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Development 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 PDFAOPs to connect food additives' effects on gut microbiota to health outcomes.
ALTEX
January 2025
Laboratory of Hepato-Gastroenterology, Institute of Experimental and Clinical Research, UCLouvain, Brussels, Belgium.
The gut microbiota is unanimously acknowledged as playing a central role in human health, notably through the production of various metabolites, including short-chain fatty acids, secondary bile acids, vitamins or neurotransmitters. Beyond contributing to gut health itself, these microbial metabolites significantly impact multiple organ systems by participating in key signaling pathways along the well documented gut-organ axes. Chemicals ingested through food might interact with our gut microbiota, altering metabolites production with consequences on health.
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 PDFCommon and distinct neural underpinnings of the association between childhood maltreatment and depression and aggressive behavior.
BMC Psychiatry
January 2025
College of Artificial Intelligence, Southwest University, Chongqing, China.
Background: Although childhood maltreatment (CM) is widely recognized as a transdiagnostic risk factor for various internalizing and externalizing psychological disorders, the neural basis underlying this association remain unclear. The potential reasons for the inconsistent findings may be attributed to the involvement of both common and specific neural pathways that mediate the influence of childhood maltreatment on the emergence of psychopathological conditions.
Methods: This study aimed to delineate both the common and distinct neural pathways linking childhood maltreatment to depression and aggression.
Risk 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.
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