Within-host model of respiratory virus shedding and antibody response to H9N2 avian influenza virus vaccination and infection in chickens
Bibliographic record
Abstract
Avian influenza virus (AIV) H9N2 subtype is an infectious pathogen that can affect both the respiratory and gastrointestinal systems in chickens and continues to have an important economic impact on the poultry industry. While the host innate immune response provides control of virus replication in early infection, the adaptive immune response aids to clear infections and prevent future invasion. Modelling virus-innate immune response pathways can improve our understanding of early infection dynamics and help to guide our understanding of virus shedding dynamics that could lead to reduced transmission between hosts. While some countries use vaccines for the prevention of H9N2 AIV in poultry, the virus continues to be endemic in regions of Eurasia and Africa, indicating a need for improved vaccine efficacy or vaccination strategies. Here we explored how three type-I interferon (IFN) pathways affect respiratory virus shedding patterns in infected chickens using a within-host model. Additionally, prime and boost vaccination strategies for a candidate H9N2 AIV vaccine are assessed for the ability to elicit seroprotective antibody titres. The model demonstrates that inclusion of virus sensitivity to intracellular type-I IFN pathways results in a shedding pattern most consistent with virus titres observed in infected chickens, and the inclusion of a cellular latent period does not improve model fit. Furthermore, early administration of a booster dose two weeks after the initial vaccine is administered results in seroprotective titres for the greatest length of time for both broilers and layers. These results demonstrate that type-I IFN intracellular mechanisms are required in a model of respiratory virus shedding in H9N2 AIV infected chickens, and also highlights the need for improved vaccination strategies for laying hens.
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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 | 0.001 | 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".