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Emerging Immunotherapies for Disease Modification of Type 1 Diabetes.
Drugs
January 2025
Division of Endocrinology, Department of Pediatrics, College of Medicine, University of Florida, 1699 SW 16th Ave, Building A, Gainesville, FL, 32608, USA.
Type 1 diabetes mellitus (T1DM) is characterized by the progressive, autoimmune-mediated destruction of β cells. As such, restoring immunoregulation early in the disease course is sought to retain endogenous insulin production. Nevertheless, in the more than 100 years since the discovery of insulin, treatment of T1DM has focused primarily on hormone replacement and glucose monitoring.
View Article and Find Full Text PDFMissing value replacement in strings and applications.
Data Min Knowl Discov
January 2025
CWI, Amsterdam, The Netherlands.
Missing values arise routinely in real-world sequential (string) datasets due to: (1) imprecise data measurements; (2) flexible sequence modeling, such as binding profiles of molecular sequences; or (3) the existence of confidential information in a dataset which has been deleted deliberately for privacy protection. In order to analyze such datasets, it is often important to replace each missing value, with one or more letters, in an efficient and effective way. Here we formalize this task as a combinatorial optimization problem: the set of constraints includes the of the missing value (i.
View Article and Find Full Text PDFSTDCformer: Spatial-temporal dual-path cross-attention model for fMRI-based autism spectrum disorder identification.
Heliyon
July 2024
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging technique widely utilized in the research of Autism Spectrum Disorder (ASD), providing preliminary insights into the potential biological mechanisms underlying ASD. Deep learning techniques have demonstrated significant potential in the analysis of rs-fMRI. However, accurately distinguishing between healthy control group and ASD has been a longstanding challenge.
View Article and Find Full Text PDFHistoNeXt: dual-mechanism feature pyramid network for cell nuclear segmentation and classification.
BMC Med Imaging
January 2025
Department of Information, Third Affiliated Hospital of Naval Medical University, No. 225 Changhai Road, Yangpu District, 200438, Shanghai, China.
Purpose: To develop an end-to-end convolutional neural network model for analyzing hematoxylin and eosin(H&E)-stained histological images, enhancing the performance and efficiency of nuclear segmentation and classification within the digital pathology workflow.
Methods: We propose a dual-mechanism feature pyramid fusion technique that integrates nuclear segmentation and classification tasks to construct the HistoNeXt network model. HistoNeXt utilizes an encoder-decoder architecture, where the encoder, based on the advanced ConvNeXt convolutional framework, efficiently and accurately extracts multi-level abstract features from tissue images.
DeSPPNet: A Multiscale Deep Learning Model for Cardiac Segmentation.
Diagnostics (Basel)
December 2024
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.
Background: Cardiac magnetic resonance imaging (MRI) plays a crucial role in monitoring disease progression and evaluating the effectiveness of treatment interventions. Cardiac MRI allows medical practitioners to assess cardiac function accurately by providing comprehensive and quantitative information about the structure and function, hence making it an indispensable tool for monitoring the disease and treatment response. Deep learning-based segmentation enables the precise delineation of cardiac structures including the myocardium, right ventricle, and left ventricle.
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