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| http://dx.doi.org/10.1148/ryai.240377 | DOI Listing |
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Genome-wide association study unravels mechanisms of brain glymphatic activity.
Nat Commun
January 2025
Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
Brain glymphatic activity, as indicated by diffusion-tensor imaging analysis along the perivascular space (ALPS) index, is involved in developmental neuropsychiatric and neurodegenerative diseases, but its genetic architecture is poorly understood. Here, we identified 17 unique genome-wide significant loci and 161 candidate genes linked to the ALPS-indexes in a discovery sample of 31,021 individuals from the UK Biobank. Seven loci were replicated in two independent datasets.
View Article and Find Full Text PDFNumerical simulation of fluid-structure interaction analysis for the performance of leaflet reimplantation with different types of artificial graft.
Comput Methods Programs Biomed
January 2025
College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China. Electronic address:
Background And Objective: In clinical practice, valve-sparing aortic root replacement surgery primarily addresses left ventricular dysfunction in patients due to severe aortic regurgitation, but there is controversy regarding the choice of surgical technique. In order to investigate which type of valve-sparing aortic root replacement surgeries can achieve better blood flow conditions, this study examines the impact of changes in the geometric morphology of the aortic root on the hemodynamic environment through numerical simulation.
Methods: An idealized model of the aortic root was established based on data obtained from clinical measurements, including using the model of the aortic root without significant lesions as the control group (Model C), while using surgical models of leaflet reimplantation with tubular graft (Model T), leaflet reimplantation with Valsalva graft (Model V), and the Florida sleeve procedure (Model F) as the experimental groups.
Discriminative, generative artificial intelligence, and foundation models in retina imaging.
Taiwan J Ophthalmol
November 2024
Sirindhorn International Institute of Technology, Thammasat University, Bangkok, Thailand.
Recent advances of artificial intelligence (AI) in retinal imaging found its application in two major categories: discriminative and generative AI. For discriminative tasks, conventional convolutional neural networks (CNNs) are still major AI techniques. Vision transformers (ViT), inspired by the transformer architecture in natural language processing, has emerged as useful techniques for discriminating retinal images.
View Article and Find Full Text PDFQuantitative investigation of a 3D bubble trapper in a high shear stress microfluidic chip using computational fluid dynamics and L*A*B* color space.
Biomed Microdevices
January 2025
Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, 111 Suwannabhumi Canal Rd, Bang Pla, Bang Phli District, Samut Prakan, 10540, Thailand.
Microfluidic chips often face challenges related to the formation and accumulation of air bubbles, which can hinder their performance. This study investigated a bubble trapping mechanism integrated into microfluidic chip to address this issue. Microfluidic chip design includes a high shear stress section of fluid flow that can generate up to 2.
View Article and Find Full Text PDFConfigurable Multi-Layer Perceptron-Based Soft Sensors on Embedded Field Programmable Gate Arrays: Targeting Diverse Deployment Goals in Fluid Flow Estimation.
Sensors (Basel)
December 2024
Intelligent Embedded Systems of Computer Science, University of Duisburg-Essen, 47057 Duisburg, Germany.
This study presents a comprehensive workflow for developing and deploying Multi-Layer Perceptron (MLP)-based soft sensors on embedded FPGAs, addressing diverse deployment objectives. The proposed workflow extends our prior research by introducing greater model adaptability. It supports various configurations-spanning layer counts, neuron counts, and quantization bitwidths-to accommodate the constraints and capabilities of different FPGA platforms.
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