Anthocyanins Protect SK-N-SH Cells Against Acrolein-Induced Toxicity by Preserving the Cellular Redox State
Bibliographic record
Abstract
In Alzheimer's disease (AD) and in mild cognitive impairment (MCI) patients, by-products of lipid peroxidation such as acrolein accumulated in vulnerable regions of the brain. We have previously shown that acrolein is a highly reactive and neurotoxic aldehyde and its toxicity involves the alteration of several redox-sensitive pathways. Recently, protein-conjugated acrolein in cerebrospinal fluid has been proposed as a biomarker to distinguish between MCI and AD. With growing evidence of the early involvement of oxidative stress in AD etiology, one would expect that a successful therapy should prevent brain oxidative damage. In this regard, several studies have demonstrated that polyphenol-rich extracts exert beneficial effect on cognitive impairment and oxidative stress. We have recently demonstrated the efficacy of an anthocyanin formulation (MAF14001) against amyloid-β-induced oxidative stress. The aim of this study is to investigate the neuroprotective effect of MAF14001 as a mixture of anthocyanins, a particular class of polyphenols, against acrolein-induced oxidative damage in SK-N-SH neuronal cells. Our results demonstrated that MAF14001, from 5μM, was able to efficiently protect SK-N-SH cells against acrolein-induced cell death. MAF14001 was able to lower reactive oxygen species and protein carbonyl levels induced by acrolein. Moreover, MAF1401 prevented glutathione depletion and positively modulated, in the presence of acrolein, some oxidative stress-sensitive pathways including the transcription factors NF-κB and Nrf2, the proteins γ-GCS and GSK3β, and the protein adaptator p66Shc. Along with its proven protective effect against amyloid-β toxicity, these results demonstrate that MAF14001 could target multiple mechanisms and could be a promising agent for AD prevention.
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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".