Attenuation of endothelin-1-induced PKB and ERK1/2 signaling, as well as Egr-1 expression, by curcumin in A-10 vascular smooth muscle cells
Bibliographic record
Abstract
Endothelin-1 (ET-1) is implicated in the pathogenesis of vascular abnormalities through the hyperactivation of growth promoting pathways, including protein kinase B (PKB) and extracellular signal-regulated kinase 1/2 (ERK1/2) signaling. ET-1 has been shown to elicit its responses through the generation of reactive oxygen species (ROS). Curcumin, the main constituent of the spice turmeric, exhibits cardio-protective, anti-proliferative, and antioxidant properties; however, the precise molecular mechanism of its action is unclear. Therefore, in the present study, we investigated the effects of curcumin on ET-1-induced PKB and ERK1/2 signaling, as well as insulin-like growth factor type receptor (IGF-1R) phosphorylation. Curcumin dose-dependently inhibited ET-1-induced phosphorylation of PKB, ERK1/2, c-Raf, and insulin-like growth factor type 1 receptor (IGF-1R), in vascular smooth muscle cells (VSMC). Furthermore, curcumin also attenuated ET-1-induced expression of early growth response (Egr)-1, a transcription factor downstream of ERK1/2 that plays a regulatory role in several cardiovascular pathological processes. In conclusion, these data demonstrate that curcumin is a potent inhibitor of ET-1-induced mitogenic and proliferative signaling events in VSMC and suggest that the ability of curcumin to attenuate these events may contribute as a potential mechanism for its cardiovascular protective response.
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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".