Lingonberry Improves Hepatic Lipid Metabolism by Targeting Notch1 Signaling
Bibliographic record
Abstract
Impaired hepatic lipid metabolism is a hallmark of non-alcoholic fatty liver disease (NAFLD), which has no effective treatment option. Recently, Notch signaling has been identified as an important mediator of hepatic lipid metabolism. Lingonberry (Vaccinium vitis-idaea L.) is an anthocyanin-rich fruit with significant lipid-lowering properties. In this study, we examined how lingonberry influenced Notch signaling and fatty acid metabolism in a mouse model of NAFLD. Mice (C57BL/6J) fed a high-fat diet (HFD) for 12 weeks developed fatty liver and activated hepatic Notch1 signaling. Lingonberry supplementation inhibited hepatic Notch1 signaling and improved lipid profile by improving the expression of the genes involved in hepatic lipid metabolism. The results were verified using a palmitic-acid-challenged cell model. Similar to the animal data, palmitic acid impaired cellular lipid metabolism and induced Notch1 in HepG2 cells. Lingonberry extract or cyanidin-3-glucoside attenuated Notch1 signaling and decreased intracellular triglyceride accumulation. The inhibition of Notch in the hepatocytes attenuated sterol-regulatory-element-binding-transcription-factor-1 (SREBP-1c)-mediated lipogenesis and increased the expression of carnitine palmitoyltransferase-I-alpha (CPTIα) and acyl-CoA oxidase1 (ACOX1). Taken together, lingonberry’s hepatoprotective effect is mediated by, in part, improving hepatic lipid metabolism via inhibiting Notch1 signaling in HFD-induced fatty liver.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".