Ginkgo Extract EGb761 Confers Neuroprotection by Reduction of Glutamate Release in Ischemic Brain
Bibliographic record
Abstract
PURPOSE: Ginkgo extract EGb761 has shown anti-edema and anti-ischemic effects in various experimental models. In the present study, we demonstrate neuroprotective effects of EGb761 in experimental stroke while monitoring brain metabolism by microdialysis. METHODS: We have used oxygen-glucose deprivation in brain slices in vitro and middle cerebral artery occlusion (MCAO) in vivo to induce ischemia in mouse brain. We used microdialysis in mouse striatum to monitor extracellular concentrations of glucose and glutamate. RESULTS: In vitro, EGb761 reduced ischemia-induced cell swelling in hippocampal slices by 60%. In vivo, administration of EGb761 (300 mg/kg) reduced cell degeneration and edema formation after MCAO by 35-50%. Immediately following MCAO, striatal glucose levels dropped to 25% of controls, and this reduction was not significantly affected by EGb761. Striatal glutamate levels, in contrast, increased 15-fold after MCAO; after pretreatment with EGb761, glutamate levels only increased by 4-5fold. CONCLUSIONS: We show that pretreatment with EGb761 strongly reduces cellular edema formation and neurodegeneration under conditions of ischemia. The mechanism of action seems to be related to a reduction of excitotoxicity, because ischemia-induced release of glutamate was strongly suppressed. Ginkgo extracts such as EGb761 may be valuable to prevent ischemia-induced damage in stroke-prone patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".