Association between Type 2 diabetes mellitus and TCF7L2 and FTO gene variants among upper Egyptian population

Background: Type 2 diabetes mellitus (T2DM) is a metabolic disorder caused by a complex interaction of genetic and environmental variables. T2DM is associated with transcription factor 7-like 2 (TCF7L2) and fat mass and obesity-associated (FTO) genetic polymorphism.Objectives: The goal of this study was to examine the common genetic risk factors of T2DM and related metabolic traits in Upper Egyptian population, in attempt to understand the genetic structure of T2DM in the Egyptian community.Methods and Materials: This case control study included 250 participants, 124 T2DM patients and 126 non-diabetics. Using mutagenically separated polymerase chain reaction (MS-PCR), genotyping of single nucleotide polymorphisms (SNP) rs7903146 of TCF7L2 and rs17817449 of FTO genes was carried out.Results: T allele of TCF7L2 variant rs7903146 confers a risk for T2DM (allelic OR=1.97, 95% CI: (1.34 to 2.88) p =<0.001). The minor G allele of FTO rs17817449 polymorphism was significantly higher in diabetics than controls (allelic OR=1.87, 95% CI=1.30 to 2.68, p<0.001). Genotype risk was evident under both recessive and dominant modes of inheritance (OR=3.18, CI (1.35-7.45), P =0.008, OR= 2.04, CI (1.23-3.38), p=0.006) for TCF7L2 and (OR= 2.55, CI (1.28 -5.09), p=0.008 and OR= 2.14, CI (1.25-3.63), p= 0.005) for FTO respectivelyConclusion: TCF7L2 rs7903146 and FTO rs17817449 variant conferred risk for T2DM in Upper Egyptian population. The study noted the interaction between certain biological and environmental risk factors including BMI, age, and sex and the conferred genetic risk

Virtual reality and machine learning for predicting visual attention in a daylit exhibition space: A proof of concept

Lighting features act as driving forces that control visual attention and perception. VR can analyse the relationship between human behavior and environment stimuli, while machine learning (ML) can exploit these data to develop a predictive model. This paper aims to develop an approach for predicting visual attention using VR and ML algorithms. VR was applied to experiment 4 virtual 360 scenes of a generic daylit exhibition space, and 41 participants were recruited. Measurements of head tracking were investigated to capture the areas of interest (AoI) within the space under different daylight conditions. Features were extracted to train the ML models through luminance, spatial contrast, and center bias. Results showed the potential of ML for predicting the visual behaviour. When comparing predicted with measured data, the accuracy reached 71 % using ensemble bagged trees algorithm. This study acts as a proof-of-concept for predicting visual attention using ML algorithms, highlighting the potentials of ML and VR for adopting human-centric design. Thus, it allows architects and lighting designers to promote more interesting and interacting visual experience