한빛사논문
- 조회398
Iris Baffour Ansah a,b, Matthew Leming c, Soo Hyun Lee a, Jun-Yeong Yang a, ChaeWon Mun a, Kyungseob Noh d,e, Timothy An d,e, Seunghun Lee a, Dong-Ho Kim a,b, Meehyein Kim d,e, Hyungsoon Im c,f, Sung-Gyu Park a
aNano-Bio Convergence Department, Korea Institute of Materials Science (KIMS), Changwon, Gyeongnam, 51508, Republic of Korea
bAdvanced Materials Engineering Division, University of Science and Technology (UST), Daejeon, 34113, Republic of Korea
cCenter for Systems Biology (CSB), Massachusetts General Hospital, Boston, MA, 02114, USA
dInfectious Diseases Therapeutic Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon, 34114, Republic of Korea
eGraduate School of New Drug Discovery and Development, Chungnam National University, Daejeon, 34134, Republic of Korea
fDepartment of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA
Corresponding authors: Meehyein Kim, Hyungsoon Im, Sung-Gyu Park
Abstract
Seasonal outbreaks of respiratory viral infections remain a global concern, with increasing morbidity and mortality rates recorded annually. Timely and false responses contribute to the widespread of respiratory pathogenic diseases owing to similar symptoms at an early stage and subclinical infection. The prevention of emerging novel viruses and variants is also a big challenge. Reliable point-of-care diagnostic assays for early infection diagnosis play a critical role in the response to threats of epidemics or pandemics. We developed a facile method for specifically identifying different viruses based on surface-enhanced Raman spectroscopy (SERS) with pathogen-mediated composite materials on Au nanodimple electrodes and machine learning (ML) analyses. Virus particles were trapped in three-dimensional plasmonic concave spaces of the electrode via electrokinetic preconcentration, and Au films were simultaneously electrodeposited, leading to the acquisition of intense and in-situ SERS signals from the Au–virus composites for ultrasensitive SERS detection. The method was useful for rapid detection analysis (<15 min), and the ML analysis for specific identification of eight virus species, including human influenza A viruses (i.e., H1N1 and H3N2 strains), human rhinovirus, and human coronavirus, was conducted. The highly accurate classification was achieved using the principal component analysis-support vector machine (98.9%) and convolutional neural network (93.5%) models. This ML-associated SERS technique demonstrated high feasibility for direct multiplex detection of different virus species for on-site applications.
논문정보
- Research article
- 2023년 05월 (BRIC 등록일 2023-06-07)
- 국내(교신)+국외 연구진
- 바이오·의료융합 > 바이오센싱 및 나노바이오물질
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