Bronchial matrix and inflammation respond to inhaled steroids despite ongoing allergen exposure in asthma
Bibliographic record
Abstract
BACKGROUND: Inflammatory and structural changes of the airway mucosa are chronic features of asthma. The mechanisms underlying these changes and their modulation by steroid prophylaxis have not been clarified. OBJECTIVE: We postulated that asymptomatic ongoing allergen exposure could drive airway inflammation as well as changes in the extracellular matrix (ECM), and that inhaled steroids could prevent this. METHODS: Therefore, we exposed patients with mild asthma to 2 weeks of repeated low-dose allergen, with concomitant inhaled steroid or placebo treatment. Bronchial biopsies, which were taken before and after this exposure, were stained and digitally analysed. The ECM proteins in asthmatics were also compared with a normal control group. RESULTS: Low-dose allergen exposure alone resulted in a significant increase of bronchial epithelial macrophages. Despite ongoing allergen exposure, inhaled steroids reduced the numbers of mucosal eosinophils, neutrophils and T lymphocytes. At baseline, the mean density of the proteoglycans (PGS) biglycan and decorin were, respectively, higher and lower in the bronchial mucosa of asthmatics as compared with normal controls. Steroid treatment, during allergen exposure, increased the mean density of the PGS biglycan and versican. CONCLUSION: We conclude that chronic allergen exposure induces inflammatory changes in the bronchial mucosa. Despite ongoing allergen exposure, steroid treatment decreases mucosal inflammatory cells while altering PG density. The latter observation highlights the need to examine steroid-induced changes closely in the airway structure in patients with asthma.
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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 | 0.000 | 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".