Effects of low doses of inhaled fluticasone propionate on inflammation and remodelling in persistent‐mild asthma
Bibliographic record
Abstract
In asthma a dysregulation of eosinophil apoptosis and an imbalance of metalloproteinase-9 (MMP-9) and tissue inhibitor metalloproteinase-1 (TIMP-1) play an important role in airway inflammation and remodelling. We evaluated the effects of a low dose of inhaled fluticasone proprionate (FP) (100 microg bid by Diskus) for 4 weeks in 24 steroid naive patients with mild persistent asthma, symptomatic and with a sputum eosinophilia >or=3% on clinical outcomes and inflammatory markers such as the induced sputum eosinophils, the induced sputum apoptotic eosinophils, the levels of MMP-9 and TIMP-1 and their molar ratio in the induced sputum supernatants. After FP treatment forced expiratory volume (FEV1) and FEV1/forced vital capacity values, PEF (L/min), sputum apoptotic eosinophils, and MMP-9/TIMP-1 molar ratio in sputum supernatants of asthmatic subjects were significantly increased in comparison with baseline, while sputum eosinophils significantly decreased. Change (Delta) in FEV1 after treatment with FP negatively correlated with the Delta in sputum eosinophils, while the Delta in MMP-9 values positively correlated with Delta in TIMP-1 values. This study shows that the clinical improvement achieved by the use of low doses of FP in asthmatics is related, at least in part, to the resolution of eosinophilic inflammation and the downregulation of remodelling markers.
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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) | 0.001 | 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 | 0.001 |
| 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".