BCG vaccination provides cross-protection against influenza infection through trained adaptive immunity.
Bibliographic record
Abstract
Abstract The ongoing COVID-19 pandemic highlights the threat emerging viruses pose to the world. While the development of a new vaccine can take at least 1–2 years, alternative approaches are urgently needed to alleviate the burden of rapid transmission. The Bacillus Calmette-Guerin (BCG) vaccine is currently used worldwide to prevent tuberculosis but has also been shown to protect against a wide range of infections. Several studies have attributed this broad protection to trained immunity. While innate immune cells usually act in a non-specific manner, in the context of trained immunity, these cells acquire an enhanced capacity to protect against different pathogens. However, preliminary work suggests that BCG may be mediating trained immunity by enhancing the adaptive immune system as well. Before the current pandemic, Influenza A virus (IAV) was responsible for the most devastating pandemic in history and remains the cause of yearly epidemics. Thus, investigating the cross-protection of BCG against IAV could determine whether BCG vaccination could offer an alternative preventive strategy against viruses with pandemic potential. To determine the protective effects of BCG against IAV infection, C57BL/6 mice were vaccinated intravenously (-iv) or subcutaneously (-sc) with BCG then infected with H1N1. We showed that BCG-iv vaccinated mice exhibited increased survival against IAV and a decreased viral load. Interestingly, BCG-iv uniquely induces the enrichment of CX3CR1+ αβ memory T cell populations which may be contributing to protective immunity against IAV infection. Overall, the present study evaluates whether beneficial effects from a widely distributed vaccine could provide some protection against emerging pulmonary pathogens.
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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) | 0.002 | 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".