Neuroprotective effects of resveratrol against <i>β</i>‐amyloid‐induced neurotoxicity in rat hippocampal neurons: involvement of protein kinase C
Bibliographic record
Abstract
1. Resveratrol, an active ingredient of red wine extracts, has been shown to exhibit neuroprotective effects in several experimental models. 2. The present study evaluated the neuroprotective effects of resveratrol against amyloid beta(Abeta)-induced toxicity in cultured rat hippocampal cells and examined the role of the protein kinase C (PKC) pathway in this effect. 3. Pre-, co- and post-treatment with resveratrol significantly attenuated Abeta-induced cell death in a concentration-dependent manner, with a concentration of 25 microm being maximally effective. 4. Pretreatment (1 h) of hippocampal cells with phorbol-12-myristate-13-acetate, a PKC activator, at increasing concentrations (1-100 ng x ml(-1)), resulted in a dose-dependent reduction in Abeta-induced toxicity, whereas the inactive 4alpha-phorbol had no effect. 5. Pretreatment (30 min) of hippocampal cells with GF 109203X (1 microm), a general PKC inhibitor, significantly attenuated the neuroprotective effect of resveratrol against Abeta-induced cell death. 6. Treatment of hippocampal cells with resveratrol (20 microm) also induced the phosphorylation of various isoforms of PKC leading to activation. 7. Taken together, the present results indicate that PKC is involved in the neuroprotective action of resveratrol against Abeta-induced toxicity.
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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 | 0.000 |
| 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".