Cognitive-Enhancing Effects of a Polyphenols-Rich Extract from Fruits without Changes in Neuropathology in an Animal Model of Alzheimer’s Disease
Bibliographic record
Abstract
No effective preventive treatment is available for age-related cognitive decline and Alzheimer's disease (AD). Epidemiological studies indicate that a diet rich in fruit is associated with cognitive improvement. It was thus proposed that high polyphenol concentrations found in berries can prevent cognitive impairment associated with aging and AD. Therefore, the Neurophenols project aimed at investigating the effects of a polyphenolic extract from blueberries and grapes (PEBG) in the triple-transgenic (3xTg-AD) mouse model of AD, which develops AD neuropathological markers, including amyloid-β plaques and neurofibrillary tangles, leading to memory deficits. In this study, 12-month-old 3xTg-AD and NonTg mice were fed a diet supplemented with standardized PEBG (500 or 2500 mg/kg) for 4 months (n = 15-20/group). A cognitive evaluation with the novel object recognition test was performed at 15 months of age and mice were sacrificed at 16 months of age. We observed that PEBG supplementation with doses of 500 or 2500 mg/kg prevented the decrease in novel object recognition observed in both 15-month-old 3xTg-AD mice and NonTg mice fed a control diet. Although PEBG treatment did not reduce Aβ and tau pathologies, it prevented the decrease in mature BDNF observed in 16-month-old 3xTg-AD mice. Finally, plasma concentrations of phenolic metabolites, such as dihydroxyphenyl valerolactone, a microbial metabolite of epicatechin, positively correlated with memory performances in supplemented mice. The improvement in object recognition observed in 3xTg-AD mice after PEBG administration supports the consumption of polyphenols-rich extracts to prevent memory impairment associated with age-related disease, without significant effects on classical AD neuropathology.
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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) | 0.000 | 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 | 0.001 |
| 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".