MEDLIFE 2019第五届健康，医学与生命科学国际会议（杭州）

Dr. Sarah Du, Associate Professor

Department of Ocean and Mechanical Engineering, Department of Biological Sciences, Florida Atlantic University, Boca Raton, USA

Biography: Dr. Sarah E Du earned a Ph.D. degree in Mechanical Engineering from Stevens Institute of Technology in 2011. She received her postdoctoral training in the Nanomechanics Lab at MIT between 2011 to 2014. After that, she joined Florida Atlantic University as an Assistant Professor in the Department of Ocean and Mechanical Engineering. Her research is focused on the cell biomechanics and biophysics under influences of human pathophysiological processes. She is also interested in development of microfluidics-based in vitro disease models and biosensors for the study of human placental malaria, sickle cell disease and nerve regeneration.

Topic: Probing Fatigue Failure of Human Red Blood Cells in Health and Disease

Abstract: Human red blood cells (RBCs) are subjected to dynamic and cyclic loads when they traverse through the cardiovascular system during their normal lifespan of approximately 90 to 120 days. Abnormal conditions such as cold storage and pathologies can affect their capability to withstand the cyclic stressing in blood circulation, leading to undermined biological functions and shortened lifespan. A quantitative measure of the dynamic behavior of RBCs under the cyclic loads, can elucidate the fatigue process of cell membranes during blood circulation. We established a new experimental approach based on amplitude modulated electrodeformation in a microfluidic platform. This approach allows generation of monotoic and cyclic loads in the forms of mathematically defined waveforms or arbitrary loading profiles for the study of fatigue failure in plasma membranes of cells. Deleterious effects of cyclic loads were quantified by the characteristic fatigue features and membrane biomechanics for both healthy and abnormal RBCs. The results showed strong correlations between cellular physiological state and fatigue life. This approach provides an accelerated lifespan testing for RBCs and can be extended to other cell types.

Dr. Fang Hu, Associate Professor

College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, China

Biography: Dr. Fang Hu received the Ph.D. degree in Complex Network from School of Computer, Central China Normal University, Wuhan, China. Dr. Hu acted as a postal-doctor researcher at University of West Florida, USA. She is currently an Associate Professor with the College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, China. She has published over 30 papers in SCI, EI journals, etc. She is the guest editor or reviewer for SCI journals. Her main research interests include complex networks, machine learning, optimization algorithms, and data modeling in various research fields.

Topic: On Herb Compatibility Rule of Insomnia Based on Machine Learning Approaches

Abstract: The herb compatibility rule of insomnia is studied using some machine learning approaches. The insomnia data set with 807 samples is extracted from the real-world Electronic Medical Records (EMRs). After cleaning and selecting the theme data referring to the prescriptions and their herbs, the herb network analysis model is constructed using complex network. Each herb node in network is trained to obtain the herb embeddings using the Skip-Gram model. After acquiring the digital vocabulary of herbs, the similarity among any two herb embeddings is calculated, and these herb embeddings are divided into seven communities using the Spectral Clustering (SC) algorithm. The experimental results shed light on that the methodologies can objectively and effectively discover the relationships among herbs and reveal the herb compatibility for clinical treatment research of insomnia.