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A Sensing Peak Identification Method for Fiber Extrinsic Fabry–Perot Interferometric Refractive Index Sensing
摘要： Chronic liver disease is a common cause of morbidity and mortality in the United States. The most common causes of liver disease include non-alcoholic fatty liver disease (NAFLD), chronic hepatitis C virus infection, alcoholic liver disease, and chronic hepatitis B virus infection. Through a discussion of various surveillance methods as well as their strengths and weaknesses, we review the epidemiology, risk factors, and natural history of each of these diseases and discuss prevention measures that have been effective in decreasing incidence rates.
关键词： Epidemiology,Hepatitis c,Liver diseases,Non-alcoholic fatty liver disease,Prevalence,Incidence,Hepatitis b,Alcoholic
更新于2025-09-23 15:23:52
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Zinc oxide nanoparticles attenuate hepatic steatosis development in high-fat-diet fed mice through activated AMPK signaling axis
摘要： Insulin resistance is thought to be a common link between obesity and Non-Alcoholic Fatty Liver Disease (NAFLD). NAFLD has now reached epidemic status worldwide and identification of molecules or pathways as newer therapeutic strategies either to prevent or overcome insulin resistance seems critical. Dysregulated hepatic lipogenesis (DNL) is a hallmark of NAFLD in humans and rodents. Therefore, reducing DNL accretion may be critical in the development of therapeutics of NAFLD. In our in vivo model (high-fat-diet fed [HFD] obese mice) we found Zinc oxide nanoparticles (ZnO NPs) significantly decreased HFD-induced hepatic steatosis and peripheral insulin resistance. This protective mechanism of ZnO NPs was signalled through hepatic SIRT1-LKB1-AMPK which restricted SREBP-1c within the cytosol limiting its transcriptional ability and thereby ameliorating HFD mediated DNL. These observations indicate that ZnO NP can serve as a therapeutic strategy to improve the physiological homeostasis during obesity and its associated metabolic abnormalities.
关键词： LKB1,Non-Alcoholic Fatty Liver Disease,AMPK,SIRT1,SREBP1c,ZnO NP
更新于2025-09-19 17:15:36
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[IEEE 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - Ostrava, Czech Republic (2018.9.17-2018.9.20)] 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) - A Novel Computer-Aided Diagnosis Framework Using Deep Learning for Classification of Fatty Liver Disease in Ultrasound Imaging
摘要： Fatty Liver Disease (FLD), if left untreated can progress into fatal chronic diseases (Eg. fibrosis, cirrhosis, liver cancer, etc.) leading to permanent liver failure. Doctors usually use ultrasound scanning as the primary modality for quantifying the amount of fat deposition in the liver tissues, to categorize the FLD into normal and abnormal. However, this quantification or diagnostic accuracy depends on the expertise and skill of the radiologist. With the advent of Health 4.0 and the Computer Aided Diagnosis (CAD) techniques, the accuracy in detection of FLD using the ultrasound by the sonographers and clinicians can be improved. Along with an accurate diagnosis, the CAD techniques will help radiologists to diagnose more patients in less time. Hence, to improve the classification accuracy of FLD using ultrasound images, we propose a novel CAD framework using convolution neural networks and transfer learning (pre-trained VGG-16 model). Performance analysis shows that the proposed framework offers an FLD classification accuracy of 90.6% in classifying normal and fatty liver images.
关键词： Computer Aided Diagnosis,VGG-16,Deep Learning,Fatty Liver Disease,Ultrasound Imaging
更新于2025-09-10 09:29:36