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Discovering the Italian phenotype of cerebral amyloid angiopathy (CAA): the SENECA project.

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Title: Discovering the Italian phenotype of cerebral amyloid angiopathy (CAA): the SENECA project.
Authors: Bersano, Anna (AUTHOR), Scelzo, Emma (AUTHOR), Pantoni, Leonardo (AUTHOR), Morotti, Andrea (AUTHOR), Erbetta, Alessandra (AUTHOR), Chiapparini, Luisa (AUTHOR), Vitali, Paolo (AUTHOR), Giaccone, Giorgio (AUTHOR), Caroppo, Paola (AUTHOR), Catania, Marcella (AUTHOR), Obici, Laura (AUTHOR), Di Fede, Giuseppe (AUTHOR), Gatti, Laura (AUTHOR), Tinelli, Francesca (AUTHOR), Di Francesco, Jacopo C. (AUTHOR), Piazza, Fabrizio (AUTHOR), Ferrarese, Carlo (AUTHOR), Gasparini, Massimo (AUTHOR), Adobbati, Laura (AUTHOR), Bianchi-Marzoli, Stefania (AUTHOR)
Source: Neurological Sciences. Aug2020, Vol. 41 Issue 8, p2193-2200. 8p. 1 Diagram, 2 Charts.
Subjects: Seneca, Lucius Annaeus, ca. 4 B.C.-65 A.D., Cerebral amyloid angiopathy, Cerebral small vessel diseases, Cerebral hemorrhage, Older patients, Disease progression, Biomarkers
Abstract: Cerebral amyloid angiopathy (CAA) is one of the major types of cerebral small vessel disease, and a leading cause of spontaneous intracerebral hemorrhage and cognitive decline in elderly patients. Although increasingly detected, a number of aspects including the pathophysiology, the clinical and neuroradiological phenotype, and the disease course are still under investigation. The incomplete knowledge of the disease limits the implementation of evidence-based guidelines on patient's clinical management and the development of treatments able to prevent or reduce disease progression. The SENECA (SEarchiNg biomarkErs of Cerebral Angiopathy) project is the first Italian multicenter cohort study aimed at better defining the disease natural history and identifying clinical and neuroradiological markers of disease progression. By a multidisciplinary approach and the collection of a large and well-phenotyped series and biorepository of CAA patients, the study is ultimately expected to improve the diagnosis and the knowledge of CAA pathophysiological mechanisms. [ABSTRACT FROM AUTHOR]
Copyright of Neurological Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Discovering the Italian phenotype of cerebral amyloid angiopathy (CAA): the SENECA project.
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  Data: Cerebral amyloid angiopathy (CAA) is one of the major types of cerebral small vessel disease, and a leading cause of spontaneous intracerebral hemorrhage and cognitive decline in elderly patients. Although increasingly detected, a number of aspects including the pathophysiology, the clinical and neuroradiological phenotype, and the disease course are still under investigation. The incomplete knowledge of the disease limits the implementation of evidence-based guidelines on patient's clinical management and the development of treatments able to prevent or reduce disease progression. The SENECA (SEarchiNg biomarkErs of Cerebral Angiopathy) project is the first Italian multicenter cohort study aimed at better defining the disease natural history and identifying clinical and neuroradiological markers of disease progression. By a multidisciplinary approach and the collection of a large and well-phenotyped series and biorepository of CAA patients, the study is ultimately expected to improve the diagnosis and the knowledge of CAA pathophysiological mechanisms. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neurological Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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