α-Amyrin induces GLUT4 translocation mediated by AMPK and PPARδ/γ in C2C12 myoblasts
Bibliographic record
Abstract
α-Amyrin, a natural pentacyclic triterpene, has an antihyperglycemic effect in mice and dual PPARδ/γ action in 3T3-L1 adipocytes, and potential in the control of type 2 diabetes (T2D). About 80% of glucose uptake occurs in skeletal muscle cells, playing a significant role in insulin resistance (IR) and T2D. Peroxisome-proliferator activated receptors (PPARs), in particular PPARδ and PPARγ, are involved in the regulation of lipids and carbohydrates and, along with adenosine-monophosphate (AMP) - activated protein kinase (AMPK) and protein kinase B (Akt), are implicated in translocation of glucose transporter 4 (GLUT4); however, it is still unknown whether α-amyrin can affect these pathways in skeletal muscle cells. Our objective was to determine the action of α-amyrin in PPARδ, PPARγ, AMPK, and Akt in C2C12 myoblasts. The expression of PPARδ, PPARγ, fatty acid transporter protein (FATP), and GLUT4 was quantified using reverse transcription quantitative PCR and Western blot. α-Amyrin increased these markers along with phospho-AMPK (p-AMPK) but not p-Akt. Molecular docking showed that α-amyrin acts as an AMPK-allosteric activator, and may be related to GLUT4 translocation, as evidenced by confocal microscopy. These data support that α-amyrin could have an insulin-mimetic action in C2C12 myoblasts and should be considered as a bioactive molecule for new multitarget drugs with utility in T2D and other metabolic diseases.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".