J Am Heart Assoc
American Heart Association Dallas TX USA.
Published: March 2025
The American Heart Association's Get With The Guidelines-Quality Improvement registry is a vital resource for real-world cardiovascular and stroke data and research, containing >14 million records from >2800 participating hospitals. To facilitate and streamline research, we aim to generate a synthetic data set that increases access to real-world data and facilitates data exploration of the Get With The Guidelines-Stroke registry. We first randomly sampled 1000 records from the entire registry data set from 2005 to 2021 containing 7.8 million records. To preserve privacy and break the links from the original data, we shifted all data time variables and replaced all patient identifiers. To evaluate the generated synthetic data, we compared the distributions of patient demographics (eg, age, race, sex) and other key stroke-related measures. The generated synthetic data exhibited similar distributions in age, race, sex, and time-sensitive metrics such as door-to-needle time and time to intravenous thrombolytic therapy, demonstrating that this open access data set can provide all researchers the opportunity to explore real-world cardiovascular and stroke data.
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http://dx.doi.org/10.1161/JAHA.124.039667 | DOI Listing |
Anal Chem
March 2025
Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Science, Frontiers Science Center for New Organic Matter, College of Chemistry, Nankai University, Tianjin 300071, China.
Protein asymmetry, while crucial for life, can arise from subtle stereoisomerization. However, a comprehensive understanding of the breadth and specificity of the whole stereoproteome (STEP) has been hindered by insufficient stereoisomeric resolution. Here, we introduce an untargeted, STEP discovery protocol for comprehensive STEP profiling and relative quantification.
View Article and Find Full Text PDFTher Adv Musculoskelet Dis
March 2025
Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, Taichung Veterans General Hospital, No. 1650, Sec. 4, Taiwan Boulevard, Taichung 40705, Taiwan.
Background: Rheumatoid arthritis (RA) is complicated by a high risk of cardiovascular disease and requires the initiation of biological or targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) for persistently active disease despite first-line therapies. The influence of b/tsDMARDs, especially tsDMARDs, on cardiovascular risk in Taiwanese patients with RA remains unclear.
Objectives: To compare the risk of major cardiovascular adverse events (MACEs) or venous thromboembolism (VTE) amongst RA patients initiating approved b/tsDMARDs for up to 5 years.
Philos Trans A Math Phys Eng Sci
March 2025
Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK.
The rapid delayed rectifier current carried by the human Ether-à-go-go-Related Gene (hERG) channel is susceptible to drug-induced reduction, which can lead to an increased risk of cardiac arrhythmia. Establishing the mechanism by which a specific drug compound binds to hERG can help reduce uncertainty when quantifying pro-arrhythmic risk. In this study, we introduce a methodology for optimizing experimental voltage protocols to produce data that enable different proposed models for the drug-binding mechanism to be distinguished.
View Article and Find Full Text PDFNutrients
March 2025
Department of Public Health and Infectious Diseases, Sapienza University of Rome, 00185 Rome, Italy.
: In recent decades, the need for sustainable alternatives to traditional foods for the global population has become urgent. To this aim, edible insects, cultivated meat, and vegetables produced through soil-less farming have been proposed. This cross-sectional study was aimed at exploring willingness to eat these novel foods and its possible correlates in young Italian adults.
View Article and Find Full Text PDFMaterials (Basel)
February 2025
Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China.
This study proposes a novel framework using graph convolutional networks to analyze and interpret X-ray diffraction patterns, addressing challenges in phase identification for multi-phase materials. By representing X-ray diffraction patterns as graphs, the framework captures both local and global relationships between diffraction peaks, enabling accurate phase identification even in the presence of overlapping peaks and noisy data. The framework outperforms traditional machine learning models, achieving a precision of 0.
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