Acute cigarette smoke exposure activates apoptotic and inflammatory programs but a second stimulus is required to induce epithelial to mesenchymal transition in COPD epithelium
Bibliographic record
Abstract
BACKGROUND: Smoking and aberrant epithelial responses are risk factors for lung cancer as well as chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis. In these conditions, disease progression is associated with epithelial damage and fragility, airway remodelling and sub-epithelial fibrosis. The aim of this study was to assess the acute effects of cigarette smoke on epithelial cell phenotype and pro-fibrotic responses in vitro and in vivo. RESULTS: Apoptosis was significantly greater in unstimulated cells from COPD patients compared to control, but proliferation and CXCL8 release were not different. Cigarette smoke dose-dependently induced apoptosis, proliferation and CXCL8 release with normal epithelial cells being more responsive than COPD patient derived cells. Cigarette smoke did not induce epithelial-mesenchymal transition. In vivo, cigarette smoke exposure promoted epithelial apoptosis and proliferation. Moreover, mimicking a virus-induced exacerbation by exposing to mice to poly I:C, exaggerated the inflammatory responses, whereas expression of remodelling genes was similar in both. CONCLUSIONS: Collectively, these data indicate that cigarette smoke promotes epithelial cell activation and hyperplasia, but a secondary stimulus is required for the remodelling phenotype associated with COPD.
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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) | 0.002 | 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".