Reproductive experience and APOEe4 genotype interact to influence neuroplasticity and neuroinflammation in the middle‐aged brain
Bibliographic record
Abstract
Abstract Background Females have a greater lifetime risk of Alzheimer’s disease (AD) compared to males, differences which are further exacerbated with possession of APOEe4 alleles, the greatest genetic risk factor for sporadic AD. Although studying sex and gender differences is important, so is studying sex‐linked factors such as parity (pregnancy and motherhood). Previous parity influences brain aging trajectories in both humans and rodents and may be associated with a greater risk of AD and greater neuropathology. Neuroinflammation is increased with AD, reduces hippocampal neurogenesis, and activates the tryptophan‐kynurenine pathway (TKP), and all these factors are influenced by aging and parity. We investigated whether previous parity influences neuroinflammation and neuroplasticity in middle‐aged rats, dependent on APOEe4 genotype. Method Age‐matched wildtype (WT) and humanized (h) APOEe4e4 knock‐in female rats were nulliparous (never mothered) or primiparous (first‐time mothers). Middle‐age (13‐14 months old) rats were euthanized to examine markers of hippocampal neuroinflammation (microglia and cytokine signalling) and neurogenesis. Result In line with previous research, wildtype primiparous rats had greater levels of neurogenesis than nulliparous rats – however, the reverse was true in hAPOEe4 rats, as hAPOEe4 primiparous rats had lower levels of neurogenesis than hAPOEe4 nulliparous rats in the ventral hippocampus. Perhaps paradoxically, hAPOEe4 primiparous rats had fewer microglia and less proinflammatory cytokine expression (IL‐l1β, IFNγ, TNFα) than wildtype primiparous rats in the ventral hippocampus. Conclusion These findings indicate that past reproductive experience and APOEe4 genotype need to be considered in aging and Alzheimer’s disease research.
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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 | 0.000 | 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) | 0.002 | 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".