Impact of the Co-Administration of N-3 Fatty Acids and Olive Oil Components in Preclinical Nonalcoholic Fatty Liver Disease Models: A Mechanistic View
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD) is present in approximately 25% of the population worldwide. It is characterized by the accumulation of triacylglycerol in the liver, which can progress to steatohepatitis with different degrees of fibrosis, stages that lack approved pharmacological therapies and represent an indication for liver transplantation with consistently increasing frequency. In view that hepatic steatosis is a reversible condition, effective strategies preventing disease progression were addressed using combinations of natural products in the preclinical high-fat diet (HFD) protocol (60% of fat for 12 weeks). Among them, eicosapentaenoic acid (C20:5n-3, EPA) and docosahexaenoic acid (C22:5n-3, DHA), DHA and extra virgin olive oil (EVOO), or EPA plus hydroxytyrosol (HT) attained 66% to 83% diminution in HFD-induced steatosis, with the concomitant inhibition of the proinflammatory state associated with steatosis. These supplementations trigger different molecular mechanisms that modify antioxidant, antisteatotic, and anti-inflammatory responses, and in the case of DHA and HT co-administration, prevent NAFLD. It is concluded that future studies in NAFLD patients using combined supplementations such as DHA plus HT are warranted to prevent liver steatosis, thus avoiding its progression into more unmanageable stages of the disease.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".