A marker combination and its use in the diagnosis of active tuberculosis and in the differentiation between latent tuberculosis infection and active tuberculosis

Abstract

The present application relates to the technical field of diagnostic markers, in particular to a marker combination and its application in diagnosing active tuberculosis and distinguishing between latent tuberculosis infection and active tuberculosis. The lectin combination provided by the present application can be used as a marker for ATB diagnosis and distinguishing between LTBI and ATB, and has high specificity and sensitivity. The lectin combination provided by the present application in combination with detection of specific antibodies of mycobacterium tuberculosis antigens can further improve the diagnostic effect. The marker and its detection products provided by the present application have good application potential in ATB diagnosis and distinguishing between LTBI and ATB, and are expected to overcome the limitations of existing diagnostic techniques, improve the diagnostic accuracy and sensitivity of tuberculosis, and provide new ideas and technical means for early detection, precise treatment and effective prevention and control of tuberculosis.

Description

A combination of biomarkers and its application in diagnosing active tuberculosis and differentiating between latent tuberculosis infection and active tuberculosis. Technical Field

[0001] This invention relates to the field of diagnostic biomarker technology, and more particularly to a biomarker combination and its application in diagnosing active tuberculosis and differentiating between latent tuberculosis infection and active tuberculosis. Background Technology

[0002] Tuberculosis (TB) is a serious chronic infectious disease caused by Mycobacterium tuberculosis (Mtb), posing a significant threat to human health. Mtb infection can lead to latent tuberculosis infection (LTBI) or active tuberculosis (ATB), with approximately 5%-10% of those with latent infection developing ATB. The high infectivity of TB presents enormous challenges to its prevention and control. Therefore, accurate detection and effective management of TB are crucial for its control.

[0003] Currently, the clinical diagnosis of tuberculosis mainly relies on traditional methods, but these methods all have limitations to varying degrees. For example, sputum smear testing has low sensitivity, bacterial culture is not only time-consuming but also has a low positive detection rate; while nucleic acid amplification technology, although it has improved sensitivity, performs poorly in low-load samples and cannot distinguish between live and dead bacteria. Furthermore, there is a lack of a "gold standard" for the clinical diagnosis of LTBI. Currently, only the tuberculin skin test (TST) and interferon-gamma release assay (IGRA) are used as auxiliary methods. However, neither of these can directly diagnose ATB, nor can they effectively distinguish between LTBI and ATB. They can only indicate the presence of tuberculosis infection in the patient (Haas MK, Belknap R W. Diagnostic Tests for Latent Tuberculosis Infection[J]. Clin Chest Med, 2019, 40(4): 829-837; Jung YE G, Schluger N W. Advances in the diagnosis and treatment of latenttuberculosis infection[J]. Curr Opin Infect Dis, 2020, 33(2): 166-172.).

[0004] Existing literature reports altered glycosylation levels, such as IgG galactosylation, in tuberculosis patients (Quantitative analysis of serum-based IgG agalactosylation for tuberculosis auxiliary diagnosis, Peng Liu et al., Glycobiology, pp. 746-759; Patent application WO2016 / 064955A1), but currently there is no combination of lectins that can accurately diagnose ATB and differentiate between ATB and LTBI.

[0005] Therefore, exploring novel tuberculosis biomarkers and diagnostic methods with high sensitivity and specificity has become a key issue that urgently needs to be addressed in the current field of tuberculosis prevention and control.

[0006] In the field of biomarkers, genomics, proteomics, and metabolomics have been widely applied. In recent years, with the continuous advancement of technology, glycoproteomics and glycomics have played an increasingly prominent role in the discovery of clinical diagnostic biomarkers. Studies have shown that antibody glycosylation profiles are closely related to the progression of various diseases, especially in autoimmune diseases, inflammatory diseases, infectious diseases, and cancer, where antibody glycosylation levels change significantly with disease status (Haslund-Gourley BS, Wigdahl B, Comunale MA. IgG N-glycan signatures as potential diagnostic and prognostic biomarkers[J]. Diagnostics (Basel), 2023,13(6).). Antibody glycosylation can be used to highly sensitively distinguish between healthy individuals and disease states, and changes in IgG N-glycan can accurately reflect the host's immune status. Therefore, antibody glycosylation indicators have great application potential as specific biomarkers. Summary of the Invention

[0007] This invention provides a combination of biomarkers and their application in diagnosing active tuberculosis and differentiating between latent tuberculosis infection and active tuberculosis.

[0008] This invention utilizes a high-throughput glycosylation analysis technique—lectin microarray technology—to comprehensively screen plasma IgG glycosylation during the pathogenesis of tuberculosis (TB). Based on changes in IgG glycosylation, it screens for biomarkers applicable to the specific diagnosis of TB. Specifically, lectin microarrays are used to detect plasma IgG glycosylation. After processing the lectin microarray data, lectins and their combinations suitable for the differential diagnosis of TB are screened, resulting in lectins and their combinations that can be used for ATB diagnosis and can differentiate between LTBI and ATB. Further model fitting of the screened lectins and their combinations confirms that the lectin combinations of this invention have good diagnostic efficacy, accurately diagnosing ATB and accurately differentiating between ATB and LTBI.

[0009] Specifically, the present invention provides the following technical solutions.

[0010] In a first aspect, the present invention provides a combination of biomarkers for diagnosing active tuberculosis or distinguishing between latent tuberculosis infection and active tuberculosis, the combination of biomarkers comprising the following lectin combination: GNL, LAL and Black.bean.crude.

[0011] The combination of the aforementioned lectins GNL, LAL, and Black bean crude specifically binds to the glycosyl groups in the IgG glycan chain to form a lectin-IgG complex. This invention reveals that the levels of the corresponding complexes of lectins GNL, LAL, and Black bean crude differ significantly between healthy controls and patients with active tuberculosis, as well as between patients with active tuberculosis and patients with latent tuberculosis infection. These complexes can serve as biomarkers for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis, exhibiting high specificity and sensitivity.

[0012] The combination of lectins GNL, LAL, and Black bean crude can be used alone as a marker for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis, or it can be used in combination with certain lectins or Mycobacterium tuberculosis-related markers.

[0013] Furthermore, the lectin combination also includes one or more lectins selected from the following: HHL, PWM, GSL.I.B4, MAL.I.

[0014] In some embodiments, the lectin combination includes GNL, LAL, Black.bean.crude, and one selected from HHL, PWM, GSL.I.B4, and MAL.I.

[0015] In some embodiments, the lectin combination includes GNL, LAL, Black.bean.crude, and two selected from HHL, PWM, GSL.I.B4, and MAL.I.

[0016] In some embodiments, the lectin combination includes GNL, LAL, Black.bean.crude, and three selected from HHL, PWM, GSL.I.B4, and MAL.I.

[0017] In some embodiments, the lectin combination includes GNL, LAL, Black.bean.crude, HHL, PWM, GSL.I.B4, and MAL.I.

[0018] This invention has found that the various combinations of lectins described above can achieve diagnostic efficacy comparable to or better than the combination of lectins GNL, LAL, and Black bean crude. For example, the combination of the seven lectins GNL, LAL, Black bean crude, HHL, PWM, GSL.I.B4, and MAL.I can further improve the accuracy of diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis compared to the combination of GNL, LAL, and Black bean crude, specifically manifested in a significant improvement in AUC value, specificity, and sensitivity.

[0019] Preferably, the lectin specifically binds to the glycosyl groups in the IgG glycan chain to form a complex.

[0020] In some embodiments, the biomarker combination comprises a lectin-IgG complex formed by the specific binding of the lectin to glycosyl groups in the IgG glycan chain. The IgG is IgG from blood, serum, or plasma. Specifically, the biomarker combination comprises a complex formed by the binding of GNL, LAL, and Black bean crude with IgG, or a complex formed by the binding of GNL, LAL, Black bean crude, HHL, PWM, GSL.I.B4, and MAL.I with IgG.

[0021] Furthermore, the biomarker combination may also include Mycobacterium tuberculosis antigen. The Mycobacterium tuberculosis antigen preferably includes one or more selected from PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B.

[0022] This invention reveals that combining the above-mentioned lectin combination with the above-mentioned Mycobacterium tuberculosis antigen for the diagnosis of active tuberculosis and the differentiation between latent tuberculosis infection and active tuberculosis also exhibits high specificity and sensitivity. In particular, combining the above-mentioned lectin combination with LAM can significantly further improve the diagnostic specificity and sensitivity.

[0023] Secondly, the present invention provides the use of a lectin combination or a lectin combination combined with Mycobacterium tuberculosis antigen in the preparation of products for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis, wherein the lectin combination includes: GNL, LAL and Black.bean.crude.

[0024] Specifically, the product uses the lectin combination to bind to IgG glycans in blood, plasma, or serum, and detects the content of glycosyl groups in the IgG glycans that specifically bind to the lectin, thereby diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis.

[0025] Thirdly, the present invention provides the application of a lectin combination or a lectin combination combined with Mycobacterium tuberculosis antigen in the diagnosis of active tuberculosis or in differentiating between latent tuberculosis infection and active tuberculosis, wherein the lectin combination includes: GNL, LAL and Black.bean.crude.

[0026] Fourthly, the present invention provides the use of a detection product for lectin combination specifically binding to glycosyl groups or a detection product for lectin combination specifically binding to glycosyl groups combined with Mycobacterium tuberculosis antigen in the preparation of products for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis, wherein the lectin combination includes: GNL, LAL and Black.bean.crude.

[0027] Fifthly, the present invention provides a detection product for lectin combinations that specifically bind to glycosyl groups, or a detection product for lectin combinations that specifically bind to glycosyl groups combined with Mycobacterium tuberculosis antigen, for the diagnosis of active tuberculosis or for differentiating between latent tuberculosis infection and active tuberculosis, wherein the lectin combination includes: GNL, LAL, and Black.bean.crude.

[0028] In the applications described in the fourth and fifth aspects above, the lectin combination specifically binds to glycosyl groups that are glycosyl groups in blood, plasma, or serum IgG glycans that specifically bind to the lectin combination.

[0029] The detection products for the specific binding of glycosyl groups of lectins may include reagents and instruments for detecting the specific binding of glycosyl groups of lectins.

[0030] Preferably, the detection product for the specific binding of glycosyl groups by the lectin combination includes the lectin combination or includes a chip loaded with the lectin combination.

[0031] The detection products for the specific binding of glycosyl groups of lectins may also include fluorescent dyes, anti-IgG antibodies, blocking buffers, PBS, mouse serum, etc. The detection products for the specific binding of glycosyl groups of lectins may also include microarray scanners.

[0032] In the applications described in the second to fifth aspects above, the lectin combination further includes one or more lectins selected from the following: HHL, PWM, GSL.I.B4, MAL.I.

[0033] In some embodiments, the lectin combination includes GNL, LAL, Black.bean.crude, and one selected from HHL, PWM, GSL.I.B4, and MAL.I.

[0034] In some embodiments, the lectin combination includes GNL, LAL, Black.bean.crude, and two selected from HHL, PWM, GSL.I.B4, and MAL.I.

[0035] In some embodiments, the lectin combination includes GNL, LAL, Black.bean.crude, and three selected from HHL, PWM, GSL.I.B4, and MAL.I.

[0036] In some embodiments, the lectin combination includes GNL, LAL, Black.bean.crude, HHL, PWM, GSL.I.B4, and MAL.I.

[0037] In this invention, the lectin combination itself has high accuracy in diagnosing active tuberculosis and differentiating between latent tuberculosis infection and active tuberculosis, and can be applied in diagnostic or differential diagnostic practice. To further improve diagnostic accuracy, it can be combined with Mycobacterium tuberculosis antigen detection. This invention does not have specific limitations on the combined Mycobacterium tuberculosis antigens; the LAM mentioned above is merely an example, and other known or new Mycobacterium tuberculosis antigens that can be used to diagnose active tuberculosis or differentiate between latent tuberculosis infection and active tuberculosis can also be selected. These antigens include, but are not limited to: secretory protein antigens (e.g., ESAT-6, CFP-10, Ag85 complex), cell wall-associated antigens (e.g., LAM, lipoprotein antigens such as LpqH, 38kDa / MPT64), and other important antigens (e.g., TB7.7 (Rv2654c), Rv2031c (HspX / α-crystallin)).

[0038] In the applications described in the second to fifth aspects above, the Mycobacterium tuberculosis antigen includes one or more selected from PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B.

[0039] In this invention, the products include detection reagents, detection kits, detection chips, etc.

[0040] Sixthly, the present invention provides a method for constructing a diagnostic model for active tuberculosis or a model for distinguishing between latent tuberculosis infection and active tuberculosis, the method comprising:

[0041] All samples are randomly divided into training and test sets;

[0042] Obtain sample data to characterize the content of lectin combinations-specifically bound glycosyl groups in the IgG glycans contained in the training set samples;

[0043] A training dataset is constructed using the tuberculosis diagnosis status corresponding to the sample as the label for the sample data;

[0044] Construct a diagnostic model for active tuberculosis or a model to distinguish between latent tuberculosis infection and active tuberculosis based on the training dataset;

[0045] The effectiveness of the model is verified using the test set.

[0046] The lectin combination includes: GNL, LAL, and Black.bean.crude.

[0047] The samples mentioned above are preferably blood, plasma, or serum samples.

[0048] Preferably, the lectin combination further includes one or more lectins selected from the following: HHL, PWM, GSL.I.B4, MAL.I.

[0049] Preferably, the model is constructed using an Artificial Neural Network (ANN) algorithm.

[0050] In the above construction method, the classification model is trained based on the training dataset, and the trained classification model is determined as a diagnostic model for active tuberculosis or a model for distinguishing between latent tuberculosis infection and active tuberculosis.

[0051] In the above construction method, the construction of the training dataset includes: grouping samples according to the tuberculosis diagnosis status of each sample as the label, using the detection signal of the agglutinin combination in each group of plasma samples, or the detection signal of the agglutinin combination in each group of plasma samples and the antibody data of Mycobacterium tuberculosis antigen detection as the sample data, and using the tuberculosis diagnosis status corresponding to the grouping of the plasma samples as the label of the sample data to construct the training dataset.

[0052] Specifically, the method for constructing the model includes the following steps:

[0053] (1) Constructing the training dataset

[0054] The training dataset may include sample data and the labels corresponding to the sample data;

[0055] Here, sample data refers to the detection results of the lectin combination in each group of plasma samples (i.e., the signal intensity obtained by combining the lectin combination with plasma IgG specific glycans); for example, each group of plasma samples specifically includes plasma samples from the healthy control group HC, the latent tuberculosis infection group LTBI, and the active tuberculosis group ATB, and the training dataset contains sample data from the above three groups of plasma samples; the label corresponding to the sample data is the tuberculosis diagnosis status corresponding to the plasma sample. For example, based on the above grouping, the label corresponding to the sample data from HC and LTBI can be negative, that is, tuberculosis has not been diagnosed, and the label corresponding to the sample data from ATB can be positive, that is, tuberculosis has been diagnosed.

[0056] (2) Training the classification model based on the training dataset

[0057] The classification model is a neural network model used to perform classification functions, such as an artificial neural network (ANN).

[0058] In the process of training a classification model based on a training dataset, sample data from the training dataset can be input into the classification model to obtain the classification results output by the model. The classification results are then compared with the labels in the training dataset to calculate the loss function. Based on the loss function, the parameters of the classification model are iterated, thereby training the classification model. During training, the classification model can learn the mapping relationship between the lectin combination detection results of the sample data and the tuberculosis diagnosis status as the label. Thus, the trained classification model has the ability to diagnose whether it is active tuberculosis or to distinguish between latent tuberculosis infection and active tuberculosis based on the input lectin combination detection results. In other words, the trained classification model can be used as a model for diagnosing active tuberculosis or distinguishing between latent tuberculosis infection and active tuberculosis.

[0059] Understandably, the model obtained thereby takes the detection results of the agglutinin combination in the plasma sample of the person being tested, or the detection results of the agglutinin combination and the antibody detection results of the Mycobacterium tuberculosis antigen, as input, and can output the diagnosis result of active tuberculosis for the person being tested, i.e., negative or positive, and can also output the diagnosis result of whether the person being tested has latent tuberculosis infection or active tuberculosis.

[0060] Specifically, the classification model is trained based on the training dataset, and the trained classification model is determined as the tuberculosis diagnostic model, including:

[0061] The training dataset is split into multiple data subsets;

[0062] The classification model is trained based on the first part of the multiple data subsets, and the trained classification model is tested based on the second part of the multiple data subsets to obtain the test results of the classification model.

[0063] Based on the test results of the classification model, a tuberculosis diagnostic model is determined from the classification model.

[0064] Specifically, during the training of a tuberculosis diagnostic model, the training dataset can be split to obtain multiple data subsets. This splitting can be random. For example, the training dataset can be split into 10 data subsets.

[0065] After obtaining multiple data subsets, cross-validation can be performed on multiple classification models based on these subsets. Here, the multiple classification models can be classification models with the same structure and parameters, or they can be classification models with different structures and / or different parameters. This embodiment of the invention does not impose specific limitations on this.

[0066] The cross-validation process involves dividing the multiple data subsets obtained from the above split into two parts: a first part and a second part. The first part is used for model training, and the second part is used for model testing. For example, in the case of 10 data subsets, 9 subsets can be used as the first part for model training, and the remaining subset as the second part for model testing. Thus, for each classification model, the first part can be used for training, and the second part can be used for testing to obtain the test result. This test result can be AUC (Area Under the Curve), which specifically refers to the area under the ROC (Receiver Operating Characteristic) curve and is commonly used to evaluate the performance of binary classification models.

[0067] Since the splitting of training data into subsets is random and the resulting subsets are different each time, and the way the first and second parts are split can be different each time, the classification model can be trained and tested again after the first and second parts are split each time. This will generate more trained classification models and obtain the corresponding test results.

[0068] Based on this, the model with the best test results from all trained classification models can be selected as the final diagnostic model or discrimination model.

[0069] (3) Validate the diagnostic model or differentiation model.

[0070] After model training is complete, the trained diagnostic model or differentiation model can be validated to verify the effectiveness of the tuberculosis diagnostic model. The validation dataset used for model validation can be constructed in the same way as the training dataset.

[0071] In a seventh aspect, the present invention provides a diagnostic model for active tuberculosis or a model for distinguishing between latent tuberculosis infection and active tuberculosis, constructed using the construction method described above.

[0072] Eighthly, the present invention provides products for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis, said products comprising a lectin combination, or a detection product comprising a lectin combination specifically binding to glycosyl groups; wherein the lectin combination comprises: GNL, LAL, and Black.bean.crude.

[0073] The lectin combination also includes one or more lectins selected from the following: HHL, PWM, GSL.I.B4, MAL.I.

[0074] Preferably, the product includes a chip loaded with the lectin combination.

[0075] The chip loaded with the aforementioned lectin combination can be a lectin chip, specifically a chip obtained by fixing each lectin in the lectin combination onto a support.

[0076] Preferably, the product further includes an anti-IgG antibody. The anti-IgG antibody is labeled with a fluorescent dye.

[0077] The product may include other reagents and instruments required for detecting the specific binding of the lectin combination to glycosyl groups, wherein the reagents may be selected from one or more of blocking buffer, PBS, and mouse serum. The instruments include microarray scanners, etc.

[0078] Furthermore, the product also includes Mycobacterium tuberculosis antigen; the Mycobacterium tuberculosis antigen preferably includes one or more selected from PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B.

[0079] Preferably, the Mycobacterium tuberculosis antigen is used to detect antibodies against the Mycobacterium tuberculosis antigen in the sample. The product may also include reagents required for detecting antibodies against the Mycobacterium tuberculosis antigen in the sample using the Mycobacterium tuberculosis antigen, such as ELISA reagents.

[0080] Ninthly, the present invention provides an apparatus for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis, the apparatus comprising:

[0081] The detection module includes a module for detecting the content of lectin combinations that specifically bind glycosyl groups in the IgG glycan chains contained in the test sample; wherein the lectin combination includes: GNL, LAL, and Black.bean.crude;

[0082] The input module is used to obtain the detection results from the detection module;

[0083] The analysis module is used to diagnose active tuberculosis or differentiate between latent tuberculosis infection and active tuberculosis based on the test results obtained from the input module.

[0084] The output module is used to output the analysis results.

[0085] Preferably, the lectin combination further includes one or more lectins selected from the following: HHL, PWM, GSL.I.B4, MAL.I.

[0086] In some embodiments, the lectin combination includes GNL, LAL, Black.bean.crude, and one selected from HHL, PWM, GSL.I.B4, and MAL.I.

[0087] In some embodiments, the lectin combination includes GNL, LAL, Black.bean.crude, and two selected from HHL, PWM, GSL.I.B4, and MAL.I.

[0088] In some embodiments, the lectin combination includes GNL, LAL, Black.bean.crude, and three selected from HHL, PWM, GSL.I.B4, and MAL.I.

[0089] In some embodiments, the lectin combination includes GNL, LAL, Black.bean.crude, HHL, PWM, GSL.I.B4, and MAL.I.

[0090] In the aforementioned detection module, the content of the lectin combination specifically binding glycosyl groups in the IgG glycan chains contained in the test sample is the content of complexes formed by the specific binding of each lectin in the lectin combination with the corresponding glycosyl groups in the IgG glycan chains. The complex is a lectin-IgG complex.

[0091] In the above detection module, the detection of the content of lectin combination-specifically bound glycosyl groups in the IgG glycan chains contained in the test sample includes:

[0092] The lectin assembly is contacted with the sample to be tested so that the lectin assembly forms a complex with IgG containing its specifically binding glycosyl group.

[0093] After removing unbound IgG, the complex is contacted with a labeled anti-IgG antibody;

[0094] Signal detection was performed after removing unbound anti-IgG antibodies.

[0095] In the above analysis module, diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis based on the test results obtained from the input module includes: comparing the content of the lectin combination specifically binding glycosyl group in the IgG glycan chain contained in the test sample with the content of the glycosyl group in the IgG glycan chain contained in the control sample, and diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis based on the comparison results.

[0096] Preferably, the detection module further includes a module for detecting the content of anti-Mycobacterium tuberculosis antigen antibodies. The Mycobacterium tuberculosis antigen preferably includes one or more selected from PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B.

[0097] Preferably, the sample to be tested is blood, serum, or plasma.

[0098] In this invention, compared with healthy controls, the levels of the GNL-related complex and the LAL-related complex were decreased in ATB patients, while the levels of the Black-bean-crude-related complex were increased. Compared with healthy controls, the levels of the HHL-related complex, the PWM-related complex, the GSL.I.B4-related complex, and the MAL.I-related complex were decreased in ATB patients.

[0099] In this invention, compared with LTBI patients, ATB patients showed decreased levels of the GNL-related complex, increased levels of the LAL-related complex, and increased levels of the Black-bean-crude-related complex. Compared with LTBI patients, ATB patients showed decreased levels of the HHL-related complex, increased levels of the PWM-related complex, decreased levels of the GSL.I.B4-related complex, and decreased levels of the MAL.I-related complex.

[0100] In a tenth aspect, the present invention provides a method for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis, the method comprising:

[0101] The content of lectin combination-specifically bound glycosyl groups in the IgG glycan chains contained in the test sample was detected.

[0102] Based on the test results, diagnose active tuberculosis or differentiate between latent tuberculosis infection and active tuberculosis;

[0103] The lectin combination includes: GNL, LAL, and Black.bean.crude.

[0104] Preferably, the lectin combination further includes one or more lectins selected from the following: HHL, PWM, GSL.I.B4, MAL.I.

[0105] Preferably, the content of the lectin combination specifically binding glycosyl groups in the IgG glycan chains contained in the test sample is the content of the complex formed by the specific binding of each lectin in the lectin combination with the corresponding glycosyl group in the IgG glycan chain. The complex is a lectin-IgG complex.

[0106] Preferably, the detection of the content of lectin combination-specifically bound glycosyl groups in the IgG glycan chains contained in the test sample includes:

[0107] The lectin assembly is contacted with the sample to be tested so that the lectin assembly forms a complex with IgG containing its specifically binding glycosyl group.

[0108] After removing unbound IgG, the complex is contacted with a labeled anti-IgG antibody;

[0109] Signal detection was performed after removing unbound anti-IgG antibodies.

[0110] Preferably, the method further includes detecting the content of anti-Mycobacterium tuberculosis antigen antibodies in the sample to be tested. The Mycobacterium tuberculosis antigen preferably includes one or more selected from PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B.

[0111] The beneficial effects of this invention include at least the following: This invention obtains a combination of lectins that can be used for ATB diagnosis and can differentiate between LTBI and ATB by detecting IgG glycosylation during the pathogenesis of tuberculosis. These lectin combinations can serve as biomarkers for ATB diagnosis and differentiation between LTBI and ATB, exhibiting high specificity and sensitivity. Combining the aforementioned lectin combinations with specific antibodies detected by Mycobacterium tuberculosis antigen can further improve diagnostic efficacy. The biomarkers and detection products provided by this invention have good application potential in ATB diagnosis and differentiation between LTBI and ATB, and are expected to overcome the limitations of existing diagnostic technologies, improve the accuracy and sensitivity of tuberculosis diagnosis, and provide new ideas and technical means for the early detection, precise treatment, and effective prevention and control of tuberculosis. Attached Figure Description

[0112] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0113] Figure 1 shows the detection results of the lectin chip in Embodiment 2 of the present invention, where (A) is a scan of the chip detection results; and (B) is a three-dimensional image of the chip detection scan data.

[0114] Figure 2 is a flowchart of biomarker screening and diagnostic model construction in Embodiment 3 of the present invention.

[0115] Figure 3 is a differential lectin volcano diagram in Example 3 of the present invention, where (A) is the differential lectin between the ATB and HC groups; and (B) is the differential lectin between the ATB and LTBI groups.

[0116] Figure 4 shows the differences between the feature aggregates LAL, GNL, HHL, PWM, Black.bean.crude, GSL.I.B4 and MAL.I in the HC, LTBI and ATB groupings in Embodiment 4 of the present invention.

[0117] Figure 5 illustrates the workflow for constructing an ANN model in Embodiment 4 of the present invention.

[0118] Figure 6 shows a schematic diagram of the ANN model and its diagnostic efficacy in Embodiment 4 of the present invention, where (A) is a schematic diagram of the ANN diagnostic model; and (B) is the diagnostic capability of a single lectin and a combination of lectins for ATB in the training set.

[0119] Figure 7 shows the interpretation of the ATB diagnostic model based on 7-lectins using the SHAP algorithm in Embodiment 5 of the present invention. (A) and (B) are global interpretations of the model, (A) is a SHAP summary bar chart, and (B) is a SHAP summary dot plot; (C) and (D) are local interpretations of the model, (C) is the ATB individual result interpretation process, and (D) is the HC individual result interpretation process.

[0120] Figure 8 shows the diagnostic efficacy analysis of the three lectin combinations GNL, LAL and Black.bean.crude in Embodiment 6 of the present invention, where (A) is the ROC curve analysis based on the training set; and (B) is the ROC curve analysis based on the test set.

[0121] Figure 9 shows the ROC analysis of the Mtb antigen combined with lectin biomarker model in Example 7 of the present invention. (A) is the ROC curve analysis based on the training set, and the ROC curve analysis based on the training set for different Mtb antigens combined with 7-lectins to construct models; (B) is the ROC curve analysis based on the test set, which corresponds to the validation results of the model in (A).

[0122] Figure 10 is a schematic diagram of the device for diagnosing active tuberculosis or distinguishing between latent tuberculosis infection and active tuberculosis in Embodiment 8 of the present invention. Detailed Implementation

[0123] In a specific embodiment of the present invention, a Lectin56™ lectin chip containing 56 lectins was used to detect the glycosylation modification of plasma IgG in clinical samples. A total of 389 clinical plasma samples were tested, including 136 healthy controls (HC), 102 latent tuberculosis infected individuals (LTBI), and 151 active tuberculosis patients (ATB). By analyzing the lectin detection data, differences in lectin signals among different groups were screened. Then, an artificial neural network (ANN) algorithm was used to construct a diagnostic model for tuberculosis identification based on the screened lectin biomarkers. The model's effectiveness was further improved by combining it with Mycobacterium tuberculosis antigen detection-specific antibody data.

[0124] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0125] Example 1: Clinical Sample Collection

[0126] This invention collected samples from three groups: HC, LTBI, and ATB. HC consisted of healthy individuals with negative IGRA test results and no clinical symptoms; LTBI consisted of infected individuals with positive IGRA test results but no clinical symptoms; and ATB patients all met the diagnostic criteria of the "Guidelines for the Diagnosis and Treatment of Pulmonary Tuberculosis." Patients with cancer, diabetes, allergic diseases, any form of infectious disease, or those in an immunocompromised state were excluded. A total of 389 clinical plasma samples were collected, including 136 HC samples, 102 LTBI samples, and 151 ATB samples. Detailed basic information and diagnostic characteristics of the subjects are shown in Table 1.

[0127] Table 1. Basic information and diagnostic characteristics of the subjects

[0128]

[0129] Example 2: Analysis of plasma IgG glycosylation using lectin microarray

[0130] A lectin microarray chip containing 56 lectins (purchased from TB Healthcare) was used to detect plasma IgG glycosylation in all samples from Example 1. The lectin microarray was incubated at room temperature with blocking buffer (PBS buffer containing 3% BSA) for 3 hours. After washing and drying, plasma samples from each patient were diluted 1:200 and added to the microarray, incubated overnight at 4°C. After washing with PBS, the microarray was incubated with 1:50 diluted mouse serum for 1 hour. After washing with PBS, Cy3 anti-Human IgG (Jackson) was diluted 1:1000, mixed, and incubated at room temperature for 1 hour. After washing and drying, the samples were analyzed using LuxScan. TM The 10K microarray chip scanner performs scans at a wavelength of 532 nm.

[0131] The median values ​​of the foreground and background at 532 nm were read using GenePix Pro v6.0 software. A blank control was used to eliminate non-specific signal errors. Background correction was performed by calculating the difference between the foreground and background values ​​(FB). Using the preprocessed net signal value as the calculation object, differentially expressed lectins were screened based on statistical methods. A Fold change ≥ 1.2 and P < 0.05 were used to determine differential expression between the two groups.

[0132] Figure 1 shows examples of chip scanning and data reading results after randomly selecting samples from different groups. The different brightness and darkness corresponding to the same position in the chip scanning image show that the IgG glycosylation level of different group samples is different. In addition, the three-dimensional map made from the fluorescence data read from the chip can more intuitively show that the IgG glycosylation level of different group samples is different.

[0133] Example 3 Screening of potential diagnostic biomarkers

[0134] To differentiate between the HC, LTBI, and ATB groups and to screen for biomarkers for diagnosing ATB, differential analysis was first performed on the data from these three groups. Then, all samples were averaged and randomly divided into training and test sets. The training set data was used to build the model, and the test set data was used to validate the model on independent samples. The general process is shown in Figure 2.

[0135] To differentiate ATB using lectins as biomarkers among HC, LTBI, and ATB, we first performed a differential analysis of IgG glycosylated lectin signals between the ATB group and the HC and LTBI groups, as shown in Figure 3. The differential analysis revealed that compared to the HC group, the ATB group upregulated 5 lectin signals and downregulated 30 lectin signals; similarly, compared to the LTBI group, the ATB group upregulated 8 lectin signals and downregulated 11 signals.

[0136] Example 4: Construction of the ATB Diagnostic Model

[0137] Feature selection was performed on differential lectins using the Random Forest algorithm. The AUC was optimal when seven lectins were combined. The seven lectins were LAL, GNL, HHL, PWM, Black.bean.crude, GSL.I.B4, and MAL.I. The differences between these seven lectins in the HC, LTBI, and ATB groups are shown in Figure 4 and Table 2. There were significant differences between ATB and HC.

[0138] Table 2 Key lectin information and analysis results

[0139]

[0140] An ATB differential diagnosis model was constructed using an Artificial Neural Network (ANN) algorithm with seven lectin biomarkers. A training set of 194 samples (69 HC, 45 LTBI, and 80 ATB) was used for model training. Ten-fold cross-validation was performed on the training set, randomly dividing the samples into ten groups, using nine groups for training and one group for testing. All samples participated in the model training and validation process, resulting in five trained models and 100 cross-validation iterations, generating a total of 500 models. During model building, the AUC of each model was calculated, and the optimal model was selected. Then, a test set of 195 samples (67 HC, 57 LTBI, and 71 ATB) was used to independently validate the model's performance using a blind test method. The workflow for building the training set model is shown in Figure 5.

[0141] Seven lectins (LAL, GNL, HHL, PWM, Blackbeancrude, GSL.I.B4, and MAL.I) were used as biomarkers for model construction. The constructed neural network model classified HC and LTBI samples as negative (Control) and ATB as positive, aiming to differentiate ATB cases from HC, LTBI, and ATB. As mentioned earlier, the constructed artificial neural network was a feedforward neural network with a single hidden layer, where the input layer had 7 neurons, the hidden layer had 4 neurons, and the output layer had 2 neurons (Figure 6A). The ANN model (7-lectins model) constructed using the combination of 7 lectins showed good diagnostic ability in identifying ATB in the training and test sets, with areas under the curve (AUC) of 0.93 and 0.85, respectively, specificities of 92.1% and 86.3%, and sensitivities of 81.3% and 71.8% (Figure 6B, Table 3). The diagnostic efficacy of the combination of seven lectins was summarized, and the results are shown in Table 3. The AUCs for distinguishing ATB from HC in the training and test sets were 0.90 and 0.83, respectively, while the AUCs for distinguishing ATB from LTBI in the same sets were 0.96 and 0.88, respectively. Therefore, the combination of seven lectins plays an important role in distinguishing ATB from HC and ATB from LTBI.

[0142] Table 3. Diagnostic model efficacy of combinations of 7 lectins

[0143]

[0144] Example 5: Explanation of the ATB differential diagnosis model

[0145] To better understand the model's working principle and decision-making process, the Shapley value of each feature was calculated using the machine learning algorithm SHAP (Shapley Additive exPlanations), which can be used to interpret the model's predictions, to interpret the constructed ANN model. The global interpretation results show that the contributions of the seven lectins in the model are ranked as follows: PWM, GSL.I.B4, MAL.I, Black.bean.crude, HHL, GNL, and LAL (Figures 7A and 7B). The local interpretation analysis examines how the model makes a certain predictive judgment for a specific individual based on individual input data. Figures 7C and 7D respectively present the proportion of each feature in the predicted outcomes for two subjects (ATB and control).

[0146] Example 6: Diagnostic efficacy of different lectin combinations for ATB

[0147] Following the method described in Example 4, a model was constructed using a combination of three lectins (GNL, LAL, and Black.bean.crude) to evaluate its diagnostic efficacy in differentiating between ATB and control samples (HC and LTBI). The model's performance was assessed using ROC curves, with AUC values ​​of 0.784 and 0.748 for the training and test sets, respectively (Figure 8).

[0148] Based on 3-lectins, models were constructed by combining one or more of HHL, PWM, GSL.I.B4, and MAL.I, and their diagnostic efficacy in differentiating ATB and control samples (HC and LTBI) was evaluated. ROC curve analysis showed that adding one or more of HHL, PWM, GSL.I.B4, and MAL.I to the 3-lectins model achieved diagnostic efficacy comparable to or improved to varying degrees, indicating that these lectin combinations all possess high diagnostic value (Table 4).

[0149] Table 4. Diagnostic efficacy of different combinations of lectins for ATB.

[0150]

[0151] Example 7: Construction of a model combining lectin combination with Mycobacterium tuberculosis antigen

[0152] To further improve the diagnostic model's effectiveness and enhance its accuracy and specificity, the levels of specific IgG antibodies against Mycobacterium tuberculosis (Mtb) antigens (PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B) in all clinical samples were detected using ELISA. Then, based on the 7-lectins agglutinin model, the agglutinin model was combined with antibody data from the detection of the seven Mtb antigens to create a co-model (Figure 9). The results showed that combining the seven Mtb antigens with the agglutinin model improved the model to varying degrees (Table 5). The combined diagnostic model demonstrated excellent performance in both training and validation, with the 7-lectins + LAM model showing the best performance, with AUCs of 0.96 and 0.93 in training and validation, respectively. This indicates that combining Mtb antigen-specific antibodies with agglutinin markers significantly improves model effectiveness, and the 7-lectins + LAM combination exhibits good diagnostic performance and can be used for the differential diagnosis of Mtb.

[0153] Table 5. Diagnostic efficacy of different biomarker combinations in differentiating ATB from HC and LTBI

[0154]

[0155] In summary, combinations of the three lectins GNL, LAL, and Black bean crude, as well as combinations of four, five, six, or seven lectins selected from HHL, PWM, GSL.I.B4, and MAL.I, can serve as diagnostic biomarkers for the diagnosis of ATB and for differentiating ATB from LTBI. The ANN model constructed using these lectin combinations can accurately diagnose ATB and differentiate it from LTBI. Furthermore, Mycobacterium tuberculosis antigens such as LAM, a cell wall component of Mycobacterium tuberculosis, can be used in combination with these lectin combinations to further enhance their diagnostic efficacy.

[0156] Example 8: A device for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis.

[0157] This embodiment provides a device for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis (Figure 10), which includes the following modules:

[0158] The detection module 100 includes a module for detecting the content of glycosyl groups specifically bound by a lectin combination in the IgG glycan chains contained in the plasma sample to be tested; wherein the lectin combination is: GNL, LAL and Black.bean.crude, or is: GNL, LAL, Black.bean.crude and one, two, three or four selected from HHL, PWM, GSL.I.B4, MAL.I;

[0159] Input module 110 is used to obtain the detection results of the detection module;

[0160] Analysis module 120 is used to diagnose active tuberculosis or differentiate between latent tuberculosis infection and active tuberculosis based on the test results obtained from the input module;

[0161] Output module 130 is used to output the analysis results.

[0162] The detection module executes the following program:

[0163] The lectin assembly is contacted with the sample to be tested so that the lectin assembly forms a complex with IgG containing its specifically binding glycosyl group.

[0164] After removing unbound IgG, the complex is contacted with a labeled anti-IgG antibody;

[0165] After removing unbound anti-IgG antibodies, signal detection was performed to obtain the content of lectin-specifically bound glycosyl groups in the IgG glycan chains contained in the test sample.

[0166] The analysis module executes the following program:

[0167] The content of the glycosyl group specifically binding to the lectin combination in the IgG glycans of the test sample is compared with the content of the glycosyl group in the IgG glycans of the control sample, and the active tuberculosis is diagnosed or the latent tuberculosis infection and active tuberculosis are distinguished based on the comparison results.

[0168] The method for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis based on the comparison results is as follows: the results of the agglutinin combination detection of the sample to be tested can be imported into the diagnostic model to output the diagnostic results.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A combination of biomarkers for distinguishing active tuberculosis from latent tuberculosis infection and healthy individuals, characterized in that, The marker combination consists of lectins GNL, LAL, and Black.bean.crude, or consists of lectins GNL, LAL, and Black.bean.crude, and one, two, or three of lectins selected from HHL, PWM, GSL.I.B4, and MAL.I.

2. The biomarker combination for distinguishing active tuberculosis from latent tuberculosis infection and healthy individuals according to claim 1, characterized in that, The lectin specifically binds to the glycosyl groups in the IgG glycan chain to form a complex.

3. A combination of biomarkers for distinguishing active tuberculosis from latent tuberculosis infection and healthy individuals, characterized in that, The biomarker combination consists of the biomarker combination for distinguishing active tuberculosis from latent tuberculosis infection and healthy individuals as described in claim 1 or 2, and Mycobacterium tuberculosis antigen.

4. The biomarker combination for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis according to claim 3, characterized in that, The Mycobacterium tuberculosis antigen includes one or more selected from PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B.

5. A combination of biomarkers for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis, characterized in that, The biomarker combination consists of lectins GNL, LAL, Blackbeancrude, HHL, PWM, GSL.I.B4, and MAL.I.

6. The biomarker combination for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis according to claim 5, characterized in that, The lectin specifically binds to the glycosyl groups in the IgG glycan chain to form a complex.

7. A combination of biomarkers for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis, characterized in that, It consists of a combination of markers for diagnosing active tuberculosis or distinguishing between latent tuberculosis infection and active tuberculosis as described in claim 5 or 6, and Mycobacterium tuberculosis antigen.

8. The biomarker combination for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis according to claim 7, characterized in that, The Mycobacterium tuberculosis antigen includes one or more selected from PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B.

9. The use of a lectin combination or a lectin combination combined with Mycobacterium tuberculosis antigen in the preparation of products for distinguishing between active tuberculosis and latent tuberculosis infection and healthy individuals, wherein the lectin combination comprises: GNL, LAL, and Black.bean.crude.

10. The application according to claim 9, characterized in that, The lectin combination also includes one, two, or three of the following lectins: HHL, PWM, GSL.I.B4, and MAL.I.

11. The application according to claim 9, characterized in that, The lectin specifically binds to the glycosyl groups in the IgG glycan chain to form a complex.

12. The application according to any one of claims 9 to 11, characterized in that, The Mycobacterium tuberculosis antigen includes one or more selected from PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B.

13. The application according to any one of claims 9 to 11, characterized in that, The product detects the content of glycosyl groups in IgG sugar chains that specifically bind to the lectin by binding the lectin combination to IgG sugar chains in blood, plasma or serum, thereby distinguishing between active tuberculosis, latent tuberculosis infection and healthy individuals.

14. The application according to claim 12, characterized in that, The product detects the content of glycosyl groups in IgG sugar chains that specifically bind to the lectin by binding the lectin combination to IgG sugar chains in blood, plasma or serum, thereby distinguishing between active tuberculosis, latent tuberculosis infection and healthy individuals.

15. The use of a combination of lectins or a combination of lectins in combination with Mycobacterium tuberculosis antigens in the preparation of products for the diagnosis of active tuberculosis or for differentiating between latent tuberculosis infection and active tuberculosis, wherein the combination of lectins includes GNL, LAL, Blackbeancrude, HHL, PWM, GSL.I.B4 and MAL.I.

16. The application according to claim 15, characterized in that, The lectin specifically binds to the glycosyl groups in the IgG glycan chain to form a complex.

17. The application according to claim 15, characterized in that, The Mycobacterium tuberculosis antigen includes one or more selected from PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B.

18. The application according to any one of claims 15 to 17, characterized in that, The product detects the content of glycosyl groups in IgG sugar chains that specifically bind to the lectin by binding the lectin combination to blood, plasma or serum, thereby diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis.

19. The use of a lectin combination-specific glycosyl-binding detection product or a lectin combination-specific glycosyl-binding detection product combined with Mycobacterium tuberculosis antigen in the preparation of a product for differentiating active tuberculosis from latent tuberculosis infection and healthy individuals, wherein the lectin combination comprises: GNL, LAL, and Black.bean.crude.

20. The application according to claim 19, characterized in that, The lectin combination also includes one, two, or three of the following lectins: HHL, PWM, GSL.I.B4, and MAL.I.

21. The application according to claim 19, characterized in that, The lectin specifically binds to the glycosyl groups in the IgG glycan chain to form a complex.

22. The application according to any one of claims 19 to 21, characterized in that, The Mycobacterium tuberculosis antigen includes one or more selected from PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B.

23. The application according to any one of claims 19 to 21, characterized in that, The lectin combination specifically binds to glycosyl groups, which are glycosyl groups in blood, plasma, or serum IgG glycans that specifically bind to the lectin combination.

24. The application according to claim 22, characterized in that, The lectin combination specifically binds to glycosyl groups, which are glycosyl groups in blood, plasma, or serum IgG glycans that specifically bind to the lectin combination.

25. The use of a lectin combination-specific glycosyl detection product or a lectin combination-specific glycosyl detection product combined with Mycobacterium tuberculosis antigen in the preparation of a product for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis, wherein the lectin combination includes GNL, LAL, Black.bean.crude, HHL, PWM, GSL.I.B4, and MAL.I.

26. The application according to claim 25, characterized in that, The lectin specifically binds to the glycosyl groups in the IgG glycan chain to form a complex.

27. The application according to claim 25, characterized in that, The Mycobacterium tuberculosis antigen includes one or more selected from PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B.

28. The application according to any one of claims 25 to 27, characterized in that, The lectin combination specifically binds to glycosyl groups, which are glycosyl groups in blood, plasma, or serum IgG glycans that specifically bind to the lectin combination.

29. A method for constructing a model to distinguish between active tuberculosis, latent tuberculosis infection, and healthy individuals for non-disease diagnostic purposes, characterized in that, The method includes: randomly dividing all samples into a training set and a test set; acquiring sample data to characterize the content of lectin combinations specifically binding to glycosyl groups in the IgG glycans contained in the training set samples; constructing a training dataset using the tuberculosis diagnosis status corresponding to the sample as the label of the sample data; constructing a model based on the training dataset to distinguish between active tuberculosis, latent tuberculosis infection, and healthy individuals; and validating the effectiveness of the model using the test set; wherein the lectin combinations include: GNL, LAL, and Black.bean.crude.

30. The method according to claim 29, characterized in that, The sample is a blood, plasma, or serum sample.

31. The construction method according to claim 29 or 30, characterized in that, The lectin combination also includes one, two, or three of the following lectins: HHL, PWM, GSL.I.B4, and MAL.I.

32. A method for constructing a diagnostic model for active tuberculosis for non-disease diagnostic purposes or a model for distinguishing between latent tuberculosis infection and active tuberculosis, characterized in that, The method includes: randomly dividing all samples into a training set and a test set; acquiring sample data to characterize the content of lectin combinations specifically binding to glycosyl groups in the IgG glycans contained in the training set samples; constructing a training dataset using the tuberculosis diagnostic status corresponding to the sample as the label of the sample data; constructing an active tuberculosis diagnostic model or a model for distinguishing between latent tuberculosis infection and active tuberculosis based on the training dataset; and validating the effectiveness of the model using the test set; wherein the lectin combinations include GNL, LAL, Black.bean.crude, HHL, PWM, GSL.I.B4, and MAL.I.

33. The construction method according to claim 32, characterized in that, The sample is a blood, plasma, or serum sample.

34. A device for distinguishing between active tuberculosis, latent tuberculosis infection, and healthy individuals, characterized in that, The device includes: a detection module, including a module for detecting the content of lectin-specifically bound glycosyl groups in the IgG glycan chains contained in the test sample; wherein the lectin combination includes: GNL, LAL, and Black.bean.crude; an input module for acquiring the detection results from the detection module; an analysis module for distinguishing between active tuberculosis, latent tuberculosis infection, and healthy individuals based on the detection results acquired by the input module; and an output module for outputting the analysis results.

35. The apparatus according to claim 34, characterized in that, The lectin combination also includes one, two, or three of the following lectins: HHL, PWM, GSL.I.B4, and MAL.I.

36. The apparatus according to claim 34 or 35, characterized in that, The content of the lectin combination specifically binding glycosyl groups in the IgG glycan chains contained in the test sample is the content of the complex formed by the specific binding of each lectin in the lectin combination with the corresponding glycosyl group in the IgG glycan chain.

37. The apparatus according to claim 34 or 35, characterized in that, The method for detecting the content of lectin-specifically bound glycosyl groups in the IgG glycan chains contained in the test sample includes: contacting the lectin-specifically bound glycosyl group with the test sample to form a complex with the lectin-specifically bound glycosyl group and IgG containing the lectin-specifically bound glycosyl group; removing unbound IgG and then contacting the complex with a labeled anti-IgG antibody; removing unbound anti-IgG antibody and then performing signal detection.

38. The apparatus according to claim 34 or 35, characterized in that, The step of distinguishing between active tuberculosis, latent tuberculosis infection, and healthy individuals based on the detection results obtained from the input module includes: comparing the content of the lectin combination specifically binding glycosyl group in the IgG glycan chain contained in the test sample with the content of the glycosyl group in the IgG glycan chain contained in the control sample, and distinguishing between active tuberculosis, latent tuberculosis infection, and healthy individuals based on the comparison results.

39. The apparatus according to claim 34 or 35, characterized in that, The detection module also includes a module for detecting the content of anti-tuberculosis antigen antibodies.

40. The apparatus according to claim 39, characterized in that, The Mycobacterium tuberculosis antigen includes one or more selected from PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B.

41. The apparatus according to claim 34, 35 or 40, characterized in that, The sample to be tested is blood, serum, or plasma.

42. A device for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis, characterized in that, The device includes: a detection module, comprising a module for detecting the content of lectin-specifically bound glycosyl groups in the IgG glycan chains contained in the test sample; wherein the lectin combination includes GNL, LAL, Blackbeancrude, HHL, PWM, GSL.I.B4, and MAL.I; an input module for acquiring the detection results from the detection module; an analysis module for diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis based on the detection results acquired by the input module; and an output module for outputting the analysis results.

43. The apparatus according to claim 42, characterized in that, The content of the lectin combination specifically binding glycosyl groups in the IgG glycan chains contained in the test sample is the content of the complex formed by the specific binding of each lectin in the lectin combination with the corresponding glycosyl group in the IgG glycan chain.

44. The apparatus according to claim 42, characterized in that, The method for detecting the content of lectin-specifically bound glycosyl groups in the IgG glycan chains contained in the test sample includes: contacting the lectin-specifically bound glycosyl group with the test sample to form a complex with the lectin-specifically bound glycosyl group and IgG containing the lectin-specifically bound glycosyl group; removing unbound IgG and then contacting the complex with a labeled anti-IgG antibody; removing unbound anti-IgG antibody and then performing signal detection.

45. The apparatus according to claim 42, characterized in that, The method of diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis based on the test results obtained from the input module includes: comparing the content of the lectin combination specifically binding glycosyl group in the IgG glycan chain contained in the test sample with the content of the glycosyl group in the IgG glycan chain contained in the control sample, and diagnosing active tuberculosis or differentiating between latent tuberculosis infection and active tuberculosis based on the comparison results.

46. ​​The apparatus according to any one of claims 42 to 45, characterized in that, The detection module also includes a module for detecting the content of anti-tuberculosis antigen antibodies.

47. The apparatus according to claim 46, characterized in that, The Mycobacterium tuberculosis antigen includes one or more selected from PPD, LAM, EC, Rv2031c, Rv0934, Ag85A, and Ag85B.

48. The apparatus according to any one of claims 42-45, 47, characterized in that, The sample to be tested is blood, serum, or plasma.

Images