Bat adaptations in inflammation and cell death regulation contribute to viral tolerance
Bibliographic record
Abstract
Bats are reservoirs for multiple viruses, some of which are known to cause global disease outbreaks. Virus spillovers from bats have been implicated in zoonotic transmission. Some bat species can tolerate viral infections, such as infections with coronaviruses and paramyxoviruses, better than humans and with less clinical consequences. Bat species are speculated to have evolved alongside these viral pathogens, and adaptations within the bat immune system are considered to be associated with viral tolerance. Inflammation and cell death in response to zoonotic virus infections prime human immunopathology. Unlike humans, bats have evolved adaptations to mitigate virus infection-induced inflammation. Inflammatory cell death pathways such as necroptosis and pyroptosis are associated with immunopathology during virus infections, but their regulation in bats remains understudied. This review focuses on the regulation of inflammation and cell death pathways in bats. We also provide a perspective on the possible contribution of cell death-regulating proteins, such as caspases and gasdermins, in modulating tissue damage and inflammation in bats. Understanding the role of these adaptations in bat immune responses can provide valuable insights for managing future disease outbreaks, addressing human disease severity, and improving pandemic preparedness.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".