Jie Zhu
Guangzhou Medical University
75 Papers
217 Citations
Jie Zhu is an academic researcher from Guangzhou Medical University. The author has contributed to research in topics: Biology & Chemistry. The author has an hindex of 27, co-authored 63 publications. Previous affiliations of Jie Zhu include Boston Children's Hospital & University of California, San Diego.
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Papers
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Daniel S. Kermany,Daniel S. Kermany,Michael H. Goldbaum,Wenjia Cai,Carolina C. S. Valentim,Huiying Liang,Sally L. Baxter,Alex McKeown,Ge Yang,Xiaokang Wu,Fangbing Yan,Justin Dong,Made K. Prasadha,Jacqueline Pei,Jacqueline Pei,Magdalene Yin Lin Ting,Jie Zhu,Christina Li,Sierra Hewett,Sierra Hewett,Jason Dong,Ian Ziyar,Alexander Shi,Runze Zhang,Lianghong Zheng,Rui Hou,William Shi,Xin Fu,Xin Fu,Yaou Duan,Viet Anh Nguyen Huu,Viet Anh Nguyen Huu,Cindy Wen,Edward Zhang,Edward Zhang,Charlotte Zhang,Charlotte Zhang,Oulan Li,Oulan Li,Xiaobo Wang,Michael A Singer,Xiaodong Sun,Jie Xu,Ali Tafreshi,M. Anthony Lewis,Huimin Xia,Kang Zhang +46 more
TL;DR: A diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases, which demonstrates performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema.
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Nanoparticle biointerfacing by platelet membrane cloaking
Che Ming Jack Hu,Ronnie H. Fang,Kuei Chun Wang,Brian T. Luk,Soracha Thamphiwatana,Diana Dehaini,Phu Nguyen,Pavimol Angsantikul,Cindy Wen,Ashley V. Kroll,Cody W. Carpenter,Manikantan Ramesh,Vivian Qu,Sherrina Patel,Jie Zhu,William Shi,Florence M. Hofman,Thomas C. Chen,Weiwei Gao,Kang Zhang,Shu Chien,Liangfang Zhang +21 more
TL;DR: The multifaceted biointerfacing enabled by the platelet membrane cloaking method provides a new approach in developing functional nanoparticles for disease-targeted delivery.
In vivo genome editing via CRISPR/Cas9 mediated homology-independent targeted integration
Keiichiro Suzuki,Yuji Tsunekawa,Reyna Hernández-Benítez,Reyna Hernández-Benítez,Jun Wu,Jun Wu,Jie Zhu,Jie Zhu,Euiseok J. Kim,Fumiyuki Hatanaka,Mako Yamamoto,Toshikazu Araoka,Toshikazu Araoka,Zhe Li,Masakazu Kurita,Tomoaki Hishida,Mo Li,Emi Aizawa,Shicheng Guo,Song Chen,April Goebl,Rupa Devi Soligalla,Jing Qu,Tingshuai Jiang,Xin Fu,Xin Fu,Maryam Jafari,Concepcion Rodriguez Esteban,W. Travis Berggren,Jeronimo Lajara,Estrella Núñez-Delicado,Pedro Guillen,Josep M. Campistol,Fumio Matsuzaki,Guang-Hui Liu,Pierre J. Magistretti,Kun Zhang,Edward M. Callaway,Kang Zhang,Juan Carlos Izpisua Belmonte +39 more
TL;DR: The HITI method presented here establishes new avenues for basic research and targeted gene therapies and demonstrates the efficacy of HITI in improving visual function using a rat model of the retinal degeneration condition retinitis pigmentosa.
Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma
Rui-Hua Xu,Wei Wei,Wei Wei,Michal Krawczyk,Wenqiu Wang,Huiyan Luo,Huiyan Luo,Ken Flagg,Shaohua Yi,William Shi,Qingli Quan,Kang Li,Lianghong Zheng,Heng Zhang,Bennett A. Caughey,Qi Zhao,Jiayi Hou,Runze Zhang,Yanxin Xu,Huimin Cai,Gen Li,Rui Hou,Zheng Zhong,Danni Lin,Xin Fu,Jie Zhu,Yaou Duan,Meixing Yu,Binwu Ying,Wengeng Zhang,Juan Wang,Edward Zhang,Charlotte Zhang,Oulan Li,Rongping Guo,Hannah Carter,Jian-Kang Zhu,Xiaoke Hao,Kang Zhang,Kang Zhang,Kang Zhang +40 more
TL;DR: This work identified an HCC-specific methylation marker panel by comparing HCC tissue and normal blood leukocytes and showed that methylation profiles of HCC tumour DNA and matched plasma ctDNA are highly correlated.
767
Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence
Huiying Liang,Brian Tsui,Hao Ni,Carolina C. S. Valentim,Sally L. Baxter,Guangjian Liu,Wenjia Cai,Daniel S. Kermany,Daniel S. Kermany,Xin Sun,Jiancong Chen,Liya He,Jie Zhu,Pin Tian,Hua Shao,Lianghong Zheng,Rui Hou,Sierra Hewett,Sierra Hewett,Gen Li,Gen Li,Ping Liang,Xuan Zang,Zhiqi Zhang,Liyan Pan,Huimin Cai,Rujuan Ling,Shuhua Li,Yongwang Cui,Shusheng Tang,Hong Ye,Xiaoyan Huang,Waner He,Wenqing Liang,Qing Zhang,Jianmin Jiang,Wei Yu,Jianqun Gao,Wanxing Ou,Yingmin Deng,Qiaozhen Hou,Bei Wang,Cuichan Yao,Yan Liang,Shu Zhang,Yaou Duan,Runze Zhang,Sarah Gibson,Charlotte Zhang,Oulan Li,Edward Zhang,Gabriel Karin,Nathan Nguyen,Xiaokang Wu,Xiaokang Wu,Cindy Wen,Jie Xu,Wenqin Xu,Bochu Wang,Winston Wang,Jing Li,Jing Li,Bianca Pizzato,Caroline Bao,Daoman Xiang,Wanting He,Wanting He,Suiqin He,Yugui Zhou,Yugui Zhou,Weldon W Haw,Weldon W Haw,Michael H. Goldbaum,Adriana H. Tremoulet,Chun-Nan Hsu,Hannah Carter,Long Zhu,Kang Zhang,Kang Zhang,Kang Zhang,Huimin Xia +80 more
TL;DR: This study shows that MLCs can query EHRs in a manner similar to the hypothetico-deductive reasoning used by physicians and unearth associations that previous statistical methods have not found, and provides a proof of concept for implementing an AI-based system to aid physicians in tackling large amounts of data, augmenting diagnostic evaluations, and to provide clinical decision support in cases of diagnostic uncertainty or complexity.
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