Neuroprotective effect of estradiol and phytoestrogens on MPP<sup>+</sup>‐induced cytotoxicity in neuronal PC12 cells
Bibliographic record
Abstract
A large body of experimental evidence supports a role for oxidative stress as a mediator of nerve cell death in Parkinson's disease. To better understand the cellular insult of oxidative stress on dopaminergic neurons, we studied the cytotoxic effect of the 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) metabolite, 1-methyl-4-phenyl pyridium (MPP(+)), on several parameters of cell distress using neuronal PC12 cells. We also measured the level of protein expression for the dopamine transporter and the estrogen receptors alpha and beta. Since estrogens have been reported to prevent neuronal degeneration caused by increased oxidative burden, we investigated the ability of 17beta-estradiol, the stereoisomer 17alpha-estradiol, and several phytoestrogens to rescue neuronal PC12 cells submitted to MPP(+)-induced cytotoxicity. Our results consistently show a protective effect of 17alpha-estradiol, 17beta-estradiol and certain phytoestrogens such as quercetin and resveratrol, in neuronal PC12 cells treated with MPP(+). In our cellular paradigm, phytoestrogens coumestrol, genistein, and kaempferol did not revert MPP(+)-induced cellular death. By Western blot, we demonstrated that administration of MPP(+) alone decrease dopamine transporter expression, while treatments with MPP(+) together with 17alpha-estradiol, 17beta-estradiol, quercetin, or resveratrol could restore dopamine transporter protein expression to control levels. Moreover, the same treatments did not modulate alpha estrogen receptor or beta estrogen receptor expression. By these studies, we aim to provide more evidence for the involvement of phytoestrogens in the process of neuroprotection and to test our hypothesis that some of these compounds may act as neuroprotective molecules and have a lesser hormonal effect than estrogens.
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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".