Pregnancy history and estradiol influence spatial memory, hippocampal plasticity, and inflammation in middle-aged rats
Bibliographic record
Abstract
Pregnancy and motherhood can have long-term effects on cognition and brain aging in both humans and rodents. Estrogens are related to cognitive function and neuroplasticity. Estrogens can improve cognition in postmenopausal women, but the evidence is mixed, partly due to differences in age of initiation, type of menopause, dose, formulation and route of administration. Additionally, past pregnancy influences brain aging and cognition as a younger age of first pregnancy in humans is associated with poorer aging outcomes. However, few animal studies have examined specific features of pregnancy history or the possible mechanisms underlying these changes. We examined whether maternal age at first pregnancy and estradiol differentially affected hippocampal neuroplasticity, inflammation, spatial reference cognition, and immediate early gene activation in response to spatial memory retrieval in middle-age. Thirteen-month-old rats (who were nulliparous (never mothered) or previously primiparous (had a litter) at three or seven months) received daily injections of estradiol (or vehicle) for sixteen days and were tested on the Morris Water Maze. An older age of first pregnancy was associated with impaired spatial memory but improved performance on reversal training, and increased number of new neurons in the ventral hippocampus. Estradiol decreased activation of new neurons in the dorsal hippocampus, regardless of parity history. Estradiol also decreased the production of anti-inflammatory cytokines based on age of first pregnancy. This work suggests that estradiol affects neuroplasticity and neuroinflammation in middle age, and that age of first pregnancy can have long lasting effects on hippocampus structure and function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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".