Activation of Innate Immune-Response Genes in Little Brown Bats (Myotis lucifugus) Infected with the Fungus Pseudogymnoascus destructans
Bibliographic record
Abstract
Recently bats have been associated with the emergence of diseases, both as reservoirs for several new viral diseases in humans and other animals and, in the northern Americas, as hosts for a devastating fungal disease that threatens to drive several bat species to regional extinction. However, despite these catastrophic events little Information is available on bat defences or how they interact with their pathogens. Even less is known about the response of bats to infection during torpor or long-term hibernation. Using tissue samples collected at the termination of an experiment to explore the pathogenesis of White Nose Syndrome in Little Brown Bats, we determined if hibernating bats infected with the fungus Pseudogymnoascus destructans could respond to infection by activating genes responsible for innate immune and stress responses. Lesions due to fungal infection and, in some cases, secondary bacterial infections, were restricted to the skin. However, we were unable to obtain sufficient amounts of RNA from these sites. We therefore examined lungs for response at an epithelial surface not linked to the primary site of infection. We found that bats responded to infection with a significant increase in lungs of transcripts for Cathelicidin (an anti-microbial peptide) as well as the immune modulators tumor necrosis factor alpha and interleukins 10 and 23. In conclusion, hibernating bats can respond to experimental P. destructans infection by activating expression of innate immune response genes.
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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".