Quercetin and Sesamin Protect Neuronal PC12 Cells from High-Glucose-Induced Oxidation, Nitrosative Stress, and Apoptosis
Bibliographic record
Abstract
Complications of diabetes are now well-known to affect sensory, motor, and autonomic nerves. Diabetes is also thought to be involved in neurodegenerative processes characteristic of several neurodegenerative diseases. Indeed, it has been acknowledged recently that hyperglycemia-induced oxidative stress contributes to numerous cellular reactions typical of central nervous system deterioration. The goal of the present study was to evaluate the effects of the polyphenol quercetin and the lignan sesamin on high-glucose (HG)-induced oxidative damage in an in vitro model of dopaminergic neurons, neuronal PC12 cells. When incubated with HG (13.5 mg/mL), neuronal PC12 cells showed a significant increase of cellular death. Our results revealed that quercetin and sesamin defend neuronal PC12 cells from HG-induced cellular demise. An elevated level of reactive oxygen and nitrogen species is a consequence of improved oxidative stress after HG administration, and we demonstrated that this production diminishes with quercetin and sesamin treatment. We also found that quercetin and sesamin elicited an increment of superoxide dismutase activity. DNA fragmentation, Bax/Bcl-2 ratio, nuclear translocation of apoptosis-inducing factor, as well as poly(adenosine diphosphate [ADP]-ribose) polymerase cleavage were significantly reduced by quercetin and sesamin administration, affirming their antiapoptotic features. Also, HG treatment impacted caspase-3 cleavage, supporting caspase-3-dependent pathways as mechanisms of apoptotic death. Our results indicate a powerful role for these natural dietary compounds and emphasize preventive or complementary nutritional strategies for diabetes control.
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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".