Shikonin inhibits TNF-α-induced growth and invasion of rat aortic vascular smooth muscle cells
Bibliographic record
Abstract
Shikonin is a naphthoquinone compound extracted from the Chinese herb purple gromwell. Shikonin has broad antibacterial, anti-inflammatory, and antitumor activities. The tumor necrosis factor-α (TNF-α)-induced proliferation and invasion of vascular smooth muscle cells (VSMCs) is an important factor that contributes to atherosclerosis. The effects of shikonin on the proliferation and apoptosis of VSMCs have been reported; however, the function of shikonin on TNF-α-mediated growth and invasion of VSMCs during atherosclerosis remains unclear. In this study, we used Western blot, flow cytometry, real-time quantitative PCR, and enzyme-linked immunosorbent assay to investigate the effect of shikonin on the TNF-α-induced growth and invasion of VSMCs and to determine the underlying mechanism. Our results showed that shikonin inhibits the TNF-α-mediated growth and invasion. Further study revealed that shikonin regulates the activation of nuclear factor kappa B and phosphatidyl inositol 3-kinase signaling pathways; modulates the expression of cyclin D1, cyclin E, B-cell lymphoma 2, and Bax; activates caspase-3 and caspase-9; induces cell cycle arrest; and promotes the apoptosis of VSMCs. Together, our results indicate that shikonin may become a promising agent for the treatment of atherosclerosis and they also establish foundation for the development of anti-atherosclerosis drugs.
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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) | 0.000 | 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) | 0.001 | 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".