演讲嘉宾

Dr. Tianru Jin, Professor

Department of Physiology, University of Toronto, Toronto, Canada

Biography: Dr. Tianru Jin is a Professor in University of Toronto. He received his PhD from University of Toronto in 1993. He then conducted his postdoctoral research at the Banting & Best Diabetes Centre under Professor Daniel J. Drucker. Since 1997, Dr. Jin has served at the University of Toronto as Assistant Professor, Associate Professor, and full Professor. Professor Jin served as Board Member or editor for several scientific journals. He is currently serving as Editor for Cell and Bioscience, Associate Chief-Editor for Obesity Medicine, and Founding Editor in Chief (2025–2030) for Metabolism and Diseases. His team has long term interests on exploring functions of GLP 1 and other metabolic hormones. His team introduced Wnt signaling pathway into the GLP-1 filed. Recently, the team expanded the research interest into the metabolic functions of polyphenolic compounds recently. Major fundings for his research group comes from the Canadian Institutes of Health Research (CIHR), Canadian Diabetes Association (CDA), JDRF (USA), and the Banting & Best Diabetes Centre (BBDC). Publications from Professor Jin’s group have appeared in Nature Communications, Hepatology, Molecular and Cellular Biology, Journal of Biological Chemistry, Endocrine Reviews, Endocrinology, Diabetes, Diabetologia, Oncogene, Molecular Metabolism, PLOS Biology, and others. As a co author, his work has also been published in PNAS, Gastroenterology, Cell Reports, Science Immunology, BioMedicine, and additional journals—over 170 publications in total. From 2017 to 2022, Dr. Jin was appointed as an Overseas Distinguished Professor by Sun Yat sen University under the Ministry of Education of PR China.

Topic: Advances in GLP 1 Research: Historical Insights, Contemporary Achievements and Future Challenges

Abstract: Glucagon-like peptide-1 (GLP 1) was identified in the 1980s as the 2nd incretin, defined as gut-produced hormones which can stimulate insulin secretion from pancreatic beta-cells. The 1st GLP-1 based therapeutic agent known as exenatide (Byetta) was approved by FDA in 2005 for diabetes treatment only. Since then, more than a dozen GLP-1-based drugs have been developed and their applications are now expanded into obesity treatment and body weight management, and potential other metabolism-related disorders. Here I will provide a historical review on the discovery of this hormone, its functional studies, as well as GLP-1-based drug development during the past two decades. More importantly, I will use liver as an example, to discuss extra-pancreatic functions of GLP-1 and its based drugs, leading to their expanded applications in the future. Finally, I will present knowledge gaps in this hormone we are facing now, and my personal perspectives on future research directions.

Dr. Alex Kuo, Professor

School of Health Information Science, University of Victoria, Victoria, Canada

Biography: Dr. Alex Kuo holds a PhD from the Department of Computer Science, University of Nottingham, UK. He is a full time professor at the School of Health Information Science, University of Victoria (UVic). He was a visiting scholar at the Department of Computer Science, Politecnico di Milano, Italy (2023), the Center for Expanded Data Annotation and Retrieval (CEDAR), School of Medicine, Stanford University, USA (2016), and the Electronic Commerce Resource Centre (ECRC), Georgia Tech (2000). Now, he is the chair of Special Interest Group on Big Data for Healthcare, Medicine and Biology at IEEE Technical Committee on Big Data. With over 20 years of programming and data analysis practical as well as research experience, he has over 170 peer-reviewed publications. His research interests include Cloud Computing & Big Data application to healthcare, health data interoperability, health database & data warehousing, data mining application in healthcare, e-health and clinical decision support system.

Topic: AI in Big Data Analytic for Healthcare

Abstract: This keynote examines the transformative influence of artificial intelligence and big data on modern healthcare systems. It brings together researchers, practitioners, and industry leaders to explore breakthroughs in AI-powered diagnostics, predictive analytics, and personalized medicine. The session also delves into big data strategies for managing large-scale healthcare datasets, strengthening clinical decision-making, and streamlining operational workflows. Core themes include an overview of cutting-edge AI methodologies, the role of AI in advancing big data analytics, and practical insights into overcoming challenges in AI adoption.