Sputum Hyaluronan and Versican in Severe Eosinophilic Asthma
Bibliographic record
Abstract
BACKGROUND: We examined levels of hyaluronan, a matrix glycosaminoglycan and versican, a matrix proteoglycan, in the sputum of asthmatics treated with mepolizumab (anti-IL-5 monoclonal antibody) versus placebo to evaluate the utility of these measurements as possible biomarkers of asthma control and airway remodeling. METHODS: Patients with severe, prednisone-dependent asthma received either mepolizumab or placebo as described in a previously published randomized, double-blind, placebo-controlled study. We measured hyaluronan and versican levels by enzyme-linked immunosorbent assay in sputum collected before and after the 16-week treatment phase. Patients underwent a predefined prednisone tapering schedule if they remained exacerbation free, and sputum eosinophil percentage, asthma control questionnaire (ACQ) and spirometry were monitored. RESULTS: After 6 months of mepolizumab therapy and prednisone tapering, there was a significant increase in sputum hyaluronan in the placebo group compared with baseline (p = 0.003). In contrast, there was a significant decrease in sputum hyaluronan in the active treatment group compared with placebo (p = 0.007), which correlated with improvements in percent forced expiratory volume in 1 s (FEV1%) (p = 0.001) and ACQ scores (p = 0.009) as well as a decrease in sputum eosinophils (p = 0.02). There was a nonsignificant increase in sputum versican in the placebo group (p = 0.16), a decrease in the mepolizumab group (p = 0.13) and a significant inverse correlation between versican reduction and FEV1% improvement (p = 0.03). CONCLUSIONS: Sputum hyaluronan values are reduced with mepolizumab therapy and correlate with improved clinical and spirometry values, suggesting this measurement may serve as a noninvasive biomarker of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".