Long-term effects of bronchial thermoplasty on airway smooth muscle and collagen deposition in severe asthma
Bibliographic record
Abstract
Background: Bronchial thermoplasty (BT) improves asthma symptoms by reducing the airway smooth muscle (ASM) mass. A durable efficacy and safety of BT on asthma control has been documented out to 5 years in patients with severe persistent asthma. The ASM reduction has been demonstrated in bronchial biopsies taken 3 weeks or 3 months post-BT. BT also decreased type 1 collagen deposition and sub-epithelial basement membrane thickening. The persistence of these structural changes over time with durable clinical benefits observed in clinical trials remains uncertain. Objectives: To assess long-term efficacy of BT on ASM mass and sub-epithelial collagen deposition. Methods: Nine subjects (age: 30-70 years; FEV1: 60-105% of predicted; ICS: 500-1500µg fluticasone equivalent) with persistent asthma underwent BT procedures. Bronchial biopsies were taken before (baseline), and at 3-14 weeks and 27-48 months after BT. Histology and immunohistochemistry studies were performed. Clinical evaluation was performed at 12 and ≥27 months post-BT. Results: ASM area decreased from 11.8% ±1.24% at baseline to 4.7± 0.95% at week 3 post BT and this decrease persisted at ≥27 months post-BT: 4.6±1.05%, P=0.0002 compared to baseline. The type I collagen deposition decreased from 6.7±0.4 µm at baseline to 4.5±0.5 µm 3 weeks post-BT and remained unchanged at long-term followup: 4.6±0.5 µm (P=0.003). At 12 and ≥27 months, BT improved significantly the Asthma Control Scoring Score compared to baseline and decreased the number of severe exacerbations. Conclusion: These data showed long-term (≥27 months) benefits from BT in terms of ASM mass reduction, sub-epithelial collagen deposition and asthma control.
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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 | 0.001 |
| 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".