https://www.selleckchem.com/products/tph104m.html INTRODUCTION AND OBJECTIVE Heredity of type 2 diabetes mellitus (T2DM) is associated with greater risk for developing T2DM. Thus, individuals who have a first-degree relative with T2DM (FDRT) provide a natural model to study factors of susceptibility towards development of T2DM, which are poorly understood. Emerging key players in T2DM pathophysiology such as adverse oxidative stress and inflammatory responses could be among possible mechanisms that predispose FDRTs to develop T2DM. Here, we aimed to examine the role of oxidative stress and inflammatory responses as mediators of this excess risk by studying dynamic postprandial responses in FDRTs. RESEARCH DESIGN AND METHODS In this open-label case-control study, we recruited normoglycemic men with (n=9) or without (n=9) a family history of T2DM. We assessed plasma glucose, insulin, lipid profile, cytokines and F2-isoprostanes, expression levels of oxidative and inflammatory genes/proteins in circulating mononuclear cells (MNC), myotubes and adipocytes at basmechanisms and future risk of FDRTs for developing T2DM and its associated complications. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.INTRODUCTION The aim of this study is to evaluate the performance of the offline smart phone-based Medios artificial intelligence (AI) algorithm in the diagnosis of diabetic retinopathy (DR) using non-mydriatic (NM) retinal images. METHODS This cross-sectional study prospectively enrolled 922 individuals with diabetes mellitus. NM retinal images (disc and macula centered) from each eye were captured using the Remidio NM fundus-on-phone (FOP) camera. The images were run offline and the diagnosis of the AI was recorded (DR present or absent). The diagnosis of the AI was compared with the image diagnosis of five retina specialists (majority diagnosis considered as ground truth). RESULTS An