Neuroprotective abilities of resveratrol and other red wine constituents against nitric oxide‐related toxicity in cultured hippocampal neurons
Bibliographic record
Abstract
Animal and epidemiological studies suggest that polyphenol constituents of red wine possess antioxidant activities that favour protection against cardiovascular disease - the so-called. 'French paradox' - and possibly, central nervous system disorders such as Alzheimer's disease (AD) and ischaemia. In the present study, the potential of three major red wine derived-polyphenols to protect against toxicity induced by the nitric oxide free radical donors sodium nitroprusside (SNP) and 3-morpholinosydnonimine (SIN-1) was examined in cultured rat hippocampal cells. Both co- and post-treatments with either the stilbene resveratrol (5 - 25 microM) or the flavonoids quercetin (5 - 25 microM) and (+)-catechin (1 - 10 microM) were capable of attenuating hippocampal cell death and intracellular reactive oxygen species accumulation produced by SNP (100 microM and 1 mM, respectively). However, among the phenolic compounds tested, only the flavonoids afforded significant protection against 5 mM SIN-1-induced toxicity. The effects of phenolic constituents were shared by Trolox (100 microM), a vitamin E analogue, but not by selective inhibitors of cyclo-oxygenases (COX) and lipoxygenases (LOX). Among the phenolic compounds tested, only quercetin (10 microM) inhibited 100 microM SNP-stimulated protein kinase C (PKC) activation, whereas none of them were able to attenuate nitrite accumulation caused by SNP (100 microM). Taken together, these data suggest that the neuroprotective abilities of quercetin, resveratrol, and (+)-catechin result from their antioxidant properties rather than their purported inhibitory effects on intracellular enzymes such as COX, LOX, or nitric oxide synthase. Quercetin, however, may also act via PKC to produce its protective effects.
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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".