Double-Stranded Ribonucleic Acid-Mediated Antiviral Response Against Low Pathogenic Avian Influenza Virus Infection
Bibliographic record
Abstract
Toll-like receptor (TLR)3 signaling pathway is known to induce type 1 interferons (IFNs) and proinflammatory mediators leading to antiviral response against many viral infections. Double-stranded ribonucleic acid (dsRNA) has been shown to act as a ligand for TLR3 and, as such, has been a focus as a potential antiviral agent in many host-viral infection models. Yet, its effectiveness and involved mechanisms as a mediator against low pathogenic avian influenza virus (LPAIV) have not been investigated adequately. In this study, we used avian fibroblasts to verify whether dsRNA induces antiviral response against H4N6 LPAIV and clarify whether type 1 IFNs and proinflammatory mediators such as interleukin (IL)-1β are contributing to the dsRNA-mediated antiviral response against H4N6 LPAIV. We found that dsRNA induces antiviral response in avian fibroblasts against H4N6 LPAIV infection. The treatment of avian fibroblasts with dsRNA increases the expressions of TLR3, IFN-α, IFN-β, and IL-1β. We also confirmed that this antiviral response elicited against H4N6 LPAIV infection correlates, but is not attributable to type 1 IFNs or IL-1β. Our findings imply that the TLR3 ligand, dsRNA, can elicit antiviral response in avian fibroblasts against LPAIV infection, highlighting potential value of dsRNA as an antiviral agent against LPAIV infections. However, further investigations are required to determine the potential role of other innate immune mediators or combination of the tested cytokines in the dsRNA-mediated antiviral response against H4N6 LPAIV infection.
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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".