Prediction of disease activity in models of multiple sclerosis by molecular magnetic resonance imaging of P-selectin
New strategies for detecting disease activity in multiple sclerosis are being investigated to ameliorate diagnosis and follow-up of patients. Today, although magnetic resonance imaging (MRI) is widely used to diagnose and monitor multiple sclerosis, no imaging tools exist to predict the evolution of...
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Published in | Proceedings of the National Academy of Sciences - PNAS Vol. 114; no. 23; pp. 6116 - 6121 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
National Academy of Sciences
06.06.2017
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Subjects | |
Online Access | Get full text |
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Summary: | New strategies for detecting disease activity in multiple sclerosis are being investigated to ameliorate diagnosis and follow-up of patients. Today, although magnetic resonance imaging (MRI) is widely used to diagnose and monitor multiple sclerosis, no imaging tools exist to predict the evolution of disease and the efficacy of therapeutic strategies. Here, we show that molecular MRI targeting the endothelial adhesion molecule P-selectin unmasks the pathological events that take place in the spinal cord of mice subjected to chronic or relapsing experimental autoimmune encephalomyelitis. This approach provides a quantitative spatiotemporal follow-up of disease course in relation to clinical manifestations. Moreover, it predicts relapse in asymptomatic mice and remission in symptomatic animals. Future molecularMRI targeting P-selectin may be used to improve diagnosis, follow-up of treatment, and management of relapse/remission cycles in multiple sclerosis patients by providing information currently inaccessible through conventional MRI techniques. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Author contributions: A.P.F., S.M.d.L., F.D., and R.M. designed research; A.P.F., A.Q., and S.M.d.L. performed research; A.P.F., S.M.d.L., F.D., and R.M. analyzed data; and A.P.F., S.M.d.L., M.G., G.D., D.V., F.D., and R.M. wrote the paper. Edited by Lawrence Steinman, Stanford University School of Medicine, Stanford, CA, and approved April 19, 2017 (received for review November 29, 2016) 1F.D. and R.M. contributed equally to this work. |
ISSN: | 0027-8424 1091-6490 |
DOI: | 10.1073/pnas.1619424114 |