The Intersection of Age and Influenza Severity: Utility of Ferrets for Dissecting the Age-Dependent Immune Responses and Relevance to Age-Specific Vaccine Development
Bibliographic record
Abstract
Many factors impact the host response to influenza virus infection and vaccination. Ferrets have been an indispensable reagent for influenza virus research for almost one hundred years. One of the most significant and well-known factors affecting human disease after infection is host age. Another significant factor is the virus, as strain-specific disease severity is well known. Studying age-related impacts on viral infection and vaccination outcomes requires an animal model that reflects both the physiological and immunological changes that occur with human aging, and sensitivity to differentially virulent influenza viruses. The ferret is uniquely susceptible to a plethora of influenza viruses impacting humans and has proven extremely useful in studying the clinical and immunological pictures of influenza virus infection. Moreover, ferrets developmentally have several of the age-related physiological changes that occur in humans throughout infancy, adulthood, old age, and pregnancy. In this review, we discuss ferret susceptibility to influenza viruses, summarize previous influenza studies using ferrets as models of age, and finally, highlight the application of ferret age models in the pursuit of prophylactic and therapeutic agents to address age-related influenza disease severity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".