Differential expression of mast cells in the small airways and alveolar septa of current smokers and patients with small airway disease and COPD
Bibliographic record
Abstract
Background COPD patients suffer from dysregulated and suppressed immune functionality, determined by their loss of degranulating capacity. Here we provide crucial information on the presence of degranulated mast cells (MCs) in COPD airways and demonstrate their relationship to lung physiology and airway remodelling. Methods Small airway lung resections from non-smoking controls (NC), normal lung function smokers (NLFS), small airway disease (SAD), and mild-to-moderate COPD current smokers (COPD-CS) and ex-smokers (COPD-ES) were dual immuno-stained with MC tryptase and degranulation marker lysosome-associated membrane protein (LAMP)-1. Total MCs, degranulating MCs and non-MCs were enumerated in small airway epithelium and subepithelium, and in alveolar septa. Results In the small airway wall subepithelial areas, COPD-CS and COPD-ES patients had significantly lower MCs than the NC group (p<0.05), although the numbers were considerably higher in the small airway epithelium (p<0.01). Degranulating non-MCs were higher in SAD (p<0.05) than in COPD in the small airway subepithelium. In contrast, there were significant increases in total MCs (degranulated and non-degranulated) and degranulated non-MCs in the alveolar septum of COPD patients compared with the NC group (p<001). The lower numbers of MCs in the subepithelium correlated with lower forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) and forced expiratory flow at 25–75% of FVC (FEF25–75%), higher smoking rates in COPD patients, and increased small airway wall thickness and extracellular matrix. The increase in MCs in the alveolar septum negatively correlated with FEF25–75%. Conclusions This study is the first to assess the differential pattern of MC, degranulating MC and non-MC populations in the small airways and alveoli of COPD patients. The spatial positioning of the MCs within the airways showed variable correlations with lung function.
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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 | 0.000 |
| 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".