Adenovirus IL-13–Induced Airway Disease in Mice
Bibliographic record
Abstract
Interleukin 13 (IL-13) is considered to be a key driver of the development of airway allergic inflammation and remodeling leading to airway hyperresponsiveness (AHR). How precisely IL-13 leads to the development of airway inflammation, AHR, and mucus production is not fully understood. In order to identify key mediators downstream of IL-13, we administered adenovirus IL-13 to specifically induce IL-13-dependent inflammation in the lungs of mice. This approach was shown to induce cardinal features of lung disease, specifically airway inflammation, elevated cytokines, AHR, and mucus secretion. Notably, the model is resistant to corticosteroid treatment and is characterized by marked neutrophilia, two hallmarks of more severe forms of asthma. To identify IL-13-dependent mediators, we performed a limited-scale two-dimensional SDS-PAGE proteomic analysis and identified proteins significantly modulated in this model. Intriguingly, several identified proteins were unique to this model, whereas others correlated with those modulated in a mouse ovalbumin-induced pulmonary inflammation model. We corroborated this approach by illustrating that proteomic analysis can identify known pathways/mediators downstream of IL-13. Thus, we have characterized a murine adenovirus IL-13 lung model that recapitulates specific disease traits observed in human asthma, and have exploited this model to identify effectors downstream of IL-13. Collectively, these findings will enable a broader appreciation of IL-13 and its impact on disease pathways in the lung.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".