Endothelial-to-Mesenchymal Transition in Cardiovascular Pathophysiology
Bibliographic record
Abstract
Under different pathophysiological conditions, endothelial cells lose endothelial phenotype and gain mesenchymal cell-like phenotype via a process known as endothelial-to-mesenchymal transition (EndMT). At the molecular level, endothelial cells lose the expression of endothelial cell-specific markers such as CD31/platelet-endothelial cell adhesion molecule, von Willebrand factor, and vascular-endothelial cadherin and gain the expression of mesenchymal cell markers such as α-smooth muscle actin, N-cadherin, vimentin, fibroblast specific protein-1, and collagens. EndMT is induced by numerous different pathways triggered and modulated by multiple different and often redundant mechanisms in a context-dependent manner depending on the pathophysiological status of the cell. EndMT plays an essential role in embryonic development, particularly in atrioventricular valve development; however, EndMT is also implicated in the pathogenesis of several genetically determined and acquired diseases, including malignant, cardiovascular, inflammatory, and fibrotic disorders. Among cardiovascular diseases, aberrant EndMT is reported in atherosclerosis, pulmonary hypertension, valvular disease, fibroelastosis, and cardiac fibrosis. Accordingly, understanding the mechanisms behind the cause and/or effect of EndMT to eventually target EndMT appears to be a promising strategy for treating aberrant EndMT-associated diseases. However, this approach is limited by a lack of precise functional and molecular pathways, causes and/or effects, and a lack of robust animal models and human data about EndMT in different diseases. Here, we review different mechanisms in EndMT and the role of EndMT in various cardiovascular diseases.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".