Lack of IL-1 Receptor Signaling Reduces Spontaneous Airway Eosinophilia in Juvenile Mice with Muco-Obstructive Lung Disease
Bibliographic record
Abstract
Abstract Previous studies demonstrated spontaneous type 2 airway inflammation with eosinophilia in juvenile Scnn1b (sodium channel, non–voltage-gated 1, β-subunit)-transgenic (Scnn1b-Tg) mice with muco-obstructive lung disease. IL-1 receptor (IL-1R) signaling has been implicated in allergen-driven airway disease; however, its role in eosinophilic inflammation in muco-obstructive lung disease remains unknown. In this study, we examined the role of IL-1R signaling in the development of airway eosinophilia and type 2 inflammation in juvenile Scnn1b-Tg mice. We determined effects of genetic deletion of Il1r1 (IL-1 receptor type I) on eosinophil counts, transcript levels of key type 2 cytokines, markers of eosinophil activation and apoptosis, and tissue morphology in lungs of Scnn1b-Tg mice at different time points during neonatal development. Furthermore, we measured endothelial surface expression of intercellular adhesion molecule 1 (ICAM-1), an integrin involved in eosinophil transendothelial migration, and determined effects of eosinophil depletion using an anti–IL-5 antibody on lung morphology. Lack of IL-1R reduced airway eosinophilia and structural lung damage, but it did not reduce concentrations of type 2 cytokines and associated eosinophil activation in Scnn1b-Tg mice. Structural lung damage in Scnn1b-Tg mice was also reduced by eosinophil depletion. Lack of IL-1R was associated with reduced expression of ICAM-1 on lung endothelial cells and reduced eosinophil counts in lungs from Scnn1b-Tg mice. We conclude that IL-1R signaling is implicated in airway eosinophilia independent of type 2 cytokines in juvenile Scnn1b-Tg mice. Our data suggest that IL-1R signaling may be relevant in the pathogenesis of eosinophilic airway inflammation in muco-obstructive lung diseases, which may be mediated in part by ICAM-1–dependent transmigration of eosinophils into the lungs.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".