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http://dx.doi.org/10.1002/ase.2200 | DOI Listing |
J Racial Ethn Health Disparities
January 2025
Center for Population Health Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Recent research shows a significant link between race-ethnicity and income concentration and premature death rates in the U.S. However, most studies focus on Black-White residential concentration, overlooking racial-ethnic diversity.
View Article and Find Full Text PDFAging Dis
December 2024
Department of Psycho-cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Angina pectoris (AP), a clinical syndrome characterized by paroxysmal chest pain, is caused by insufficient blood supply to the coronary arteries and sudden temporary myocardial ischemia and hypoxia. Long-term AP typically induces other cardiovascular events, including myocardial infarction and heart failure, posing a serious threat to patient safety. However, AP's complex pathological mechanisms and developmental processes introduce significant challenges in the rapid diagnosis and accurate treatment of its different subtypes, including stable angina pectoris (SAP), unstable angina pectoris (UAP), and variant angina pectoris (VAP).
View Article and Find Full Text PDFPredicting health trajectories and accurately measuring aging processes across the human lifespan remain profound scientific challenges. Assessing the effectiveness and impact of interventions targeting aging is even more elusive, largely due to the intricate, multidimensional nature of aging-a process that defies simple quantification. Traditional biomarkers offer only partial perspectives, capturing limited aspects of the aging landscape.
View Article and Find Full Text PDFEpilepsia
January 2025
Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada.
Objective: Somatic variants causing epilepsy are challenging to detect, as they are only present in a subset of brain cells (e.g., mosaic), resulting in low variant allele frequencies.
View Article and Find Full Text PDFBackground: Early detection and accurate forecasting of AD progression are crucial for timely intervention and management. This study leverages multi-modal data, including MRI scans, brain volumetrics, and clinical notes, utilizing Machine Learning (ML), Deep Learning (DL) and a range of ensemble methods to enhance the forecasting accuracy of Alzheimer's disease.
Method: We utilize the OASIS-3 longitudinal dataset, tracking 1,098 patients over 30 years.
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