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An Artificial Intelligent Signal Amplification System for in vivo Detection of miRNA.

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机构: [1]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China [2]Schoolof Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China [3]International Co-operation Laboratory onSignal Transduction, Eastern Hepatobiliary Surgery Institute, Second Military Medical University, Shanghai, China [4]NationalLaboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, ChineseAcademy of Sciences, Beijing, China [5]Department of Radiology, West China Second University Hospital, Sichuan University,Chengdu, China [6]Experimental Center of Advanced Materials School of Materials Science & Engineering, School ofMaterials Science & Engineering, Beijing Institute of Technology, Beijing, China [7]Beijing Advanced Innovation Center for BigData-Based Precision Medicine, Beihang University, Beijing, China
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MicroRNAs (miRNA) have been identified as oncogenic drivers and tumor suppressors in every major cancer type. In this work, we design an artificial intelligent signal amplification (AISA) system including double-stranded SQ (S, signal strand; Q, quencher strand) and FP (F, fuel strand; P, protect strand) according to thermodynamics principle for sensitive detection of miRNA in vitro and in vivo. In this AISA system for miRNA detection, strand S carries a quenched imaging marker inside the SQ. Target miRNA is constantly replaced by a reaction intermediate and circulatively participates in the reaction, similar to enzyme. Therefore, abundant fluorescent substances from S and SP are dissociated from excessive SQ for in vitro and in vivo visualization. The versatility and feasibility for disease diagnosis using this system were demonstrated by constructing two types of AISA system to detect Hsa-miR-484 and Hsa-miR-100, respectively. The minimum target concentration detected by the system in vitro (10 min after mixing) was 1/10th that of the control group. The precancerous lesions of liver cancer were diagnosed, and the detection accuracy were larger than 94% both in terms of location and concentration. The ability to establish this design framework for AISA system with high specificity provides a new way to monitor tumor progression and to assess therapeutic responses. Copyright © 2019 Ma, Chen, Yang, Zhang, Wang, Zhang, Zheng, Zhu, Sun, Zhang, Guo, Liang, Wang and Tian.

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出版当年[2019]版:
大类 | 3 区 工程技术
小类 | 3 区 综合性期刊
最新[2025]版:
大类 | 3 区 生物学
小类 | 3 区 生物工程与应用微生物 4 区 工程:生物医学
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出版当年[2019]版:
Q2 MULTIDISCIPLINARY SCIENCES
最新[2024]版:
Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Q2 ENGINEERING, BIOMEDICAL

影响因子: 最新[2024版] 最新五年平均 出版当年[2019版] 出版当年五年平均 出版前一年[2018版] 出版后一年[2020版]

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第一作者机构: [1]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China [2]Schoolof Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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通讯机构: [1]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China [2]Schoolof Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China [7]Beijing Advanced Innovation Center for BigData-Based Precision Medicine, Beihang University, Beijing, China
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