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Alterations of amino acids in older adults with Alzheimer's Disease and Vascular Dementia.
Amino Acids
January 2025
Institute of Brain Science, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230022, P. R. China.
Metabolomics provide a promising tool for understanding dementia pathogenesis and identifying novel biomarkers. This study aimed to identify amino acid biomarkers for Alzheimer's Disease (AD) and Vascular Dementia (VD). By amino acid metabolomics, the concentrations of amino acids were determined in the serum of AD and VD patients as well as age-matched healthy controls.
View Article and Find Full Text PDFDevelopment and Validation of KCPREDICT: A Deep Learning Model for Early Detection of Coronary Artery Lesions in Kawasaki Disease Patients.
Pediatr Cardiol
January 2025
Department of Infectious Disease, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, No. 1678 Dongfang Road, Pudong New Area, Shanghai, 200127, China.
Kawasaki disease (KD) is a febrile vasculitis disorder, with coronary artery lesions (CALs) being the most severe complication. Early detection of CALs is challenging due to limitations in echocardiographic equipment (UCG). This study aimed to develop and validate an artificial intelligence algorithm to distinguish CALs in KD patients and support diagnostic decision-making at admission.
View Article and Find Full Text PDFMitigating bias in AI mortality predictions for minority populations: a transfer learning approach.
BMC Med Inform Decis Mak
January 2025
Department of Clinical Pharmacy and Translational Science, The University of Tennessee Health Science Center, Memphis, TN, USA.
Background: The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health disparities due to demographic biases, particularly affecting racial and ethnic minorities. The objective of this study is to investigate the demographic biases in AI models predicting COVID-19 mortality and to assess the effectiveness of transfer learning in improving model fairness across diverse demographic groups.
View Article and Find Full Text PDFRisk factors for long-term severe tricuspid regurgitation following mitral valve replacement: a retrospective study.
BMC Cardiovasc Disord
January 2025
Department of Cardiology, Xuzhou Central Hospital, No.199 Jiefang South Road, Quanshan District, Xuzhou, 221009, People's Republic of China.
Background: The aim of this study is to identify factors associated with the development of long-term severe tricuspid regurgitation (TR) following mitral valve replacement (MVR).
Methods: A retrospective analysis was conducted involving 308 patients who underwent single-valve MVR at Xuzhou Central Hospital between April 2017 and December 2022. Preoperative color Doppler ultrasound indicated that all patients had either no or mild to moderate tricuspid regurgitation.
MGATAF: multi-channel graph attention network with adaptive fusion for cancer-drug response prediction.
BMC Bioinformatics
January 2025
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 611756, Sichuan, China.
Background: Drug response prediction is critical in precision medicine to determine the most effective and safe treatments for individual patients. Traditional prediction methods relying on demographic and genetic data often fall short in accuracy and robustness. Recent graph-based models, while promising, frequently neglect the critical role of atomic interactions and fail to integrate drug fingerprints with SMILES for comprehensive molecular graph construction.
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