Retinoic Acid Attenuates Cell Death and Reduces Tyrosine Hydroxylase Expression in Ethanol-Treated Human SH-SY5Y Neuroblastoma Cells
Bibliographic record
Abstract
Background: Retinoic acid (RA) plays an important role in embryonic development and central nervous system function, and has been shown to exert anti-apoptotic effects in various cells. In contrast, ethanol probably exerts its toxicity via pro-apoptotic effects. Here, we investigated the effects of all-trans RA on ethanol-induced cell death in human dopaminergic SH-SY5Y neuroblastoma cells. Methods: Cell viability of SH-SY5Y cells exposed to 25, 50, 100, and 200 mM ethanol for 72 hours, with or without 10 µ M RA, was measured using an MTT assay. Expression of p53, Bcl, and Bax mRNA levels in untreated SH-SY5Y cells and cells exposed to 200 mM ethanol, 10 µ M RA, or both for 24 hours was analyzed using quantitative real-time RT-PCR. Tyrosine hydroxylase (TH) of SH-SY5Y cells exposed to 25, 50, 100, and 200 mM ethanol for 72 hours, with or without 10 µ M RA, was measured using western blotting analysis. Results: The effect of ethanol on cell viability was dose-dependent, and was accompanied by the significant reduction of p53 and Bax mRNA expression, including Bax/Bcl-2 ratio. Western blotting analysis showed that 72 hours of treatment with 200 mM ethanol significantly increased TH expression, but the expression was significantly decreased when cells were co-cultured with 10 µ M RA and 200 mM ethanol. Conclusions: All-trans RA could protect against apoptosis via a p53-dependent pathway and reduce the biochemical adaptation of ethanol-treated SH-SY5Y cells. J Neurol Res. 2012;2(5):204-210 doi: https://doi.org/10.4021/jnr147w
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".