MMP-2: is too low as bad as too high in the cardiovascular system?
Bibliographic record
Abstract
Matrix metalloproteinase (MMP)-2 cleaves a broad spectrum of substrates, including extracellular matrix components (responsible for normal tissue remodeling) and cytokines (modulators of the inflammatory response to physiological insults such as tissue damage). MMP-2 expression is elevated in many cardiovascular pathologies (e.g., myocardial infarction, hypertensive heart disease) where tissue remodeling and inflammatory responses are perturbed. Thus, it has generally been assumed that blockade of MMP-2 activity will yield therapeutic effects. Here, we provide a counterargument to this dogma based on 1) preclinical studies on Mmp2-null ( Mmp2−/−) mice and 2) clinical studies on patients with inactivating MMP2 gene mutations. Furthermore, we put forward the hypothesis that, when MMP-2 activity falls below baseline, the bioavailability of proinflammatory cytokines normally cleaved and inactivated by MMP-2 increases, leading to the production of cytokines and cardiac secretion of phospholipase A2activity into the circulation, which stimulate systemic inflammation that perturbs lipid metabolism in target organs. Finally, we suggest that insufficient understanding of the consequences of MMP-2 deficiency remains a major factor in the failure of MMP-2 inhibitor-based therapeutic approaches. This paucity of knowledge precludes our ability to effectively intervene in cardiovascular and noncardiovascular pathologies at the level of MMP-2.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".