TyG index and insulin resistance in beta-thalassemia

Insulin resistance (IR) underlies some glucose metabolism abnormalities in thalassemia major. Recently, triglyceride glucose index (TyG) has been proposed for evaluating insulin resistance as a simple, low cost, and accessible tool. In this study, the TyG index were studied for IR monitoring in beta-thalassemia major (βTM) patients. The participants were 90 βTM patients on chronic regular transfusion therapy. The TyG index was computed based on fasting plasma glucose (FPG) and triglyceride (TG). The time gap between the first and the second TyG index survey (TyG.1 and TyG.2) was 2 years. The agreement between TyG and HOMA-IR were studied with the extension of limit of agreement (LOA). We included 90 patients 53.3 % men (n = 48). Among them, 14.4 % (14.6 % male, 14.3 % female) had impaired fasting glucose level (e.g., 100–125 mg/dl) at first test. It rose to 37.8 % (27.1 % male, 50 % female) during 2 years. Based on TyG.1, the 34.4 % of patients was detected as IR cases. After 2 years, the percent of IR based on TyG.2 was 82.2 %. The mean differences between TyG.1 and TyG.2 and their differences from the considered cutoff values were significant (P < 0.001). The prediction limits between TyG and HOMA-IR had good agreement. These data may suggest the use of TyG index for detection/monitoring of IR in βTM patients. © 2015, Research Society for Study of Diabetes in India

Identification of gene network motifs for cancer disease diagnosis

All networks, including biological networks, computer networks, social networks and more can be represented as graphs, which include a number of small module such as subgraph, also called as network motifs. Network motifs are subgraph which recur themselves in a specific network or different networks. In biological networks, these network motifs plays very important role to identify diseases in human beings. In this paper we have developed a module to identify common network motifs types from cancer pathways and Signal Transduction Networks (STNs). It also identifies the topological behaviors of cancer networks and STNs. In this study, we have implemented five motif algorithms such as Auto-Regulation Loop (ARL), Feed Backward Loop (FBL), Feed Forward Loop (FFL), Single-Input Motif (SIM) and Bi-fan. These algorithms gives correct results in terms of network motifs for human cancer and STNs. Finding network motifs by using online tool is limited to three nodes, but our proposed work provides facility to find network motifs up to any number of nodes. We applied five motif algorithms to human cancer networks and Signal Transduction Networks (STNs) which are collected from KEGG database as a result we got ”Frequent Occurrences of Network Motifs (FONMs)”. These FONMs acts as a references for an oncologist in order to find type of cancer in human being