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Myelin oligodendrocyte glycoprotein antibody-associated disease mimicking neuromyelitis optica spectrum disorder.
BMJ Case Rep
January 2025
Division of Paediatric Neurology, Department of Neurology, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India.
Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and neuromyelitis optica spectrum disorders (NMOSD) are two rare autoimmune inflammatory demyelinating diseases involving the central nervous system, which are often seen with combined involvement of the optic nerve and spinal cord. MOGAD can be confused with multiple sclerosis or NMOSD, due to its clinical presentation that may be similar and its characteristic to progress with habitual attacks. Although the clinical course of the above-mentioned three diseases is similar, their diagnosis and management are different.
View Article and Find Full Text PDFConformational Antibodies to Proteolipid Protein-1 and Its Peripheral Isoform DM20 in Patients With CNS Autoimmune Demyelinating Disorders.
Neurol Neuroimmunol Neuroinflamm
March 2025
Neuroimmunology Laboratory and Neuroimmunology Research Section, IRCCS Mondino Foundation, Pavia, Italy.
Background And Objectives: Antibodies to proteolipid protein-1 (PLP1-IgG), a major central myelin protein also expressed in the peripheral nervous system (PNS) as the isoform DM20, have been previously identified mostly in patients with multiple sclerosis (MS), with unclear clinical implications. However, most studies relied on nonconformational immunoassays and included few patients with non-MS CNS autoimmune demyelinating disorders (ADDs). We aimed to investigate conformational PLP1-IgG in the whole ADD spectrum.
View Article and Find Full Text PDFBrain tissue integrity in neuromyelitis optica spectrum disorder through T1-w/T2-w ratio, MTR and DTI.
J Neurol
January 2025
Department of Radiology and Oncology, Instituto de Radiologia. Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Rua Dr. Ovídio Pires de Campos, 75, Cerqueira César, São Paulo, 05403010, Brazil.
Background: The presence of diffuse brain damage in normal-appearing white matter (NAWM) and gray matter (NAGM) in neuromyelitis optica spectrum disorder (NMOSD) remains controversial. We aimed to address this controversy by applying a multiparametric MRI approach. Additionally, the association between MRI metrics and clinical variables was explored.
View Article and Find Full Text PDFAnalysis of changes in the chemokine CXC ligand 13 in serum and cerebrospinal fluid of patients with neuromyelitis optica.
Sci Rep
January 2025
Department of Neurology, Chenzhou First People's Hospital, Chenzhou City, 423000, Hunan Province, China.
To determine correlation between the Extended Disability Status Scale(EDSS) grade and the progression of neuromyelitis optica(NMO) patients' levels of the chemokine CXC ligand 13 (CXCL13) in their serum and cerebrospinal fluid. This research included forty-one patients diagnosed with neuromyelitis optica(NMO) and forty-three patients diagnosed with multiple sclerosis(MS). The control group consisted of forty-three non-inflammatory neurological disease(NND) patients.
View Article and Find Full Text PDFDifferentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis.
Sci Rep
January 2025
Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China.
The conventional statistical approach for analyzing resting state functional MRI (rs-fMRI) data struggles to accurately distinguish between patients with multiple sclerosis (MS) and those with neuromyelitis optic spectrum disorders (NMOSD), highlighting the need for improved diagnostic efficacy. In this study, multilevel functional metrics including resting state functional connectivity, amplitude of low frequency fluctuation (ALFF), and regional homogeneity (ReHo) were calculated and extracted from 116 regions of interest in the anatomical automatic labeling atlas. Subsequently, classifiers were developed using different combinations of these selected features to distinguish between MS and NMOSD.
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