Viral Booster Vaccines Improve <i>Mycobacterium bovis</i> BCG-Induced Protection against Bovine Tuberculosis
Bibliographic record
Abstract
Previous work with small-animal laboratory models of tuberculosis has shown that vaccination strategies based on heterologous prime-boost protocols using Mycobacterium bovis bacillus Calmette-Guérin (BCG) to prime and modified vaccinia virus Ankara strain (MVA85A) or recombinant attenuated adenoviruses (Ad85A) expressing the mycobacterial antigen Ag85A to boost may increase the protective efficacy of BCG. Here we report the first efficacy data on using these vaccines in cattle, a natural target species of tuberculous infection. Protection was determined by measuring development of disease as an end point after M. bovis challenge. Either Ad85A or MVA85A boosting resulted in protection superior to that given by BCG alone: boosting BCG with MVA85A or Ad85A induced significant reduction in pathology in four/eight parameters assessed, while BCG vaccination alone did so in only one parameter studied. Protection was particularly evident in the lungs of vaccinated animals (median lung scores for naïve and BCG-, BCG/MVA85A-, and BCG/Ad85A-vaccinated animals were 10.5, 5, 2.5, and 0, respectively). The bacterial loads in lymph node tissues were also reduced after viral boosting of BCG-vaccinated calves compared to those in BCG-only-vaccinated animals. Analysis of vaccine-induced immunity identified memory responses measured by cultured enzyme-linked immunospot assay as well as in vitro interleukin-17 production as predictors of vaccination success, as both responses, measured before challenge, correlated positively with the degree of protection. Therefore, this study provides evidence of improved protection against tuberculosis by viral booster vaccination in a natural target species and has prioritized potential correlates of vaccine efficacy for further evaluation. These findings also have implications for human tuberculosis vaccine 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".