Cigarette Smoke Drives Small Airway Remodeling by Induction of Growth Factors in the Airway Wall
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it