Pterostilbene and its nicotinate derivative ameliorated vascular endothelial senescence and elicited endothelium-dependent relaxations via activation of sirtuin 1
Bibliographic record
Abstract
Vascular endothelial cell senescence is a leading cause of age-associated diseases and cardiovascular diseases. Interventions and therapies targeting endothelial cell senescence and dysfunction would have important clinical implications. This study evaluated the effect of 10 resveratrol analogues, including pterostilbene (Pts) and its derivatives, against endothelial senescence and dysfunction. All the tested compounds at the concentrations from 10−9 M to 10−6 M did not show cytotoxicity in endothelial cells by MTT assay. Among the 10 resveratrol analogues, Pts and Pts nicotinate attenuated the expression of senescence-associated β-galactosidase, downregulated p21 and p53, and increased the production of nitric oxide (NO) in both angiotensin II – and hydrogen peroxide – induced endothelial senescence models. In addition, Pts and Pts nicotinate elicited endothelium-dependent relaxations, which were attenuated in the presence of endothelial NO synthase (eNOS) inhibitor L-NAME or sirtuin 1 (SIRT1) inhibitor sirtinol. Pts and Pts nicotinate did not alter SIRT1 expression but enhanced its activity. Both Pts and Pts nicotinate have high binding activities with SIRT1, according to surface plasmon resonance results and the molecular docking analysis. Inhibition of SIRT1 by sirtinol reversed the anti-senescent effects of Pts and Pts nicotinate. Moreover, Pts and Pts nicotinate shared similar ADME (absorption, distribution, metabolism, excretion) profiles and physiochemical properties. This study suggests that the Pts and Pts nicotinate ameliorate vascular endothelial senescence and elicit endothelium-dependent relaxations via activation of SIRT1. These two compounds may be potential drugs for the treatment of cardiovascular diseases related to endothelial senescence and dysfunction.
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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".