Phenolic compounds isolated from fermented blueberry juice decrease hepatocellular glucose output and enhance muscle glucose uptake in cultured murine and human cells
Bibliographic record
Abstract
BACKGROUND: We recently reported that blueberry juice fermented (FJ) with Serratia vaccinii bacterium has antidiabetic activities both in vivo and in vitro. The purpose of this project was to elucidate the effect of FJ on glucose homeostasis in liver and skeletal muscle cells and to identify active fractions/compounds responsible for this effect. METHODS: FJ was fractionated using standard chromatography procedures. Hepatic (H4IIE, HepG2) and skeletal muscle cells (C2C12) were treated with maximum non-toxic concentrations of FJ, fractions and isolated compounds thereof. Glucose-6-phosphatase (G6Pase) activity was measured using glucose oxidase method. To measure glucose uptake and glycogen synthase (GS) activity, radioactive assays were used. RESULTS: Fractionation of FJ yielded seven fractions. FJ and its phenolic fractions F2, F3-1 and F3-2 respectively inhibited G-6Pase by 31, 45, 51 and 26%; activated GS by 2.3-, 2.3-, 2.2- and 2-fold; and stimulated glucose uptake by 19, 25, 18 and 15%, as compared to DMSO vehicle control. Subfractionation of the active fractions yielded 4 compounds (catechol, chlorogenic, gallic and protocatechuic acid). Catechol, yielding the greatest bioactivity in G6Pase and glucose uptake assays, decreased G6Pase activity by 54%, increased GS by 2-fold and stimulated glucose uptake by 44% at 45.5 μM. CONCLUSIONS: This study identifies novel potential antidiabetic compounds that can help standardize FJ.
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) | 0.001 | 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".