Estradiol and GPER Activation Differentially Affect Cell Proliferation but Not GPER Expression in the Hippocampus of Adult Female Rats
Bibliographic record
Abstract
Estradiol increases cell proliferation in the dentate gyrus of the female rodent but it is not known whether the G protein-coupled estrogen receptor (GPER), a membrane receptor, is involved in this process, nor whether there are regional differences in estradiol's effects on cell proliferation. Thus, we investigated whether estradiol exerts its effects on cell proliferation in the dorsal and ventral dentate gyrus through GPER, using the GPER agonist, G1, and antagonist, G15. Ovariectomized adult female rats received a single injection of either: 17β-estradiol (10 μg), G1 (0.1, 5, 10 μg), G15 (40 μg), G15 and estradiol, or vehicle (oil, DMSO, or oil+DMSO). After 30 min, animals received an injection of bromodeoxyuridine (BrdU) and were perfused 24 h later. Acute treatment with estradiol increased, while the GPER agonist G1 (5 μg) decreased, the number of BrdU+ cells in the dentate gyrus relative to controls. The GPER antagonist, G15 increased the number of BrdU+ cells relative to control in the dorsal region and decreased the number of BrdU+ cells in the ventral region. However, G15 treatment in conjunction with estradiol partially eliminated the estradiol-induced increase in cell proliferation in the dorsal dentate gyrus. Furthermore, G1 decreased the expression of GPER in the dentate gyrus but not the CA1 and CA3 regions of the hippocampus. In summary, we found that activation of GPER decreased cell proliferation and GPER expression in the dentate gyrus of young female rats, presenting a potential and novel estrogen-independent role for this receptor in the adult hippocampus.
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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) | 0.001 | 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) | 0.002 | 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".