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Interaction between SIRT1 and non-coding RNAs in different disorders
SIRT1 is a member of the sirtuin family functioning in the process of removal of acetyl groups from different proteins. This protein has several biological functions and is involved in the pathogenesis of metabolic diseases, malignancy, aging, neurodegenerative disorders and inflammation. Several long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and circular RNAs (circRNAs) have been found to interact with SIRT1. These interactions have been assessed in the contexts of sepsis, cardiomyopathy, heart failure, non-alcoholic fatty liver disease, chronic hepatitis, cardiac fibrosis, myocardial ischemia/reperfusion injury, diab...
Source: Frontiers in Genetics - June 27, 2023 Category: Genetics & Stem Cells Source Type: research

Causal relationships between obesity and the leading causes of death in women and men
by Jenny C. Censin, Sanne A. E. Peters, Jonas Bovijn, Teresa Ferreira, Sara L. Pulit, Reedik M ägi, Anubha Mahajan, Michael V. Holmes, Cecilia M. Lindgren Obesity traits are causally implicated with risk of cardiometabolic diseases. It remains unclear whether there are similar causal effects of obesity traits on other non-communicable diseases. Also, it is largely unexplored whether there are any sex-specific differences in the causal effects of obe sity traits on cardiometabolic diseases and other leading causes of death. We constructed sex-specific genetic risk scores (GRS) for three obesity traits; body mass index (BM...
Source: PLoS Genetics - October 23, 2019 Category: Genetics & Stem Cells Authors: Jenny C. Censin Source Type: research

A Novel Deep Neural Network Model for Multi-Label Chronic Disease Prediction
Conclusions concludes this work along with future work. Dataset and Data Preprocessing In the work, we mainly focus on multiple chronic disease classification. It can be formulated into a multi-label classification problem. There are three common chronic diseases are selected from the physical examination records: hypertension (H), diabetes (D), and fatty liver (FL). In the experiments, the physical examination datasets are collected from a local medical center, which contain 110,300 physical examination records from about 80,000 anonymous patients (Li et al., 2017a,b). Sixty-two feature items are selected from over 100...
Source: Frontiers in Genetics - April 23, 2019 Category: Genetics & Stem Cells Source Type: research