Insulin-like growth-factor-1-induced PKB signaling and Egr-1 expression is inhibited by curcumin in A-10 vascular smooth muscle cells
Bibliographic record
Abstract
Insulin-like growth factor 1 (IGF-1) is a mitogenic factor that stimulates the signaling pathways responsible for inducing hypertrophic and proliferative responses in vascular smooth muscle cells (VSMC). We have previously demonstrated that IGF-1 receptor (IGF-1R) plays a key role in transducing the hypertrophic and proliferative responses of angiotensin II (Ang-II) and endothelin-1 (ET-1). Curcumin, a polyphenolic compound derived from the spice turmeric is known to possess antiproliferative properties and exerts vasculoprotective effects. However, the ability of curcumin to modulate IGF-1-induced signaling responses in VSMC remains to be investigated. In this study, we determined the effect of curcumin on IGF-1-induced phosphorylation of protein kinase B (PKB), glycogen synthase kinase-3β (GSK-3β), and IGF-1R in VSMC. Curcumin inhibited IGF-1-induced phosphorylation of PKB and GSK-3β as well as the IGF-1R β subunit in a dose-dependent fashion. In addition, IGF-1-induced expression of early growth response protein 1 (Egr-1) which plays a pathogenic role in vascular dysfunctions, was also attenuated by curcumin. In conclusion, these results indicate that curcumin is a potent inhibitor of key components of the IGF-1-induced mitogenic and proliferative signaling system in VSMC, and suggest that curcumin-induced attenuation of these signaling components may constitute a potential mechanism for its vasculoprotective effects.
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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 | 0.001 |
| 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 | 0.001 |
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".