The Inhibitory Mechanisms of Amlodipine in Human Vascular Smooth Muscle Cell Proliferation.
Bibliographic record
Abstract
The abnormal proliferation of vascular smooth muscle cells (VSMCs) is closely related to vascular diseases. There is growing evidence that calcium antagonists inhibit VSMC growth/proliferation, yet their molecular mechanisms remain to be determined. Recent reports suggest that p42/p44 mitogen-activated protein kinases (MAPKs) play an important role in cell growth and proliferation induced by growth factors. This study was designed to determine whether these MAPKs are involved in VSMC proliferation induced by basic fibroblast growth factor (bFGF) and to examine the inhibitory effect of amlodipine. Human VSMCs were obtained from inner mammary artery. p42/p44 MAPKs activity was measured by immunoblotting assay using anti-p42/p44 phospho-MAPK antibody. 1) bFGF (20 ng/ml) significantly activated p42/p44 MAPKs with a peak time of 5-15 min, which was maintained for 3 h. PD98059 (100 nM-10 microM), a specific inhibitor of MAPK kinase, inhibited bFGF-induced p42/p44 MAPKs activation in a dose-dependent manner. 2) Amlodipine (1-100 nM) dose-dependently inhibited p42/p44 MAPKs activation by bFGF. 3) Amlodipine (10 nM) could inhibit both short-term and long-term p42/p44 MAPKs activation by bFGF. Our results indicate that bFGF could activate p42/p44 MAPKs. Amlodipine, which could inhibit bFGF-induced human VSMC proliferation, inhibited both short-term and sustained p42/p44 MAPKs activation by bFGF, suggesting that bFGF-induced VSMC proliferation may be related to p42/p44 MAPKs activation, and that the antiproliferative effect of amlodipine may be related to its inhibition of p42/p44 MAPKs activation.
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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) | 0.001 | 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".