Role of extracellular matrix in vascular remodeling of hypertension
Bibliographic record
Abstract
PURPOSE OF REVIEW: Arterial stiffness due to alterations in extracellular matrix is one of the mechanisms responsible for increased peripheral resistance in hypertension. Recent evidence points to arterial stiffness as an independent predictor of cardiovascular events. This review focuses on recent advances in the biology of extracellular matrix proteins involved in hypertension-associated vascular changes. RECENT FINDINGS: The vascular extracellular matrix is a complex heterogeneous tissue comprising collagens, elastin, glycoproteins, and proteoglycans. These constituents not only provide mechanical integrity to the vessel wall but also possess a repertoire of insoluble ligands that induce cell signaling to control proliferation, migration, differentiation, and survival. It is now evident that it is not only the quantity but also the quality of the new synthesized extracellular matrix that determines changes in vascular stiffness in hypertension. Also, the control of cross-linking and the interactions between the extracellular matrix and vascular cells seem to be important. SUMMARY: It is now evident that some of the currently used antihypertensive therapies can correct vascular stiffness and fibrosis. A better understanding of molecular mechanisms underlying alterations in extracellular matrix in hypertension will provide insights into novel therapies to reduce arterial stiffness and will identify new roles of established antihypertensive drugs.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".