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| http://dx.doi.org/10.3389/fgene.2023.1194788 | DOI Listing |
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Predicting cognitive decline from neuropsychiatric symptoms and Alzheimer's disease biomarkers: A machine learning approach to a population-based data.
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Department of Neurology and the Franke Barrow Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ, USA.
Background: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults.
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Periodic paralysis across the life course: age-related phenotype transition and sarcopenia overlap.
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In Periodic Paralysis (PP), a rare inherited condition caused by mutation in skeletal muscle ion channels, the phenotype changes with age, transitioning from the episodic attacks of weakness that give the condition its name, to a more degenerative phenotype of permanent progressive weakness and myopathy. This leads to disability and reduced quality of life. Neither the cause of this phenotype transition, nor why it occurs around the age of 40 is known.
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