Resveratrol inhibits high-glucose-induced inflammatory “metabolic memory” in human retinal vascular endothelial cells through SIRT1-dependent signaling
Bibliographic record
Abstract
Diabetes induces vascular endothelial damage and this study investigated high-glucose-induced inflammation "metabolic memory" of human retinal vascular endothelial cells (HRVECs), the effects of resveratrol on HRVECs, and the underlying signaling. HRVECs were grown under various conditions and assayed for levels of sirtuin 1 (SIRT1); acetylated nuclear factor κB (Ac-NF-κB); NOD-like receptor family, pyrin domain containing 3 (NLRP3); and other inflammatory cytokines; and cell viability. A high glucose concentration induced HRVEC inflammation metabolic memory by decreasing SIRT1 and increasing Ac-NF-κB, NLRP3, caspase 1, interleukin-1β, inducible nitric oxide synthase, and tumor necrosis factor α, whereas exposure of HRVECs to a high glucose medium for 4 days, followed by a normal glucose concentration for an additional 4 days, failed to reverse these changes. A high glucose concentration also significantly reduced HRVEC viability. In contrast, resveratrol, a selective SIRT1 activator, markedly enhanced HRVEC viability and reduced the inflammatory cytokines expressions. In addition, high glucose reduced AMP-activated protein kinase (AMPK) phosphorylation and retained during the 4 days of the reversal period of culture. The effects of resveratrol were abrogated after co-treatment with the SIRT1 inhibitor nicotinamide and the AMPK inhibitor compound C. In conclusion, resveratrol was able to reverse high-glucose-induced inflammation "metabolic memory" of HRVECs by activation of the SIRT1/AMPK/NF-κB pathway.
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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".