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Eleven neurology-related proteins measured in serum are positively correlated to the severity of diabetic neuropathy.
Sci Rep
July 2024
Pain and Rehabilitation Center, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden.
About 20% of patients with diabetes suffer from chronic pain with neuropathic characteristics. We investigated the multivariate associations between 92 neurology-related proteins measured in serum from 190 patients with painful and painless diabetic neuropathy. Participants were recruited from the Pain in Neuropathy Study, an observational cross-sectional multicentre study in which participants underwent deep phenotyping.
View Article and Find Full Text PDFWhat is associated with painful polyneuropathy? A cross-sectional analysis of symptoms and signs in patients with painful and painless polyneuropathy.
Pain
December 2024
Division of Neurological Pain Research and Therapy, Department of Neurology, University Hospital of Schleswig-Holstein, Campus Kiel, Germany.
Article Synopsis
- The study investigates the reasons behind why some patients experience painful polyneuropathy while others do not, utilizing data from 1181 patients in the DOLORISK database.
- Researchers used multivariate logistic regression and machine learning to identify key factors related to painful neuropathy, including severity of neuropathy, family history of chronic pain, fatigue, depression scores, and pain-related worrying.
- The findings suggest that emotional and clinical factors play a significant role in the development of painful neuropathy, with predictive models achieving over 76% accuracy, which could help in identifying patients at risk in the future.
Disease modeling and pharmacological rescue of autosomal dominant retinitis pigmentosa associated with copy number variation.
Elife
April 2024
Department of Ophthalmology, University of California, San Francisco, San Francisco, United States.
Article Synopsis
- Retinitis pigmentosa (RP) is an inherited retinal disorder leading to gradual vision loss, with around 25% of autosomal dominant cases linked to mutations in the rhodopsin gene.
- The study identifies a novel cause of autosomal dominant RP through a unique mono-allelic copy number variation (CNV) in the rhodopsin gene, showcasing advanced retinal degeneration in a 68-year-old patient with multiple copies of the gene.
- Researchers also explored using a small molecule, Photoregulin3 (PR3), to manage disease progression, which showed promise in correcting mislocalization of the rhodopsin protein in patient-derived retinal organoids, indicating potential for personalized medicine approaches in treating RP.
A Biobanking System for Diagnostic Images: Architecture Development, COVID-19-Related Use Cases, and Performance Evaluation.
JMIR Form Res
December 2023
Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
Background: Systems capable of automating and enhancing the management of research and clinical data represent a significant contribution of information and communication technologies to health care. A recent advancement is the development of imaging biobanks, which are now enabling the collection and storage of diagnostic images, clinical reports, and demographic data to allow researchers identify associations between lifestyle and genetic factors and imaging-derived phenotypes.
Objective: The aim of this study was to design and evaluate the system performance of a network for an operating biobank of diagnostic images, the Bio Check Up Srl (BCU) Imaging Biobank, based on the Extensible Neuroimaging Archive Toolkit open-source platform.
A medical multimodal large language model for future pandemics.
NPJ Digit Med
December 2023
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Since most neural networks are supervised, their performance heavily depends on the volume and quality of available labels. However, few such labels exist for rare diseases (e.
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