Cell-Matrix Interactions and Matricrine Signaling in the Pathogenesis of Vascular Calcification
Bibliographic record
Abstract
Vascular calcification is a complex pathological process occurring in patients with atherosclerosis, type 2 diabetes, and chronic kidney disease. The extracellular matrix, via matricrine-receptor signaling plays important roles in the pathogenesis of calcification. Calcification is mediated by osteochondrocytic-like cells that arise from transdifferentiating vascular smooth muscle cells. Recent advances in our understanding of the plasticity of vascular smooth muscle cell and other cells of mesenchymal origin have furthered our understanding of how these cells transdifferentiate into osteochondrocytic-like cells in response to environmental cues. In the present review, we examine the role of the extracellular matrix in the regulation of cell behavior and differentiation in the context of vascular calcification. In pathological calcification, the extracellular matrix not only provides a scaffold for mineral deposition, but also acts as an active signaling entity. In recent years, extracellular matrix components have been shown to influence cellular signaling through matrix receptors such as the discoidin domain receptor family, integrins, and elastin receptors, all of which can modulate osteochondrocytic differentiation and calcification. Changes in extracellular matrix stiffness and composition are detected by these receptors which in turn modulate downstream signaling pathways and cytoskeletal dynamics, which are critical to osteogenic differentiation. This review will focus on recent literature that highlights the role of cell-matrix interactions and how they influence cellular behavior, and osteochondrocytic transdifferentiation in the pathogenesis of cardiovascular calcification.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| 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".