Artificial intelligence‐assisted point‐of‐care testing system for ultrafast and quantitative detection of drug‐resistant bacteria
Yang Ding,
Jingjie Chen,
Qiong Wu,
Bin Fang,
Wenhui Ji,
Xin Li,
Changmin Yu,
Xuchun Wang,
Xiamin Cheng,
Hai‐Dong Yu,
Zhangjun Hu,
Kajsa Uvdal,
Peng Li,
Lin Li,
Wei Huang
Affiliations
Yang Ding
Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) Nanjing China
Jingjie Chen
Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an China
Qiong Wu
Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) Nanjing China
Bin Fang
Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an China
Wenhui Ji
Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) Nanjing China
Xin Li
Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an China
Changmin Yu
Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) Nanjing China
Xuchun Wang
College of Chemistry and Material Engineering University of Science and Technology of Anhui Bengbu China
Xiamin Cheng
Institute of Advanced Synthesis School of Chemistry and Molecular Engineering, Nanjing Tech University (NanjingTech) Nanjing China
Hai‐Dong Yu
Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an China
Zhangjun Hu
Department of Physics, Chemistry and Biology Linköping University Linköping Sweden
Kajsa Uvdal
Department of Physics, Chemistry and Biology Linköping University Linköping Sweden
Peng Li
Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an China
Lin Li
Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) Nanjing China
Wei Huang
Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech) Nanjing China
Abstract As one of the major causes of antimicrobial resistance, β‐lactamase develops rapidly among bacteria. Detection of β‐lactamase in an efficient and low‐cost point‐of‐care testing (POCT) way is urgently needed. However, due to the volatile environmental factors, the quantitative measurement of current POCT is often inaccurate. Herein, we demonstrate an artificial intelligence (AI)‐assisted mobile health system that consists of a paper‐based β‐lactamase fluorogenic probe analytical device and a smartphone‐based AI cloud. An ultrafast broad‐spectrum fluorogenic probe (B1) that could respond to β‐lactamase within 20 s was first synthesized, and the detection limit was determined to be 0.13 nmol/L. Meanwhile, a three‐dimensional microfluidic paper‐based analytical device was fabricated for integration of B1. Also, a smartphone‐based AI cloud was developed to correct errors automatically and output results intelligently. This smart system could calibrate the temperature and pH in the β‐lactamase level detection in complex samples and mice infected with various bacteria, which shows the problem‐solving ability in interdisciplinary research, and demonstrates potential clinical benefits.
