Transcriptional regulation of endothelial-to-mesenchymal transition in cardiac fibrosis: role of myocardin-related transcription factor A and activating transcription factor 3
Bibliographic record
Abstract
The etiology of cardiac fibrogenesis is quite diverse, but a common feature is the presence of activated fibroblasts. Experimental evidence suggests that a subset of cardiac fibroblasts is derived via transition of vascular endothelial cells into fibroblasts by endothelial-to-mesenchymal transition (EndMT). During EndMT, endothelial cells lose their endothelial characteristics and acquire a mesenchymal phenotype. Molecular mechanisms and the transcriptional mediators controlling EndMT in heart during development or disease remain relatively undefined. Myocardin-related transcription factor A facilitates the transcription of cytoskeletal genes by serum response factor during fibrosis; therefore, its specific role in cardiac EndMT might be of importance. Activation of activating transcription factor 3 (ATF-3) during cardiac EndMT is speculative, since ATF-3 responds to a transforming growth factor β (TGF-β) stimulus and controls the expression of the primary epithelial-to-mesenchymal transition markers Snail, Slug, and Twist. Although the role of TGF-β in EndMT-mediated cardiac fibrosis has been established, targeting of the TGF-β ligand has not proven to be a viable anti-fibrotic strategy owing to the broad functional importance of this ligand. Thus, targeting of downstream transcriptional mediators may be a useful therapeutic approach in attenuating cardiac fibrosis. Here, we discuss some of the transcription factors that may regulate EndMT-mediated cardiac fibrosis and their involvement in type 2 diabetes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".