Whole-food diet worsened cognitive dysfunction in an Alzheimer's disease mouse model
Bibliographic record
Abstract
Food combinations have been associated with lower incidence of Alzheimer's disease. We hypothesized that a combination whole-food diet containing freeze-dried fish, vegetables, and fruits would improve cognitive function in TgCRND8 mice by modulating brain insulin signaling and neuroinflammation. Cognitive function was assessed by a comprehensive battery of tasks adapted to the Morris water maze. Unexpectedly, a "Diet × Transgene" interaction was observed in which transgenic animals fed the whole-food diet exhibited even worse cognitive function than their transgenic counterparts fed the control diet on tests of spatial memory (p < 0.01) and strategic rule learning (p = 0.034). These behavioral deficits coincided with higher hippocampal gene expression of tumor necrosis factor-α (p = 0.013). There were no differences in cortical amyloid-β peptide species according to diet. These results indicate that a dietary profile identified from epidemiologic studies exacerbated cognitive dysfunction and neuroinflammation in a mouse model of familial Alzheimer's disease. We suggest that normally adaptive cellular responses to dietary phytochemicals were impaired by amyloid-beta deposition leading to increased oxidative stress, neuroinflammation, and behavioral deficits.
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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 | 0.000 |
| 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 | 0.001 |
| 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".