Impact of former smoking exposure on airway eosinophilic activation and autoimmunity in patients with severe asthma
Bibliographic record
Abstract
INTRODUCTION: Severe eosinophilic asthma is characterised by frequent exacerbations and a relative insensitivity to steroids. Experimentally, smoking may induce eosinophilic airway inflammation, but the impact in patients with severe asthma is not clear. OBJECTIVE: To investigate the association between smoking exposure in patients with severe asthma, and eosinophilic inflammation and activation, as well as airway autoimmunity and steroid responsiveness. METHODS: Patients with severe asthma according to European Respiratory Society/American Thoracic Society criteria were assessed with sputum samples, analysed by cell differential count, and for the presence of free eosinophil granules (FEGs), autoantibodies against eosinophil peroxidase (EPX) and macrophage receptor with collagenous structure (MARCO). A subgroup of patients with eosinophilic airway inflammation was re-assessed after a 2-week course of prednisolone. RESULTS: 132 severe asthmatics were included in the study. 39 (29.5%) patients had ≥10 pack-years of smoking history: 36 (27.3%) were former smokers and three (2.3%) current smokers; and 93 (70.5%) had <10 pack-years exposure. Eosinophilic airway inflammation was more prevalent among patients with ≥10 pack-years (66.7%), compared to patients with <10 pack-years (38.7%, p=0.03), as was the level of FEGs (p=0.001) and both anti-EPX and anti-MARCO (p<0.05 and p<0.0001, respectively). Omitting current smokers did not affect these associations. Furthermore, prednisolone reduced, but did not normalise, sputum eosinophils in patients with a ≥10 pack-year smoking history. CONCLUSION: In patients with severe asthma, a former smoking history is associated with eosinophilic airway inflammation and activation and relative insensitivity to steroids, as well as airway autoimmunity.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".