Tetrahydroxystilbene glucoside inhibits α-synuclein aggregation and apoptosis in A53T α-synuclein-transfected cells exposed to MPP<sup>+</sup>
Bibliographic record
Abstract
Increasing evidence has solidified the involvement of α-synuclein (α-Syn) and neurotoxins in the pathogenesis of Parkinson’s disease (PD), suggesting a combination of genetic and environmental influences. 2,3,5,4′-Tetrahydroxystilbene-2-O-β-D-glucoside (TSG) is one of the main active components extracted from Polygonum multiflorum. The purpose of the present study was to investigate the effects of TSG on α-Syn aggregation, mitochondrial dysfunction, oxidative stress, and apoptosis in vitro. A53T mutant α-synuclein-transfected cells (A53T AS cells) plus MPP+ exposure were used as a complex cell model of PD. The expression of proteins was determined by Western blot assay. Flow cytometry was utilized to measure mitochondrial membrane potential and apoptosis. The results showed that MPP+ exposure for 24 h induced more severe damage in A53T AS cells than in vector control cells. Pretreatment of TSG for 24 h significantly increased the cell viability; decreased lactate dehydrogenase leakage; inhibited α-Syn over-expression and aggregation; elevated mitochondrial membrane potential; decreased reactive oxygen species, Bax/Bcl-2 ratio, and caspase-3 activity; and inhibited apoptosis in A53T AS cells exposed to MPP+. These results suggest that TSG may be an attractive candidate for PD therapy.
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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) | 0.000 | 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".