Inhibition of Matrix Metalloproteinases (MMPs) as a Potential Strategy to Ameliorate Hypertension-Induced Cardiovascular Alterations
Bibliographic record
Abstract
A group of proteases, the matrix metalloproteinases (MMPs) are well known for their capacity to degrade extracellular matrix (ECM) proteins. Particularly MMP-2 and MMP-9 contribute to the degradation and reorganization of the ECM components and are involved in the pathophysiology of cardiovascular remodeling. Imbalanced MMP activity promotes vascular smooth muscle cells and migration and proliferation and endothelial dysfunction, thus resulting in increased cardiovascular stiffness and hypertrophy. Furthermore, MMP-2 cleaves non-ECM protein substrates including cellular receptors and intracellular proteins, thus causing cardiac and vascular dysfunction. It is now becoming clear that increased MMP activity promotes long-lasting cardiovascular structural and functional alterations in both experimental and clinical hypertension, and this alteration may contribute to sustained hypertension and its complications. Other pathogenic mechanisms including activation of the renin-angiotensin-aldosterone system and oxidative stress activate and upregulate MMPs. Therefore, MMP inhibition may prevent the deleterious consequences of hypertension to the cardiovascular system. This review article will focus on growing evidence supporting the relevance of MMPs in hypertension and the effects of MMP inhibitors. Particularly, the effects of doxycycline used as a non selective MMP inhibitor in experimental and clinical studies will be discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".