Accelerated ovarian failure results in brain alterations related to Alzheimer's disease that are not recovered by high‐intensity interval training in mice
Bibliographic record
Abstract
INTRODUCTION: The menopausal decline in ovarian estrogen production is thought to increase the risk of Alzheimer's disease; however, this link requires further investigation. The chronological development of this connection is not well defined because of the lack of animal models that recapitulate the time course of menopause. This study characterized the cognitive and neuronal effects of the 4-vinylcyclohexene diepoxide (VCD) model of ovarian failure in female mice and assessed whether high-intensity interval training (HIIT) would attenuate impairments. METHODS: Female mice were injected with VCD for 15 days. Novel object recognition tests (NORT) were conducted during (perimenopause) and after (menopause) ovarian failure (n = 7). HIIT was initiated in menopause and mice underwent NORT testing after 2 and 8 weeks of HIIT (n = 5). RESULTS: VCD mice had a lower discrimination index, and lower SNAP25 and NeuN expression in perimenopause. HIIT did not recover memory in VCD mice. DISCUSSION: Neuronal changes arise early in the perimenopausal transition and HIIT did not improve recognition memory when initiated in menopause. HIGHLIGHTS: The menopausal decline in ovarian estrogen production increases the risk of Alzheimer's disease (AD). The chronological development of this connection is not well defined because of the lack of animal models that recapitulate the time course of menopause. 4-vinylcyclohexene diepoxide (VCD)-induced ovarian failure provides a model that simulates the average human experience in the transition from perimenopause to menopause. We demonstrate that cognitive and biochemical effects related to AD pathology are present from the earliest available timepoint in perimenopause in VCD mice. This work highlights the importance of examining the time course in the progression to menopause and the use of VCD as a model to investigate changes in the brain.
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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) | 0.000 | 0.001 |
| 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) | 0.003 | 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".