PPARα/γ, adiponectin, and GLUT4 overexpression induced by moronic acid methyl ester influenced glucose and triglyceride levels of experimental diabetic mice
Bibliographic record
Abstract
The current study aimed to determine the antidiabetic and antidyslipidemic activities of moronic acid methyl ester (1) (compound 1) by in vivo, in vitro, in silico, and molecular biology studies. Compound 1 was evaluated to establish its dose-dependent antidiabetic and antihyperglycemic (50 mg/kg) activities, in diabetic and normoglycemic male CD1 mice, respectively. Also, compound 1 was subjected to a subacute study (50 mg/kg per day for 8 days) to determine blood biochemical profiles and the expression of protein tyrosine phosphatase 1B (PTP-1B), glucose transporter type 4 (GLUT4), peroxisome proliferator-activated receptor α (PPAR-α), PPAR-γ, adiponectin, interleukin-1β (IL-1β), and monocyte chemoattractant protein 1 (MCP-1) in adipose tissue of animals after treatment. Different doses in acute administration of compound 1 decreased glycemia (p < 0.05) compared with vehicle, showing greater effectiveness in the range 50–160 mg/kg. Also, the oral glucose tolerance test showed that compound 1 induced a significant antihyperglycemic action by opposing the hyperglycemic peak (p < 0.05). Moreover, compound 1 subacute administration decreased glucose and triglyceride levels after treatment (p < 0.05); while the expression of PPAR-α and PPAR-γ, adiponectin, and GLUT4 displayed an increase (p < 0.05) compared with the diabetic control group. In conclusion, compound 1 showed antihyperglycemic, antidiabetic, and antidyslipidemic effects in normal and diabetic mice, probably due to insulin sensitization through increased mRNA expression of GLUT4, PPAR-α, PPAR-γ, and adiponectin genes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".