Omega‐3 fatty acid eicospentaenoic acid attenuates <scp>MPP</scp><sup>+</sup>‐induced neurodegeneration in fully differentiated human <scp>SH</scp>‐<scp>SY</scp>5Y and primary mesencephalic cells
Bibliographic record
Abstract
Eicosapentaenoic acid (EPA), a neuroactive omega-3 fatty acid, has been demonstrated to exert neuroprotective effects in experimental models of Parkinson's disease (PD), but the cellular mechanisms of protection are unknown. Here, we studied the effects of EPA in fully differentiated human SH-SY5Y cells and primary mesencephalic neurons treated with MPP(+) . In both in-vitro models of PD, EPA attenuated an MPP(+) -induced reduction in cell viability. EPA also prevented the presence of electron-dense cytoplasmic inclusions in SH-SY5Y cells. Then, possible mechanisms of the neuroprotection were studied. In primary neurons, EPA attenuated an MPP(+) -induced increase in Tyrosine-related kinase B (TrkB) receptors. In SH-SY5Y cells, EPA down-regulated reactive oxygen species and nitric oxide. This antioxidant effect of EPA may have been mediated by its inhibition of neuronal NADPH oxidase and cyclo-oxygenase-2 (COX-2), as MPP(+) increased the expression of these enzymes. Furthermore, EPA prevented an increase in cytosolic phospholipase A2 (cPLA2), an enzyme linked with COX-2 in the potentially pro-inflammatory arachidonic acid cascade. Lastly, EPA attenuated an increase in the bax:bcl-2 ratio, and cytochrome c release. However, EPA did not prevent mitochondrial enlargement or a decrease in mitochondrial membrane potential. This study demonstrated cellular mechanisms by which EPA provided neuroprotective effects in experimental PD.
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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) | 0.001 | 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 | 0.001 |
| 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".