Molecular Regulations Induced by Acrolein in Neuroblastoma SK-N-SH Cells: Relevance to Alzheimer's Disease
Bibliographic record
Abstract
Acrolein is the most reactive aldehyde among the by-products of lipid peroxidation. Growing evidence indicates that acrolein may play an important role in the pathogenesis of Alzheimer's disease (AD). In AD, levels of acrolein are significantly higher in hippocampus and temporal cortex regions of the brain. However, little is known about its toxicity in neuronal cells. Using the neuroblastoma cell line SK-N-SH, our results show that acrolein is toxic from 10 μM, but its toxicity does not induce the activation of caspase-3 and DNA fragmentation. Protein carbonylation and 4-hydroxynonenal levels were increased after 0.5 hr and 1 hr of treatment, respectively. Furthermore acrolein induced a biphasic effect on glutathione levels with a rapid depletion followed by a progressive increase. We have further investigated the regulation of different redox signaling pathways. A treatment with 10 μM of acrolein for 30 min activated NFκB while this activation was suppressed after a 24 hrs of treatment. In contrast, Nrf2 was activated only after 24 hrs of acrolein exposure. Consequently, the expression of heme oxygenase-1 and γ-glutamyl-cysteine-synthase were elevated after 24 hrs of acrolein treatment. Sirt-1 was also upregulated after 24 hrs of acrolein treatment. The p66Shc and ERK1/2 proteins are known to be involved in oxidative stress. Acrolein, at 10 μM, induced the phosphorylation of p66Shc and ERK1/2 only after a short period of treatment. Collectively, these data strengthen the contribution of acrolein in the induction of oxidative stress as observed in mild cognitive impairment and in AD brain.
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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".