The ginkgo biloba extract (EGb 761) protects hippocampal neurons against cell death induced by β‐amyloid
Bibliographic record
Abstract
Substantial evidence suggests that the accumulation of beta-amyloid (Abeta)-derived peptides, and to a lesser extent free radicals, may contribute to the aetiology and/or progression of Alzheimer's disease (AD). Ginkgo biloba extract (EGb 761) is a well-defined plant extract containing two major groups of constituents, i.e. flavonoids and terpenoids. It is viewed as a polyvalent agent with a possible therapeutic use in the treatment of neurodegenerative diseases of multifactorial origin, e.g. AD. We have investigated here the potential effectiveness of EGb 761 against toxicity induced by (Abeta)-derived peptides (Abeta25-35, Abeta1-40 and Abeta1-42) on hippocampal primary cultured cells, this area being severely affected in AD. A co-treatment with EGb 761 concentration-dependently (10-100 microg/mL) protected hippocampal neurons against toxicity induced by Abeta fragments, with a maximal and complete protection at the highest concentration tested. Similar, albeit less potent protective effects were seen with the flavonoid fraction of the extract (CP 205), while the terpenes were ineffective. Most interestingly, EGb 761 (100 microg/mL) was even able to protect (up to 8 h) hippocampal cells from a pre-exposure to Abeta25-35 and Abeta1-40. EGb 761 was also able to both protect and rescue hippocampal cells from toxicity induced by H2O2 (50-150 microM), a major peroxide possibly involved in mediating Abeta toxicity. Moreover, EGb 761 (10-100 microg/mL), and to a lesser extent CP 205 (10-50 microg/mL), completely blocked Abeta-induced events, e.g. reactive oxygen species accumulation and apoptosis. These results suggest that the neuroprotective effects of EGb 761 are partly associated with its antioxidant properties and highlight its possible effectiveness in neurodegenerative diseases, e.g. AD via the inhibition of Abeta-induced toxicity and cell death.
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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".