Cigarette Smoke Drives Small Airway Remodeling by Induction of Growth Factors in the Airway Wall
Bibliographic record
Abstract
BACKGROUND: Small airway remodeling (SAR) is an important cause of airflow obstruction in cigarette smokers with chronic obstructive pulmonary disease, but the pathogenesis of SAR is not understood. OBJECTIVE: To determine whether smoke causes production of profibrotic growth factors in the airway wall. METHODS: We exposed C57Bl/6 mice to cigarette smoke for up to 6 mo and examined growth factor/procollagen gene expression in laser-capture microdissected small airways by real-time reverse transcription-polymerase chain reaction. RESULTS: With a single smoke exposure, increases in procollagen, connective tissue growth factor (CTGF), transforming growth factor (TGF)-beta(1), platelet-derived growth factor (PDGF)-A and -B expression were seen 2 h after the start of smoking and declined to baseline by 24 h. With repeated exposures and at killing of animals 24 h after the last exposure, increases in procollagen, CTGF, PDGF-B, and (minimally) PDGF-A expression persisted through 1 wk, 1 mo, and 6 mo. TGF-beta(1) gene expression declined over time; however, increased immunochemical staining for phopho-Smad 2 was present at all time points, indicating continuing TGF-beta downstream signaling. Morphometric analysis showed that the small airways in smoke-exposed mice had more collagen at 6 mo. CONCLUSIONS: These findings suggest that smoke can induce growth factor and procollagen production in small airways in a time frame that initially is too short for a significant inflammatory response and that profibrotic growth factor and procollagen gene expression become self-sustaining with repeated smoke exposures. These results imply that the pathogenesis of and possible treatment approaches to emphysema and small airway remodeling might be quite different.
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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 | 0.000 |
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".