A dietary polyphenol resveratrol acts to provide neuroprotection in recurrent stroke models by regulating <scp>AMPK</scp> and <scp>SIRT</scp>1 signaling, thereby reducing energy requirements during ischemia
Post-publication record
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Bibliographic record
Abstract
Polyphenol resveratrol (RSV) has been associated with Silent Information Regulator T1 (SIRT1) and AMP-activated protein kinase (AMPK) metabolic stress sensors and probably responds to the intracellular energy status. Our aim here was to investigate the neuroprotective effects of RSV and its association with SIRT1 and AMPK signaling in recurrent ischemia models. In this study, elderly male Wistar rats received a combination of two mild transient middle cerebral artery occlusions (tMCAOs) as an in vivo recurrent ischemic model. Primary cultured cortical neuronal cells subjected to combined oxygen-glucose deprivation (OGD) were used as an in vitro recurrent ischemic model. RSV administration significantly reduced infarct volumes, improved behavioral deficits and protected neuronal cells from cell death in recurrent ischemic stroke models in vivo and in vitro. RSV treatments significantly increased the intracellular NAD(+) /NADH ratio, AMPK and SIRT1 activities, decreased energy assumption and restored cell energy ATP level. SIRT1 and AMPK inhibitors and specific small interfering RNA (siRNA) for SIRT1 and AMPK significantly abrogated the neuroprotection induced by RSV. AMPK-siRNA and inhibitor decreased SIRT1 activities; however, SIRT1-siRNA and inhibitor had no impact on phospho-AMPK (p-AMPK) levels. These results indicated that the neuroprotective effects of RSV increased the intracellular NAD(+) /NADH ratio as well as AMPK and SIRT1 activities, thereby reducing energy ATP requirements during ischemia. SIRT1 is a downstream target of p-AMPK signaling induced by RSV in the recurrent ischemic stroke model.
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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 | 0.000 |
| 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".