Impaired respiratory health induced by cannabis smoking is associated with a sparse airway epithelium and elevated MUC5AC expression
Bibliographic record
Abstract
Introduction: Cannabis is used by 147 million people globally, with smoking as the main method consumption. However, the impact of cannabis smoking on the lungs is not fully understood. Objective: Investigate how cannabis smoking affects airway epithelial morphology and association with respiratory health outcomes. Methods: 11 non-smoking controls (NS) and 20 cannabis smokers (CS) completed St. George’s Respiratory Questionnaires (SGRQ) and underwent bronchoscopy. Epithelial cells from bronchial brushings were cultured as air-liquid interface to establish a pseudostratified epithelium which was stained for epithelial morphometry and mucin quantification. Results: Total SGRQ scores were higher in CS than NS (P<0.01). Compared with NS (Fig. 1A), epithelial cell number was lower in CS (Fig. 1B; P=0.01). Lower epithelial cell number was correlated with increased cough (P<0.01) and worse SGRQ symptom scores (P=0.02). Relative to NS (Fig. 1C), CS (Fig. 1D) had 86% greater MUC5AC expression (P=0.01), with worse SGRQ symptom scores associated with higher MUC5AC expression (P<0.05) across the cohort. Epithelial thickness, cilia length, and MUC5B expression did not differ between groups. Conclusion: Cannabis smoking impairs respiratory health, underpinned by a sparse airway epithelium and greater MUC5AC expression; epithelial disruption may contribute to obstructive airway disease pathology. erj;66/suppl_69/PA3500/F1 F1 F1
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".