文献类型：期刊文献

英文题名：Deep learning-assisted surface-enhanced Raman spectroscopy detection of intracellular reactive oxygen species

作者：Chen, Hua-Ying[1];He, Yue[2];Wang, Xiao-Yuan[2];Ye, Ming-Jie[2];Chen, Chao[1];Qian, Ruo-Can[2];Li, Da-Wei[2]

机构：[1]Zhejiang Shuren Univ, Coll Biol & Environm Engn, Hangzhou 310015, Peoples R China;[2]East China Univ Sci & Technol, Sch Chem & Mol Engn, Key Lab Adv Mat, Shanghai Key Lab Funct Mat Chem, Shanghai 200237, Peoples R China

年份：2025

卷号：284

外文期刊名：TALANTA

收录：;EI(收录号：20244717376670);WOS:【SCI-EXPANDED(收录号:WOS:001360248900001)】；

基金：This work was financially supported by the National Natural Science Foundation of China (Grant Nos. 22176058) , Science and Technology Commission of Shanghai Municipality (Grant Nos. 22ZR1416800 and 23ZR1416100) , the Program of Introducing Talents of Discipline to Universities (B16017) , the Fundamental Research Funds for the Central Universities (222201717003) , and Talent Introduction Project of Zhe-jiang Shuren University (2023R070) . The authors thank Research Center of Analysis and Test of East China University of Science and Technology for the help on the characterization.

语种：英文

外文关键词：SERS; Nanoprobes; Single-cell analysis; ROS; Deep learning

摘要：Realizing the intelligent analysis of the intracellular reactive oxygen species (ROS) is beneficial to quick diagnosis of diseases. Herein, surface-enhanced Raman spectroscopy (SERS) technology was combined with deep learning to establish a smart detection method of intracellular ROS based on neural network to improve the SERS analysis ability. Taking the simultaneous detection of peroxynitrite (ONOO- ) and hypochlorite (ClO- ) as the templates, 4-mercaptophenylboric acid (4-MPBA) and 2-mercapto-4-methoxyphenol (2-MP) molecules were modified on the AuNPs to prepare AuNP/4-MPBA/2-MP nanoprobes. The SERS spectra of AuNP/4-MPBA/2-MP nanoprobes before and after the specific response of ONOO- and ClO- were collected to construct a database, and the neural network model for extraction (ENN) and one-dimensional convolutional neural network model (1D-CNN) for quantification were built. The cosine similarity values of ENN model for ONOO- and ClO- correlation spectra were 0.997 and 0.995, respectively. In addition, the qualitative and quantitative results of the models were basically consistent with the experimental results. Moreover, the models can accurately extract the SERS response spectral information of ONOO- and ClO- and realize their preliminary prediction of concentration in living cells, which has great potential in the high-throughput smart processing and accurate analysis of large-scale complicated SERS data from biological system.