Sex-focused analyses of M83 A53T hemizygous mouse model with recombinant human alpha-synuclein preformed fibril injection identifies female resilience to disease progression: A combined magnetic resonance imaging and behavioural study
Bibliographic record
Abstract
Abstract Alpha-synuclein (aSyn) pathology has been extensively studied in mouse models harbouring human mutations. In spite of the known sex differences in age of onset, prevalence and disease presentation in human synucleinopathies, the impact of sex on aSyn propagation has received very little attention. To address this need, we examined sex differences in whole brain signatures of neurodegeneration due to aSyn toxicity in the M83 mouse model using longitudinal magnetic resonance imaging (MRI; T1-weighted; 100 μm 3 isotropic voxel; acquired −7, 30, 90 and 120 days post-injection [dpi]; n≥8 mice/group/sex/time point). To initiate aSyn spreading, M83 mice were inoculated with recombinant human aSyn preformed fibrils (Hu-PFF) or phosphate buffered saline (PBS) injected in the right dorsal striatum. We observed more aggressive neurodegenerative profiles over time for male M83 Hu-PFF-injected mice when examining voxel-wise trajectories. However, at 90 dpi, we observed widespread patterns of neurodegeneration in the female Hu-PFF-injected mice. These differences were not accompanied with any differences in motor symptom onset between the male and female Hu-PFF-injected mice. However, male Hu-PFF-injected mice reached their humane endpoint sooner. These findings suggest that post-motor symptom onset, even though more accelerated disease trajectories were observed for male Hu-PFF-injected mice, neurodegeneration may appear sooner in female Hu-PFF-injected mice (prior to motor symptomatology). These findings suggest that sex-specific synucleinopathy phenotypes urgently need to be considered to improve our understanding of neuroprotective and neurodegenerative mechanisms.
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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 | 0.000 |
| 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) | 0.003 | 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".