Age at Mycobacterium bovis BCG Priming Has Limited Impact on Anti-Tuberculosis Immunity Boosted by Respiratory Mucosal AdHu5Ag85A Immunization in a Murine Model
Bibliographic record
Abstract
Tuberculosis (TB) remains a global pandemic despite the use of Bacillus Calmette-Guérin (BCG) vaccine, partly because BCG fails to effectively control adult pulmonary TB. The introduction of novel boost vaccines such as the human Adenovirus 5-vectored AdHu5Ag85A could improve and prolong the protective immunity of BCG immunization. Age at which BCG immunization is implemented varies greatly worldwide, and research is ongoing to discover the optimal stage during childhood to administer the vaccine, as well as when to boost the immune response with potential novel vaccines. Using a murine model of subcutaneous BCG immunization followed by intranasal AdHu5Ag85A boosting, we investigated the impact of age at BCG immunization on protective efficacy of BCG prime and AdHu5Ag85A boost immunization-mediated protection. Our results showed that age at parenteral BCG priming has limited impact on the efficacy of BCG prime-AdHu5Ag85A respiratory mucosal boost immunization-enhanced protection. However, when BCG immunization was delayed until the maturity of the immune system, longer sustained memory T cells were generated and resulted in enhanced boosting effect on T cells of AdHu5Ag85A respiratory mucosal immunization. Our findings hold implications for the design of new TB immunization protocols for humans.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".