Early Diagnosis of Influenza Virus A Using Surface-enhanced Raman Scattering-based Lateral Flow Assay
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Hyun Ji | - |
dc.contributor.author | Yang, Sung Chul | - |
dc.contributor.author | Choo, Jaebum | - |
dc.date.accessioned | 2021-06-18T08:43:58Z | - |
dc.date.available | 2021-06-18T08:43:58Z | - |
dc.date.issued | 2016-12 | - |
dc.identifier.issn | 0253-2964 | - |
dc.identifier.issn | 1229-5949 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/45658 | - |
dc.description.abstract | We report a surface-enhanced Raman scattering (SERS)-based lateral flow assay (LFA) kit for the rapid diagnosis of influenza virus A. Influenza virus A is highly infectious and causes acute respiratory diseases. Therefore, it is important to diagnose the virus early to prevent a pandemic and to provide appropriate treatment to the patient and vaccination of high-risk individuals. Conventional diagnostic tests, including virus cell culture and real-time polymerase chain reaction, take longer than 1day to confirm the disease. In contrast, a commercially available rapid influenza diagnostic test can detect the infection within 30min, but it is hard to confirm viral infection using only this test because of its low sensitivity. Therefore, the development of a rapid and simple test for the early diagnosis of influenza infection is urgently needed. To resolve these problems, we developed a SERS-based LFA kit in which the gold nanoparticles in the commercial rapid kit were replaced with SERS-active nano tags. It is possible to quantitatively detect the influenza virus A with high sensitivity by measuring the enhanced Raman signal of these SERS nano tags on the LFA strip. The limit of detection (LOD) using our proposed SERS-based LFA kit was estimated to be 1.9x10(4) PFU/mL, which is approximately one order of magnitude more sensitive than the LOD determined from the colorimetric LFA kit. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | WILEY-V C H VERLAG GMBH | - |
dc.title | Early Diagnosis of Influenza Virus A Using Surface-enhanced Raman Scattering-based Lateral Flow Assay | - |
dc.type | Article | - |
dc.identifier.doi | 10.1002/bkcs.11021 | - |
dc.identifier.bibliographicCitation | BULLETIN OF THE KOREAN CHEMICAL SOCIETY, v.37, no.12, pp 2019 - 2024 | - |
dc.identifier.kciid | ART002172607 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000390270200021 | - |
dc.identifier.scopusid | 2-s2.0-85003598128 | - |
dc.citation.endPage | 2024 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 2019 | - |
dc.citation.title | BULLETIN OF THE KOREAN CHEMICAL SOCIETY | - |
dc.citation.volume | 37 | - |
dc.type.docType | Article | - |
dc.publisher.location | 독일 | - |
dc.subject.keywordAuthor | Influenza virus A | - |
dc.subject.keywordAuthor | Surface-enhanced Raman scattering | - |
dc.subject.keywordAuthor | Lateral flow assay | - |
dc.subject.keywordAuthor | Gold nanoparticles | - |
dc.subject.keywordPlus | RESPIRATORY SYNCYTIAL VIRUS | - |
dc.subject.keywordPlus | SWINE-ORIGIN | - |
dc.subject.keywordPlus | MONOCLONAL-ANTIBODIES | - |
dc.subject.keywordPlus | SENSITIVE DETECTION | - |
dc.subject.keywordPlus | H1N1 VIRUS | - |
dc.subject.keywordPlus | A VIRUS | - |
dc.subject.keywordPlus | IMMUNOASSAY | - |
dc.subject.keywordPlus | SERS | - |
dc.subject.keywordPlus | PERFORMANCE | - |
dc.subject.keywordPlus | INFECTION | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.description.journalRegisteredClass | sci | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.