Pattern Recognition Receptor Signaling and Innate Responses to Influenza A Viruses in the Mallard Duck, Compared to Humans and Chickens
Bibliographic record
Abstract
Mallard ducks are a natural host and reservoir of avian Influenza A viruses. While most influenza strains can replicate in mallards, the virus typically does not cause substantial disease in this host. Mallards are often resistant to disease caused by highly pathogenic avian influenza viruses, while the same strains can cause severe infection in humans, chickens, and even other species of ducks, resulting in systemic spread of the virus and even death. The differences in influenza detection and antiviral effectors responsible for limiting damage in the mallards are largely unknown. Domestic mallards have an early and robust innate response to infection that seems to limit replication and clear highly pathogenic strains. The regulation and timing of the response to influenza also seems to circumvent damage done by a prolonged or dysregulated immune response. Rapid initiation of innate immune responses depends on viral recognition by pattern recognition receptors (PRRs) expressed in tissues where the virus replicates. RIG-like receptors (RLRs), Toll-like receptors (TLRs), and Nod-like receptors (NLRs) are all important influenza sensors in mammals during infection. Ducks utilize many of the same PRRs to detect influenza, namely RIG-I, TLR7, and TLR3 and their downstream adaptors. Ducks also express many of the same signal transduction proteins including TBK1, TRIF, and TRAF3. Some antiviral effectors expressed downstream of these signaling pathways inhibit influenza replication in ducks. In this review, we summarize the recent advances in our understanding of influenza recognition and response through duck PRRs and their adaptors. We compare basal tissue expression and regulation of these signaling components in birds, to better understand what contributes to influenza resistance in the duck.
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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) | 0.001 | 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 | 0.000 | 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".