Beyond Motor Deficits: Environmental Enrichment Mitigates Huntington’s Disease Effects in YAC128 Mice
Bibliographic record
Abstract
Huntington's disease (HD) is a neurodegenerative genetic disorder characterized by motor, psychiatric, cognitive, and peripheral symptoms without effective therapy. Evidence suggests that lifestyle factors can modulate disease onset and progression, and environmental enrichment (EE) has emerged as a potential approach to mitigate the progression and severity of neurodegenerative processes. Wild-type (WT) and yeast artificial chromosome (YAC) 128 mice were exposed to different EE conditions. Animals from cohort 1 were exposed to EE between postnatal days 21 and 60, and animals from cohort 2 were exposed to EE between postnatal days 60 and 120. Motor and non-motor behavioral tests were employed to evaluate the effects of EE on HD progression. Monoamine levels, hippocampal cell proliferation, neuronal differentiation, and dendritic arborization were also assessed. Here we show that EE had an antidepressant-like effect and slowed the progression of motor deficits in HD mice. It also reduced monoamine levels, which correlated with better motor performance, particularly in the striatum. EE also modulated neuronal differentiation in the YAC128 hippocampus. These results confirm that EE can impact behavior, hippocampal neuroplasticity, and monoamine levels in YAC128 mice, suggesting this could be a therapeutic strategy to modulate neuroplasticity deficits in HD. However, further research is needed to fully understand EE's mechanisms and long-term effects as an adjuvant therapy for this debilitating condition.
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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) | 0.002 | 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".