17<i>β</i>-Estradiol Pretreatment Reduces Bleeding and Brain Injury after Intracerebral Hemorrhagic Stroke in Male Rats
Bibliographic record
Abstract
17beta-estradiol reduces cell death after global and focal ischemia and subarachnoid hemorrhage in rodents. Presently, we tested whether estrogen improves outcome after intracerebral hemorrhage (ICH) in male rats. Rats were implanted subcutaneously with 0.05, 0.25, or 0.50 mg pellets of estrogen (21-day release) or subjected to a sham procedure. Two weeks after implantation, they were given a striatal ICH via an infusion of collagenase. The three estrogen groups had significantly smaller lesions at a 7-day survival. Some rats had core temperature measured with an implanted telemetry probe, which also measured whole-body movements. Estrogen did not affect temperature nor activity levels after ICH. A second study with 0.25 mg pellets, administered once or twice, showed persistent histologic protection (30 days) and some functional benefit (e.g., elevated beam). A spectrophotometric hemoglobin assay showed that the 0.25 mg dose significantly reduced hemorrhagic blood volume at 12 hours after ICH. Regardless, estrogen did not lessen cerebral edema at 2 days after ICH and functional benefits were not consistently found on all tests (e.g., cylinder task). In summary, estrogen pretreatment reduces injury after ICH, in part by reducing bleeding. Estrogen may thus lessen injury and improve outcome after ICH in humans.
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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 | 0.001 |
| 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".