Effects of a high-fat diet and an exercise-based rescue on neuroanatomy in the triple-transgenic mouse model of Alzheimer’s disease
Bibliographic record
Abstract
Alzheimer’s disease (AD) is the main cause of dementia worldwide and the 7th leading cause ofdeath in Canada. The absence of cure or treatment foreshadows a major public health crisis forour rapidly aging society and has led researchers to turn towards preventive strategies instead.Indeed, up to 30% of dementia cases may be attributable to potentially modifiable lifestyle-relatedrisk factors. In particular, midlife obesity and its associated conditions, such as physicalinactivity, are known to increase risk for dementia. Previous work has also found thathigh-fat diet (HFD)-induced obesity causes neuroanatomical changes and cognitive impairmentin a mouse model of AD.In this project, I assessed the potential for rescue of three interventions following exposure toHFD: return to a low-fat diet, voluntary exercise, or the combination of both. In order to studythe interaction of AD-like pathology with intervention, I used a triple transgenic mouse model ofAD (3Tg-AD) and its wild-type counterpart. Neuroanatomical changes were monitored from 2 to6 months using Magnetic Resonance Imaging. Object recognition and spatial memory wereassessed at 6 months.It was found that HFD induced volumetric decline in regions involved in spatial cognition (e.g.the hippocampus and the entorhinal and retrosplenial cortices), but this effect was rescued byexercise. The rescue effect of return to a low-fat diet was more localized. Interestingly, the 3Tg-AD mice were benefitting less from the exercise-based rescues. This was also seen in terms ofbehavior, with exercise improving spatial memory performance mostly in the wild-type mice. Onthe other hand, it appeared that object recognition was only improved by return to a low-fat diet,independently of genotype
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".