The Effects and Mechanism of Saponins of <i>Panax notoginseng</i> on Glucose Metabolism in 3T3-L1 Cells
Bibliographic record
Abstract
This study was carried out to determine the effect of saponins of Panax notoginseng (SPN), a naturally occurring cardiovascular agent, on: (1) glucose uptake, (2) GLUT4 translocation and (3) glycogen synthesis in 3T3-L1 adipocytes. Electrospray ionization-Mass spectrometry (ESI-MS) was used to determine the structural characterization of the major active components of SPN. 3T3-L1 adipocytes were cultured and treated with 100 nM insulin alone or with 10, 50 and 100 microg/ml of SPN. [(3)H]2-deoxyglucose glucose uptake, GLUT4 immunofluorescence imaging and glycogen synthesis assay were carried out to determine the effects of SPN on glucose metabolism. Under insulin stimulation, SPN significantly increased glucose uptake in a dose-dependent manner; 50 microg/ml of SPN increased glucose uptake by 64% (p < 0.001). Immunofluorescence imaging and analysis have revealed that 50 and 100 microg/ml of SPN increased GLUT4 in the plasma membrane by 3-fold and 6-fold respectively (p < 0.001). Furthermore, the incorporation of D-[U-(14)C] glucose into glycogen was enhanced by 53% in 3T3-L1 cells treated with 100 microg/ml of SPN (p < 0.01 vs. insulin stimulation alone). SPN, a naturally occurring agent used to treat ischemic cardio-cerebral vascular disease in China, enhanced insulin-stimulated glucose uptake and glycogen synthesis in adipocytes. The results of this study indicate that SPN may have a therapeutic potential for hyperglycaemia in type 2 diabetes.
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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) | 0.000 | 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".