MiR-122 knockdown regulates vascular smooth muscle cells phenotypic switching through enhanced FOXO3 expression
Bibliographic record
Abstract
Vascular smooth muscle cells (VSMCs) phenotypic switching is identified as enhanced dedifferentiation, proliferation, and migration ability of VSMCs, in which microRNAs have been identified as important regulators of the process. The present study is aimed to explore the pathophysiological effect of miR-122 on VSMC phenotypic modulation. Here, the result showed that the decreased miR-122 expression was found in VSMCs subjected to platelet-derived growth factor-BB (PDGF-BB) treatment. Next, we investigated the response of miR-122 knockdown in VSMCs with PDGF-BB stimulation. MiR-122 silencing showed increased proliferation and migration capability, whereas attenuated the differentiation markers expression. The above results were reversed by miR-122 overexpression. Finally, we further demonstrated that FOXO3 was an important target for miR-122. Collectively, we demonstrated that miR-122 silencing promoted VSMC phenotypic modulation partially through upregulated FOXO3 expression that indicated miR-122 may be a novel therapeutic target for neointimal formation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".