Structural plasticity of the hippocampus in response to estrogens in female rodents
Bibliographic record
Abstract
It is well established that estrogens affect neuroplasticity in a number of brain regions. In particular, estrogens modulate and mediate spine and synapse formation as well as neurogenesis in the hippocampal formation. In this review, we discuss current research exploring the effects of estrogens on dendritic spine plasticity and neurogenesis with a focus on the modulating factors of sex, age, and pregnancy. Hormone levels, including those of estrogens, fluctuate widely across the lifespan from early life to puberty, through adulthood and into old age, as well as with pregnancy and parturition. Dendritic spine formation and modulation are altered both by rapid (likely non-genomic) and classical (genomic) actions of estrogens and have been suggested to play a role in the effects of estrogens on learning and memory. Neurogenesis in the hippocampus is influenced by age, the estrous cycle, pregnancy, and parity in female rodents. Furthermore, sex differences exist in hippocampal cellular and molecular responses to estrogens and are briefly discussed throughout. Understanding how structural plasticity in the hippocampus is affected by estrogens and how these effects can influence function and be influenced by other factors, such as experience and sex, is critical and can inform future treatments in conditions involving the hippocampus.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".