Protection and diagnostic interference induced by heat-inactivated, phage-inactivated and live vaccine prototypes against animal tuberculosis
Bibliographic record
Abstract
Introduction Vaccination emerges as a promising cost-effective tool to reduce the impact and spread of animal tuberculosis, especially in regions where test-and-slaughter eradication strategy is socioeconomically unfeasible or unfruitful for different reasons, provided it is safe, efficacious and compatible with diagnosis. Methods In this study, we preliminarily evaluated the diagnostic interference (using guinea pigs) and the protective efficacy (using mice) of three heat-inactivated, three phage-inactivated and one live attenuated vaccine prototypes prepared from M. bovis, M. caprae , and M. microti . Results and discussion Phage-inactivation killed almost all (96.41–99.92%) bacteria to be included in vaccines and filtering was used to remove the remaining viable cells. All the assayed vaccines induced skin test reactions in response to bovine tuberculin, but they were smaller in the phage-inactivated vaccine groups. All the vaccines were diagnosis-compatible with defined skin test antigens based on ESAT-6, CFP-10, and Rv3615c. In contrast with the rest of prototypes, vaccination with heat- and phage-inactivated M. microti did not prompt the production of detectable anti-MPB70+MPB83 antibodies. Mean bacterial burden was lower in all vaccinated groups in comparison with the control, being significantly reduced in the lungs of the heat-inactivated M. microti and M. caprae and phage-inactivated M. caprae groups. Considering both diagnostic interference and protection collectively, the heat-inactivated M. microti vaccine showed the best performance. Further studies to evaluate these vaccines and to improve phage-driven inactivation are warranted.
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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 | 0.000 |
| 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".