Identification of baicalin from Bofutsushosan and Daisaikoto as a potent inducer of glucose uptake and modulator of insulin signaling-associated pathways
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) is a chronic disease characterized by hyperglycemia that can lead to long-term complications including heart diseases, stroke, retinopathy, and renal failure. Treatment strategies include stimulating glucose uptake and controlling blood glucose level. Bofutsushosan (BOF) and Daisaikoto (DAI) are two herb-based kampo medicines that have been demonstrated to improve metabolism-associated disorders including obesity, hyperlipidemia, and nonalcoholic fatty liver. Given their bioactivities against metabolic syndromes, we explored in this study the effect of BOF and DAI extracts on glucose absorption and used them as source to identify phytochemical stimulator of glucose absorption. Glucose uptake and mechanistic studies were evaluated in differentiated C2C12 skeletal muscle cells, and HPLC analysis was used to determine the molecular bioactive constituents. Our results indicated that the ethanolic extracts of BOF and DAI (BOFEE and DAIEE, respectively) enhanced the glucose uptake ratio in the differentiated C2C12 cells, and further analysis identified the flavone baicalin as a major constituent capable of efficiently stimulating glucose absorption. Mechanistic studies revealed that the effect from baicalin involved the activation of IRS-1 and GLUT-4, and implicated the AMPK, PI3K/Akt, and MAPK/ERK signaling cascades. Due to its potency, we suggest that baicalin merit further evaluation as a potential candidate anti-hyperglycemic agent for the treatment and management of T2DM.
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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 | 0.000 |
| 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".