文献类型：期刊文献

英文题名：Worm Generator: A System for High-Throughput in Vivo Screening

作者：Yang, Anqi[1];Lin, Xiang[1];Liu, Zijian[1];Duan, Xin[1];Yuan, Yurou[1];Zhang, Jiaxuan[1];Liang, Qilin[1];Ji, Xianglin[2];Sun, Nannan[3];Yu, Huajun[3];He, Weiwei[4];Zhu, Lili[4];Xu, Bingzhe[1];Lin, Xudong[1]

机构：[1]Sun Yat sen Univ, Sch Biomed Engn, Guangdong Prov Key Lab Sensor Technol & Biomed Ins, Shenzhen Campus, Shenzhen 518000, Peoples R China;[2]City Univ Hong Kong, Dept Biomed Engn, Kowloon, Hong Kong 999077, Peoples R China;[3]Guangdong Med Univ, Dept Biochem & Mol Biol, Zhanjiang 524023, Peoples R China;[4]East China Univ Sci & Technol, Sch Pharm, Shanghai 200237, Peoples R China

年份：2023

卷号：23

期号：4

起止页码：1280

外文期刊名：NANO LETTERS

收录：;EI(收录号：20230613538850);WOS:【SCI-EXPANDED(收录号:WOS:000926986900001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant 81901837) , the National Natural Science Foundation of Guangdong Province (Grant 2021A15-15010266) , the Shenthen Science and Technology Program (Grants 202206193000001 and 20220816161126002) , the Postdoctoral Science Foundation of China (Grants 2021TQ0382 and 2022M723669) , and the Foundation of Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument (Grant 2020B1212060077) .

语种：英文

外文关键词：triboelectric nanogenerator; microfluidics; Caenorhabditis elegans; high-throughput; drug screening

摘要：Large-scale screening of molecules in organisms requires high-throughput and cost-effective evaluating tools during preclinical development. Here, a novel in vivo screening strategy combining hierarchically structured biohybrid triboelectric nano-generators (HB-TENGs) arrays with computational bioinformatics analysis for high-throughput pharmacological evaluation using Caenorhabditis elegans is described. Unlike the traditional methods for behavioral monitoring of the animals, which are laborious and costly, HB-TENGs with micropillars are designed to efficiently convert animals' behaviors into friction deformation and result in a contact-separation motion between two triboelectric layers to generate electrical outputs. The triboelectric signals are recorded and extracted to various bioinformation for each screened compound. Moreover, the information-rich electrical readouts are successfully demonstrated to be sufficient to predict a drug's identity by multiple-Gaussian-kernels-based machine learning methods. This proposed strategy can be readily applied to various fields and is especially useful in in vivo explorations to accelerate the identification of novel therapeutics.