Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy
Jiabao Xu(University of Glasgow), Wei E. Huang(University of Oxford), Yizhi Song(Chinese Academy of Sciences), Yunsong Yu(Xi'an Jiaotong University), Xiaogang Xu(Fudan University), Xiaofei Yi(Fudan University), Yanjun Luo, Xiaoting Hua(Sir Run Run Shaw Hospital), Jingkai Wang(University of Science and Technology of China), Weiming Tu(Nanyang Technological University), Yuguo Tang(Chinese Academy of Sciences), Qiwen Yang(Chinese Academy of Medical Sciences & Peking Union Medical College), Huabing Yin(China Ocean Shipping (China))
Cited by 29
Related Papers
European Society of Clinical Microbiology and Infectious Diseases (ESCMID) guidelines for the treatment of infections caused by multidrug-resistant Gram-negative bacilli (endorsed by European society of intensive care medicine)
|Clinical Microbiology and Infection|2021|1k
RT‐LAMP for rapid diagnosis of coronavirus SARS‐CoV‐2
|Microbial Biotechnology|2020|527
Tracking heavy water (D <sub>2</sub> O) incorporation for identifying and sorting active microbial cells
|Proceedings of the National Academy of Sciences|2014|474
Raman Microscopic Analysis of Single Microbial Cells
|Analytical Chemistry|2004|416