Evaluating the sensitivity of the bovine BCG challenge model using a prime boost Ad85A vaccine regimen
Bibliographic record
Abstract
In the absence of biomarkers of protective immunity, newly developed vaccines against bovine tuberculosis need to be evaluated in virulent Mycobacterium bovis challenge experiments, which require the use of expensive and highly in demand Biological Safety Level 3 (BSL3) animal facilities. The recently developed bovine BCG challenge model offers a cheaper and faster way to test new vaccine candidates and additionally reduces the severity of the challenge compared to virulent M. bovis challenge in line with the remits of the NC3Rs. In this work we sought to establish the sensitivity of the BCG challenge model by testing a prime boost vaccine regimen that previously increased protection over BCG alone against M. bovis challenge. All animals, except the control group, were vaccinated subcutaneously with BCG Danish, and half of those were then boosted with a recombinant adenoviral vector expressing Antigen 85A, Ad85A. All animals were challenged with BCG Tokyo into the prescapular lymph node and the bacterial load within the lymph nodes was established. All vaccinated animals, independent of the vaccination regimen, cleared BCG significantly faster from the lymph node than control animals, suggesting a protective effect. There was however, no difference between the BCG and the BCG-Ad85A regimens. Additionally, we analysed humoral and cellular immune responses taken prior to challenge for possible predictors of protection. Cultured ELISpot identified significantly higher IFN-ɣ responses in protected vaccinated animals, relative to controls, but not in unprotected vaccinated animals. Furthermore, a trend for protected animals to produce more IFN-ɣ by quantitative PCR and intracellular staining was observed. Thus, this model can also be an attractive alternative to M. bovis challenge models for the discovery of protective biomarkers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".