Sex and region-specific disruption of autophagy and mitophagy in Alzheimer’s disease: linking cellular dysfunction to cognitive decline
Bibliographic record
Abstract
Macroautophagy and mitophagy are critical processes in Alzheimer's disease (AD), yet their links to behavioral outcomes, particularly sex-specific differences, are not fully understood. This study investigates autophagic (LC3B-II, SQSTM1) and mitophagic (BNIP3L, BNIP3, BCL2L13) markers in the cortex and hippocampus of male and female 3xTg-AD mice, using western blotting, transmission electron microscopy (TEM), and behavioral tests (novel object recognition and novel object placement). Significant sex-specific differences emerged: female 3xTg-AD mice exhibited autophagosome accumulation due to impaired degradation in the cortex, while males showed fewer autophagosomes, especially in the hippocampus, without significant degradation changes. TEM analyses demonstrated variations in mitochondrial and mitophagosome numbers correlated with memory outcomes. Females had enhanced mitophagy, with higher BNIP3L and BCL2L13 levels, whereas males showed elevated BNIP3 dimers. Cognitive deficits in females correlated with mitochondrial dysfunction in the cortex, while in males, higher LC3B-II levels associated positively with cognitive performance, suggesting protective autophagy effects. Using machine learning, we predicted mitophagosome and mitochondrial numbers based on behavioral data, pioneering a predictive approach to cellular outcomes in AD. These findings underscore the importance of sex-specific regulation of autophagy and mitophagy in AD and support personalized therapeutic approaches targeting these pathways. Integrating machine learning emphasizes its potential to advance neurodegenerative research. Sex-specific differences in autophagy and mitophagy regulation in Alzheimer's disease (AD) are highlighted. Female 3xTg-AD mice show autophagosome accumulation and cognitive deficits, while males exhibit variations in mitophagy markers and behavior.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".