DNA and RNA vaccines against tuberculosis: a scoping review of human and animal studies
Bibliographic record
Abstract
Introduction To comprehensively identify and provide an overview of in vivo or clinical studies of nucleic acids (NA)-based vaccines against TB we included human or animal studies of NA vaccines for the prevention or treatment of TB and excluded in vitro or in silico research, studies of microorganisms other than M. tuberculosis, reviews, letters, and low-yield reports. Methods We searched PubMed, Scopus, Embase, selected Web of Science and ProQuest databases, Google Scholar, eLIBRARY.RU, PROSPERO, OSF Registries, Cochrane CENTRAL, EU Clinical Trials Register, clinicaltrials.gov, and others through WHO International Clinical Trials Registry Platform Search Portal, AVMA and CABI databases, bioRxiv, medRxiv, and others through OSF Preprint Archive Search. We searched the same sources and Google for vaccine names (GX-70) and scanned reviews for references. Data on antigenic composition, delivery systems, adjuvants, and vaccine efficacy were charted and summarized descriptively. Results A total of 18,157 records were identified, of which 968 were assessed for eligibility. No clinical studies were identified. 365 reports of 345 animal studies were included in the review. 342 (99.1%) studies involved DNA vaccines, and the remaining three focused on mRNA vaccines. 285 (82.6%) studies used single-antigen vaccines, while 48 (13.9%) used multiple antigens or combinations with adjuvants. Only 12 (3.5%) studies involved multiepitope vaccines. The most frequently used antigens were immunodominant secretory antigens (Ag85A, Ag85B, ESAT6), heat shock proteins, and cell wall proteins. Most studies delivered naked plasmid DNA intramuscularly without additional adjuvants. Only 4 of 17 studies comparing NA vaccines to BCG after M. tuberculosis challenge demonstrated superior protection in terms of bacterial load reduction. Some vaccine variants showed better efficacy compared to BCG. Systematic review registration https://osf.io/ , identifier F7P9G.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".