Polyphenol-rich extract from grape and blueberry attenuates cognitive decline and improves neuronal function in aged mice
Bibliographic record
Abstract
Abstract Ageing is characterised by memory deficits, associated with brain plasticity impairment. Polyphenols from berries, such as flavan-3-ols, anthocyanins, and resveratrol, have been suggested to modulate synaptic plasticity and cognitive processes. In the present study we assessed the preventive effect of a polyphenol-rich extract from grape and blueberry (PEGB), with high concentrations of flavonoids, on age-related cognitive decline in mice. Adult and aged (6 weeks and 16 months) mice were fed a PEGB-enriched diet for 14 weeks. Learning and memory were assessed using the novel object recognition and Morris water maze tasks. Brain polyphenol content was evaluated with ultra-high-performance LC-MS/MS. Hippocampal neurotrophin expression was measured using quantitative real-time PCR. Finally, the effect of PEGB on adult hippocampal neurogenesis was assessed by immunochemistry, counting the number of cells expressing doublecortin and the proportion of cells with dendritic prolongations. The combination of grape and blueberry polyphenols prevented age-induced learning and memory deficits. Moreover, it increased hippocampal nerve growth factor (Ngf) mRNA expression. Aged supplemented mice displayed a greater proportion of newly generated neurons with prolongations than control age-matched mice. Some of the polyphenols included in the extract were detected in the brain in the native form or as metabolites. Aged supplemented mice also displayed a better survival rate. These data suggest that PEGB may prevent age-induced cognitive decline. Possible mechanisms of action include a modulation of brain plasticity. Post-treatment detection of phenolic compounds in the brain suggests that polyphenols may act directly at the central level, while they can make an impact on mouse survival through a potential systemic effect.
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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".