THE RELATION OF CIGARETTE SMOKING TO INCREASED EXTRACELLULAR MATRIX IN THE LAYER OF AIRWAY SMOOTH MUSCLE
Bibliographic record
Abstract
Reduced FEV1 in smokers may be due to the combined effects of pre-existing airway remodeling and/or progressive loss of lung function (due to emphysema or airway remodeling). We have previously observed an inverse relationship between FEV1 and the volume fraction of extracellular matrix (VVECM) in the layer of airway smooth muscle (ASM). Given the increased decline in FEV1 in smokers, we hypothesized that this relationship reflects progressive remodeling of the ASM in smokers. Aim: To examine the relationship between smoke exposure and VVECM within the ASM layer in smokers and non-smokers undergoing lung resection. Methods: Prior to surgery, FEV1 was measured and smoking history was recorded. Post-operative tissue was obtained from current smokers (n=10), ex-smokers (n=20) and never smokers (n=9). Transverse sections (0.5μm thick) of large and small airway were stained with the Masson9s trichrome technique. VVECM within the ASM layer was estimated and related to age, duration of smoking and number cigarettes smoked per week. Results: Mean pack-years of smoking was 49±39 (range 4-160). VVECM was increased in current (p=0.001) and ex-smokers (p=0.016) compared with non-smokers in the large airways. VVECM was not related to pack years (r2=0.036, p=0.38), duration of smoking (r2=0.035, p=0.41), number of cigarettes smoked (r2=0.008, p=0.69) or age (r2=0.007, p=0.68) in large or small airways. Conclusion: Although increase VVECM in the ASM layer is related to FEV1 and current smoking, we could not detect a dose effect of smoking. This suggests a non-progressive effect of smoking and the presence of pre-existing remodeling of the ASM layer or a threshold effect of cigarette smoking.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".