Açaí (<i>Euterpe oleracea</i>Mart.) presents anti-neuroinflammatory capacity in LPS-activated microglia cells
Bibliographic record
Abstract
INTRODUCTION: Neuropsychiatric diseases are responsible for one of the highest burden of morbidity and mortality worldwide. These illnesses include schizophrenia, bipolar disorder, and major depression. Individuals affected by these diseases may present mitochondrial dysfunction and oxidative stress. Additionally, patients also have increased peripheral and neural chronic inflammation. The Brazilian fruit, açaí, has been demonstrated to be a neuroprotective agent through its recovery of mitochondrial complex I activity. This extract has previously shown anti-inflammatory effects in inflammatory cells. However, there is a lack of understanding of potential anti-neuroinflammatory mechanisms, such as cell cycle involvement. OBJECTIVE: The objective of this study is to evaluate the anti-neuroinflammatory potential of an açaí extract in lipopolysaccharide-activated BV-2 microglia cells. METHODS: Açaí extract was produced and characterized through high performance liquid chromatography. Following açaí extraction and characterization, BV-2 microglia cells were activated with LPS and a dose-response curve was generated to select the most effective açaí dose to reduce cellular proliferation. This dose was then used to assess reactive oxygen species (ROS) production, double-strand DNA release, cell cycle modulation, and cytokine and caspase protein expression. RESULTS: Characterization of the açaí extract revealed 10 bioactive molecules. The extract reduced cellular proliferation, ROS production, and reduced pro-inflammatory cytokines and caspase 1 protein expression under 1 μg/mL in LPS-activated BV-2 microglia cells but had no effect on double strand DNA release. Additionally, açaí treatment caused cell cycle arrest, specifically within synthesis and G2/Mitosis phases. CONCLUSION: These results suggest that the freeze-dried hydroalcoholic açaí extract presents high anti-neuroinflammatory potential.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".