Effects of resveratrol postconditioning on cerebral ischemia in mice: role of the sirtuin-1 pathway
Bibliographic record
Abstract
Evidence has demonstrated that resveratrol preconditioning exhibits neuroprotection against cerebral ischemia-reperfusion (IR) injury. The current investigation aimed to explore whether pharmacological postconditioning, by administering resveratrol, after a sustained ischemia and prior to prolonged reperfusion abrogates cerebral IR injury. Cerebral IR-induced injury mice model was employed in this study to evaluate the neuroprotective effects of pharmacological postconditioning with resveratrol (30 mg/kg; i.p.) administered 5 min before reperfusion. We administered sirtinol, a SIRT1/2 selective inhibitor (10 mg/kg; i.p.) 10 min before ischemia (17 min) and reperfusion (24 h), to elucidate whether the neuroprotection with resveratrol postconditioning depends on SIRT1 activation. Various biochemical and behavioural parameters and histopathological changes were assessed to examine the effect of pharmacological postconditioning. Infarct size is estimated using TTC staining. It was established that resveratrol postconditioning abrogated the deleterious effects of IR injury expressed with regard to biochemical parameters of oxidative stress (TBARS, SOD, GSH), acetylcholinesterase activity, behavioural parameters (memory, motor coordination), infarct size, and histopathological changes. Sirtinol significantly reversed the effect of resveratrol postconditioning. We conclude that induced neuroprotective benefits of resveratrol postconditioning may be the consequence of SIRT1 activation and resveratrol can be considered, for further studies, as potential agent inducing pharmacological postconditioning in clinical situations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".