P1‐213: Resveratrol and, to a lesser extent, docosahexaenoic acid, induce disease‐modifying effects in old 3xTg‐AD mice
Bibliographic record
Abstract
There is a general agreement that disease-modifying treatments will be required for Alzheimer's disease (AD). In this study, we tested the therapeutic potential of two potential disease modifying drugs, namely the n-3 polyunsaturated fatty acid docosahexaenoic acid (DHA) and resveratrol (RVT), a polyphenolic compound derived from grapes, in the 3xTg-AD mouse model of AD. Four groups of 3xTg-AD mice received from the age of 12 months a DHA (∼0.8 g.kg -1.day -1) or a RVT (∼1 g.kg -1.day -1) supplementation for a period of either 6 months or 3 months followed by 3 months of a washout period, to test disease modification. All animals were fed a westernized diet until sacrifice at 18 months of age. Both RVT and DHA improved the exploratory behavior of 3xTg-AD mice. RVT induced decreases of soluble Aβ42/Aβ40 ratio (-42%) and insoluble tau (-93%) in the parieto-temporal cortex of 3xTg-AD mice. Both effects remained significant after a 3-month washout. DHA treatment also led to a decrease of insoluble tau (-74%), an effect lost after the washout period. Finally, both DHA and RVT protected from the increase of soluble drebrin observed in 3xTg-AD mice fed the control diet. These data confirm previous report on a beneficial impact of DHA and RVT in AD models and provide new evidence of disease-modifying effects of RVT on Aβ, tau and synaptic markers, remaining significant 3 months after the end of treatment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".