Matrix metalloproteinase activation by free neutrophil elastase contributes to bronchiectasis progression in early cystic fibrosis
Bibliographic record
Abstract
Neutrophil elastase is the most significant predictor of bronchiectasis in early-life cystic fibrosis; however, the causal link between neutrophil elastase and airway damage is not well understood. Matrix metalloproteinases (MMPs) play a crucial role in extracellular matrix modelling and are activated by neutrophil elastase. The aim of this study was to assess if MMP activation positively correlates with neutrophil elastase activity, disease severity and bronchiectasis in young children with cystic fibrosis.Total MMP-1, MMP-2, MMP-7, MMP-9, tissue inhibitor of metalloproteinase (TIMP)-2 and TIMP-1 levels were measured in bronchoalveolar lavage fluid collected from young children with cystic fibrosis during annual clinical assessment. Active/pro-enzyme ratio of MMP-9 was determined by gelatin zymography. Annual chest computed tomography imaging was scored for bronchiectasis.A higher MMP-9/TIMP-1 ratio was associated with free neutrophil elastase activity. In contrast, MMP-2/TIMP-2 ratio decreased and MMP-1 and MMP-7 were not detected in the majority of samples. Ratio of active/pro-enzyme MMP-9 was also higher in the presence of free neutrophil elastase activity, but not infection. Across the study cohort, both MMP-9/TIMP-1 and active MMP-9 were associated with progression of bronchiectasis.Both MMP-9/TIMP-1 and active MMP-9 increased with free neutrophil elastase and were associated with bronchiectasis, further demonstrating that free neutrophil elastase activity should be considered an important precursor to cystic fibrosis structural disease.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".