alpha.
In one embodiment, the quantitative detection reagent comprises an antibody that specifically binds to a cytokine.
In one embodiment, the predictive product includes a kit for predicting the risk of aids-associated active tuberculosis.
In one embodiment, the kit comprises capture microspheres with antibodies that specifically bind to cytokines bound to their surfaces and a fluorescein-labeled fluorescent detection reagent.
In one embodiment, the kit further comprises reagents for detection of a subpopulation of human Th1/Th2 based on flow fluorescence luminescence.
In one embodiment, the kit further comprises a support vector machine model for predicting the risk of aids patients with combined active tuberculosis.
In one embodiment, the predictive product includes a predictive system for predicting the risk of an aids patient to develop active tuberculosis.
In one embodiment, a prediction system includes:
a data acquisition unit: the method comprises the steps of detecting cytokines in a sample to obtain the concentrations of cytokines IL6, IL10, IFN-gamma and TNF alpha in the sample;
a data analysis unit: the method comprises the steps of carrying out normalization processing on concentration, and inputting the concentration into a support vector machine model based on Python scikit-learn to obtain a prediction probability value of a sample;
data prediction unit: and the method is used for comparing the predicted probability value with a threshold value to predict the risk of the combined active tuberculosis of the AIDS patient.
In an embodiment, the data prediction unit further comprises a threshold value for risk of aids complicated with active tuberculosis, and when the predicted probability value is higher than the threshold value, the risk of aids complicated with active tuberculosis is high; when the sample prediction probability value is lower than the threshold value, the risk of the AIDS patient combining active tuberculosis is low.
In one embodiment, the active tuberculosis of the aids-associated active tuberculosis includes at least one of tuberculosis, extrapulmonary tuberculosis, and blood group-spreading tuberculosis.
Experiments show that 4 cytokines (namely IL6, IL10, IFN-gamma and TNF alpha) have content differences in patients with AIDS complicated with active tuberculosis and patients with AIDS, and whether the patients with AIDS complicated with active tuberculosis can be predicted by carrying out statistical analysis on the content levels of the 4 cytokines in blood plasma and calculating by using a related model. Therefore, the embodiment of the application can utilize the quantitative detection reagent of the cytokines related to the AIDS-associated active tuberculosis to prepare a predicted product of the AIDS-associated active tuberculosis, has high specificity and good accuracy and sensitivity, and can realize early prediction of the non-phlegm-based AIDS-associated active tuberculosis so as to reduce the incidence rate and the death rate of the AIDS-associated active tuberculosis of patients.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a graph showing comparison of the amounts of 4 cytokines in patients with AIDS (HIV) and patients with AIDS-associated active tuberculosis (HIV-TB) in the examples of the present application;
FIG. 2 is a graph showing comparison of AUC values of 4 cytokines in the example of the present application for distinguishing patients with AIDS and patients with combined active tuberculosis, respectively;
FIG. 3 is a ROC graph showing the difference between an AIDS patient and an AIDS combined active tuberculosis patient in a training set (Train), a Validation set (Validation) and a Test set (Test) based on SVM models constructed by 4 cytokine combinations in the embodiment of the present application;
FIG. 4 is a ROC graph showing the difference between AIDS patients and AIDS-associated and hematogenous tuberculosis patients in training set (Train), validation set (Validation) and Test set (Test), respectively, based on SVM models constructed from 4 cytokine combinations in the embodiment of the present application.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the application is further described in detail below with reference to the embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In the present application, "at least one" means one or more, and "a plurality" means two or more. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s).
It should be understood that, in various embodiments of the present application, the sequence number of each process described above does not mean that the execution sequence of some or all of the steps may be executed in parallel or executed sequentially, and the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
The terminology used in the embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "predictive probability value", "threshold value" as used in the present application refer to the mathematical score calculated by the SVM algorithm; the predicted probability value is the mathematical score calculated after cytokine data of interleukin 6 (IL 6), interleukin 10 (IL 10), interferon gamma (IFN-gamma) and tumor necrosis factor alpha (TNF alpha) of a sample of a subject are input into an SVM model, and the range is 0-1; the threshold value refers to a calculation method for evaluating the prediction capability of a model, the application uses a Youden index (Youden index) to calculate the threshold value, and when a sample ' prediction probability value ' is larger than the threshold value ', the model predicts that the risk of combining active tuberculosis of an AIDS subject is high; when the sample 'prediction probability value' is smaller than the 'threshold', the model predicts that the risk of the combined active tuberculosis of the AIDS subjects is low.
The term "content difference" used in the present application means that the levels of cytokines IL6, IL10, IFN-gamma, TNF alpha in different samples are changed, and has statistical significance; the cytokines are higher in patients with AIDS complicated with active tuberculosis than in patients with AIDS.
The SVM model is a support vector machine (support vector machines, SVM), which is a blank model, and is obtained by correspondingly setting parameters of an experimenter to obtain a model applied to a specific experiment for modeling, and the model obtained by modeling is adopted for data analysis.
The training set is an experimental group for constructing SVM model, and the data of the training set are from cytokines IL6, IL10, IFN-gamma and TNF alpha detection contents of AIDS patients and AIDS combined active tuberculosis patients. The validation set is an experimental group for verifying the efficacy of the SVM model constructed by the training set, and the data of the validation set are from cytokines IL6, IL10, IFN-gamma and TNF alpha detection contents of another group of AIDS patients and AIDS combined active tuberculosis patients. The test set is an experimental set for performing actual tests based on the SVM model constructed by the training set, and the data of the test set are from the cytokines IL6, IL10, IFN-gamma and TNF alpha detection contents of another group (different from the training set and the verification set) of AIDS patients and AIDS combined active tuberculosis patients.
The embodiment of the application provides application of a quantitative detection reagent of cytokines related to AIDS-associated active tuberculosis, wherein the quantitative detection reagent is used for preparing a predicted product of the AIDS-associated active tuberculosis, and the cytokines comprise IL6, IL10, IFN-gamma and TNF alpha.
The embodiment of the application provides an application of a quantitative detection reagent of cytokines related to AIDS-associated active tuberculosis in preparing a predicted product of the AIDS-associated active tuberculosis. Specifically, the embodiment of the application discovers that the 4 cytokines of IL6, IL10, IFN-gamma and TNF alpha have content difference between AIDS patients with combined active tuberculosis and AIDS patients through experiments, and can predict whether the AIDS patients with combined active tuberculosis by carrying out statistical analysis on the content level of the 4 cytokines in blood plasma and calculating by using a related support vector machine model. Therefore, the embodiment of the application can utilize the quantitative detection reagent of the cytokines related to the AIDS-associated active tuberculosis to prepare a predicted product of the AIDS-associated active tuberculosis, and the predicted product applied not only has high specificity, but also has good accuracy and sensitivity, and can realize early prediction of the AIDS-associated active tuberculosis of the non-phlegm-based AIDS patients, thereby being more beneficial to comprehensively and accurately predicting the disease risk of the AIDS-associated active tuberculosis and effectively reducing the incidence rate and the death rate of the AIDS-associated active tuberculosis of the AIDS patients.
In one embodiment, the quantitative detection reagent comprises an antibody that specifically binds to a cytokine, specifically an antibody that specifically binds to IL6, IL10, IFN-gamma, TNF-alpha. Based on the specifically bound cytokine antibodies, the IL6, IL10, IFN-gamma and TNF alpha content in the sample can be better quantitatively detected. Therefore, the antibody capable of specifically binding to IL6, IL10, IFN-gamma and TNF alpha can be used for preparing a predicted product of the combined active tuberculosis of the AIDS.
In some embodiments, the predictive product includes at least one of a kit, a chip, a system. The quantitative detection reagent of cytokines (including IL6, IL10, IFN-gamma and TNF alpha) related to the combined active tuberculosis of the AIDS is applied to the preparation of a kit, a chip or a system, and can improve the prediction accuracy and the reliability of the risk of the combined active tuberculosis of the AIDS.
In one embodiment, the predictive product is a kit for predicting the risk of aids-associated active tuberculosis. The kit comprises a quantitative detection reagent of cytokines related to the AIDS combined active tuberculosis. Specifically, the embodiment of the application provides an application of a quantitative detection reagent of cytokines (including IL6, IL10, IFN-gamma and TNF alpha) related to AIDS-associated active tuberculosis in preparing a prediction kit of the AIDS-associated active tuberculosis.
In one embodiment, the kit comprises capture microspheres with antibodies that specifically bind to cytokines bound to their surfaces and a fluorescein-labeled fluorescent detection reagent. After the capture microspheres are respectively combined with IL6, IL10, IFN-gamma and TNF alpha in the plasma of a subject in a specific way, the capture microspheres are combined with a fluorescent detection reagent marked by fluorescein to form a double-antibody sandwich compound (capture microspheres+sample to be detected+detection antibody), and the content of IL6, IL10, IFN-gamma and TNF alpha of the sample to be detected is obtained by analyzing the fluorescence intensity of the double-antibody sandwich compound. In specific embodiments, the sample IL6, IL10, IFN-gamma, TNF alpha concentrations of a subject can be analyzed by flow cytometry.
Specifically, the capture microsphere may be a polystyrene microsphere, the surface of which binds to a specific antibody of the above cytokine. The fluorescent detection reagent marked by fluorescein can be a fluorescent detection reagent marked by Phycoerythrin (PE), and concretely can be a detection antibody marked by PE, so that a double-antibody sandwich complex can be formed during detection.
In one embodiment, the kit further comprises reagents for detection of a subpopulation of human Th1/Th2 based on flow fluorescence luminescence. For example, the reagent may be Phosphate Buffered Saline (PBS) buffer, bovine serum albumin, or the like. Namely, the quantitative detection reagent of the cytokines can be used for preparing a detection kit (flow fluorescence luminescence method) of the human Th1/Th2 subgroup. The cytokine content is detected based on a flow fluorescent light-emitting method, and IFN-gamma and TNF alpha secreted by Th1 cells, IL6 and IL10 secreted by Th2 cells are quantitatively detected, so that the method can be suitable for various flow cytometry.
In one embodiment, the kit further comprises a support vector machine model for predicting the risk of aids patients combining active tuberculosis.
Specifically, the support vector machine model is built based on a Python scikit-learn module, and samples of an AIDS patient and an AIDS combined active tuberculosis patient are taken as 4: the 1 proportion is randomly divided into a training set and a verification set, the model is constructed through the training set, and meanwhile, the verification set is used for verifying the model efficiency. The automatic parameter tuning method selects GridSearchCV, and sets 5 times of cross validation to fit the best performance of the model to the data, and final modeling is carried out according to the best parameters selected by GridSearchCV. And further carrying out probability calibration, and calculating an optimal threshold of the model by utilizing the about log index according to the predicted values of the training set, the verification set and the test set. The SVM model can be constructed and developed into visual software which is easy to operate by 4 kinds of cytokine data, and comprises a data input module and a prediction module, wherein the prediction module predicts the risk of an AIDS patient for suffering from active tuberculosis by using the SVM model.
In one embodiment, the predictive product includes a predictive system for predicting the risk of an aids patient combining active tuberculosis. Specifically, the embodiment of the application provides an application of a quantitative detection reagent of cytokines (including IL6, IL10, IFN-gamma and TNF alpha) related to AIDS complicated active tuberculosis in preparing a prediction system of the combined active tuberculosis of AIDS patients.
In one embodiment, a prediction system includes:
a data acquisition unit: the method comprises the steps of detecting cytokines in a sample to obtain the concentrations of cytokines IL6, IL10, IFN-gamma and TNF alpha in the sample;
a data analysis unit: the method comprises the steps of carrying out normalization processing on concentration, and inputting the concentration into a support vector machine model based on Python scikit-learn to obtain a prediction probability value of a sample;
data prediction unit: and the method is used for comparing the predicted probability value with a threshold value and predicting the risk of combining active tuberculosis of the AIDS patient.
The data acquisition unit is used for acquiring the concentration of cytokines IL6, IL10, IFN-gamma and TNF alpha in the sample. Specifically, the method comprises the following steps: (1) Collecting and treating the blood plasma sample of the subject, such as collecting venous blood sample of AIDS patients and AIDS complicated with active tuberculosis patients by EDTA anticoagulation tube; (2) flow-through fluorescence detection: preparing a calibrator tube, a negative control tube and a sample tube by using a quantitative detection reagent, and performing fluorescence detection on a flow cytometer in a calibrated state to obtain the contents of 4 cytokines in AIDS-combined active tuberculosis patients and AIDS patients. The sample is venous blood plasma of the subject, and the plasma sample can be obtained from an AIDS subject who may or may not have active tuberculosis. The negative control sample is a sample of an active tuberculosis negative AIDS patient.
The data analysis unit constructs a support vector machine model based on the contents of 4 cytokines in the AIDS combined active tuberculosis patients and the AIDS patients obtained by the data acquisition unit to obtain a prediction probability value, and the content of the cytokines reflects the level of the cytokines in the AIDS patients and the AIDS combined active tuberculosis patients. Specifically, the method is used for inputting the normalized concentration into a support vector machine model based on Python scikit-learn to obtain a predicted probability value (between 0 and 1) of a sample, and through data normalization processing, the method has the advantages of simple process, smaller error range and more reliable result. The support vector machine model can be established based on a Python scikit-learn module, and samples of an AIDS patient and an AIDS combined active tuberculosis patient are taken as 4: the 1 proportion is randomly divided into a training set and a verification set, the model is constructed through the training set, and meanwhile, the verification set is used for verifying the model efficiency. The automatic parameter tuning method selects GridSearchCV, and sets 5 times of cross validation to fit the best performance of the model to the data, and final modeling is carried out according to the best parameters selected by GridSearchCV.
The data prediction unit is used for predicting the risk of the combined active tuberculosis of the AIDS patients. Namely, the SVM model of the data analysis unit is utilized to predict the risk of the AIDS patient for suffering from active tuberculosis. Specifically, the data prediction unit further comprises a threshold value for risk of combining active tuberculosis of the aids patient, and when the prediction probability value is higher than the threshold value, the risk of combining active tuberculosis of the aids patient is high; when the sample prediction probability value is lower than the threshold value, the risk of the AIDS patient combining active tuberculosis is low. Therefore, the embodiment of the application can realize the medical prediction of artificial intelligence by judging the risk of the active tuberculosis of the AIDS patient by utilizing the SVM model.
In one embodiment, the active tuberculosis of the aids-associated active tuberculosis includes at least one of tuberculosis, extrapulmonary tuberculosis, and blood group-spreading tuberculosis.
According to the embodiment of the application, through carrying out statistical analysis on the cytokine level in the blood plasma and calculating through the established SVM model, whether an AIDS patient has active tuberculosis can be predicted; in the test set samples, the AUC values for the 4 factor combinations were 0.8 (95% ci:0.69, 0.91), accuracy 0.8 (95% ci:0.71, 0.87), sensitivity 0.83 (95% ci:0.73, 0.9), specificity 0.7 (95% ci:0.5, 0.86). Wherein, the AUC value of the SVM model for predicting whether the AIDS patient has combined blood-spreading tuberculosis is 0.95 (95% CI:0.9, 1.0), the accuracy is 0.9 (95% CI:0.83, 0.94), the sensitivity is 0.9 (95% CI:0.82, 0.94), and the specificity is 0.89 (95% CI:0.72, 0.98). Therefore, the quantitative detection of the combination of cytokines IL6, IL10, IFN-gamma and TNF alpha has wide application prospect in predicting the combined active tuberculosis of AIDS.
The following description is made with reference to specific embodiments.
Example 1
Collection and processing of a subject's plasma sample
(1) The embodiment comprises 145 cases of AIDS patients and 97 cases of AIDS combined active tuberculosis patients, wherein the number of the blood circulation spreading tuberculosis patients is 35; then, the training set and the verification set are randomly divided in a ratio of 4:1. In addition, 83 cases of AIDS patients and 27 cases of AIDS-complicated active tuberculosis patients were additionally included as test sets, wherein 9 cases of blood-disseminated tuberculosis patients were included.
(2) Collection and treatment of a subject's plasma sample: venous blood samples were collected using EDTA anticoagulant tubes and centrifuged at 2000-4000rpm for 20 minutes to obtain 0.5mL of plasma for review.
Preparation of the laboratory tube
The experimental tube includes: calibrator tube, negative control tube, sample tube.
(1) The calibrator (IL 6, IL10, IFN-. Gamma., TNF-. Alpha.four cytokine lyophilized powder) was opened, transferred to a centrifuge tube, and the calibrator tube was labeled at the highest concentration.
(2) The calibrator was resuspended in 2mL of sample dilution (PBS buffer) and left to stand at room temperature for 15 minutes.
(3) Mixing the calibrator with the suction head; 9 loaded centrifuge tubes were taken as sample tubes, labeled 1:2, 1:4, 1:8, 1:16, 1:32, 1:64, 1:128, 1:256, 1:512, respectively, with 300uL of sample diluent added per tube.
(4) Extracting 300uL of liquid from the highest concentration calibrator tube into a sample tube marked as 1:2, blowing and mixing uniformly, then taking 300uL of liquid from the sample tube marked as 1:2 into a sample tube marked as 1:4, blowing and mixing uniformly, and the like until the sample tube marked as 1:512.
(5) The mixture of capture microspheres (wherein the surface of the capture microspheres contains IL6, IL10, IFN-gamma and TNF alpha specific antibodies, and the rest solution is PBS buffer solution added with bovine serum albumin and sodium chloride) is centrifuged for 5 minutes by using a low-speed centrifuge with 200g, the supernatant is sucked away, microsphere buffer solution (PBS buffer solution added with bovine serum albumin and sodium chloride) with the same volume as the supernatant is sucked away is added, and after vortex and full mixing, incubation is carried out for 30 minutes in a dark place.
(6) The mixture of capture microspheres was vortexed and 25uL of the mixture of capture microspheres was added to each experimental tube.
(7) The 25uL gradient diluted calibrator was added to the calibrator tube at the concentrations shown in Table 1 below.
Table 1 calibrator concentration
(8) 25uL of the plasma sample to be tested was added to each tube of the sample tube.
(9) All tubes were added with 25uL of fluorescent detection reagent (PE-labelled detection antibody) and vortexed well before incubation for 2.5 hours at room temperature in the dark.
1mL of PBS buffer was added to each tube of the tube, and after centrifugation at 200g for 5 minutes, the supernatant was carefully aspirated; 100uL of PBS buffer was added to each tube, and the mixture was allowed to stand for detection.
Flow type fluorescence luminescence detection
(1) And (3) sequentially carrying out fluorescence detection on the experiment tubes on the flow cytometer in a calibrated state according to the sequence of the calibrator tube, the negative control tube and the sample tube, and immediately detecting each experiment tube after vortex mixing for 3-5 seconds.
(2) The highest value of the measured value of 4 cytokines in the strong positive sample is not more than 2500pg/mL, and the sample result is higher than 2500pg/mL, and the sample is diluted by an appropriate multiple by using sample diluent.
Comparison of the content of 4 cytokines in patients with AIDS and patients with active tuberculosis complicated with AIDS
(1) The results of the flow-type fluorescence detection of the training set are shown in tables 2 and 3 (the content units in the tables are pg/mL), and the difference of the content of 4 cytokines in the training set in two groups of patients is analyzed by using a scatter diagram, as shown in FIG. 1: it can be seen that the content of 4 cytokines in patients with AIDS complicated with active tuberculosis is higher than that in patients with AIDS.
TABLE 2 training set AIDS patient 4 cytokine levels
TABLE 3 training set AIDS complicated with active tuberculosis patients 4 cytokine contents
(2) The area under the curve (AUC) of 4 cytokines alone to distinguish aids patients from aids combined active tuberculosis patients was further analyzed and the results are shown in fig. 2 and table 4: the results show that IFN-gamma distinguishes between the two with the largest AUC areas, followed by IL10, IL6 and TNF alpha. In patients with tuberculosis with predicted blood circulation, IFN-gamma has the largest AUC area, and IL6, IL10 and TNF alpha are the second.
Table 4 cytokines alone predict AUC values (95% confidence interval) for active tuberculosis
Cytokines and methods of use Predicting active tuberculosis Predicting tuberculosis of blood circulation spreading type
IL6 0.79(0.73，0.85) 0.92(0.88，0.97)
IL10 0.79(0.73，0.85) 0.84(0.77，0.92)
TNFα 0.75(0.68，0.82) 0.81(0.73，0.89)
IFN-γ 0.87(0.82，0.92) 0.93(0.88，0.99)
Example 2 construction and testing of SVM model
(1) And building an SVM model based on the Python scikit-leam module, building the model through a training set, and simultaneously verifying the model efficiency by using a verification set. The automatic parameter tuning method selects GridSearchCV, sets 5 times of cross validation to fit the optimal performance of the model to the data, carries out final modeling according to the optimal parameters selected by GridSearchCV, further calculates the optimal threshold of the model by utilizing the Jordon index according to the predicted values of the training set, the validation set and the test set after probability calibration.
(2) The performance parameters of the SVM model constructed by the 4 kinds of cytokine combinations in the training set, the validation set and the test set are shown in Table 5, and the ROC curves of the AIDS patients and the combined active tuberculosis patients based on the 4 kinds of cytokine combinations in the training set (train), the validation set (validation) and the test set (test) are shown in FIG. 3.
The results show that: the model optimal critical point ("threshold") is 0.44. AUC of training set was 0.83 (95% ci:0.77, 0.89), sensitivity was 0.76 (95% ci:0.67, 0.84), specificity was 0.78 (95% ci:0.67, 0.86), accuracy was 0.77 (95% ci:0.7, 0.82), recall F1 was 0.79, positive and negative predictive values were 0.83 (95% ci:0.74, 0.89) and 0.7 (95% ci:0.59, 0.79), respectively. AUC of the validation set was 0.89 (95% ci:0.76, 1.0), sensitivity was 0.88 (95% ci:0.71, 0.96), specificity was 0.76 (95% ci:0.5, 0.93), accuracy was 0.84 (95% ci:0.7, 0.93), recall F1 was 0.88, positive and negative predictive values were 0.88 (95% ci:0.71, 0.96) and 0.76 (95% ci:0.5, 0.93), respectively. AUC of the test set was 0.8 (95% ci:69, 0.91), sensitivity was 0.83 (95% ci:0.73, 0.9), specificity was 0.7 (95% ci:0.5, 0.86), accuracy was 0.8 (95% ci:0.71, 0.87), recall F1 was 0.86, positive and negative predictive values were 0.9 (95% ci:0.81, 0.95) and 0.58 (95% ci:0.39, 0.75), respectively.
TABLE 5 SVM model Performance parameters constructed based on 4 cytokine combinations
Training set (95% CI) Verification set (95% CI) Test set (95% CI)
AUC(95％CI) 0.83(0.77，0.89) 0.89(0.76，1.0) 0.8(0.69，0.91)
Accuracy rate of 0.77(0.7，0.82) 0.84(0.7，0.93) 0.8(0.71，0.87)
Sensitivity to 0.76(0.67，0.84) 0.88(0.71，0.96) 0.83(0.73，0.9)
Specificity (specificity) 0.78(0.67，0.86) 0.76(0.5，0.93) 0.7(0.5，0.86)
Positive predictive value 0.83(0.74，0.89) 0.88(0.71，0.96) 0.9(0.81，0.95)
Negative predictive value 0.7(0.59，0.79) 0.76(0.5，0.93) 0.58(0.39，0.75)
F1 value 0.79 0.88 0.86
(3) The ROC curves of the SVM model for predicting whether the AIDS patient has the blood-spreading tuberculosis in the training set, the verification set and the test set are shown in figure 4, and the performance parameters of the SVM model for predicting whether the AIDS patient has the blood-spreading tuberculosis are shown in table 6.
The results show that: AUC of training set was 0.95 (95% ci:0.9, 1.0), sensitivity was 0.9 (95% ci:0.83, 0.94), specificity was 0.89 (95% ci:0.72, 0.98), accuracy was 0.9 (95% ci:0.83, 0.94), recall F1 was 0.93, positive and negative predictive values were 0.97 (95% ci:0.92, 0.99) and 0.68 (95% ci:0.5, 0.82), respectively. AUC of the validation set was 0.94 (95% ci:0.85, 1.0), sensitivity was 0.77 (95% ci:0.58, 0.9), specificity was 1.0 (95% ci:0.59, 1.0), accuracy was 0.81 (95% ci:0.65, 0.92), recall F1 was 0.87, positive and negative predictive values were 1.0 (95% ci:0.85, 1.0) and 0.5 (95% ci:0.23, 0.77), respectively. AUC of the test set was 0.88 (95% ci:0.75, 1.0), sensitivity was 0.82 (95% ci:0.72, 0.9), specificity was 0.78 (95% ci:0.4, 0.97), accuracy was 0.81 (95% ci:0.72, 0.89), recall F1 was 0.89, positive and negative predictive values were 0.97 (95% ci:0.9, 1.0) and 0.32 (95% ci:0.14, 0.55), respectively.
Table 6 SVM model predicts Performance parameters for AIDS patients with combined blood circulation spreading tuberculosis
Training set (95% CI) Verification set (95% CI) Test set (95% CI)
AUC(95％CI) 0.95(0.9，1.0) 0.94(0.85，1.0) 0.88(0.75，1.0)
Accuracy rate of 0.9(0.83，0.94) 0.81(0.65，0.92) 0.81(0.72，0.89)
Sensitivity to 0.9(0.82，0.94) 0.77(0.58，0.9) 0.82(0.72，0.9)
Specificity (specificity) 0.89(0.72，0.98) 1.0(0.59，1.0) 0.78(0.4，0.97)
Positive predictive value 0.97(0.92，0.99) 1.0(0.85，1.0) 0.97(0.9，1.0)
Negative predictive value 0.68(0.5，0.82) 0.5(0.23，0.77) 0.32(0.14，0.55)
F1 value 0.93 0.87 0.89
In summary, the embodiment of the application can predict whether AIDS patients combine active tuberculosis or not through the calculation of the established SVM model by carrying out statistical analysis on the levels of four cytokines of IL6, IL10, IFN-gamma and TNF alpha in blood plasma.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.

Claims (10)

1. Use of a quantitative detection reagent for cytokines associated with aids-associated active tuberculosis, wherein the quantitative detection reagent is used for preparing a predictive product of aids-associated active tuberculosis, and the cytokines comprise IL6, IL10, IFN- γ and tnfα.
2. The use of a quantitative detection reagent for cytokines associated with aids-associated active tuberculosis according to claim 1, wherein the quantitative detection reagent comprises an antibody capable of binding specifically to the cytokines.
3. Use of a quantitative detection reagent for cytokines associated with aids-associated active tuberculosis according to claim 1 or 2, wherein the predictive product comprises a kit for predicting the risk of aids-associated active tuberculosis.
4. The use of a quantitative detection reagent for cytokines associated with aids-associated active tuberculosis according to claim 3, wherein the kit comprises a capture microsphere and a fluorescent detection reagent labeled with fluorescein, and the surface of the capture microsphere is combined with an antibody capable of specifically binding to the cytokines.
5. The use of a reagent for quantitative detection of cytokines associated with aids-associated active tuberculosis as described in claim 3, wherein the kit further comprises a reagent for detection of human Th1/Th2 subpopulations based on the flow fluorescence method.
6. The use of a quantitative detection reagent for cytokines associated with aids-associated active tuberculosis as described in claim 3, wherein the kit further comprises a support vector machine model for predicting the risk of aids-associated active tuberculosis.
7. Use of a quantitative detection reagent for cytokines associated with aids-associated active tuberculosis according to claim 1 or 2, characterized in that the predictive product comprises a predictive system for predicting the risk of aids-associated active tuberculosis.
8. The use of a reagent for quantitative detection of cytokines associated with aids-associated active tuberculosis as described in claim 7, wherein said prediction system comprises:
a data acquisition unit: the method comprises the steps of detecting cytokines in a sample, and obtaining the concentrations of cytokines IL6, IL10, IFN-gamma and TNF alpha in the sample;
a data analysis unit: the method comprises the steps of carrying out normalization processing on the concentration, and inputting the concentration into a support vector machine model based on Python scikit-learn to obtain a prediction probability value of the sample;
data prediction unit: and the prediction probability value is used for comparing with a threshold value to predict the risk of the combined active tuberculosis of the AIDS.
9. The use of a quantitative detection reagent for cytokines associated with aids-associated active tuberculosis according to claim 8, wherein the data prediction unit further comprises a threshold value for the risk of aids-associated active tuberculosis, and when the prediction probability value is higher than the threshold value, the risk of aids patients associated with active tuberculosis is high; when the predicted probability value is lower than the threshold value, the risk of the AIDS patient combining active tuberculosis is low.
10. The use of a reagent for quantitative detection of cytokines associated with aids-associated active tuberculosis according to claim 8, wherein the aids-associated active tuberculosis comprises at least one of tuberculosis, extrapulmonary tuberculosis and blood group-spreading tuberculosis.
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