Lingonberry (<i>Vaccinium vitis-idaea</i> L.) Exhibits Antidiabetic Activities in a Mouse Model of Diet‐Induced Obesity
Bibliographic record
Abstract
Vaccinium vitis-idaea, commonly known as lingonberry, has been identified among species used by the Cree of Eeyou Istchee of northern Quebec to treat symptoms of diabetes. In a previous study, the ethanol extract of berries of V. vitis-idaea enhanced glucose uptake in C2C12 muscle cells via stimulation of AMP-activated protein kinase (AMPK) pathway. The purpose of this study was to examine the effect of plant extract in a dietary mouse model of mild type 2 diabetes. C57BL/6 mice fed a high-fat diet (HFD, ∼35% lipids) for 8 weeks that become obese and insulin-resistant (diet-induced obesity, DIO) were used. Treatment began by adding V. vitis-idaea extract to HFD at 3 different concentrations (125, 250, and 500 mg/Kg) for a subsequent period of 8 weeks (total HFD, 16 weeks). The plant extract significantly decreased glycemia and strongly tended to decrease insulin levels in this model. This was correlated with a significant increase in GLUT4 content and activation of the AMPK and Akt pathways in skeletal muscle. V. vitis-idaea treatment also improved hepatic steatosis by decreasing hepatic triglyceride levels and significantly activated liver AMPK and Akt pathways. The results of the present study confirm that V. vitis-idaea represents a culturally relevant treatment option for Cree diabetics and pave the way to clinical studies.
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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 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".