Results 21 to 30 of about 88,920 (217)

Exosome Proteomics of SOD1D90A Mutation Suggest Early Disease Mechanisms, and FN1 as a Biomarker

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Amyotrophic lateral sclerosis (ALS) is a neuromuscular disease. Super oxide dismutase 1 (SOD1) gene mutations cause ALS, and the D90A mutation is associated with primarily upper motor neuron (UMN) loss. Objective Our goal is to reveal the early cellular events in ALS pathology and identify potential pharmacokinetic biomarkers, using well ...
Mukesh Gautam, Ali Laith, Aslihan Gunel, Melda Yilmaz, Nazli Basak, Halil Idrisoglu, P. Hande Ozdinler   +6 more
wiley   +1 more source

Embedding Privacy in Computational Social Science and Artificial Intelligence Research

SSRN Electronic Journal
International Association for the Advancement of Artificial Intelligence (AAAI) Conference on Web and Social Media (ICWSM) Workshops (Disrupt, Ally, Resist, Embrace (DARE) Workshop ...
Jones, Keenan, Zahrah, Fatima, Nurse, Jason R. C.   +2 more
openaire   +3 more sources

Diffusion Tractography Biomarker for Epilepsy Severity in Children With Drug‐Resistant Epilepsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To develop a novel deep‐learning model of clinical DWI tractography that can accurately predict the general assessment of epilepsy severity (GASE) in pediatric drug‐resistant epilepsy (DRE) and test if it can screen diverse neurocognitive impairments identified through neuropsychological assessments.
Jeong‐Won Jeong, Min‐Hee Lee, Hiroshi Uda, Yoon Ho Hwang, Michael Behen, Aimee Luat, Csaba Juhász, Eishi Asano   +7 more
wiley   +1 more source

Impact of Stress‐Induced Hyperglycemia on In‐Hospital Medical Complications in Patients With Acute Stroke: From a Large‐Scale Nationwide Longitudinal Registry

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Aims This study aimed to explore the relationship between stress‐induced hyperglycemia (SIH) and in‐hospital medical complications in patients with acute stroke. Methods We enrolled 865,765 patients with acute stroke from the Chinese Stroke Center Alliance cohort.
Xintong Song, Yi Ju, Hongqiu Gu, Zhikai Zhu, Zixiao Li, Qian Zhang, Xingquan Zhao   +6 more
wiley   +1 more source

Effect of Nonsteroidal Anti‐Inflammatory Drugs on Sacroiliac Joint Inflammation, as Seen on Magnetic Resonance Imaging, in Axial Spondyloarthritis

Arthritis Care &Research, EarlyView.
Objective Imaging evidence of active sacroiliitis is important for diagnosis, classification, and monitoring of axial spondyloarthritis (axSpA). However, there is no consistent guidance on whether patients should temporarily stop nonsteroidal anti‐inflammatory drugs (NSAIDs) before magnetic resonance imaging (MRI).
Gareth T. Jones, Alexander N. Bennett, Raj Sengupta, Pedro M. Machado, Helena Marzo‐Ortega, Lorna Aucott, Margaret A. Hall‐Craggs, Timothy J. P. Bray, Alan Bainbridge, Ruaridh M. Gollifer, Gary J. Macfarlane   +10 more
wiley   +1 more source

Where, how, and how much? A multicenter cohort study of the relationship between lupus decision aid modality, place of administration, interruption and viewing completeness and patient‐reported outcomes

Arthritis Care &Research, Accepted Article.
Objective We assessed whether shared decision‐making (SDM), and patient acceptability, feasibility, and overall satisfaction with a computerized patient decision aid (PtDA) for patients with systemic lupus erythematosus (SLE), differs by PtDA setting, modality, and the viewing experience.
Jasvinder A. Singh, Mark Beasley, Larry R. Hearld, for the Implementing DEcision‐Aid for Lupus in clinics (IDEAL) Consortium   +3 more
wiley   +1 more source

Hip morphology‐based osteoarthritis risk prediction models: development and external validation using individual participant data from the World COACH Consortium

Arthritis Care &Research, Accepted Article.
Objectives This study aims to develop hip morphology‐based radiographic hip osteoarthritis (RHOA) risk prediction models and investigates the added predictive value of hip morphology measurements and the generalizability to different populations. Methods We combined data from nine prospective cohort studies participating in the World COACH consortium ...
Myrthe A. van den Berg, Fleur Boel, Michiel M.A. van Buuren, Noortje S. Riedstra, Jinchi Tang, Harbeer Ahedi, Nigel K. Arden, Sita M.A. Bierma‐Zeinstra, Cindy G. Boer, Flavia M. Cicuttini, Timothy F. Cootes, David T. Felson, Willem Paul Gielis, Joshua Heerey, Graeme Jones, Stefan Kluzek, Nancy E. Lane, Claudia Lindner, Joyce B.J. van Meurs, Andrea Mosler, Amanda E. Nelson, Michael C. Nevitt, Edwin H. Oei, Jos Runhaar, Harrie Weinans, Jesse H. Krijthe, Rintje Agricola   +26 more
wiley   +1 more source

Optimized Time–Frequency Analysis for Induction Motor Fault Detection Using Hybrid Differential Evolution and Deep Learning Techniques

International Journal of Adaptive Control and Signal Processing, EarlyView.
Workflow of the parameter optimization process for ITSC fault detection, applying Differential Evolution optimization and the Smooth Pseudo Wigner‐Ville Distribution for signal processing. The optimized parameters are then used in the failure identification pipeline, which combines the signal processing with a YOLO‐based architecture for fault severity
Rafael Martini Silva, Avyner Lorran de Oliveira Vitor, Marcelo Favoretto Castoldi, Alessandro Goedtel, Wesley Angelino Souza   +4 more
wiley   +1 more source

Detecting Dengue in Flight: Leveraging Machine Learning to Analyze Mosquito Flight Patterns for Infection Detection

Advanced Biology, EarlyView.
Dengue infection alters mosquito flight behavior, enabling detection using machine learning classifiers. This study analyzes 3D flight trajectories and evaluates multiple models, showing that longer sequence lengths improve classification performance.
Nouman Javed, Adam J. López‐Denman, Prasad N. Paradkar, Asim Bhatti   +3 more
wiley   +1 more source

Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics

Advanced Engineering Materials, EarlyView.
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani, Aditi Singh, Behnam Ashrafi   +2 more
wiley   +1 more source