Fermented Canadian lowbush blueberry juice stimulates glucose uptake and AMP-activated protein kinase in insulin-sensitive cultured muscle cells and adipocytesThis article is one of a selection of papers published in this special issue (part 1 of 2) on the Safety and Efficacy of Natural Health Products.
Bibliographic record
Abstract
Extracts of the Canadian lowbush blueberry (Vaccinium angustifolium Ait.) have recently been demonstrated to possess significant antidiabetic potential, in accordance with the traditional use of this plant as an antidiabetic natural health product. Fermentation of blueberry juice with the Serratia vaccinii bacterium is known to modify the phenolic content and increase antioxidant activity. The present study evaluated the effects of fermented blueberry juice on glucose uptake, adipogenesis, and the signaling pathways that regulate glucose transport in muscle cells and adipocytes. A 6-hour treatment with fermented juice potentiated glucose uptake by 48% in C2C12 myotubes and by 142% in 3T3-L1 adipocytes, in the presence or absence of insulin, whereas nonfermented juice had no effect on transport. Fermented juice dramatically inhibited triglyceride content during adipogenesis of 3T3-L1 cells. Chlorogenic acid and gallic acid, both major phenolic components of fermented juice, had no effect on glucose uptake. Western blot analysis of the insulin-independent AMP-activated protein kinase revealed increased phosphorylation resulting from a 6-hour treatment. This activation or the increase in glucose uptake could not be explained by increased cytosolic calcium. Fermentation with S. vaccinii is concluded to confer antidiabetic activities to blueberry juice. Although the active principles and their mechanisms of action remain to be identified, transformed blueberry juice may nevertheless represent a novel complementary therapy and a source of novel therapeutic agents against diabetes mellitus.
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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 | 0.000 | 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 | 0.000 |
| 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".