'All In One' SARS-CoV-2 variant recognition platform: Machine learning-enabled point of care diagnostics
Ilton de Oliveira Filho, José
Salama, Khaled N.
KAUST DepartmentSensors Lab
Material Science and Engineering Program
Physical Science and Engineering (PSE) Division
Electrical and Computer Engineering Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Advanced Membranes and Porous Materials Center
Permanent link to this recordhttp://hdl.handle.net/10754/674934
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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.
CitationBeduk, D., Ilton de Oliveira Filho, J., Beduk, T., Harmanci, D., Zihnioglu, F., Cicek, C., … Timur, S. (2022). “All In One” SARS-CoV-2 variant recognition platform: Machine learning-enabled point of care diagnostics. Biosensors and Bioelectronics: X, 100105. doi:10.1016/j.biosx.2022.100105
SponsorsAuthors 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).
JournalBiosensors and Bioelectronics: X