Sex-Dependent Synaptic Alterations in a Mouse Model of Alzheimer's Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is a devastating memory disorder affecting 32 million people worldwide (Drew et al., 2023). AD is characterized by aggregation of misfolded amyloid and tau proteins, disruption of neural circuits, and death of neural cells (Wang and Holtzman, 2020). Circuit dysfunction is a critical feature of disease progression in AD, and work in mouse AD models has demonstrated both hyperactivity (e.g., neocortex) and a reduction in the number of synaptic inputs (e.g., the hippocampus; Ye et al., 2022; Meftah and Gan, 2023). Interestingly, the degree of neural circuitry impairment in AD may be sex-dependent. In humans, females are at a higher risk of developing AD compared with males (Nebel et al., 2018), and mouse studies have demonstrated sex-dependent changes to neural circuitry (e.g., in the hippocampus; Ye et al., 2022). However, few investigations have examined sex differences across brain regions in AD, leaving the extent of these variations largely unknown. An area of keen interest is the subiculum, because this region is impaired early in AD pathogenesis (Kampmann, 2024) and is connected with many areas throughout the brain. Not only are neurons of the subiculum vulnerable to AD pathology, but AD-related changes in the interactions between this region and the rest of the brain vary by sex (Ye et al., 2022). Given this, a recent paper published in The Journal of Neuroscience aimed to document sex-dependent synaptic inputs to the subiculum in the most widely used mouse model of AD, the 5xFAD mouse line, that overexpress a form of the AD-linked amyloid protein harboring five mutations linked to familial AD. Ye et al. (2024) compared AD … Correspondence should be addressed to Brittany J. Dugan at brittany.dugan{at}mail.utoronto.ca.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".