Neuroprotection by Estrogen in Animal Models of Global and Focal Ischemia
Bibliographic record
Abstract
Estrogen has been demonstrated to protect against brain injury, neurodegeneration, and cognitive decline. Furthermore, estrogen seems to specifically protect cortical and hippocampal neurons from ischemic injury. Here our data evaluating the neuroprotective effects of estrogens, the selective estrogen receptor modulators (SERMs), and estrogen receptor alpha- and beta-selective ligands in animal models of ischemic injury are discussed. In rats and mice, the middle cerebral artery occlusion (MCAO) model was used as models representing cerebrovascular stroke, while in gerbils the two-vessel occlusion model, resenting acute heart attack, was used. Using focal ischemia in ovariectomized ERalphaKO, ERbetaKO, and wild-type mice, we clearly established that the ERalpha subtype is the critical ER-mediating neuroprotection in mouse focal ischemia. Because of the characteristic blood supply of the gerbil, the gerbil global ischemia model was used to evaluate the neuroprotective effects of estrogen, SERMs, and ERalpha- and ERbeta-selective compounds in the hippocampus. Analysis of neurogranin mRNA, a marker of viability of hippocampal neurons, with in situ hybridization, revealed that estrogen treatment resulted in a complete protection in the CA1 regions not only when administered before, but also when given 1 hour after occlusion. Our in vivo binding studies with (125)I-estrogen in gerbils revealed the presence of nuclear estrogen binding sites primarily in CA1 neurons, but not in the CA3 region, as we saw in rats and mice. Together, these observations demonstrate that estrogen protects from ischemic injury in both the focal and global ischemia models by acting primarily via classical nuclear receptors.
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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) | 0.001 | 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".