Upsala journal of medical sciences
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The electronic medical record (EMR) offers unique possibilities for clinical research, but some important patient attributes are not readily available due to its unstructured properties. We applied text mining using machine learning to enable automatic classification of unstructured information on smoking status from Swedish EMR data. ⋯ A model using machine-learning algorithms to automatically classify patients' smoking status was successfully developed. Such algorithms may enable automatic assessment of smoking status and other unstructured data directly from EMRs without manual classification of complete case notes.
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During the Covid-19 pandemic, the protection of healthcare workers has been in focus throughout the world, but the availability and quality of personal protective equipment has at times and in some settings been suboptimal. ⋯ Our findings indicate that SARS-CoV-2 transmission is related to inpatient healthcare work, and illustrate the need for a high standard of basic hygiene routines in all inpatient care settings.
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Alzheimer's disease (AD) is one the most common types of dementia. Plaques of amyloid beta and neurofibrillary tangles of tau are two major hallmarks of AD. Metabolism of these two proteins, in part, depends on autophagy pathways. Autophagy dysfunction and protein aggregation in AD may be involved in a vicious circle. The aim of this study was to investigate whether tau or amyloid beta 42 (Aβ42) could affect expression of autophagy genes, and whether they exert their effects in the same way or not. ⋯ We conclude that both Aβ42 and Tau R406W may affect autophagy through dysregulation of autophagy genes. Interestingly, it seems that these pathological proteins exert their toxic effects on autophagy through different pathways and independently.