Curcumin inhibits proliferation, migration and neointimal formation of vascular smooth muscle via activating miR-22
Bibliographic record
Abstract
Curcumin has antitumor, antioxidative, anti-inflammatory, and anti-proliferative properties. To investigate the role of miR-22 during curcumin-induced changes in vascular smooth muscle cells (VSMC) and neointima formation in balloon-injured rat abdominal aorta. Sprague-Dawley rats were randomised to the sham-operated (n = 10), operated control (injured, n = 10), and curcumin treatment (n = 10) groups. miR-22 expression was determined by real-time PCR. SP1 was assessed by western blot and real-time PCR. Rat aortic smooth muscle A7r5 cells were used to determine VSMC proliferation and migration, which were measured by the MTS, EdU staining, Transwell, and wound healing assays. miR-22 levels declined following arterial balloon injury in vivo (48% at 3d, p < 0.05) and serum stimulation in vitro (45% at 24 h, p < 0.01). Functional studies revealed that miR-22 negatively regulated the proliferation and migration of VSMCs by directly targeting the SP1 transcription factor in VSMCs. Curcumin increased the expression of miR-22 (81%, p < 0.05) and decreased the protein expression of SP1 in VSMCs (25%, p < 0.05). miR-22 inhibition was found to attenuate the effects of curcumin on VSMC functions. Curcumin increased miR-22 (46%, p < 0.01), decreased the SP1 protein (19%, p < 0.05), and inhibited vascular neointimal area (48%, p < 0.01) in vivo. The miR-22/SP1 pathway is involved in the protective role of curcumin during arterial balloon injury, but the mechanisms remain unclear. miR-22 is involved in the inhibitory effects of curcumin on VSMCs’ proliferation, migration and neointima hyperplasia after arterial balloon injury in rats. Curcumin could be used to prevent neointimal hyperplasia after angioplasty.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".