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首页 > 商务会议 > 医疗医学会议 > MEDLIFE 2019第五届健康,医学与生命科学国际会议(杭州) 更新时间:2019-11-08T10:46:01

MEDLIFE 2019第五届健康,医学与生命科学国际会议(杭州)
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MEDLIFE 2019第五届健康,医学与生命科学国际会议(杭州) 已截止报名

会议时间:2019-12-16 08:00至 2019-12-18 18:00结束

会议地点: 杭州  详细地址会前通知   周边酒店预订

主办单位: 上海来溪会务服务有限公司

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        会议介绍

        会议内容 主办方介绍


        MEDLIFE 2019第五届健康,医学与生命科学国际会议(杭州)

        MEDLIFE 2019第五届健康,医学与生命科学国际会议(杭州)宣传图

        2019第五届健康,医学生命科学国际会议为广大从事健康,医学与生命科学等相关领域的研究学者、专家提供交流平台。会议组委会诚邀全球相关领域的学者、专家参加此次国际会议,就健康,医学与生命科学为主题的相关热点问题进行探讨、交流,共同促进全球健康,医学与生命科学的发展。

        2019第五届健康,医学与生命科学国际会议将于2019年12月16-18日在中国杭州召开。杭州是浙江省的省会,中国七大古都之一。意大利旅行家马可·波罗曾在他的游记中惊叹:杭州是世界上最美丽华贵的天城。杭州西湖位于杭州市西面,它以其秀丽的湖光山色和众多的名胜古迹而闻名中外,是中国著名的旅游胜地,被誉为“人间天堂”。此外,杭州也以“中国茶都”和“丝绸之府”著称。2022亚运会将在杭州举办,这让杭州成为中国第三个荣获亚运会举办权的城市。同时,中国最大的网络公司和世界第二大网络公司——阿里巴巴的总部,也坐落于杭州。

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        会议日程

        (最终日程以会议现场为准)


        12月16日 11:00-17:00 报到
        12月17日 09:00-12:00 特邀嘉宾演讲
        12:00-14:00 午餐
        14:00-17:30 口头报告
        18:30-20:00 晚宴
        12月18日 08:00-17:00 学术交流

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        会议嘉宾

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        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.

        MEDLIFE 2019第五届健康,医学与生命科学国际会议(杭州)

        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.

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