Effects of Ginkgo Biloba Extract on A53T α-Synuclein Transgenic Mouse Models of Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is a degenerative disorder of the central nervous system mainly affecting the motor system. Presently, there is no effective and safe drug to treat patients with PD. Ginkgo biloba extract (GBE), obtained from leaves of the Ginkgo biloba tree, is a complex mixture of ingredients primarily containing two active components: flavonoids and terpenoids. In this study, we investigated the effects of GBE on A53T α-synuclein transgenic mice, a PD model that has better simulated the progression of PD patients than other models such as the 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine-induced PD model. METHODS: Fifty α-synuclein A53T transgenic mice were fed and treated with GBE, and locomotor activity was detected by pole test, forced swim test, and wire-hang test. The expression of tyrosine hydroxylase and dopamine transporters was detected using immunohistochemistry. Superoxide dismutase activity, glutathione peroxidase activity, and malondialdehyde expression were detected using an assay kit. RESULTS: Our results show that GBE treatment improved locomotor activity and that superoxide dismutase and glutathione peroxidase inhibited the expression of methane dicarboxylic aldehyde and recovered the expression of tyrosine hydroxylase and dopamine transporters. CONCLUSIONS: The GBE treatment improved locomotor activity and inhibited the development of PD in the A53T α-synuclein transgenic mice, which may be partly responsible for decreased oxidative damage and maintain the normal dopamine homeostasis.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".