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      Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders

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          Abstract

          Histological analysis of hepatic tissue specimens is essential for evaluating the pathology of several liver disorders such as chronic liver diseases, hepatocellular carcinomas, liver steatosis, and infectious liver diseases. Manual examination of histological slides on the microscope is a classically used method to study these disorders. However, it is considered time-consuming, limited, and associated with intra- and inter-observer variability. Emerging technologies such as whole slide imaging (WSI), also termed virtual microscopy, have increasingly been used to improve the assessment of histological features with applications in both clinical and research laboratories. WSI enables the acquisition of the tissue morphology/pathology from glass slides and translates it into a digital form comparable to a conventional microscope, but with several advantages such as easy image accessibility and storage, portability, sharing, annotation, qualitative and quantitative image analysis, and use for educational purposes. WSI-generated images simultaneously provide high resolution and a wide field of observation that can cover the entire section, extending any single field of view. In this review, we summarize current knowledge on the application of WSI to histopathological analyses of liver disorders as well as to understand liver biology. We address how WSI may improve the assessment and quantification of multiple histological parameters in the liver, and help diagnose several hepatic conditions with important clinical implications. The WSI technical limitations are also discussed.

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          Common and unique mechanisms regulate fibrosis in various fibroproliferative diseases.

          Fibroproliferative diseases, including the pulmonary fibroses, systemic sclerosis, liver cirrhosis, cardiovascular disease, progressive kidney disease, and macular degeneration, are a leading cause of morbidity and mortality and can affect all tissues and organ systems. Fibrotic tissue remodeling can also influence cancer metastasis and accelerate chronic graft rejection in transplant recipients. Nevertheless, despite its enormous impact on human health, there are currently no approved treatments that directly target the mechanism(s) of fibrosis. The primary goals of this Review series on fibrotic diseases are to discuss some of the major fibroproliferative diseases and to identify the common and unique mechanisms of fibrogenesis that might be exploited in the development of effective antifibrotic therapies.
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            Schistosomiasis

            Schistosomiasis (bilharzia) is a neglected tropical disease caused by parasitic flatworms (blood flukes) of the genus Schistosoma, with considerable morbidity in parts of the Middle East, South America, Southeast Asia and, particularly, in sub-Saharan Africa. Infective larvae grow in an intermediate host (fresh-water snails) before penetrating the skin of the definitive human host. Mature adult worms reside in the mesenteric (Schistosoma mansoni and Schistosoma japonicum) or pelvic (Schistosoma haematobium) veins, where female worms lay eggs, which are secreted in stool or urine. Eggs trapped in the surrounding tissues and organs, such as the liver and bladder, cause inflammatory immune responses (including granulomas) that result in intestinal, hepato-splenic or urogenital disease. Diagnosis requires the detection of eggs in excreta or worm antigens in the serum, and sensitive, rapid, point-of-care tests for populations living in endemic areas are needed. The anti-schistosomal drug praziquantel is safe and efficacious against adult worms of all the six Schistosoma spp. infecting humans; however, it does not prevent reinfection and the emergence of drug resistance is a concern. Schistosomiasis elimination will require a multifaceted approach, including: treatment; snail control; information, education and communication; improved water, sanitation and hygiene; accurate diagnostics; and surveillance-response systems that are readily tailored to social-ecological settings.
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              Machine Learning Methods for Histopathological Image Analysis

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                Author and article information

                Contributors
                Journal
                Front Med (Lausanne)
                Front Med (Lausanne)
                Front. Med.
                Frontiers in Medicine
                Frontiers Media S.A.
                2296-858X
                08 January 2020
                2019
                : 6
                : 310
                Affiliations
                [1] 1Laboratory of Cellular Biology, Department of Biology, Federal University of Juiz de Fora , Juiz de Fora, Brazil
                [2] 2Faculty of Medical Sciences, Radboud University , Nijmegen, Netherlands
                Author notes

                Edited by: Behzad Bozorgtabar, École Polytechnique Fédérale de Lausanne, Switzerland

                Reviewed by: Luigi M. Terracciano, University of Basel, Switzerland; Christine Sempoux, Lausanne University Hospital (CHUV), Switzerland

                *Correspondence: Rossana C. N. Melo rossana.melo@ 123456ufjf.edu.br

                This article was submitted to Pathology, a section of the journal Frontiers in Medicine

                Article
                10.3389/fmed.2019.00310
                6960181
                31970160
                0a75b4e2-47b2-4807-bb1e-36505ad1981b
                Copyright © 2020 Melo, Raas, Palazzi, Neves, Malta and Silva.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 02 June 2019
                : 09 December 2019
                Page count
                Figures: 4, Tables: 0, Equations: 0, References: 101, Pages: 13, Words: 9039
                Funding
                Funded by: Conselho Nacional de Desenvolvimento Científico e Tecnológico 10.13039/501100003593
                Funded by: Fundação de Amparo à Pesquisa do Estado de Minas Gerais 10.13039/501100004901
                Categories
                Medicine
                Review

                whole slide imaging,digital pathology,digital slide,virtual microscopy,histopathology,liver disorders,histology,hepatic tissue

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