Oophorectomy Reduces Estradiol Levels and Long-Term Spontaneous Neurovascular Recovery in a Female Rat Model of Focal Ischemic Stroke
Bibliographic record
Abstract
Although epidemiological evidence suggests significant sex and gender-based differences in stroke risk and recovery, females have been widely under-represented in preclinical stroke research. The neurovascular sequelae of brain ischemia in females, in particular, are largely uncertain. We set out to address this gap by a multimodal in vivo study of neurovascular recovery from endothelin-1 model of cortical focal-stroke in sham vs. ovariectomized female rats. Three weeks post ischemic insult, sham operated females recapitulated the phenotype previously reported in male rats in this model, of normalized resting perfusion but sustained peri-lesional cerebrovascular hyperreactivity. In contrast, ovariectomized (Ovx) females showed reduced peri-lesional resting blood flow, and elevated cerebrovascular responsivity to hypercapnia in the peri-lesional and contra-lateral cortices. Electrophysiological recordings showed an attenuation of theta to low-gamma phase-amplitude coupling in the peri-lesional tissue of Ovx animals, despite relative preservation of neuronal power. Further, this chronic stage neuronal network dysfunction was inversely correlated with serum estradiol concentration. Our pioneering data demonstrate dramatic differences in spontaneous recovery in the neurovascular unit between Ovx and Sham females in the chronic stage of stroke, underscoring the importance of considering hormonal-dependent aspects of the ischemic sequelae in the development of novel therapeutic approaches and patient recruitment in clinical trials.
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 | 0.001 | 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 | 0.000 |
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".