Variable Virulence and Efficacy of BCG Vaccine Strains in Mice and Correlation With Genome Polymorphisms
Bibliographic record
Abstract
Bacille Calmette-Guérin (BCG), an attenuated strain of Mycobacterium bovis, is the only vaccine available for tuberculosis (TB) control. However, BCG is not an ideal vaccine and has two major limitations: BCG exhibits highly variable effectiveness against the development of TB both in pediatric and adult populations and can cause disseminated BCG disease in immunocompromised individuals. BCG comprises a number of substrains that are genetically distinct. Whether and how these genetic differences affect BCG efficacy remains largely unknown. In this study, we performed comparative analyses of the virulence and efficacy of 13 BCG strains, representing different genetic lineages, in SCID and BALB/c mice. Our results show that BCG strains of the DU2 group IV (BCG-Phipps, BCG-Frappier, BCG-Pasteur, and BCG-Tice) exhibit the highest levels of virulence, and BCG strains of the DU2 group II (BCG-Sweden, BCG-Birkhaug) are among the least virulent group. These distinct levels of virulence may be explained by strain-specific duplications and deletions of genomic DNA. There appears to be a general trend that more virulent BCG strains are also more effective in protection against Mycobacterium tuberculosis challenge. Our findings have important implications for current BCG vaccine programs and for future TB 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".