Epithelial Expression of Profibrotic Mediators in a Model of Allergen-Induced Airway Remodeling
Bibliographic record
Abstract
Airway remodeling, including subepithelial fibrosis, is a characteristic feature of asthma and likely contributes to the pathogenesis of airway hyperresponsiveness. We examined expression of genes related to airway wall fibrosis in a model of chronic allergen-induced airway dysfunction using laser capture microdissection and quantitative real-time PCR. BALB/c mice were sensitized and subjected to chronic ovalbumin exposure over a 12-wk period, after which they were rested and then harvested 2 and 8 wk after the last exposure. Chronic allergen-exposed mice had significantly increased indices of airway remodeling and airway hyperreactivity at all time points, although no difference in expression of fibrosis-related genes was found when mRNA extracted from whole lung was examined. In contrast, fibrosis-related gene expression was significantly upregulated in mRNA obtained from microdissected bronchial wall at 2 wk after chronic allergen exposure. In addition, when bronchial wall epithelium and smooth muscle were separately microdissected, gene expression of transforming growth factor-beta1 and plasminogen activating inhibitor-1 were significantly upregulated only in the airway epithelium. These data suggest that transforming growth factor-beta1 and other profibrotic mediators produced by airway wall, and specifically, airway epithelium, play an important role in the pathophysiology of airway remodeling.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".