MicroRNA regulation of vascular smooth muscle cells and its significance in cardiovascular diseases
Bibliographic record
Abstract
Cardiovascular disease (CVD) is among the leading causes of death worldwide. MicroRNAs (miRNAs), regulatory molecules that repress protein expression, have attracted considerable attention in CVD research. The vasculature plays a big role in CVD development and progression and dysregulation of vascular cells underlies the root of many vascular diseases. This review provides a brief introduction of the biogenesis of miRNAs and exosomes, followed by overview of the regulatory mechanisms of miRNAs in vascular smooth muscle cells (VSMCs) intracellular signaling during phenotypic switching, senescence, calcification, and neointimal hyperplasia. Evidence of extracellular signaling of VSMCs and other cells via exosomal and circulating miRNAs is also presented. Lastly, current drawbacks and limitations of miRNA studies in CVD research and potential ways to overcome these disadvantages are discussed in detail. In-depth understanding of VSMC regulation via miRNAs will add substantial knowledge and advance research in diagnosis, disease progression, and (or) miRNA-derived therapeutic approaches in CVD research.
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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 | 0.001 | 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 | 0.002 |
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".