Browsing by Author "Dao, Ming"

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    Microfluidics guided by deep learning for cancer immunotherapy screening
    (National Academy of Science, 2022) Ao, Zheng; Cai, Hongwei; Wu, Zhuhao; Hu, Liya; Nunez, Asael; Zhou, Zhuolong; Liu, Hongcheng; Bondesson, Maria; Lu, Xiongbin; Lu, Xin; Dao, Ming; Guo, Feng; Medical and Molecular Genetics, School of Medicine
    Immune-cell infiltration and cytotoxicity to pathogens and diseased cells are ubiquitous in health and disease. To better understand immune-cell behavior in a 3D environment, we developed an automated high-throughput microfluidic platform that enables real-time imaging of immune-cell infiltration dynamics and killing of the target cancer cells. We trained a deep learning algorithm using clinical data and integrated the algorithm with our microfluidic platform to effectively identify epigenetic drugs that promote T cell tumor infiltration and enhance cancer immunotherapy efficacy both in vitro and in vivo. Our platform provides a unique method to investigate immune-tissue interactions, which can be widely applied to oncology, immunology, neurology, microbiology, tissue engineering, regenerative medicine, translational medicine, and so on.