Genetic and Pharmacological Manipulation of Urotensin II Ameliorate the Metabolic and Atherosclerosis Sequalae in Mice
Bibliographic record
Abstract
OBJECTIVE: Urotensin II (UII) is a potent vasoactive peptide that binds to the urotensin receptor-coupled receptor-14 (known as UT) and exerts a wide range of actions in humans and experimental animals. We tested the hypothesis that UII gene deletion or UT blockade ameliorate experimental atherosclerosis. METHODS AND RESULTS: We observed a significant reduction in weight gain, visceral fat, blood pressure, circulating plasma lipids, and proatherogenic cytokines and improvement of glucose tolerance in UII knockout mice compared with wild type (P<0.05). Deletion of UII after an apolipoprotein E knockout resulted in a significant reduction in serum cytokines, adipokines, and aortic atherosclerosis compared with apolipoprotein E knockout mice. Similarly, treatment of apolipoprotein E knockout mice fed on high-fat diet with the UT antagonist SB657510A reduced weight gain, visceral fat, and hyperlipidemia and improved glucose tolerance (P<0.05) and attenuated the initiation and progression of atherosclerosis. The UT antagonist also decreased aortic extracellular signal-regulated kinase 1/2 phosphorylation and oxidant formation and serum level of cytokines (P<0.05). CONCLUSIONS: These findings demonstrate for the first time the role of UII gene deletion in atherosclerosis and suggest that the use of pharmaceutical agents aimed at blocking the UII pathway may provide a novel approach in the treatment of atherosclerosis and its associated precursors such as obesity, hyperlipidemia, diabetes mellitus, and hypertension.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".