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 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".