Curcumin protects neuronal‐like cells against acrolein by restoring <scp>A</scp>kt and redox signaling pathways
Bibliographic record
Abstract
SCOPE: The aim of the present study was to examine the neuroprotective effect of curcumin against the toxicity induced by acrolein and to identify its cellular mechanisms and targets. METHODS AND RESULTS: Human neuroblastoma cells SK-N-SH were treated with acrolein. Curcumin, from 5 μM, was able to protect SK-N-SH cells against acrolein toxicity. The addition of curcumin restored the expression of γ-glutamylcysteine synthetase, reactive oxygen species, and reactive nitrogen species levels but had no effect on the decrease of glutathione (GSH) and on the elevation of protein carbonyls. Acrolein induced the activity of Nrf2, NF-κB, and Sirt1. These activations were prevented by the presence of curcumin. Acrolein also induced a decrease of the pAkt, which was counteracted by curcumin. To increase its solubility, we have encapsulated curcumin in a biodegradable poly(lactide-co-glycolide) based nanoparticulate formulation (Nps-Cur). Our results showed that 0.5 μM of Nps-Cur can protect neuronal cells challenged with acrolein while free curcumin was not able to display neuroprotection. CONCLUSION: Our results provided evidence that curcumin was able to protect SK-N-SH cells against acrolein toxicity. This protection is mediated through the antioxidant, the redox, and the survival regulated pathways by curcumin. Moreover, our results demonstrated that Nps-Cur had higher capacity than curcumin to protect SK-N-SH cells against acrolein.
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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) | 0.002 | 0.001 |
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".