Treating older patients with neurodegenerative disorders involves numerous challenges. The older patient population is expected to increase appreciably in the coming years; thus, there will be increasing numbers of these individuals requiring treatment. As a result, the appropriate choice of psychopharmacologic agents becomes an important decision in treating older patients with atypical antipsychotics. The atypical antipsychotic medications are replacing the high-potency conventional antipsychotics in the long-term care setting because of the lower risks of side effects. For instance, atypical antipsychotics have lower rates of extrapyramidal side effects and tardive dyskinesia. Double-blind placebo-controlled trials examining the use of risperidone and olanzapine have been published and indicate that both agents safely and effectively reduce agitation symptoms in long-term care patients with neurodegenerative disorders. For instance, based on these studies, the doses that appear efficacious in treating behavioral agitation in dementia are 0.5 to 1.5 mg per day of risperidone and 5 to 10 mg per day of olanzapine. In addition, there are open-label studies examining the use of quetiapine, which suggest that this agent is also safe and efficacious in patients with dementia. Doses used range approximately from 25 to 350 mg per day. Very few studies are available examining the newest atypical antipsychotics, ziprasidone and aripiprazole, in patients with neurodegenerative disorders. These studies do suggest that ziprasidone and aripiprazole are worth further study in the long-term care setting.
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http://dx.doi.org/10.1097/01.JAM.0000129822.54487.14 | DOI Listing |
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December 2024
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.
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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
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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December 2024
Center for Bioelectronics and Biosensors, Biodesign Institute, Arizona State University, 1001 S McAllister Ave, Tempe, AZ 85281, USA.
Alzheimer's disease (AD) and Alzheimer's Related Dementias (ADRD) are projected to affect 50 million people globally in the coming decades. Clinical research suggests that Mild Cognitive Impairment (MCI), a precursor to dementia, offers a critical window of opportunity for lifestyle interventions to delay or prevent the progression of AD/ADRD. Previous research indicates that lifestyle changes, including increased physical exercise, reduced caloric intake, and mentally stimulating activities, can reduce the risk of MCI.
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