Matrix metalloproteinases in acute lung injury: mediators of injury and drivers of repair
Bibliographic record
Abstract
Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) comprise a spectrum of acute inflammatory pulmonary oedema resulting in refractory hypoxaemia in the absence of an underlying cardiogenic cause. There are multiple pulmonary and extrapulmonary causes and ALI/ARDS patients are a clinically heterogeneous group with associated high morbidity and mortality. Inflammatory injury to the alveolar epithelial and endothelial capillary membrane is a central event in the pathogenesis of ALI/ARDS, and involves degradation of the basement membrane. Matrix metalloproteinases (MMPs) have been implicated in a wide variety of pulmonary pathologies and are capable of degrading all components of the extracellular matrix including the basement membrane and key non-matrix mediators of lung injury such as chemokines and cell surface receptors. While many studies implicate MMPs in the injurious process, there are significant gaps in our knowledge of the role of specific proteases at different phases of injury and repair. This article examines the role of MMPs in injury and repair of the alveolar epithelial-endothelial capillary barrier and discusses the potential for MMP modulation in the prevention and treatment of ALI. The need for further mechanistic and in vivo studies to inform appropriate subsequent clinical trials of MMP modulation will be highlighted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".