Antibody Profiles Characteristic of<i>Mycobacterium tuberculosis</i>Infection State
Bibliographic record
Abstract
The relationship between specific antibody profiles and tuberculosis (TB) state was investigated by measuring serum antibody levels to six Mycobacterium tuberculosis antigens in human subjects grouped into four diagnostic categories: active disease, inactive (past) tuberculosis, latent infection without radiographic chest abnormalities, and infection free. Statistical data analyses showed that the latter two groups were serologically indistinguishable and that active tuberculosis and inactive tuberculosis were characterized by different antibody profiles. Antibodies to the 38-kDa antigen, alanine dehydrogenase, and Rv2626c were associated with active TB, while antibodies to the 16-kDa antigen, ferredoxin A, and ESAT-6 were associated with inactive TB. Thus, the targets of the immune response vary with tuberculosis state. The correlation between bacterial antigen production and infection stage was investigated in mice infected with M. tuberculosis by bacterial transcription profiling. It was found that levels of transcripts encoding the six M. tuberculosis antigens varied during infection. Together, the data indicate that antigen composition of tubercle bacilli varies with stage of infection and that immunoprofiling can distinguish between tuberculosis states.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".