Protective effects of berry polyphenols against age-related cognitive impairment
Bibliographic record
Abstract
A growing body of evidence suggests that dietary interventions may delay or halt the progression of age-related health disorders and cognitive decline.Among the components of the human diet, polyphenols from berries are essential micronutrients that have been particularly studied for improving cognitive functions.In the present review, we highlight the health impact of major polyphenolic classes found in berries: flavanols, anthocyanins and stilbenes, focusing on resveratrol.The reports of beneficial effects of berry consumption on age-related cognitive decline and associated neurobiological processes in animals and human are underscored.We then discuss the potential benefit of each category of polyphenols on memory impairment and in neurodegenerative diseases.Berry polyphenols improve several types of memory and have a global effect on brain plasticity, partly through their antioxidant activity and/or their effect on neuronal signal transduction and neuroinflammation.Interestingly, accumulated bioavailability data suggest that most polyphenols, or at least key metabolites, can access the brain in sufficient concentrations.Collectively, the data accumulated so far suggest that dietary polyphenols can modulate brain health and function, and strengthen the importance of fruit consumption for a healthy brain aging and the prevention of age-related diseases.However, further preclinical work is needed to determine the most neuroactive nutraceutical formulations, whether through the diet or supplement, to subsequently design and perform informative clinical trials.
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) | 0.002 | 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".