Cigarette Smoke Produces Airway Wall Remodeling in Rat Tracheal Explants
Bibliographic record
Abstract
Small airway remodeling ("small airways disease") is a common finding in cigarette smokers and is an important cause of airflow obstruction. Airway remodeling is usually attributed to the effects of cigarette smoke-induced inflammation in the airway wall, but little is actually known about its pathogenesis. We exposed rat tracheal explants to cigarette smoke and then maintained them in air organ culture. At 24 hours after smoke exposure, there was a dose-dependent increase in gene expression of procollagen and a significant increase in tissue hydroxyproline, a measure of collagen content. Greater increases in procollagen gene expression were found with repeated smoke exposures. Increased procollagen gene expression could be prevented with SN50, a selective inhibitor of nuclear factor-kappaB activation, and superoxide dismutase, catalase, and tetramethylthiourea, scavengers of active oxygen species. AG1478, an inhibitor of epidermal growth factor receptor signaling, also prevented increased procollagen gene expression, but PD98059 and SB203580, inhibitors of mitogen-activated protein kinases, did not. These findings indicate that cigarette smoke can directly induce airway remodeling, specifically airway wall fibrosis, probably through active oxygen species-dependent transactivation of the epidermal growth factor receptor and subsequent nuclear factor-kappaB activation. Smoke-evoked inflammatory cells are not required for this process.
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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) | 0.000 | 0.001 |
| 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 | 0.001 |
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".