Influence of smoking on airway inflammation and remodelling in asthma
Bibliographic record
Abstract
BACKGROUND: Although exposure to tobacco smoke has been associated with increased morbidity and mortality, cigarette smoking is still common in the asthmatic population. Induced sputum neutrophilia has been observed in asthmatic smokers, but the effects of regular smoking on their bronchial mucosa morphology remain to be defined. This study documents the inflammatory and remodelling features in bronchial biopsies of smoking compared with non-smoking asthmatics. METHODS: We analysed bronchial biopsies from 24 steroid-naïve young subjects with mild asthma: 12 non-smoking and 12 currently smoking subjects. In addition to airway morphology assessment, inflammation and remodelling were analysed by immunohistochemistry using antibodies against CD3, CD68, major basic protein, neutrophil elastase, and tryptase. Expression of the cytokines IL-4, IL-5, IL-8, IFN-gamma, transforming growth factor-beta, and TNF was determined by in situ hybridization. RESULTS: Compared with non-smoking asthmatic subjects, smoking asthmatics' bronchial mucosa showed squamous cell metaplasia, in addition to increased expression of subepithelial neutrophil elastase, IFN-gamma, and intraepithelial IL-8. CONCLUSIONS: Smoking status modifies morphological and inflammatory processes in young subjects with mild asthma. The changes may possibly affect asthma treatment responses and clinical outcomes.
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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 | 0.000 | 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 | 0.000 |
| 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".