Induction of ferroptosis promotes vascular smooth muscle cell phenotypic switching and aggravates neointimal hyperplasia in mice
Bibliographic record
Abstract
BACKGROUND: Stent implantation-induced neointima formation is a dominant culprit in coronary artery disease treatment failure after percutaneous coronary intervention. Ferroptosis, an iron-dependent regulated cell death, has been associated with various cardiovascular diseases. However, the effect of ferroptosis on neointima formation remains unclear. METHODS: The mouse common right carotid arteries were ligated for 16 or 30 days, and ligated tissues were collected for further analyses. Primary rat vascular smooth muscle cells (VSMCs) were isolated from the media of aortas of Sprague-Dawley (SD) rats and used for in vitro cell culture experiments. RESULTS: Ferroptosis was positively associated with neointima formation. In vivo, RAS-selective lethal 3 (RSL3), a ferroptosis activator, aggravated carotid artery ligation-induced neointima formation and promoted VSMC phenotypic conversion. In contrast, a ferroptosis inhibitor, ferrostatin-1 (Fer-1), showed the opposite effects in mice. In vitro, RSL3 promoted rat VSMC phenotypic switching from a contractile to a synthetic phenotype, evidenced by increased contractile markers (smooth muscle myosin heavy chain and calponin 1), and decreased synthetic marker osteopontin. The induction of ferroptosis by RSL3 was confirmed by the increased expression level of ferroptosis-associated gene prostaglandin-endoperoxide synthase 2 (Ptgs2). The effect of RSL3 on rat VSMC phenotypic switching was abolished by Fer-1. Moreover, N-acetyl-L-cysteine (NAC), the reactive oxygen species inhibitor, counteracted the effect of RSL3 on the phenotypic conversion of rat VSMCs. CONCLUSIONS: Ferroptosis induces VSMC phenotypic switching and accelerates ligation-induced neointimal hyperplasia in mice. Our findings suggest inhibition of ferroptosis as an attractive strategy for limiting vascular restenosis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".