DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jayeon Song | - |
dc.contributor.author | B Cha | - |
dc.contributor.author | Jeong Moon | - |
dc.contributor.author | Hyowon Jang | - |
dc.contributor.author | S Kim | - |
dc.contributor.author | J Jang | - |
dc.contributor.author | D Yong | - |
dc.contributor.author | Hyung-Jun Kwon | - |
dc.contributor.author | In Chul Lee | - |
dc.contributor.author | Eun Kyung Lim | - |
dc.contributor.author | Juyeon Jung | - |
dc.contributor.author | H G Park | - |
dc.contributor.author | Taejoon Kang | - |
dc.date.accessioned | 2022-08-03T16:33:40Z | - |
dc.date.available | 2022-08-03T16:33:40Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 1936-0851 | - |
dc.identifier.uri | https://oak.kribb.re.kr/handle/201005/30144 | - |
dc.description.abstract | Coronavirus disease (COVID-19) has affected people for over two years. Moreover, the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants has raised concerns regarding its accurate diagnosis. Here, we report a colorimetric DNAzyme reaction triggered by loop-mediated isothermal amplification (LAMP) with clustered regularly interspaced short palindromic repeats (CRISPR), referred to as DAMPR assay for detecting SARS-CoV-2 and variants genes with attomolar sensitivity within an hour. The CRISPR-associated protein 9 (Cas9) system eliminated false-positive signals of LAMP products, improving the accuracy of DAMPR assay. Further, we fabricated a portable DAMPR assay system using a three-dimensional printing technique and developed a machine learning (ML)-based smartphone application to routinely check diagnostic results of SARS-CoV-2 and variants. Among blind tests of 136 clinical samples, the proposed system successfully diagnosed COVID-19 patients with a clinical sensitivity and specificity of 100% each. More importantly, the D614G (variant-common), T478K (delta-specific), and A67V (omicron-specific) mutations of the SARS-CoV-2 S gene were detected selectively, enabling the diagnosis of 70 SARS-CoV-2 delta or omicron variant patients. The DAMPR assay system is expected to be employed for on-site, rapid, accurate detection of SARS-CoV-2 and its variants gene and employed in the diagnosis of various infectious diseases. | - |
dc.publisher | Amer Chem Soc | - |
dc.title | Smartphone-based SARS-CoV-2 and variants detection system using colorimetric DNAzyme reaction triggered by loop-mediated isothermal amplification (LAMP) with clustered regularly interspaced short palindromic repeats (CRISPR) | - |
dc.title.alternative | Smartphone-based SARS-CoV-2 and variants detection system using colorimetric DNAzyme reaction triggered by loop-mediated isothermal amplification (LAMP) with clustered regularly interspaced short palindromic repeats (CRISPR) | - |
dc.type | Article | - |
dc.citation.title | ACS Nano | - |
dc.citation.number | 7 | - |
dc.citation.endPage | 11314 | - |
dc.citation.startPage | 11300 | - |
dc.citation.volume | 16 | - |
dc.contributor.affiliatedAuthor | Jayeon Song | - |
dc.contributor.affiliatedAuthor | Jeong Moon | - |
dc.contributor.affiliatedAuthor | Hyowon Jang | - |
dc.contributor.affiliatedAuthor | Hyung-Jun Kwon | - |
dc.contributor.affiliatedAuthor | In Chul Lee | - |
dc.contributor.affiliatedAuthor | Eun Kyung Lim | - |
dc.contributor.affiliatedAuthor | Juyeon Jung | - |
dc.contributor.affiliatedAuthor | Taejoon Kang | - |
dc.contributor.alternativeName | 송자연 | - |
dc.contributor.alternativeName | 차백동 | - |
dc.contributor.alternativeName | 문정 | - |
dc.contributor.alternativeName | 장효원 | - |
dc.contributor.alternativeName | 김선주 | - |
dc.contributor.alternativeName | 장지은 | - |
dc.contributor.alternativeName | 용동은 | - |
dc.contributor.alternativeName | 권형준 | - |
dc.contributor.alternativeName | 이인철 | - |
dc.contributor.alternativeName | 임은경 | - |
dc.contributor.alternativeName | 정주연 | - |
dc.contributor.alternativeName | 박현규 | - |
dc.contributor.alternativeName | 강태준 | - |
dc.identifier.bibliographicCitation | ACS Nano, vol. 16, no. 7, pp. 11300-11314 | - |
dc.identifier.doi | 10.1021/acsnano.2c04840 | - |
dc.subject.keyword | SARS-CoV-2 | - |
dc.subject.keyword | Variants | - |
dc.subject.keyword | Smartphone | - |
dc.subject.keyword | CRISPR-Cas9 | - |
dc.subject.keyword | Machine learning | - |
dc.subject.local | SARS-CoV-2 | - |
dc.subject.local | SARS-Cov-2 | - |
dc.subject.local | variant | - |
dc.subject.local | Variant | - |
dc.subject.local | Variants | - |
dc.subject.local | Smartphone | - |
dc.subject.local | smartphone | - |
dc.subject.local | CRISPR-Cas9 | - |
dc.subject.local | CRISPR/Cas9 | - |
dc.subject.local | CRISPRCas9 | - |
dc.subject.local | crispr/cas9 | - |
dc.subject.local | machine learning | - |
dc.subject.local | Machine learning | - |
dc.description.journalClass | Y | - |
There are no files associated with this item.
Items in OpenAccess@KRIBB are protected by copyright, with all rights reserved, unless otherwise indicated.