Matrix Metalloproteinase-2 Contributes to Ischemia-Reperfusion Injury in the Heart
Bibliographic record
Abstract
BACKGROUND: Matrix metalloproteinases (MMPs) contribute to collagen degradation and remodeling of the extracellular matrix after myocardial infarction; however, their role in myocardial dysfunction immediately after ischemia and reperfusion is unknown. METHODS AND RESULTS: We measured the release of MMPs into the coronary effluent of isolated, perfused rat hearts during aerobic perfusion and reperfusion after ischemia. Aerobically perfused control hearts expressed pro-MMP-2 and MMP-2, as well as an unidentified 75-kDa gelatinase. These enzymes were also detected in the coronary effluent. After 20 minutes of global no-flow ischemia, there was a marked increase in pro-MMP-2 in the coronary effluent that peaked within the first minute of reperfusion. The release of pro-MMP-2 into the coronary effluent during reperfusion was enhanced with increasing duration of ischemia and correlated negatively with the recovery of mechanical function during reperfusion (r(2)=0.99). MMP-2 antibody (1.5 to 15 microg/mL) and the inhibitors of MMPs doxycycline (10 to 100 micromol/L) and o-phenanthroline (3 to 100 micromol/L) improved whereas MMP-2 worsened the recovery of mechanical function during reperfusion. CONCLUSIONS: These results show that acute release of MMP-2 during reperfusion after ischemia contributes to cardiac mechanical dysfunction. The inhibition of MMPs may be a novel pharmacological strategy for the treatment of ischemia-reperfusion injury.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".