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Xianhua Liao,1 Qianqian Ma,1 Tingfeng Wu,1 Congxiang Shao,1 Yansong Lin,1 Yanhong Sun,2 Shiting Feng,3 Wei Wang,4 Junzhao Ye,1 Bihui Zhong1 1Department of Gastroenterology of The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China; 2Department of Laboratory of The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China; 3Department of Radiology of The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China; 4Department of Medical Ultrasonics of The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of ChinaCorrespondence: Bihui Zhong; Junzhao Ye, Department of Gastroenterology of the First Affiliated Hospital, Sun Yat-sen University, No. 58 Zhongshan II Road, Yuexiu District, Guangzhou, 510080, People’s Republic of China, Tel +86 135 0307 9262 ; +86 135 0151 9252, Email zhongbh@mail.sysu.edu.cn; yejzh@mail2.sysu.edu.cnPurpose: Effective treatment of dyslipidemia with lipid-lowering agents is pivotal in the management of metabolic-associated fatty liver disease (MAFLD) for preventing cardiovascular complications. We explored the associations between improvements in liver injuries indicated by changes in transaminases and a reduction in lipid levels in MAFLD patients with dyslipidemia and elevated transaminases during lipid-lowering therapies.Methods: This prospective, cohort study enrolled consecutive MAFLD patients with hyperlipidemia and elevated transaminases. Patients were divided into a group receiving lipid-lowering agents and an age-, sex- and baseline lipid level-matched control group without receiving lipid-lowering agents. Clinical visits were performed at the 1st month and then every 3 months for 1 year.Results: This study included 541 MAFLD patients (lipid-lowering group: 325 patients; control group: 216 patients). Compared with controls, there was a substantially greater reduction in alanine aminotransferase (ALT)

Hepatocellular carcinoma (HCC) is the second most frequent cause of malignancy-related death and is one of the diseases with the highest incidence in the world. Because the liver is the only organ in the human body that is supplied by two major vessels: the hepatic artery and the portal vein, various types of malignant tumors can spread from other organs to the liver. And due to the liver masses' heterogeneous and diffusive shape, the tumor lesions are very difficult to be recognized, thus automatic lesion detection is necessary for the doctors with huge workloads. To assist doctors, this work uses the existing large-scale annotation medical image data to delve deep into liver lesion detection from multiple directions. To solve technical difficulties, such as the image-recognition task, traditional deep learning with convolution neural networks (CNNs) has been widely applied in recent years. However, this kind of neural network, such as Faster Regions with CNN features (R-CNN), cannot leverage the spatial information because it is applied in natural images (2D) rather than medical images (3D), such as computed tomography (CT) images. To address this issue, we propose a novel algorithm that is appropriate for liver CT imaging. Furthermore, according to radiologists' experience in clinical diagnosis and the characteristics of CT images of liver cancer, a liver cancer-detection framework with CNN, including image processing, feature extraction, region proposal, image registration, and classification recognition, was proposed to facilitate the effective detection of liver lesions.