Neuroprotective Effects of Açaí (<i>Euterpe oleracea</i> Mart.) against Rotenone <i>In Vitro</i> Exposure
Bibliographic record
Abstract
Neuropsychiatric diseases, such as bipolar disorder (BD) and schizophrenia (SCZ), have a very complex pathophysiology. Several current studies describe an association between psychiatric illness and mitochondrial dysfunction and consequent cellular modifications, including lipid, protein, and DNA damage, caused by cellular oxidative stress. Euterpe oleracea (açaí) is a powerful antioxidant fruit. Açaí is an Amazonian palm fruit primarily found in the lowlands of the Amazonian rainforest, particularly in the floodplains of the Amazon River. Given this proposed association, this study analyzed the potential in vitro neuropharmacological effect of Euterpe oleracea (açaí) extract in the modulation of mitochondrial function and oxidative metabolism. SH‐SY5Y cells were treated with rotenone to induce mitochondrial complex I dysfunction and before and after we exposed the cells to açaí extract at 5 μg/mL. Treated and untreated cells were then analyzed by spectrophotometric, fluorescent, immunological, and molecular assays. The results showed that açaí extract can potentially increase protein amount and enzyme activity of mitochondrial complex I, mainly through NDUFS7 and NDUFS8 overexpression. Açaí extract was also able to decrease cell reactive oxygen species levels and lipid peroxidation. We thus suggest açaí as a potential candidate for drug development and a possible alternative BD therapy.
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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".