Protective effect of estrogen on apoptosis in a cell culture model of Parkinson's disease
Bibliographic record
Abstract
OBJECTIVES: The protective effect of estrogen on the neurons in Parkinson's disease (PD) is unclear. The present study aimed to investigate the effect of estrogen on the apoptosis and dopaminergic function on a cultured cell model of PD. METHODS: The PD model was established by addition of 1-methyl-4-phenylpyridinium (MPP+) to PC12 cell culture. Estrogen was added to cell groups with MPP+ (Estrogen+MPP+), and without MPP+ (Estrogen only group). Cell viability, content of tyrosine hydroxylase (TH), apoptosis ratio, expression of apoptosis-suppression protein Bcl-x and apoptosis-acceleration protein IL-1 beta converting enzyme (ICE) were measured. RESULTS: Cell viability in the Estrogen+MPP+ group was similar to the control group but was higher than in the MPP+ group (P < 0.05). The apoptosis ratios in the Estrogen+MPP+ group (33.6%), and the control group (31.3%), were also similar, but it was lower than in the MPP+ group (63.5%, P < 0.05). Concentrations of Bcl-x were higher in the Estrogen+MPP+ group, whereas ICE concentrations were lower than in the MPP+ group (P < 0.05). CONCLUSIONS: Estrogen suppresses apoptosis and improves cell viability in MPP+ induced injuries in the PC12 cells. The beneficial effects of estrogen on the PD model are due to the suppression of pro-apoptotic protein ICE, and stimulation of anti-apoptotic protein Bcl-x.
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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".