Author Interviews, Dermatology, Technology / 03.04.2020

MedicalResearch.com Interview with: Jung-Im Na, MD PhD Associate Professor, Department of Dermatology Seoul National University Bundang Hospital Korea MedicalResearch.com: What is the background for this study? Would you briefly explain what is meant by a convolutional neural network? Response: When a very young child looks at a picture, she can easily identify cats and dogs, however, even the most advanced computers had struggled at this task until recently. Computers began to “see” with the recent advancement of Deep Learning techniques. Deep Learning is a machine learning technique that teaches computers to learn from raw data. Most deep learning methods use artificial neural network architectures, imitating human brain, and convolutional neural networks (CNN) is a particular type of deep learning architecture, imitating the visual cortex. CNN is especially powerful for recognizing images. CNN exploit the information contained in image datasets to automatically learn features and patterns. (more…)
Author Interviews, Dermatology, Microbiome, Pediatrics / 14.08.2019

MedicalResearch.com Interview with: Zhe-Xue Quan, PhD Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering Institute of Biodiversity Science School of Life Sciences, Fudan University Shanghai, China MedicalResearch.com: What is the background for this study? What are the main findings? Response: The maturation of skin microbial communities during childhood is important for the skin health of children and development of the immune system into adulthood. This necessitates a better characterization of the environmental and genetic factors influencing these microbiome dynamics. We investigated the skin microbiota of children (158 subjects between 1 and 10 years old) and their mothers using 16S rRNA gene amplicon sequencing. Sample location and age were the primary factors determining a child’s skin bacterial composition. Relative abundances of Streptococcus and Granulicatella were negatively correlated with age, and the alpha diversity at all body sites examined increased during the first 10 years of life, especially on the face. The facial bacterial composition of 10-year-old children was strongly associated with delivery mode at birth. (more…)