Intranasal Administration of Alpha Interferon Reduces Seasonal Influenza A Virus Morbidity in Ferrets
Bibliographic record
Abstract
The type I interferon (IFN) response represents one of the first lines of defense against influenza virus infections. In this study, we assessed the protective potential of exogenous IFN-alpha against seasonal and highly pathogenic influenza viruses in ferrets. Intranasal treatment with IFN-alpha several hours before infection with the H1N1 influenza A virus strain A/USSR/90/77 reduced viral titers in nasal washes at least 100-fold compared to mock-treated controls. IFN-treated animals developed only mild and transient respiratory symptoms, and the characteristic fever peak seen in mock-treated ferrets 2 days after infection was not observed. Repeated application of IFN-alpha substantially increased the protective effect of the cytokine treatment. IFN-alpha did not increase survival after infection with the highly pathogenic H5N1 avian influenza A virus strain A/Vietnam/1203/2004. However, viral titers in nasal washes were significantly reduced at days 1 and 3 postinfection. Our study shows that intranasal application of IFN-alpha can protect ferrets from seasonal influenza viruses, which replicate mainly in the upper respiratory tract, but not from highly pathogenic influenza viruses, which also disseminate to the lung. Based on these results, a more intensive evaluation of IFN-alpha as an emergency drug against pandemic influenza A is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".