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scTRIM44 Positively Regulated Siniperca Chuatsi Rhabdovirus Through RIG-I- and MDA5-Mediated Interferon Signaling.
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Ministry of Agriculture and Rural Affairs, Guangdong Provincial Key Laboratory of Aquatic Animal Immunology and Sustainable Aquaculture, Key Laboratory of Fishery Drug Development, Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510380, China.
Tripartite Motif-Containing 44 (TRIM44) is responsible for cancers, neurodegenerative diseases, and viral infections. However, the role of TRIM44 (scTRIM44) during viral infection remains unclear. In the present study, we analyzed the molecular characteristics of scTRIM44 and its role in infectious spleen and kidney necrosis virus (ISKNV), largemouth bass virus (LMBV), and Siniperca chuatsi rhabdovirus (SCRV) infection.
View Article and Find Full Text PDFReal-Time Freezing of Gait Prediction and Detection in Parkinson's Disease.
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School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Freezing of gait (FOG) is a walking disturbance that can lead to postural instability, falling, and decreased mobility in people with Parkinson's disease. This research used machine learning to predict and detect FOG episodes from plantar-pressure data and compared the performance of decision tree ensemble classifiers when trained on three different datasets. Dataset 1 ( = 11) was collected in a previous study.
View Article and Find Full Text PDFSafety of Immersive Virtual Reality for the Management of Parkinson's Disease.
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Centro de Estudos Egas Moniz, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal.
Virtual reality (VR) has been used in research and clinical practice in the management of Parkinson's disease (PD), potentially enhancing physiotherapy. Adverse events (AEs) associated with VR applications in PD have been poorly explored. We conducted a randomized controlled trial to compare two 12-week interventions using physiotherapy and immersive VR, and analyzed the frequency and type of AEs occurring in 30 people with PD.
View Article and Find Full Text PDFMachine Learning Recognizes Stages of Parkinson's Disease Using Magnetic Resonance Imaging.
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Faculty of Computer Science, Polish-Japanese Academy of Information Technology, 86 Koszykowa Street, 02-008 Warsaw, Poland.
Neurodegenerative diseases (NDs), such as Alzheimer's disease (AD) and Parkinson's disease (PD), are debilitating conditions that affect millions worldwide, and the number of cases is expected to rise significantly in the coming years. Because early detection is crucial for effective intervention strategies, this study investigates whether the structural analysis of selected brain regions, including volumes and their spatial relationships obtained from regular T1-weighted MRI scans ( = 168, PPMI database), can model stages of PD using standard machine learning (ML) techniques. Thus, diverse ML models, including Logistic Regression, Random Forest, Support Vector Classifier, and Rough Sets, were trained and evaluated.
View Article and Find Full Text PDFMachine Learning and Statistical Analyses of Sensor Data Reveal Variability Between Repeated Trials in Parkinson's Disease Mobility Assessments.
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Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.
Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson's disease (PD) on motor control, balance, and cognitive function. We assess the test-retest reliability of these tasks in 262 PD participants and 50 controls by evaluating machine learning models based on wearable-sensor-derived measures and statistical metrics. This evaluation examines total duration, subtask duration, and other quantitative measures across two trials.
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