A non-invasive method for evaluating changes in subcutaneous fat layer of mice induced by diglycerides and their compositions

Optical Coherence Tomography (OCT) enables the non-invasive acquisition of high resolution three-dimensional cross-sectional images at a micrometer scale for (epi-)dermis layer and subcutaneous fat layer of mouse skin. In this paper, we present a method to predict individual weight loss based on a deep-learning algorithm for the automatic segmentation of several layers of mouse skin in OCT image using Convolutional Neural Network (CNN). Our results demonstrate that individual weight loss is related to the subcutaneious fat layer of mice. Then Diacylglycerol, composition containing Diacylglycerol and Xylooligosaccharide, and composition containing Diacylglycerol, medium chain triglycerides, Medium- and long-chain triacylglycerol oil, and Xylooligosaccharide have a small positive effect on weight loss, since that the subcutaneious fat layer become thinning. The potential suggests the subcutaneous fat layer of mice based on OCT combined with CNN could be used as a method to evaluate the weight loss. It was shown that composition containing Diacylglycerol, medium chain triglycerides, Medium- and long-chain triacylglycerol oil, and Xylooligosaccharide had the greatest effect on the adipose layer, which resulted in the greatest degree of adiposity thinning.