The Urotensin II System and Carotid Atherosclerosis: A Role in Vascular Calcification
Bibliographic record
Abstract
BACKGROUND AND AIMS: The aims of the present study were to determine the expression of urotensin II (UII), urotensin-II related peptide (URP), and their receptor (UT) in stable and unstable carotid atherosclerosis, and determine the effects of UII on human aortic smooth muscle cell (SMCs) calcification. METHODS AND RESULTS: We examined UII, URP, and UT protein expression in 88 carotid endarterectomy specimens using immunohistochemistry. Expression of UII, URP, and UT was more evident in unstable compared to stable plaques (P < 0.05). Multivariate Spearman correlation analyses revealed significant positive correlations between UII, URP and UT overall staining and presence of calcification, severity of stenosis and inflammation (P < 0.05). Subjects undergoing carotid endarterectomy had significantly higher plasma UII levels, as assessed by ELISA, when compared with normolipidemic healthy control subjects (P < 0.05). Incubation of human aortic SMCs cultured in phosphate media with varying concentrations of UII resulted in a significant increase in calcium deposition and alkaline phosphatase activity. UII also significantly increased β-catenin translocation and expression of ALPL, BMP2, ON, and SOX9 (P < 0.05). Incubation of cells with phosphate medium alone increased the expression of the pre-UT and mature UT (P < 0.01), and addition of UII had a synergistic effect on pre-UT protein expression (P < 0.001) compared to phosphate medium alone. CONCLUSIONS: Upregulation of UII, URP, and UT in unstable carotid endarterectomy plaques and plasma, and the stimulatory effect of UII on vascular smooth muscle cell calcification suggest that the UII system may play a role in the pathogenesis of vascular calcification and stability of atherosclerosis.
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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) | 0.001 | 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".