Acute and chronic metabolic effects of carvedilol in high-fructose, high-fat diet–fed mice: implication of β-arrestin2 pathway
Bibliographic record
Abstract
We aimed to investigate the acute and chronic effects of carvedilol on insulin resistance in high-fructose, high-fat diet (HFrHFD) - fed mice and the implication of the β-arrestin2 pathway. The acute effect of carvedilol (10 mg/kg, i.p.) on glucose tolerance and hepatic lipid signaling in normal and insulin resistant mice was investigated. Then, the chronic effect of carvedilol on insulin resistance and dyslipidemia in HFrHFD-fed mice was examined. Changes in β-arrestin2 and its downstream signals in liver, skeletal muscle, and adipose tissue were measured. This involved measuring phosphatidylinositol 4,5-bisphosphate (PIP2) and diacylglycerol (DAG) levels and protein kinase B (AKT) activity. Carvedilol acutely reduced fasting blood glucose levels in both normal and insulin resistant mice without significantly affecting the glucose tolerance. These acute effects were associated with increased hepatic PIP2 but decreased hepatic DAG levels. Chronic administration of carvedilol significantly ameliorated insulin resistance and dyslipidemia in HFrHFD-fed mice. These chronic effects were associated with increased β-arrestin2, PIP2, and AKT activity levels but decreased DAG levels in the classical insulin target tissues. In conclusion, carvedilol acutely maintains glucose homeostasis and chronically ameliorates insulin resistance and dyslipidemia in HFrHFD-fed mice. The insulin sensitizing effects of carvedilol are highly correlated with the upregulation of β-arrestin2 pathway.
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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 | 0.000 | 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 | 0.000 |
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".