Combined serum marker detection reagent for screening and diagnosis of tuberculosis
A technology for detection reagents and detection kits, which is applied in the fields of biomedicine and clinical diagnosis, can solve the problems of difficult standardization of sample processing, unstable sample components, and difficulty in obtaining effective samples, so as to simplify the composition and detection operation process and facilitate industrialization The effect of promoting and reducing testing costs
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Embodiment 1
[0021] Example 1: Tuberculosis Screening Test
[0022] Study population: people with suspicious symptoms of tuberculosis who need differential diagnosis of tuberculosis (those with cough symptoms for more than 2 weeks);
[0023] Included sample size: 300 subjects;
[0024] Test samples: subjects' blood samples (300 in total);
[0025] Detection method: 71 serum markers in each blood sample were quantitatively tested by Single Molecule Array (SiMOA), and the Mycobacterium tuberculosis Ag85B antibody was quantitatively detected by Bead Array method;
[0026] Result analysis: use the machine learning method (TreeNet: Stochastic Gradient Boosting) to analyze the test results;
[0027] Test results: It was found that only the combination of Mycobacterium tuberculosis Ag85B antibody and four serum markers of interleukin-6 (IL-6), interleukin-8 (IL-8) and interleukin-18 (IL-18) can detect The results can reach the detection target of clinical use (see attached figure 1 ).
[002...
Embodiment 2
[0034] Example 2: Tuberculosis Screening Expanded Trial
[0035] Based on the results of Example 1, we verified on a larger sample.
[0036] The population included in the study: tuberculosis patients from multiple countries and those with suspicious symptoms of tuberculosis who need to be differentially diagnosed with tuberculosis;
[0037] Included sample size: 583 subjects, including 278 patients diagnosed with tuberculosis and 305 non-tuberculosis patients;
[0038] Test samples: subjects' blood samples (583 in total);
[0039] Detection method: Quantitative detection of serum markers in each blood sample by single molecule protein array detection method (Single Molecule Array, SiMOA);
[0040] Result analysis: use the machine learning method (TreeNet: Stochastic Gradient Boosting) to analyze the test results;
[0041] Test results: The results confirmed that only the combination of Mycobacterium tuberculosis Ag85B antibody and four serum markers of interleukin-6 (IL-6), ...
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