Innovative Anthocyanin/Anthocyanidin Formulation Protects SK-N-SH Cells Against the Amyloid-β Peptide-Induced Toxicity: Relevance to Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder of aging. It is a multifactorial disease with several overlapping pathways. Therefore, successful therapy should target several pathological features simultaneously. In this regard, cumulative data have demonstrated that polyphenols can display neuroprotective effects through different mechanisms. In this study, we tested the hypothesis that a mixture of anthocyanins/anthocyanidins presents in the formulation MAF14001 may mitigate the amyloid-β peptide (Aβ) toxicity. Anthocyanins are a class of polyphenols capable to cross the blood brain barrier and their intake is associated to a reduced risk of some several chronic diseases. Our results showed that the formulation MAF14001 can protect SK-N-SH cells against Aβ-induced toxicity. From 5 µM, MAF14001 protected SK-N-SH cells against Aβ toxicity by preventing oxidative stress, mitochondrial dysfunction and apoptosis. Furthermore, MAF14001 might directly interact with Aβ to prevent its aggregation process, a key process on Aβ-induced oxidative stress. Indeed, in the presence of MAF14001, Aβ was less susceptible to fibrillation. Finally, MAF14001 decreased the tau phosphorylation (Ser-202) induced by Aβ. Altogether, these results demonstrated that MAF14001 could target multiple mechanisms involved in the etiology of AD and could be useful in preventing and treating AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".