Soluble IL-4 Receptor Inhibits Airway Inflammation Following Allergen Challenge in a Mouse Model of Asthma
Bibliographic record
Abstract
In vitro and in vivo studies, in both animal models and human asthmatics, have implicated IL-4 as an important inflammatory mediator in asthma. In a murine asthma model, we examined the anti-inflammatory activities of soluble IL-4R (sIL-4R). In this model, mice sensitized to OVA by i.p. and intranasal (i.n.) routes are challenged with the allergen by i.n. administration. The OVA challenge elicits an eosinophil infiltration into the lungs, with widespread mucus occlusion of the airways, and results in bronchial hyperreactivity. sIL-4R (0.1-100 microgram) was administered by either i.n. or i.p. routes before OVA challenge in OVA-sensitized mice. Both blood and bronchoalveolar lavage fluid levels of sIL-4R were significantly elevated compared with controls by i.n. delivery of 100 microgram sIL-4R; i.p. delivery of 100 microgram sIL-4R only raised blood levels of sIL-4R. The i.n. administration of 100 microgram sIL-4R before allergen challenge significantly reduced late phase pulmonary inflammation, blocking airway eosinophil infiltration, VCAM-1 expression, and mucus hypersecretion. In contrast, i.p. delivery of 100 microgram sIL-4R inhibited only the influx of eosinophils into the lungs, but not airway mucus release. Furthermore, sIL-4R treatment by either i.n. or i.p. routes did not reduce airway hyperreactivity in response to methacholine challenge. Thus, elevating airway levels of sIL-4R through the administration of exogenous sIL-4R is effective in blocking the late phase pulmonary inflammation that occurs in this murine allergen-challenge asthma model. These results suggest that sIL-4R may have beneficial anti-inflammatory effects in asthmatic patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".