Bisphenol A (BPA) is an endocrine-disrupting chemical that affects lipid metabolism and contributes to non-alcoholic fatty liver disease (NAFLD). The mechanism of BPA exposure in hepatic lipid accumulation and its potential effect on NAFLD remain unclear. This study investigated the effect of BPA-exposure-induced hepatic lipid deposition on the pathology of NAFLD and its underlying mechanism in vitro and in vivo. BPA increased intracellular reactive oxygen species (ROS) levels, and promoted fatty acid uptake through upregulation of a free fatty acid uptake transporter, cluster of differentiation 36 (CD36), in HUH-7 cells. Additionally, C57BL/6 mice administered a high-fat/high-cholesterol/high-cholic acid diet (HFCCD) and BPA (50 mg/kg body weight) for 8 weeks developed a steatohepatitis-like phenotype, characterized by alpha-smooth muscle actin (α-SMA, an indicator of hepatic fibrosis) and cleaved caspase 3 (an indicator of apoptosis) in hepatic tissue; moreover, they had a higher oxidative stress index of 8-hydroxydeoxyguanosine (8-OHdG) in liver tissue compared to the control group. Treatment with ROS scavenger n-acetylcysteine (NAC) ameliorated BPA-mediated HFCCD-induced lipid accumulation and steatohepatitis in the livers of treated mice. Our study indicates that BPA acts synergistically to increase hepatic lipid uptake and promote NAFLD development by stimulating ROS-induced CD36 overexpression.

According to a classification of central nervous system tumors by the World Health Organization, diffuse gliomas are classified into grade 2, 3, and 4 gliomas in accordance with their aggressiveness. To quantitatively evaluate a tumor’s malignancy from brain magnetic resonance imaging, this study proposed a computer-aided diagnosis (CAD) system based on a deep convolutional neural network (DCNN). Gliomas from a multi-center database (The Cancer Imaging Archive) composed of a total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were used for the training and evaluation of the proposed CAD. Using transfer learning to fine-tune AlexNet, a DCNN, its internal layers, and parameters trained from a million images were transferred to learn how to differentiate the acquired gliomas. Data augmentation was also implemented to increase possible spatial and geometric variations for a better training model. The transferred DCNN achieved an accuracy of 97.9% with a standard deviation of ±1% and an area under the receiver operation characteristics curve (Az) of 0.9991 ± 0, which were superior to handcrafted image features, the DCNN without pretrained features, which only achieved a mean accuracy of 61.42% with a standard deviation of ±7% and a mean Az of 0.8222 ± 0.07, and the DCNN without data augmentation, which was the worst with a mean accuracy of 59.85% with a standard deviation ±16% and a mean Az of 0.7896 ± 0.18. The DCNN with pretrained features and data augmentation can accurately and efficiently classify grade 2, 3, and 4 gliomas. The high accuracy is promising in providing diagnostic suggestions to radiologists in the clinic.