Advancing Immunotherapeutic Vaccine Strategies Against Pulmonary Tuberculosis
Bibliographic record
Abstract
Chemotherapeutic intervention remains the primary strategy in treating and controlling tuberculosis (TB). However, a complex interplay between therapeutic and patient-related factors leads to poor treatment adherence. This in turn continues to give rise to unacceptably high rates of disease relapse and the growing emergence of drug-resistant forms of TB. As such, there is considerable interest in strategies that simultaneously improve treatment outcome and shorten chemotherapy duration. Therapeutic vaccines represent one such approach which aims to accomplish this through boosting and/or priming novel anti-TB immune responses to accelerate disease resolution, shorten treatment duration, and enhance treatment success rates. Numerous therapeutic vaccine candidates are currently undergoing pre-clinical and clinical assessment, showing varying degrees of efficacy. By dissecting the underlying mechanisms/correlates of their successes and/or shortcomings, strategies can be identified to improve existing and future vaccine candidates. This mini-review will discuss the current understanding of therapeutic TB vaccine candidates, and discuss major strategies that can be implemented in advancing their development.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".