Transforming Growth Factor-β1 Drives Airway Remodeling in Cigarette Smoke–Exposed Tracheal Explants
Bibliographic record
Abstract
Small airway remodeling (SAR) is an important cause of airflow obstruction in cigarette smokers, but whether SAR represents a response to smoke-evoked inflammation or is directly mediated by smoke-induced growth factor production is disputed. To examine this process, we exposed rat tracheal explants, a model free of exogenous inflammatory cells, to cigarette smoke in vitro. Cigarette smoke caused release of active transforming growth factor (TGF)-beta1, and this was prevented by the oxidant scavenger tetramethythiourea. Nuclear immunostaining for phospho-Smad2, a TGF-beta downstream signaling molecule, was present in epithelial and interstitial cells within 1 h after exposure. Smoke caused upregulation of gene expression of connective tissue growth factor (CTGF), a mediator of TGF-beta fibrogenic effects, within 2 h, and upregulation of procollagen gene expression at 24 h; both changes could be prevented by the TGF-beta antagonist fetuin (alpha2-HS-glycoprotein). In a cell-free system, recombinant human TGF-beta latency-associated peptide was oxidized by cigarette smoke, and smoke released active TGF-beta1 from recombinant latent TGF-beta1 via an oxidant mechanism. These experiments suggest that SAR in cigarette smokers may be caused by direct, smoke-mediated, oxidant-driven induction of growth factor signaling in the airway wall, and that SAR does not necessarily require exogenous inflammatory cells.
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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 | 0.000 | 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".