CD177 + Use of granulocyte subsets in the preparation of a kit for the auxiliary diagnosis of tuberculosis

Abstract

This invention relates to the field of biomedical technology, and provides CD177. + Application of granulocyte subsets in the preparation of kits for the auxiliary diagnosis of tuberculosis. The CD177... + Granulocyte subsets include CD177 + Neutrophils and CD177 + Low-density granulocytes; the kit detects CD177. + The proportion of neutrophil-positive cells and CD177 + The mean fluorescence intensity of low-density granulocytes aids in the diagnosis of tuberculosis. This invention utilizes CD177. + Granulocyte subsets, as biomarkers, play an important role in auxiliary diagnosis, disease activity monitoring, and improving diagnostic efficiency. Based on CD177... + The proportion of neutrophil-positive cells, CD177 + By using the mean fluorescence intensity of low-density granulocytes, the ratio of neutrophils to lymphocytes, the ratio of platelets to lymphocytes, and the systemic inflammatory index, a predictive model for the auxiliary diagnosis of tuberculosis can be constructed. This model can more accurately identify true tuberculosis patients and effectively exclude non-patients, reducing the risk of misdiagnosis and missed diagnosis.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, and particularly relates to CD177. + Application of granulocyte subsets in the preparation of kits for the auxiliary diagnosis of tuberculosis. Background Technology

[0002] Mycobacterium tuberculosis (Mtb) is an obligate aerobic, non-motile pathogen primarily transmitted through respiratory droplets. Tuberculosis (TB), caused by Mtb, is a chronic infectious disease that mainly affects the lungs, causing pulmonary tuberculosis (PTB), while extrapulmonary tuberculosis (EPTB) also accounts for a significant proportion. Extrapulmonary tuberculosis can infect organs and tissues outside the lungs, such as the pleura, lymph nodes, and central nervous system. Due to its low bacterial load and diverse clinical manifestations, diagnosis is often more challenging. The pathophysiological mechanisms of tuberculosis are complex, involving the host's immune response, the biological characteristics of the bacteria, and various environmental influences. Therefore, accurate and timely diagnosis of tuberculosis has become a crucial core element in effectively preventing further disease progression, minimizing its transmission risk in the community, and ultimately improving long-term treatment outcomes and quality of life for patients.

[0003] Current diagnostic methods for tuberculosis primarily rely on a combination of classical and modern techniques, but each method has its limitations. Traditional acid-fast staining and microscopic examination of sputum smears is simple and inexpensive, but its sensitivity is generally low, typically requiring a high concentration of bacteria in the sample to produce a positive result. This makes it easy to miss cases with low bacterial loads, such as early infection, extrapulmonary tuberculosis, or HIV co-infection. Solid or liquid culture of Mycobacterium tuberculosis, while providing definitive etiological evidence and suitable for drug susceptibility testing, suffers from a lengthy culture period that cannot meet the urgent clinical need for rapid diagnosis. Molecular biological detection techniques can improve diagnostic speed and sensitivity through nucleic acid amplification, but their complex procedures and high cost limit their widespread application in resource-constrained areas. Furthermore, their detection range is mainly limited to specific drug resistance genes and cannot comprehensively replace culture to obtain a complete drug susceptibility spectrum. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides CD177. + The application of granulocyte subsets in the preparation of kits for the auxiliary diagnosis of tuberculosis aims to address the problems mentioned in the background section.

[0005] In a first aspect, the present invention provides CD177 + The application of granulocyte subsets in the preparation of kits for the auxiliary diagnosis of tuberculosis, wherein CD177 + Granulocyte subsets include CD177 + Neutrophils and CD177+ Low-density granulocytes.

[0006] Furthermore, the kit detects CD177. + The proportion of neutrophil-positive cells and CD177 + The mean fluorescence intensity of low-density granulocytes aids in the diagnosis of tuberculosis.

[0007] Furthermore, the kit contains an antibody that specifically binds to the CD177 protein.

[0008] Furthermore, the antibody is a fluorescently labeled antibody.

[0009] Furthermore, the kit is used to test peripheral blood samples.

[0010] Furthermore, the peripheral blood sample contains neutrophils and low-density granulocytes.

[0011] Furthermore, when CD177 + The proportion of positive neutrophil cells decreased, CD177 + An increase in the mean fluorescence intensity of low-density granulocytes is a diagnostic criterion for tuberculosis in the examinee.

[0012] Secondly, the present invention provides a computer program for assisting in the diagnosis of tuberculosis, wherein when the computer program is executed, it performs the following specific steps: Based on CD177 + The proportion of neutrophil-positive cells, CD177 + The mean fluorescence intensity of low-density granulocytes, the neutrophil-to-lymphocyte ratio, the platelet-to-lymphocyte ratio, and the systemic inflammatory index were used to construct a predictive model for the auxiliary diagnosis of tuberculosis. The formula for the predictive model is expressed as follows: Q=2.26×NLR+0.01×PLR-0.004×SII+0.019×C1-0.06×C2-2.45; In the formula: Q is the predicted value; NLR is the neutrophil-to-lymphocyte ratio; PLR is the platelet-to-lymphocyte ratio; SII is the systemic inflammatory complex index; C1 is CD177. + Mean fluorescence intensity of low-density granulocytes; C2 is CD177 + The percentage of neutrophil-positive cells.

[0013] Furthermore, when the predicted value is >0.41, the subject is diagnosed as a tuberculosis patient.

[0014] The present invention has the following technical effects: (1) CD177 +Granulocyte subsets were used to prepare a kit for the auxiliary diagnosis of tuberculosis by detecting CD177. + The proportion of neutrophil-positive cells and CD177 + Mean fluorescence intensity of low-density granulocytes aids in the diagnosis of tuberculosis. CD177 + Granulocyte subsets, as biomarkers, have important value in assisting diagnosis, monitoring disease activity, and improving diagnostic efficiency.

[0015] (2) Based on CD177 + The proportion of neutrophil-positive cells, CD177 + By using the mean fluorescence intensity of low-density granulocytes, the ratio of neutrophils to lymphocytes, the ratio of platelets to lymphocytes, and the systemic inflammatory index, a predictive model for the auxiliary diagnosis of tuberculosis can be constructed. This model can more accurately identify true tuberculosis patients and effectively exclude non-patients, reducing the risk of misdiagnosis and missed diagnosis. Attached Figure Description

[0016] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures: Figure 1 The results are obtained by flow cytometry analysis of CD177 expression in peripheral blood low-density granulocytes from the tuberculosis group and the healthy control group in Example 2 of this invention, wherein: Figure 1 In this context, A represents the gating strategy of flow cytometry. Cells are first selected through size (FSC) and granularity (SSC) analysis. LDG was identified as CD14 in PBMCs. low CD15 + Cells; NDG is defined as CD15 after purification of erythrocyte components. high CD14 - Neutrophil CD177 expression level was defined as CD177-PE fluorescence intensity being higher than the threshold level set for isotype controls. Figure 1 In the diagram, B represents low-density granulocytes in the PBMC layer that exhibit neutrophil morphology after Wright staining. Figure 1 The purity of C neutrophils in the image is determined by flow cytometry after labeling with the specific markers CD66b and CD11b. More than 95% of the neutrophils are CD66b. + CD11b + ; Figure 1 In this context, D represents the percentage of CD177 expression in peripheral blood low-density granulocytes in the tuberculosis group and the healthy control group, with no statistically significant difference between the two groups. Figure 1E in the figure represents the percentage of CD177 expression in peripheral blood neutrophils in the tuberculosis group and the healthy control group, P<0.05; Figure 1 In this context, F represents the average fluorescence intensity of CD177 expression in peripheral blood low-density granulocytes from the tuberculosis group and the healthy control group, with P < 0.001. Figure 1 J in the figure represents the average fluorescence intensity of CD177 expression in peripheral blood neutrophils of the tuberculosis group and the healthy control group, with no statistically significant difference between the two groups.

[0017] Figure 2 This is a graph showing the correlation analysis results between the expression of CD177 in low-density granulocytes in the tuberculosis group and the clinical characteristics of tuberculosis patients in Example 3 of this invention, wherein: Figure 2 Figure A in the figure shows the correlation analysis results between the percentage of CD177 expression in low-density granulocytes and PLT, with P < 0.05. Figure 2 Figure B in the graph shows the correlation analysis results between the percentage of CD177 expression in low-density granulocytes and NLR, P<0.05; Figure 2 In the figure, C represents the correlation analysis results between the percentage of CD177 expression in low-density granulocytes and TNF-α, P<0.05; Figure 2 In the figure, D represents the correlation analysis results between the average fluorescence intensity of CD177 expression in neutrophils and IL-4, P<0.05; Figure 2 E in the figure represents the correlation analysis results between the average fluorescence intensity of CD177 expression in neutrophils and IFN-γ, P<0.05.

[0018] Figure 3 This is a graph showing the ROC curve analysis results of Embodiment 4 of the present invention, wherein: Figure 3 In Figure A, the ROC curve analysis results of the average fluorescence intensity of CD177 expression in low-density granulocytes of the tuberculosis group and the healthy control group are shown, with P<0.05. Figure 3 B in the figure represents the ROC curve analysis results of the percentage of CD177 expression in neutrophils and the healthy control group, P<0.05.

[0019] Figure 4 This is the ROC curve analysis result of the prediction model in Embodiment 5 of the present invention, distinguishing between the tuberculosis group and the healthy control group, wherein: Figure 4 In the figure, A is a statistical graph of the predicted values ​​between the tuberculosis patient group and the healthy control group, P<0.001; Figure 4B in the equation represents the ROC analysis of the predictive model, distinguishing between tuberculosis patients and healthy controls, with an AUC of 0.921. Detailed Implementation

[0020] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0022] This invention provides CD177 + Application of granulocyte subsets in the preparation of kits for the auxiliary diagnosis of tuberculosis, CD177 + Granulocyte subsets include CD177 + Neutrophils and CD177 + Low-density granulocytes.

[0023] In some embodiments, the kit detects CD177. + The proportion of neutrophil-positive cells and CD177 + The mean fluorescence intensity of low-density granulocytes aids in the diagnosis of tuberculosis.

[0024] In some embodiments, the kit contains an antibody that specifically binds to the CD177 protein.

[0025] In some embodiments, the antibody is a fluorescently labeled antibody.

[0026] In some embodiments, the kit is used to test peripheral blood samples.

[0027] In some embodiments, the peripheral blood sample contains neutrophils and low-density granulocytes.

[0028] In some embodiments, when CD177 + The proportion of positive neutrophil cells decreased, CD177 + An increase in the mean fluorescence intensity of low-density granulocytes is a diagnostic criterion for tuberculosis in the examinee.

[0029] In some embodiments, the present invention provides a computer program for assisting in the diagnosis of tuberculosis. When the computer program is executed, it performs the following specific steps: Based on CD177 + The proportion of neutrophil-positive cells, CD177 +The mean fluorescence intensity of low-density granulocytes, the neutrophil-to-lymphocyte ratio, the platelet-to-lymphocyte ratio, and the systemic inflammatory index were used to construct a predictive model for the auxiliary diagnosis of tuberculosis. The formula for the predictive model is expressed as follows: Q=2.26×NLR+0.01×PLR-0.004×SII+0.019×C1-0.06×C2-2.45; In the formula: Q is the predicted value; NLR is the neutrophil-to-lymphocyte ratio; PLR is the platelet-to-lymphocyte ratio; SII is the systemic inflammatory complex index; C1 is CD177. + Mean fluorescence intensity of low-density granulocytes; C2 is CD177 + The percentage of neutrophil-positive cells.

[0030] In some embodiments, when the predicted value is >0.41, the subject is diagnosed as a tuberculosis patient.

[0031] Sample collection and analysis: (1) Study population: From June 2024 to May 2025, a tuberculosis cohort of 43 patients was recruited from the Departments of Respiratory Medicine, Gastroenterology, and Orthopedics at the First Affiliated Hospital of Nanchang University (Nanchang, China), including 23 patients with pulmonary tuberculosis (PTB) and 20 patients with extrapulmonary tuberculosis (EPTB) (24 males, 19 females; mean age ± standard error, 51.40 ± 17.38 years); and a healthy control group of 31 age- and sex-matched individuals (19 males, 12 females; mean age ± standard error, 49.10 ± 16.06 years), who were T-spot negative and had no other inflammatory diseases. This study followed the Declaration of Helsinki and was approved by the Medical Ethics Committee of the First Affiliated Hospital of Nanchang University (2024) CDYFYYLK (07-046).

[0032] Diagnosis of pulmonary tuberculosis relies on a combination of clinical symptoms (persistent cough, fever, or weight loss) and radiological evidence. Microbiological confirmation includes sputum smear microscopy, Mycobacterium tuberculosis culture, or molecular tests such as polymerase chain reaction (PCR), GeneXpert MTB / RIF, or next-generation sequencing.

[0033] The diagnosis of extrapulmonary tuberculosis follows the definition and classification in the Chinese National Standard for the Diagnosis of Extrapulmonary Tuberculosis. Extrapulmonary tuberculosis includes intestinal tuberculosis, bone tuberculosis, tuberculous lymphadenitis, and abdominal tuberculosis. Exclusion criteria include individuals with concurrent chronic diseases such as chronic infections, malignancies, diabetes, or HIV, as well as patients receiving immunosuppressive therapy.

[0034] (2) Assessment of CD177 expression levels on low-density granulocytes and neutrophils by flow cytometry: Approximately 3 ml of blood was collected from the antecubital vein and transferred to a tube coated with EDTA. Peripheral blood mononuclear cells (PBMCs) were then separated by density gradient centrifugation using Ficoll-diatrizoate meglumine according to the Ficoll separation method. In short, the venous blood sample (3 ml) was mixed with an equal volume of sterile saline solution. The diluted sample was centrifuged at 300 g for 20 minutes. After centrifugation, PBMCs were collected at the plasma-lymphocyte interface to separate low-density granulocytes.

[0035] PBMC fractions were treated with a specialized erythrocyte lysis buffer (OptiLyse C; Beckman Coulter) to remove erythrocyte contamination. Neutrophils were then separated from the residual erythrocyte pellet using erythrocyte lysis. Subsequently, 50 μL of freshly prepared PBMC (5 × 10⁻⁶) was... 5 ) or 100uL NDG (6×10 5 Cells were co-incubated for 15 minutes in the dark with 4 μL of ECD-labeled anti-CD14, 4 μL of PC5-labeled anti-CD15, and 4 μL of PE-labeled anti-CD177. Cells incubated with PE-labeled mouse IgG served as isotype controls. Cells were then evaluated using a CYTOMICS FC 500 flow cytometer and CXP software (Beckman Coulter). Flow cytometry analysis was validated using isotype controls, with a background signal of less than 5%.

[0036] (3) Collect complete blood count measurements and derived inflammatory indices, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and serum cytokines, including interleukin (IL)-1β, IL-2, IL-4, IL-5, IL-6, IL-8, tumor necrosis factor-α (TNF-α), and interferon-γ (IFN-γ). Thermo Fisher Scientific X2100 (Hanshin Thermo Fisher Scientific, Japan) was used to calculate derived inflammatory indices based on the complete blood count measurements, including LMR, NLR, PLR, SII, PMR, MNR, and PNR. CRP was measured in serum using a turbidimetric assay with an Immunosorbent Assay 800 system (Beckman Coulter). Erythrocyte sedimentation rate (ESR) was measured according to the manufacturer's instructions. The levels of IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, TNF-α and IFN-γ in serum were measured using a Cytokine Profiling Kit (Siemens Healthcare (Pty) Ltd.) and an Immulite® 1000 Immunoassay System (Siemens Healthcare (Pty) Ltd.), according to the manufacturer’s instructions.

[0037] (4) Statistical analysis was performed using FlowJo software version 10.8.1 (BD, BD Inc., USA) and GraphPad Prism. The expression of each surface marker was expressed as mean fluorescence intensity or percentage. Data are expressed as mean ± standard deviation or median and interquartile range (IQR, 25th to 75th percentile). Comparisons between two groups of unpaired data were performed using independent samples unpaired t-tests or nonparametric Mann-Whitney U tests. Correlation analysis was performed using the Spearman correlation test. ROC curves and area under the curve (AUC) were analyzed using GraphPad Prism software. P < 0.05 was considered statistically significant.

[0038] Example 1: We analyzed the detailed demographic characteristics of 74 participants (including 23 patients with pulmonary tuberculosis, 20 patients with extrapulmonary tuberculosis, and 31 healthy controls).

[0039] The statistical results are shown in Table 1. The results showed no significant differences in age or sex distribution between the tuberculosis group and the healthy control group. The affected anatomical sites in tuberculosis patients were, in descending order: lungs, intestines, lumbar spine, lymph nodes, cervical spine, abdomen, pleura, spine, and esophagus. Furthermore, cough with sputum was the most common symptom in 9 tuberculosis patients (20.93%), followed by pain (18.60%), fever (9.30%), and chest pain (6.98%). Hematological parameters showed significantly lower levels of hepatitis B (HB) and hemolytic uremic response (HCT) in the tuberculosis group compared to the healthy controls.

[0040] Table 1. Clinical characteristics of the tuberculosis group and the healthy control group

[0041] Note: All data in Table 1 has been rounded to two decimal places.

[0042] Example 2: (1) The expression levels of CD177 protein in neutrophils and low-density granulocytes in the tuberculosis group and the healthy control group were compared by flow cytometry.

[0043] Gate strategy for streaming graphs, such as Figure 1 As shown in A, cells were first selected by size (FSC) and particle size (SSC) analysis, and LDG was identified as CD14 in PBMCs. low CD15 + Cells. NDG is defined as CD15 after purification of erythrocyte components. high CD14 -Neutrophil CD177 expression level was defined as CD177-PE fluorescence intensity exceeding the threshold level set for isotype controls.

[0044] like Figure 1 As shown in B, the low-density granulocytes in the PBMC layer exhibit neutrophil morphology after Wright staining; as shown in Figure B. Figure 1 As shown in Figure C, after neutrophils were labeled with the specific markers CD66b and CD11b by flow cytometry, more than 95% of the neutrophils were CD66b. + CD11b + .

[0045] Flow cytometry results as follows Figure 1 D- Figure 1 As shown in G, the results indicate that the percentage of CD177 expression in peripheral blood neutrophils in the tuberculosis group was lower than that in the healthy control group. Figure 1 In the E group, the mean fluorescence intensity of CD177 expression in peripheral blood low-density granulocytes was higher than that in the healthy control group. Figure 1 (F in the middle).

[0046] Example 3: To analyze the correlation between CD177 protein expression levels in neutrophils and low-density granulocytes of tuberculosis patients and the clinical characteristics of tuberculosis patients.

[0047] The correlation between CD177 protein expression levels in neutrophils and low-density granulocytes and clinical characteristics of tuberculosis patients is shown in the figure below. Figure 2 As shown, the results indicated that the percentage of CD177 expression in low-density granulocytes was positively correlated with elevated levels of systemic inflammatory markers, including PLT (r=0.3193), NLR (r=0.3267), and the pro-inflammatory cytokine TNF-α (r=0.5053). In contrast, the mean fluorescence intensity of CD177 expression in neutrophils was positively correlated with the levels of T helper cytokines IL-4 (r=0.5277) and IFN-γ (r=0.5329).

[0048] Example 4: The expression levels of CD177 in neutrophils and low-density granulocytes in the tuberculosis group and the healthy control group were detected by flow cytometry. ROC curves were plotted, and AUC, 95% confidence interval (95% CI), sensitivity and specificity were calculated.

[0049] The ROC curve analysis results are as follows: Figure 3As shown, the results indicated that the mean fluorescence intensity of CD177 expression in low-density granulocytes showed the highest potential in distinguishing the tuberculosis group from the healthy control group, with an AUC of 0.733 (95% CI: 0.616–0.850), a sensitivity of 76.74%, and a specificity of 64.52%. Figure 3 The percentage of CD177 expression in neutrophils had an AUC of 0.691 (95% CI: 0.546-0.837) in differentiating between the tuberculosis group and the healthy control group, with a sensitivity of 60.71% and a specificity of 80.00%. Figure 3 (B in the text). This indicates that CD177, as a diagnostic biomarker for tuberculosis patients, has the sensitivity and specificity available for clinical auxiliary diagnosis.

[0050] Example 5: Peripheral blood neutrophil and low-density granulocyte CD177 levels were combined with conventional clinical indicators such as neutrophil / monocyte ratio, monocyte / neutrophil ratio, neutrophil / platelet ratio, neutrophil / lymphocyte ratio, lymphocyte / monocyte ratio, platelet / lymphocyte ratio, systemic inflammation index, and derived neutrophil / lymphocyte ratio. A predictive model for diagnosing tuberculosis was constructed through univariate and multivariate analyses, and the results are shown in Table 2.

[0051] Table 2 Univariate and multivariate analyses of risk factors associated with tuberculosis

[0052] Build a computer program to assist in the diagnosis of tuberculosis. When the computer program is executed, it will perform the following specific steps: The following parameters were determined: neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, systemic inflammatory complex index, mean fluorescence intensity of CD177 expression in low-density granulocytes, and percentage of CD177 expression in neutrophils (CD177). + The proportion of neutrophil-positive cells is the optimal model variable, and the formula for constructing the predictive model is expressed as: Q=2.26×NLR+0.01×PLR-0.004×SII+0.019×C1-0.06×C2-2.45; In the formula: Q is the predicted value; NLR is the neutrophil-to-lymphocyte ratio; PLR is the platelet-to-lymphocyte ratio; SII is the systemic inflammatory complex index; C1 is CD177. + Mean fluorescence intensity of low-density granulocytes; C2 is CD177 + The percentage of neutrophil-positive cells.

[0053] The analysis results of the constructed prediction model are as follows Figure 4As shown in the results, the higher the predicted value, the higher the risk of tuberculosis. Figure 4 (A in the original text); The predictive model performed well in distinguishing between tuberculosis patients and healthy controls, with an AUC of 0.921 and a 95% confidence interval of 0.851–0.992. Figure 4 In section B), it is superior to a single indicator. It is worth noting that, according to the analysis of Graphpadprism 10.1.2 software, when the optimal cutoff value of the prediction model is 0.41, it has high specificity (84.00%) and high sensitivity (92.59%), that is, when the prediction value is >0.41, it can diagnose the subject as a tuberculosis patient.

[0054] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. CD177 + The application of granulocyte subsets in the preparation of kits for the auxiliary diagnosis of tuberculosis is characterized by: The CD177 + Granulocyte subsets include CD177 + Neutrophils and CD177 + Low-density granulocytes.

2. The application as described in claim 1, characterized in that: The kit detects CD177. + The proportion of neutrophil-positive cells and CD177 + The mean fluorescence intensity of low-density granulocytes aids in the diagnosis of tuberculosis.

3. The application as described in claim 2, characterized in that: The kit contains an antibody that specifically binds to the CD177 protein.

4. The application as described in claim 3, characterized in that: The antibody is a fluorescently labeled antibody.

5. The application as described in claim 4, characterized in that: The kit is used to test peripheral blood samples.

6. The application as described in claim 5, characterized in that: The peripheral blood sample contained neutrophils and low-density granulocytes.

7. The application as described in claim 6, characterized in that: When CD177 + The proportion of positive neutrophil cells decreased, CD177 + An increase in the mean fluorescence intensity of low-density granulocytes is a diagnostic criterion for tuberculosis in the examinee.

8. A computer program for assisting in the diagnosis of tuberculosis, characterized in that: When the computer program is executed, it performs the following specific steps: Based on CD177 + The proportion of neutrophil-positive cells, CD177 + The mean fluorescence intensity of low-density granulocytes, the neutrophil-to-lymphocyte ratio, the platelet-to-lymphocyte ratio, and the systemic inflammatory index were used to construct a predictive model for the auxiliary diagnosis of tuberculosis. The formula for the predictive model is expressed as follows: Q=2.26×NLR+0.01×PLR-0.004×SII+0.019×C1-0.06×C2-2.45; In the formula: Q is the predicted value; NLR is the neutrophil-to-lymphocyte ratio; PLR is the platelet-to-lymphocyte ratio; SII is the systemic inflammatory complex index; C1 is CD177. + Mean fluorescence intensity of low-density granulocytes; C2 is CD177 + The percentage of neutrophil-positive cells.

9. The application as described in claim 8, characterized in that: When the predicted value is >0.41, the subject is diagnosed as a tuberculosis patient.

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