The article presents a case of a teenage female patient illustrating the difficulties of diagnosis and treatment of mental pathology at the onset of juvenile parkinsonism, which developed as a result of compound heterozygous mutations and proceeds with moderate akinetic-rigid and pseudopyramidal syndromes and combined tremor. It is shown that before the appearance of distinct symptoms of parkinsonism and the genetic study that confirmed the diagnosis, the patient had been observed for several years with various psychiatric diagnoses and received psychotropic therapy. This case demonstrates the difficulties of differential diagnosis of juvenile parkinsonism in children at the initial stage due to multiple comorbid motor, affective and behavioral disorders. When neurological symptoms appear in patients, a multidisciplinary approach is required, including joint diagnostics by psychiatrists and neurologists to establish an accurate diagnosis and select the optimal therapy. The introduction of principles of interdisciplinary interaction in the management of such patients will improve the timeliness of diagnosis and pathogenetically based therapy.
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http://dx.doi.org/10.17116/jnevro202412411296 | DOI Listing |
PLoS One
January 2025
College of Computer and Information Technology, Northeast Petroleum University, China.
Background: There had been extensive research on the role of the gut microbiota in human health and disease. Increasing evidence suggested that the gut-brain axis played a crucial role in Parkinson's disease, with changes in the gut microbiota speculated to be involved in the pathogenesis of Parkinson's disease or interfere with its treatment. However, studies utilizing deep learning methods to predict Parkinson's disease through the gut microbiota were still limited.
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December 2024
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.
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December 2024
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.
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December 2024
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.
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December 2024
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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