Sesamin Modulates Tyrosine Hydroxylase, Superoxide Dismutase, Catalase, Inducible No Synthase and Interleukin‐6 Expression in Dopaminergic Cells Under Mpp+‐Induced Oxidative Stress
Bibliographic record
Abstract
Oxidative stress is regarded as a mediator of nerve cell death in several neurodegenerative disorders, such as Parkinson's disease. Sesamin, a lignan mainly found in sesame oil, is currently under study for its anti-oxidative and possible neuroprotective properties. We used 1-methyl-4-phenyl-pyridine (MPP(+)) ion, the active metabolite of the potent parkinsonism-causing toxin 1-methyl-4-phenyl-1,2,5,6-tetrahydropyridine, to produce oxidative stress and neurodegeneration in neuronal PC12 cells, which express dopamine, as well as neurofilaments. Our results show that picomolar doses of sesamin protected neuronal PC12 cells from MPP(+)-induced cellular death, as revealed by colorimetric measurements and production of reactive oxygen species. We also demonstrated that sesamin acted by rescuing tyrosine hydroxylase levels from MPP(+)-induced depletion. Sesamin, however, did not modulate dopamine transporter levels, and estrogen receptor-alpha and -beta protein expression. By examining several parameters of cell distress, we found that sesamin also elicited a strong increase in superoxide dismutase activity as well as protein expression and decreased catalase activity and the MPP(+) stimulated inducible nitric oxide synthase protein expression, in neuronal PC12 cells. Finally, sesamin possessed significant anti-inflammatory properties, as disclosed by its potential to reduce MPP(+)-induced interleukin-6 mRNA levels in microglia. From these studies, we determined the importance of the lignan sesamin as a neuroprotective molecule and its possible role in complementary and/or preventive therapies of neurodegenerative diseases.
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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".