Neural stem cells and adult brain fatty acid metabolism: Lessons from the 3xTg model of Alzheimer's disease
Bibliographic record
Abstract
Neural stem cell (NSC) activity and adult neurogenesis are physiologically relevant regulators of adult brain structure, function and repair. Given these roles, the NSC impairments observed in a wide range of neurodegenerative and psychiatric conditions likely factor into the overall cognitive dysfunction in these conditions. We investigated NSC regulation in the context of Alzheimer's disease (AD) using the well-characterised triple transgenic (3xTg) model of AD. In this review, we describe our recent findings that link 3xTg-AD neurogenesis impairments to AD-associated abnormalities in brain fatty acid metabolism. Notably, we identified an accumulation of triglycerides rich in oleic acid, a mono-unsaturated fatty acid, within the forebrain NSC niche in AD. Inhibiting the local conversion of saturated to mono-unsaturated fatty acids within the brain was sufficient to counteract the loss of NSC activity in 3xTg-AD mice (Hamilton et al., 2015). We place these findings within the context of recent evidence that dynamic changes in lipid metabolism occur during the transition from NSC quiescence to activation. The picture that emerges is that the critical NSC quiescence-to-activation decision is sensitive to the local levels of specific fatty acids and can be impaired by a disease-associated shift in brain fatty acid balance.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".