Possible Involvement of Programmed Cell Death Pathways in the Neuroprotective Action of Polyphenols
Bibliographic record
Abstract
One of the hallmarks of Alzheimer's disease is the accumulation of senile plaques composed of extra-cellular aggregates of beta-amyloid (Aβ) peptides. It is well established that at least in vitro, Aβ triggers apoptotic cell death via the activation of caspase-dependent and -independent cell death effectors, namely caspase-3 and apoptosis inducing factor (AIF), respectively. Epidemiological studies have reported that elderly people have a lower risk (up to 50%) of developing dementia if they regularly eat fruits and vegetables and drink tea and red wine (in moderation). Numerous studies indicate that polyphenols derived from these foods and beverages account for the observed neuroprotective effects. In particular, we have reported that polyphenols extracted from green tea (i.e. epigallocatechin gallate or EGCG) and red wine (i.e. resveratrol) block Aβ-induced hippocampal cell death, by at least partially inhibiting Aβ fibrillisation. It has been shown that polyphenols may also modulate caspase-dependent and -independent programmed cell death (PCD) pathways. Indeed, polyphenols including resveratrol, EGCG and luteolin significantly inhibit the activation of the key apoptotic executioner, caspase-3 and are able to modulate mitogen-activated protein kinases known to play an important role in neuronal apoptosis. Moreover, it has been reported that polyphenols may exert their anti-apoptotic action by inhibiting AIF release from mitochondria, thus providing new mechanism of action for polyphenols. This review aims to update the current knowledge regarding the differential effects of polyphenols on PCD pathways and discuss their putative neuroprotective action resulting from their capacity to modulate these pathways.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".