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      Cerebral Small Vessel Disease: A Review Focusing on Pathophysiology, Biomarkers, and Machine Learning Strategies

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          Abstract

          Cerebral small vessel disease (cSVD) has a crucial role in lacunar stroke and brain hemorrhages and is a leading cause of cognitive decline and functional loss in elderly patients. Based on underlying pathophysiology, cSVD can be subdivided into amyloidal and non-amyloidal subtypes. Genetic factors of cSVD play a pivotal role in terms of unraveling molecular mechanism. An important pathophysiological mechanism of cSVD is blood-brain barrier leakage and endothelium dysfunction which gives a clue in identification of the disease through circulating biological markers. Detection of cSVD is routinely carried out by key neuroimaging markers including white matter hyperintensities, lacunes, small subcortical infarcts, perivascular spaces, cerebral microbleeds, and brain atrophy. Application of neural networking, machine learning and deep learning in image processing have increased significantly for correct severity of cSVD. A linkage between cSVD and other neurological disorder, such as Alzheimer’s and Parkinson’s disease and non-cerebral disease, has also been investigated recently. This review draws a broad picture of cSVD, aiming to inculcate new insights into its pathogenesis and biomarkers. It also focuses on the role of deep machine strategies and other dimensions of cSVD by linking it with several cerebral and non-cerebral diseases as well as recent advances in the field to achieve sensitive detection, effective prevention and disease management.

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          Most cited references121

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          Global Burden of Stroke.

          On the basis of the GBD (Global Burden of Disease) 2013 Study, this article provides an overview of the global, regional, and country-specific burden of stroke by sex and age groups, including trends in stroke burden from 1990 to 2013, and outlines recommended measures to reduce stroke burden. It shows that although stroke incidence, prevalence, mortality, and disability-adjusted life-years rates tend to decline from 1990 to 2013, the overall stroke burden in terms of absolute number of people affected by, or who remained disabled from, stroke has increased across the globe in both men and women of all ages. This provides a strong argument that "business as usual" for primary stroke prevention is not sufficiently effective. Although prevention of stroke is a complex medical and political issue, there is strong evidence that substantial prevention of stroke is feasible in practice. The need to scale-up the primary prevention actions is urgent.
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            A Survey on Deep Learning in Medical Image Analysis

            Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.
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              Tight junctions of the blood-brain barrier: development, composition and regulation.

              1. The blood-brain barrier is essential for the maintenance and regulation of the neural microenvironment. The main characteristic features of blood-brain barrier endothelial cells are an extremely low rate of transcytotic vesicles and a restrictive paracellular diffusion barrier. 2. Endothelial blood-brain barrier tight junctions differ from epithelial tight junctions, not only by distinct morphological and molecular properties, but also by the fact that endothelial tight junctions are more sensitive to microenvironmental than epithelial factors. 3. Many ubiquitous molecular tight junction components have been identified and characterized including claudins, occludin, ZO-1, ZO-2, ZO-3, cingulin and 7H6. Signaling pathways involved in tight junction regulation include G-proteins, serine-, threonine- and tyrosine-kinases, extra and intracellular calcium levels, cAMP levels, proteases and cytokines. Common to most of these pathways is the modulation of cytoskeletal elements and the connection of tight junction transmembrane molecules to the cytoskeleton. Additionally, crosstalk between components of the tight junction- and the cadherin-catenin system of the adherens junction suggests a close functional interdependence of the two cell-cell contact systems. 4. Important new molecular aspects of tight junction regulation were recently elucidated. This review provides an integration of these new results.
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                Author and article information

                Journal
                J Stroke
                J Stroke
                JOS
                Journal of Stroke
                Korean Stroke Society
                2287-6391
                2287-6405
                September 2018
                30 September 2018
                : 20
                : 3
                : 302-320
                Affiliations
                [a ]Department of Neurology, Hospital del Mar Medical Research Institute, Barcelona, Spain
                [b ]Amity Institute of Biotechnology, Amity University, Gwalior, India
                [c ]Department of Computer Science & Engineering and Information Technology, Madhav Institute of Technology and Science, Gwalior, India
                [d ]Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
                [e ]Department of Biological Engineering, IQS School of Engineering, Barcelona, Spain
                [f ]Department of Cardiology, St. Helena Hospital, St. Helena, CA, USA
                [g ]Deparment of Neurology, University Medical Centre Maribor, Maribor, Slovenia
                [h ]Brown University, Providence, RI, USA
                [i ]Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus
                [j ]Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
                [k ]Department of Cardiology, Apollo Hospital, New Delhi, India
                [l ]Stroke Monitoring Division, AtheroPoint, Roseville, CA, USA
                Author notes
                Correspondence: Elisa Cuadrado-Godia Department of Neurology, Hospital del Mar Medical Research Institute, Passeig Marítim 25-29, Barcelona 08003, Spain Tel: +34-667564588 Fax: +34-932483236 E-mail: ecuadrado@ 123456parcdesalutmar.cat
                Article
                jos-2017-02922
                10.5853/jos.2017.02922
                6186915
                30309226
                4f67f94d-70cf-440d-89a1-0d414230eb3b
                Copyright © 2018 Korean Stroke Society

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 December 2017
                : 5 March 2018
                : 2 April 2018
                Categories
                Review

                small vessel disease,neuroimaging,biomarkers,blood-brain barrier,machine learning

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