Vascular smooth muscle remodeling in health and disease
Bibliographic record
Abstract
In blood vessels, vascular smooth muscle cells (VSMCs) generally exist in two major phenotypes: contractile and non-contractile (synthetic). The contractile phenotype is predominant and includes quiescent or differentiated VSMCs, which function as the regulators of blood vessel diameter and blood flow. According to some literature in the field, contractile VSMCs do not switch to the non-contractile phenotype due to the activation of specific transcription factors that are considered as guardians of the contractile phenotype. However, a vast amount of the literature uses the terms remodeling and phenotype switching of contractile VSMCs interchangeably based mainly on studies dealing with atherosclerosis. The use of the terms remodeling and switching to describe changes in phenotype based on morphological criteria can be confusing. The term remodeling was first used to describe morphological changes in the heart and was soon used to describe phenotype changes of contractile VSMCs based on morphological criteria. The latter were introduced in early studies, and new molecular criteria were later added, including changes in gene expression, which could be irreversible. In this review, we will discuss the different views concerning remodeling and possible switching of contractile VSMCs to a non-contractile phenotype. We conclude that only remodeling of contractile VSMCs may take place upon vascular injury and disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".