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首頁 > 商務會議 > 醫療醫學會議 > MEDLIFE 2019第五屆健康,醫學與生命科學國際會議(杭州) 更新時間:2019-11-08T10:46:01

MEDLIFE 2019第五屆健康,醫學與生命科學國際會議(杭州)
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MEDLIFE 2019第五屆健康,醫學與生命科學國際會議(杭州) 已截止報名

會議時間: 2019-12-16 08:00至 2019-12-18 18:00結束

會議地點: 杭州  詳細地址會前通知   周邊酒店預訂

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        會議內容 主辦方介紹


        MEDLIFE 2019第五屆健康,醫學與生命科學國際會議(杭州)

        MEDLIFE 2019第五屆健康,醫學與生命科學國際會議(杭州)宣傳圖

        2019第五屆健康,醫學生命科學國際會議為廣大從事健康,醫學與生命科學等相關領域的研究學者、專家提供交流平臺。會議組委會誠邀全球相關領域的學者、專家參加此次國際會議,就健康,醫學與生命科學為主題的相關熱點問題進行探討、交流,共同促進全球健康,醫學與生命科學的發展。

        2019第五屆健康,醫學與生命科學國際會議將于2019年12月16-18日在中國杭州召開。杭州是浙江省的省會,中國七大古都之一。意大利旅行家馬可·波羅曾在他的游記中驚嘆:杭州是世界上最美麗華貴的天城。杭州西湖位于杭州市西面,它以其秀麗的湖光山色和眾多的名勝古跡而聞名中外,是中國著名的旅游勝地,被譽為“人間天堂”。此外,杭州也以“中國茶都”和“絲綢之府”著稱。2022亞運會將在杭州舉辦,這讓杭州成為中國第三個榮獲亞運會舉辦權的城市。同時,中國最大的網絡公司和世界第二大網絡公司——阿里巴巴的總部,也坐落于杭州。

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        會議日程

        (最終日程以會議現場為準)


        12月16日 11:00-17:00 報到
        12月17日 09:00-12:00 特邀嘉賓演講
        12:00-14:00 午餐
        14:00-17:30 口頭報告
        18:30-20:00 晚宴
        12月18日 08:00-17:00 學術交流

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        會議嘉賓

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        MEDLIFE 2019第五屆健康,醫學與生命科學國際會議(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第五屆健康,醫學與生命科學國際會議(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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