Mucosal delivery of tuberculosis vaccines: a review of current approaches and challenges
Bibliographic record
Abstract
Introduction: Tuberculosis (TB) remains a major health threat and it is now clear that the current vaccine, BCG, is unable to arrest the global TB epidemic. A new vaccine is needed to either replace or boost BCG so that a better level of protection could be achieved. The route of entry of Mycobacterium tuberculosis, the causative organism, is via inhalation making TB primarily a respiratory disease. There is therefore good reason to hypothesize that a mucosally delivered vaccine against TB could be more effective than one delivered via the systemic route.Areas covered: This review summarizes the progress that has been made in the area of TB mucosal vaccines in the last few years. It highlights some of the strengths and shortcomings of the published evidence and aims to discuss immunological and practical considerations in the development of mucosal vaccines.Expert opinion: There is a growing body of evidence that the mucosal approach to vaccination against TB is feasible and should be pursued. However, further key studies are necessary to both improve our understanding of the protective immune mechanisms operating in the mucosa and the technical aspects of aerosolized delivery, before such a vaccine could become a feasible, deployable strategy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.003 | 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".