IL-4Rα blockade reduces influenza-associated morbidity in a murine model of allergic asthma
Bibliographic record
Abstract
Abstract Background Asthma was identified as the most common comorbidity in hospitalized patients during the 2009 H1N1 influenza pandemic. We determined using a murine model of allergic asthma whether these mice experienced increased morbidity from pandemic H1N1 (pH1N1) viral infection and whether blockade of interleukin-4 receptor α (IL-4Rα), a critical mediator of Th2 signalling, improved their outcomes. Methods Male BALB/c mice were intranasally sensitized with house dust mite antigen (Der p 1) for 2 weeks; the mice were then inoculated intranasally with a single dose of pandemic H1N1 (pH1N1). The mice were administered intraperitoneally anti-IL-4Rα through either a prophylactic or a therapeutic treatment strategy. Results Infection with pH1N1 of mice sensitized to house dust mite (HDM) led to a 24% loss in weight by day 7 of infection (versus 14% in non-sensitized mice; p < .05). This was accompanied by increased viral load in the airways and a dampened anti-viral host responses to the infection. Treatment of HDM sensitized mice with a monoclonal antibody against IL-4Rα prior to or following pH1N1 infection prevented the excess weight loss, reduced the viral load in the lungs and ameliorated airway eosinophilia and systemic inflammation related to the pH1N1 infection. Conclusion Together, these data implicate allergic asthma as a significant risk factor for H1N1-related morbidity and reveal a potential therapeutic role for IL-4Rα signalling blockade in reducing the severity of influenza infection in those with allergic airway disease.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".