1,892 research outputs found

    Occult HCV Infection: An Unexpected Finding in a Population Unselected for Hepatic Disease

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    BACKGROUND:Occult Hepatitis C virus (HCV) infection is a new pathological entity characterized by presence of liver disease and absence or very low levels of detectable HCV-RNA in serum. Abnormal values of liver enzymes and presence of replicative HCV-RNA in peripheral blood mononuclear cells are also observed. Aim of the study was to evaluate occult HCV occurrence in a population unselected for hepatic disease. METHODOLOGY/PRINCIPAL FINDINGS:We chose from previous epidemiological studies three series of subjects (n = 276, age range 40-65 years) unselected for hepatic disease. These subjects were tested for the presence of HCV antibodies and HCV-RNA in plasma and in the peripheral blood mononuclear cells (PBMCs) by using commercial systems. All subjects tested negative for HCV antibodies and plasma HCV-RNA and showed normal levels of liver enzymes; 9/276 patients (3.3%) were positive for HCV-RNA in PBMCs, identifying a subset of subjects with potential occult HCV infection. We could determine the HCV type for 8 of the 9 patients finding type 1a (3 patients), type 1b (2 patients), and type 2a (3 patients). CONCLUSIONS:The results of this study show evidence that occult HCV infection may occur in a population unselected for hepatic disease. A potential risk of HCV infection spread by subjects harbouring occult HCV infection should be considered. Design of prospective studies focusing on the frequency of infection in the general population and on the clinical evolution of occult HCV infection will be needed to verify this unexpected finding

    Direct Classification of Type 2 Diabetes From Retinal Fundus Images in a Population-based Sample From The Maastricht Study

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    Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to blindness and cardiovascular disease. Information about early stage T2D might be present in retinal fundus images, but to what extent these images can be used for a screening setting is still unknown. In this study, deep neural networks were employed to differentiate between fundus images from individuals with and without T2D. We investigated three methods to achieve high classification performance, measured by the area under the receiver operating curve (ROC-AUC). A multi-target learning approach to simultaneously output retinal biomarkers as well as T2D works best (AUC = 0.746 [±\pm0.001]). Furthermore, the classification performance can be improved when images with high prediction uncertainty are referred to a specialist. We also show that the combination of images of the left and right eye per individual can further improve the classification performance (AUC = 0.758 [±\pm0.003]), using a simple averaging approach. The results are promising, suggesting the feasibility of screening for T2D from retinal fundus images.Comment: to be published in the proceeding of SPIE - Medical Imaging 2020, 6 pages, 1 figur
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