Zaozao Chen, Ph.D.
Associate Professor,School
of Biomedical Engineering
Southeast University
101012282@seu.edu.cn
Biography:
Zaozao Chen is the associate professor in the School of Biomedical Engineering at Southeast University. He is also the technical director of Institute of Biomedical Devices of Southeast University. Dr. Chen received his Ph.D. in Cell Biology from University of North Carolina at Chapel Hill, and was Postdoc and Associate Research Scientist at Duke University and Columbia University in New York City. His research interests include: understanding of cell-biomaterial interactions, and development of organs-on-a-chip systems (or microphysiological systems) for drug screening and disease modeling. He joined in MPS consortium in U.S.A. to develop different organs-on-a-chip products. He has published more than 20 peer reviewed papers on SCI journals including Nature Cell Biology, Biomaterials, Lab on a Chip, etc.
Topic title：Development of a "Smart" System for Organs-on-a-Chip Research
Abstract：Organs-on-a-chip (OOC) system, or microphysiological system (MPS), is a new biomedical research field that aims to recapitulate organ-level tissue structures and organ functions for drug evaluation and disease modeling. In previous study, we have developed multiple OOC and MPS systems including blood vessels, heart, liver, tumor, etc. Our previous work demonstrated that the miniature organs made with advanced microfabrication, 3D printing, microfluidics, and tissue engineering techniques could form tissue-specific structures and could maintain desirable organ functions for more than four weeks.
In this work, we report the research and development of an automated “SMART” system for OOC imaging and analysis. This advanced 3D imaging system allows us to characterize OOC tissue and analyze its morphology and other functional features automatically and quantitatively. For example, this system could offer packaged solutions to analyze the tumor spheroids/organoid viability and invasiveness, together with the prediction of the drug classification and mechanism with the deep-learning based AI-algorisms, thus this system could be very useful for oncology drug screening and evaluation.
