The <i>Ginkgo biloba</i> Extract (EGb 761) Protects and Rescues Hippocampal Cells Against Nitric Oxide‐Induced Toxicity
Bibliographic record
Abstract
An excess of the free radical nitric oxide (NO) is viewed as a deleterious factor involved in various CNS disorders. Numerous studies have shown that the Ginkgo biloba extract EGb 761 is a NO scavenger with neuroprotective properties. However, the mechanisms underlying its neuroprotective ability remain to be fully established. Thus, we investigated the effect of different constituents of EGb 761, i.e., flavonoids and terpenoids, against toxicity induced by NO generators on cells of the hippocampus, a brain area particularly susceptible to neurodegenerative damage. Exposure of rat primary mixed hippocampal cell cultures to either sodium nitroprusside (SNP; 100 microM) or 3-morpholinosydnonimine resulted in both a decrease in cell survival and an increase in free radical accumulation. These SNP-induced events were blocked by either EGb 761 (10-100 microg/ml) or its flavonoid fraction CP 205 (25 microg/ml), as well as by inhibitors of protein kinase C (PKC; chelerythrine) and L-type calcium channels (nitrendipine). In contrast, the terpenoid constituents of EGb 761, known as bilobalide and ginkgolide B, as well as inhibitors of phospholipases A [3-[(4-octadecyl)benzoyl]acrylic acid (OBAA)] and C (U-73122), failed to display any significant effects. Moreover, EGb 761 (50 microm) CP 205 (25 microg/ml), and chelerythrine were also able to rescue hippocampal cells preexposed to SNP (up to 1 mM). Finally, EGb 761 (100 microg/ml) was shown to block the activation of PKC induced by SNP (100 microM). These data suggest that the protective and rescuing abilities of EGb 761 are not only attributable to the antioxidant properties of its flavonoid constituents but also via their ability to inhibit NO-stimulated PKC activity.
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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".