Determining the Protective Efficacy of Toll-Like Receptor Ligands to Minimize H9N2 Avian Influenza Virus Transmission in Chickens
Bibliographic record
Abstract
Low-pathogenicity avian influenza viruses (AIV) of the H9N2 subtype can infect and cause disease in chickens. Little is known about the efficacy of immune-based strategies for reducing the transmission of these viruses. The present study investigated the efficacy of Toll-like receptor (TLR) ligands (CpG ODN 2007 and poly(I:C)) to reduce H9N2 AIV transmission from TLR-treated seeder (trial 1) or inoculated chickens (trial 2) to naive chickens. The results from trial 1 revealed that a low dose of CpG ODN 2007 led to the highest reduction in oral shedding, and a high dose of poly(I:C) was effective at reducing oral and cloacal shedding. Regarding transmission, the recipient chickens exposed to CpG ODN 2007 low-dose-treated seeder chickens showed a maximum reduction in shedding with the lowest number of AIV+ chickens. The results from trial 2 revealed a maximum reduction in oral and cloacal shedding in the poly(I:C) high-dose-treated chickens (recipients), followed by the low-dose CpG ODN 2007 group. In these two groups, the expression of type I interferons (IFNs), protein kinase R (PKR), interferon-induced transmembrane protein 3 (IFITM3), viperin, and (interleukin) IL-1β, IL-8, and 1L-18 was upregulated in the spleen, cecal tonsils and lungs. Hence, TLR ligands can reduce AIV transmission in chickens.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".