Cognitive and behavioral evaluation of nutritional interventions in rodent models of brain aging and dementia
Bibliographic record
Abstract
Devin Wahl,1,2 Sean CP Coogan,1,3 Samantha M Solon-Biet,1,2 Rafael de Cabo,4 James B Haran,5 David Raubenheimer,1,6,7 Victoria C Cogger,1,2 Mark P Mattson,8 Stephen J Simpson,1,2,7 David G Le Couteur1,2 1Charles Perkins Centre, University of Sydney, Sydney, 2Aging and Alzheimers Institute, ANZAC Research Institute, Concord Clinical School/Sydney Medical School, Concord, NSW, Australia; 3Department of Renewable Resources, University of Alberta, Edmonton, AB, Canada; 4Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA; 5Philadelphia College of Osteopathic Medicine, Philadelphia, PA, USA; 6Faculty of Veterinary Science, 7School of Life and Environmental Sciences, University of Sydney, Sydney, NSW, Australia; 8Laboratory of Neurosciences, National Institute on Aging’s Intramural Research Program, National Institutes of Health, Baltimore, MD, USA Abstract: Evaluation of behavior and cognition in rodent models underpins mechanistic and interventional studies of brain aging and neurodegenerative diseases, especially dementia. Commonly used tests include Morris water maze, Barnes maze, object recognition, fear conditioning, radial arm water maze, and Y maze. Each of these tests reflects some aspects of human memory including episodic memory, recognition memory, semantic memory, spatial memory, and emotional memory. Although most interventional studies in rodent models of dementia have focused on pharmacological agents, there are an increasing number of studies that have evaluated nutritional interventions including caloric restriction, intermittent fasting, and manipulation of macronutrients. Dietary interventions have been shown to influence various cognitive and behavioral tests in rodents indicating that nutrition can influence brain aging and possibly neurodegeneration. Keywords: calorie restriction, intermittent fasting, aging, memory, macronutrients
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".