Regions of Differences Encoded Antigens as Targets for Immunodiagnosis of Tuberculosis in Humans
Bibliographic record
Abstract
Tuberculosis is one of the major global health problems causing nearly 2 million deaths every year. It continues to be a leading cause of morbidity and mortality in developing countries. Accurate early diagnosis and proper treatment can control the spread of tuberculosis in the community. Currently used diagnostic tests have certain limitations such as low sensitivity and suboptimal turn-around times. Hence, introduction of diagnostic methods that are comparatively more sensitive and specific can increase the efficiency of strategies to control tuberculosis. In recent years, there has been a remarkable progress in identifying new and potentially useful antigens for diagnosis of both latent and active tuberculosis. Regions of differences (RD) encoded proteins are among such promising candidate antigens (RD antigens). Some of these antigens are encoded by regions of differences located in the genome of Mycobacterium tuberculosis, M. africanum, M. bovis but are absent in all the Bacillus Calmette Guerin substrains and many of the environmental mycobacteria. Over the past few years, RD antigens, particularly RD-based diagnostic methods such as improved tuberculin skin testing, interferon-gamma release assays, and RD1-based serological assays are being tested and have shown promising results. This article provides an overview of the use of RD antigens in the immunodiagnosis of tuberculosis infection and disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".