Glutathione protects GH4 pituitary lactotrope tumor cells from apoptosis induced by dopamine
Bibliographic record
Abstract
Objective To explore mechanisms of dopamine(DA) inducing GH4 cell apoptosis and glutathione(GSH)protecting GH4 cells from apoptosis induced by DA.Methods ① GH4 pituitary cells were treated with DA at 0,100,300 and 500μmol/L for 24h,then treated with DA at 500μmol/L for 0,1,3,5,12 and 24h to select the appropriate concentration and time.② Then GH4 cells were treated with raclopride(a D2 receptor antagonist,Rac)and GSH to explore the effects of Rac and GSH on apoptosis.③Apoptotic cells were counted by an inverted phase contrast microscope.Morphological appearance was observed by PI labeling,and expressions of Bcl-2 and PARP-1 were detected by Western blot.Results DA induced concentration-and time-dependent GH4 cell apoptosis.A selective D2 receptor antagonist could not block the cytotoxic effect.PI revealed that exposure to GSH(1mmol/L) for 1h prior to the DA treatment attenuated DA-induced apoptosis.Western blot showed up-regulation of Bcl-2 and down-regulation of PARP-1.Conclusion DA exerts cytotoxic effects on GH4 cells mainly through auto-oxidation in the intracellular space.A selective D2 receptor antagonist cannot block DA-induced apoptosis,while GSH can block it,which may be relevant to regulation of Bcl-2 and PARP-1.
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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".