Abstract
The pericardium is a thin membrane sac that covers the heart. As such, the segmentation of the pericardium in computed tomography (CT) can have several clinical applications, namely as a preprocessing step for extraction of different clinical parameters. However, manual segmentation of the pericardium can be challenging, time-consuming and subject to observer variability, which has motivated the development of automatic pericardial segmentation methods. In this study, a method to automatically segment the pericardium in CT using a U-Net framework is proposed. Two datasets were used in this study: the publicly available Cardiac Fat dataset and a private dataset acquired at the hospital centre of Vila Nova de Gaia e Espinho (CHVNGE). The Cardiac Fat database was used for training with two different input sizes - 512 512 and 256 256. A superior performance was obtained with the 256 256 image size, with a mean Dice similarity score (DCS) of 0.871 ± 0.01 and 0.807 ± 0.06 on the Cardiac Fat test set and the CHVNGE dataset, respectively. Results show that reasonable performance can be achieved with a small number of patients for training and an off-the-shelf framework, with only a small decrease in performance in an external dataset. Nevertheless, additional data will increase the robustness of this approach for difficult cases and future approaches must focus on the integration of 3D information for a more accurate segmentation of the lower pericardium. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Correction to: Scientific Reports, published online 19 May 2023 The original PDF version of this Article contained formatting errors in Equation 10. (Formula presented.) now reads: Additionally, the Funding section in the original version of this Article was omitted. The Funding section now reads: “This research was funded by the Health Research Council of New Zealand (programme grant 17/608) and National Heart Foundation of New Zealand (project 1834).” The original Article has been corrected. © 2023, Springer Nature Limited.

Abstract
The aim of this study was to identify and validate distinct patterns of vascular aging, focusing on a novel high-risk vascular aging (HRVA) cluster. Key biomarkers such as aortic pulse wave velocity, glycated hemoglobin, pulse pressure, and advanced glycation end-products were used to enhance cardiovascular risk stratification and explore implications for targeted interventions. Data from multiple studies were integrated, and K-means clustering identified three vascular aging patterns: healthy vascular aging (HVA), early vascular aging (EVA), and high-risk vascular aging (HRVA). ROC analysis determined optimal thresholds for key biomarkers. ANOVA and Chi-square tests evaluated differences and associations across clusters, supported by contingency tables and residual analysis. The HRVA cluster exhibited significantly elevated biomarker levels compared to the HVA and EVA clusters. Statistically significant differences were observed across clusters (p <=\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le $$\end{document} 0.001), confirmed by ANOVA. Chi-square tests revealed strong associations between cluster membership and categorical variables, further validating the distinct profiles. The HRVA group demonstrated a particularly high risk of adverse cardiovascular events, emphasizing the clinical relevance. The identification of the HRVA cluster provides new insights into vascular aging, suggesting the need for intensive, personalized interventions. Future research should focus on validating these clusters longitudinally and exploring genetic, environmental, and lifestyle factors to improve cardiovascular outcomes.