Matrix Metalloproteinase-9 Increases in the Sputum from Allergic Occupational Asthma Patients after Specific Inhalation Challenge
Bibliographic record
Abstract
BACKGROUND: Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) play a role in the pathogenesis of asthma. MMP-9 increases in the sputum of asthmatic patients after bronchial challenge with common allergens. We sought to assess whether a high-molecular-weight occupational allergen was able to induce changes in MMP-9 as well as in other MMPs and TIMPs in subjects with occupational asthma. METHODS: Ten patients underwent specific inhalation challenge (SIC) on 2 consecutive days. We monitored changes in lung function by measuring FEV(1) for 7 h. Induced sputum test was performed at 6 h after sham and flour challenge. The total and differential cell counts were analyzed. Levels of MMPs (specifically MMP-2, MMP-7, MMP-9 and MMP-13) were measured using Fluorokine® MultiAnalyte Profiling kits and a Luminex® Bioanalyzer, while levels of TIMP-1 and TIMP-2 were measured by ELISA. RESULTS: Flour challenge increased the percentage of eosinophils in sputum samples. Asthmatic reactions induced by flour were associated with a significant increase in the sputum level of MMP-9 (p = 0.05), but not in the levels of MMP-2, MMP-7, MMP-13, TIMP-1 and TIMP-2. Sputum levels of MMP-9 measured after flour challenge were nearly significantly correlated (r = 0.67; p = 0.06) with the maximal fall in FEV(1) observed during the asthmatic reaction, but they did not correlate with the number of neutrophils (r = 0.18; p = 0.7) and eosinophils (r = 0.55; p = 0.2). CONCLUSIONS: This study showed that MMP-9 increases in sputum samples from sensitized occupational asthma patients after SIC with flour.
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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 | 0.001 | 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".