'All In One' SARS-CoV-2 variant recognition platform: Machine learning-enabled point of care diagnostics

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dc.authorscopusid57224474138
dc.authorscopusid57222170609
dc.authorscopusid57205630119
dc.authorscopusid55935858800
dc.authorscopusid6602258835
dc.authorscopusid35611300300
dc.authorscopusid55662941400
dc.contributor.authorBeduk D.
dc.contributor.authorIlton de Oliveira Filho J.
dc.contributor.authorBeduk T.
dc.contributor.authorHarmanci D.
dc.contributor.authorZihnioglu F.
dc.contributor.authorCicek C.
dc.contributor.authorSertoz R.
dc.date.accessioned2023-01-12T20:22:39Z
dc.date.available2023-01-12T20:22:39Z
dc.date.issued2022
dc.departmentN/A/Departmenten_US
dc.description.abstractPoint of care (PoC) devices are highly demanding to control current pandemic, originated from severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). Though nucleic acid-based methods such as RT-PCR are widely available, they require sample preparation and long processing time. PoC diagnostic devices provide relatively faster and stable results. However they require further investigation to provide high accuracy and be adaptable for the new variants. In this study, laser-scribed graphene (LSG) sensors are coupled with gold nanoparticles (AuNPs) as stable promising biosensing platforms. Angiotensin Converting Enzyme 2 (ACE2), an enzymatic receptor, is chosen to be the biorecognition unit due to its high binding affinity towards spike proteins as a key-lock model. The sensor was integrated to a homemade and portable potentistat device, wirelessly connected to a smartphone having a customized application for easy operation. LODs of 5.14 and 2.09 ng/mL was achieved for S1 and S2 protein in the linear range of 1.0–200 ng/mL, respectively. Clinical study has been conducted with nasopharyngeal swabs from 63 patients having alpha (B.1.1.7), beta (B.1.351), delta (B.1.617.2) variants, patients without mutation and negative patients. A machine learning model was developed with accuracy of 99.37% for the identification of the SARS-Cov-2 variants under 1 min. With the increasing need for rapid and improved disease diagnosis and monitoring, the PoC platform proved its potential for real time monitoring by providing accurate and fast variant identification without any expertise and pre sample preparation, which is exactly what societies need in this time of pandemic. © 2022en_US
dc.description.sponsorshipASCRS Research Foundation, ASCRS: 2016K121190, TOA-2020-21862; Ege Üniversitesi; King Abdullah University of Science and Technology, KAUSTen_US
dc.description.sponsorshipAuthors would like to express their acknowledgments to the financial support of funding from the Ege University, Research Foundation (Project number: TOA-2020-21862), Republic of Turkey, Ministry of Development (Project Grant No: 2016K121190) and King Abdullah University of Science and Technology (KAUST) Smart Health Initiative, Saudi Arabia. In addition, authors thank the laboratories of the Ege University Central Research Testing and Analysis Laboratory Research and Application Center (EGE-MATAL).en_US
dc.description.sponsorshipAuthors would like to express their acknowledgments to the financial support of funding from the Ege University, Research Foundation (Project number: TOA-2020-21862 ), Republic of Turkey, Ministry of Development (Project Grant No: 2016K121190 ) and King Abdullah University of Science and Technology ( KAUST ) Smart Health Initiative, Saudi Arabia. In addition, authors thank the laboratories of the Ege University Central Research Testing and Analysis Laboratory Research and Application Center (EGE-MATAL).en_US
dc.identifier.doi10.1016/j.biosx.2022.100105
dc.identifier.issn25901370
dc.identifier.issn2590-1370en_US
dc.identifier.scopus2-s2.0-85122650697en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.biosx.2022.100105
dc.identifier.urihttps://hdl.handle.net/11454/79531
dc.identifier.volume10en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofBiosensors and Bioelectronics: Xen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCOVID-19en_US
dc.subjectLaser-scribed grapheneen_US
dc.subjectMachine learningen_US
dc.subjectPoint-of-careen_US
dc.subjectSARS-CoV-2en_US
dc.subjectSensoren_US
dc.subjectBinding energyen_US
dc.subjectDiagnosisen_US
dc.subjectDiseasesen_US
dc.subjectGold nanoparticlesen_US
dc.subjectMachine learningen_US
dc.subjectPolymerase chain reactionen_US
dc.subjectProteinsen_US
dc.subjectSARSen_US
dc.subjectControl currenten_US
dc.subjectCOVID-19en_US
dc.subjectLaser-scribed grapheneen_US
dc.subjectLongest processing timeen_US
dc.subjectPoint of careen_US
dc.subjectPoint of care diagnosticen_US
dc.subjectSample preparationen_US
dc.subjectSensoren_US
dc.subjectSevere acute respiratory syndrome coronavirusen_US
dc.subjectSevere acute respiratory syndrome coronavirus 2en_US
dc.subjectGrapheneen_US
dc.subjectangiotensin converting enzyme 2en_US
dc.subjectcoronavirus spike glycoproteinen_US
dc.subjectgold nanoparticleen_US
dc.subjectArticleen_US
dc.subjectbinding affinityen_US
dc.subjectcoronavirus disease 2019en_US
dc.subjecthumanen_US
dc.subjectmachine learningen_US
dc.subjectmajor clinical studyen_US
dc.subjectnasopharyngeal swaben_US
dc.subjectnonhumanen_US
dc.subjectpatient monitoringen_US
dc.subjectpoint of care testingen_US
dc.subjectSARS-Cov-2 variant 501Y.V1en_US
dc.subjectSARS-CoV-2 variant 501Y.V2en_US
dc.subjectSARS-CoV-2 variant VUI-21APR-01en_US
dc.subjectSevere acute respiratory syndrome coronavirus 2en_US
dc.subjectvirus identificationen_US
dc.subjectvirus strainen_US
dc.title'All In One' SARS-CoV-2 variant recognition platform: Machine learning-enabled point of care diagnosticsen_US
dc.typeArticleen_US

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