Azelnidipine, Not Amlodipine, Induces Secretion of Vascular Endothelial Growth Factor From Smooth Muscle Cells and Promotes Endothelial Tube Formation
Bibliographic record
Abstract
Background: We previously reported that the calcium channel blocker (CCB) nifedipine-induced secretion of vascular endothelial growth factor (VEGF) from human coronary smooth muscle cells (HCSMCs) promoted human coronary endothelial cell (HCEC) tube formation. Therefore, we analyzed whether other CCBs, azelnidipine and amlodipine, also induced the secretion of VEGF and promoted HCEC tube formation, and the underlying molecular mechanisms. Methods: To evaluate the tube formation, HCECs were grown on Matrigel for 18 hours in the supernatants from HCSMCs that had been treated with different kinds of reagents. Concentrations of VEGF in cultured HCSMCs were determined by specific enzyme immunoassays. Nuclear extracts from HCSMCs were prepared, and nuclear factor-kappa B (NF-kappaB) activation was measured by EZ-Detect TM Transcription Factor Kits for NF-kappaB p50 or p65. Results: Although azelnidipine dose-dependently stimulated the significant secretion of VEGF from HCSMCs and this stimulation was abolished by a protein kinase C inhibitor, amlodipine-induced secretion of VEGF was significantly lower than that induced by azelnidipine. The medium derived from azelnidipine (at up to 2 uM)-treated HCSMCs led to HCEC tube formation, whereas that obtained with amlodipine did not. Azelnidipine-induced tube formation was blocked by an inhibitor of kinase insert domain-containing receptor/fetal liver kinase-1 tyrosine kinase. Azelnidipine at up to 2uM induced NF-kappaB activation. Conclusions: Azelnidipine, but not amlodipine, stimulated the secretion of VEGF from HCSMCs and induced HCEC tube formation. This secretion is mediated at least in part via the activation of NF-kappaB. Azelnidipine may have a novel beneficial effect in improving coronary microvascular blood flow in addition to its main effect of lowering blood pressure. Cardiol Res. 2014;5(5):145-150 doi: http://dx.doi.org/10.14740/cr352w
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".