Diagnostic Value of Specific Protein Fragment RV3425 and Fusion Protein CFP10-ESAT6 in Tuberculosis Antibody Detection
Bibliographic record
Abstract
Objective:Bioinformatics analysis and epitope prediction of Rv3425 in Mycobacterium tuberculosis.Evaluating the diagnostic value of Rv342519-176 recombinant protein and CFP10-ESAT6 fusion protein in tuberculosis(TB).Methods:The gene coding 19～176 amino acid fragment of Rv3425 was amplified from Mycobacterium tuberculosis genome by PCR.Prokaryotic expression vector of Rv342519-176 was constructed and recombinant protein(rRv342519-176) and CFP10-ESAT6 fusion protein(rCFP10-ESAT6) were expressed and purified.Establishing ELISA testing method with recombinant protein and fusion protein for antigen,and assessing the combined value of two proteins in TB-antibody detection.Results:The rRv342519-176 protein fragment with dominant epitope and rCFP10-ESAT6 were expressed effectively in E.coli.The ELISA result showed certain sensibility(39%) and high specificity(97.5%),when rRv342519-167 was antigen for clinical diagnosis in tuberculosis.The sensibility and specificity were respectively 37% and 95%,when rCFP10-ESAT6 was antigen.Furthermore,two proteins conjoint analysis by ELISA showed higher sensibility(57%) and specificity(95%).Conclusion:The epitope of Rv3425 was predicted by bioinformatics.The expressed and purified rRv342519-167 and rCFP10-ESAT6 had a certain complementary antigen in TB-antibody detection,and provided a reference with improving the diagnostic sensitivity in joint diagnostic antigen with keeping high specificity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".