Lingonberry Improves Non-Alcoholic Fatty Liver Disease by Reducing Hepatic Lipid Accumulation, Oxidative Stress and Inflammatory Response
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease globally and there is a pressing need for effective treatment. Lipotoxicity and oxidative stress are the important mediators in NAFLD pathogenesis. Lingonberry (Vaccinium vitis-idaea L.) is rich in anthocyanins that have antioxidant and anti-inflammatory properties. The present study investigated the effect of lingonberry supplementation on liver injury in C57BL/6J male mice fed a high-fat diet (HFD) for 12 weeks. Mice fed HFD displayed liver injury with steatosis, increased lipid peroxidation and inflammatory cytokine expression in the liver as compared to mice fed a control diet. Lingonberry supplementation for 12 weeks alleviated HFD-induced liver injury, attenuated hepatic lipid accumulation, and inflammatory cytokine expression. Lingonberry supplementation inhibited the expression of sterol regulatory element-binding protein-1c (SREBP-1c) and acetyl-CoA carboxylase-1 (AAC-1) as well as activated AMP-activated protein kinase (AMPK) in the liver. It also decreased HFD-induced hepatic oxidative stress and aggregation of inflammatory foci. This was associated with a restoration of nuclear factor erythroid 2–related factor 2 (Nrf2) and glutathione level in the liver. These results suggest that lingonberry supplementation can protect against HFD-induced liver injury partly through attenuation of hepatic lipid accumulation, oxidative stress, and inflammatory response.
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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 | 0.001 |
| 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".