Innate immune responses in raccoons after raccoon rabies virus infection
Bibliographic record
Abstract
Zoonotic wildlife diseases pose significant health risks not only to their primary vectors but also to humans and domestic animals. Rabies is a lethal encephalitis caused by rabies virus (RV). This RNA virus can infect a range of terrestrial mammals but each viral variant persists in a particular reservoir host. Active management of these host vectors is needed to minimize the negative impacts of this disease, and an understanding of the immune response to RV infection aids strategies for host vaccination. Current knowledge of immune responses to RV infection comes primarily from rodent models in which an innate immune response triggers activation of several genes and signalling pathways. It is unclear, however, how well rodent models represent the immune response of natural hosts. This study investigates the innate immune response of a primary host, the raccoon, to a peripheral challenge using the raccoon rabies virus (RRV). The extent and temporal course of this response during RRV infection was analysed using genes predicted to be upregulated during infection (IFNs; IFN regulatory factors; IL-6; Toll like receptor-3; TNF receptor). We found that RRV activated components of the innate immune system, with changes in levels of transcripts correlated with presence of viral RNA. Our results suggest that natural reservoirs of rabies may not mimic the immune response triggered in rodent models, highlighting the need for further studies of infection in primary hosts.
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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".