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Multilayer network analysis in patients with end-stage kidney disease.
Sci Rep
December 2024
Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Republic of Korea.
This study aimed to investigate alterations in a multilayer network combining structural and functional layers in patients with end-stage kidney disease (ESKD) compared with healthy controls. In all, 38 ESKD patients and 43 healthy participants were prospectively enrolled. They exhibited normal brain magnetic resonance imaging (MRI) without any structural lesions.
View Article and Find Full Text PDFMicrostructural mapping of neural pathways in Alzheimer's disease using macrostructure-informed normative tractometry.
Alzheimers Dement
December 2024
Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA.
Introduction: Diffusion-weighted magnetic resonance imaging (dMRI) is sensitive to the microstructural properties of brain tissues and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest without considering the underlying fiber geometry.
Methods: We propose a novel macrostructure-informed normative tractometry (MINT) framework to investigate how white matter (WM) microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia.
GADIFF: a transferable graph attention diffusion model for generating molecular conformations.
Brief Bioinform
November 2024
School of Information Science and Technology, Northeast Normal University, 130117 Changchun, China.
The diffusion generative model has achieved remarkable performance across various research fields. In this study, we propose a transferable graph attention diffusion model, GADIFF, for a molecular conformation generation task. With adopting multiple equivariant networks in the Markov chain, GADIFF adds GIN (Graph Isomorphism Network) to acquire local information of subgraphs with different edge types (atomic bonds, bond angle interactions, torsion angle interactions, long-range interactions) and applies MSA (Multi-head Self-attention) as noise attention mechanism to capture global molecular information, which improves the representative of features.
View Article and Find Full Text PDFA Rare Case of Nasal myiasis following Kidney Transplantation.
Arch Razi Inst
June 2024
Nephrology and Urology Research Center, Clinical Science Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
In this case report, we present a 63-year-old man with a history of diabetes mellitus and kidney transplantation who was diagnosed with nasal myiasis. The patient presented with symptoms of nasal myiasis infestation, including epistaxis, nasal obstruction, nasal discharge, and the presence of larvae. The patient had risk factors for poor wound healing, such as hyperglycemia, and the presence of diabetes mellitus, hypertension, and kidney transplantation indicated the presence of predisposing factors for myiasis.
View Article and Find Full Text PDFInterpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features.
BMC Med Imaging
December 2024
Department of MRI, Xinxiang Central Hospital (The Fourth Clinical College of Xinxiang Medical University), 56 Jinsui Road, Xinxiang, Henan, 453000, China.
Background: To develop and validate an interpretable machine learning model based on intratumoral and peritumoral radiomics combined with clinicoradiological features and metabolic information from magnetic resonance spectroscopy (MRS), to predict clinically significant prostate cancer (csPCa, Gleason score ≥ 3 + 4) and avoid unnecessary biopsies.
Methods: This study retrospectively analyzed 350 patients with suspicious prostate lesions from our institution who underwent 3.0 Tesla multiparametric magnetic resonance imaging (mpMRI) prior to biopsy (training set, n = 191, testing set, n = 83, and a temporal validation set, n = 76).
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