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

被引：31

作者：

Beduk D. ^{[1
]}

Ilton de Oliveira Filho J. ^{[2
]}

Beduk T. ^{[2
]}

Harmanci D. ^{[1
]}

Zihnioglu F. ^{[3
]}

Cicek C. ^{[4
]}

Sertoz R. ^{[4
]}

Arda B. ^{[5
]}

Goksel T. ^{[6
,7
]}

Turhan K. ^{[8
]}

Salama K.N. ^{[2
]}

Timur S. ^{[1
,3
]}

机构：

[1] Central Research Test and Analysis Laboratory Application and Research Center, Ege University, Bornova, Izmir

[2] Sensors Lab, Advanced Membranes and Porous Materials Center, Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal

[3] Department of Biochemistry, Faculty of Science, Ege University, Bornova, Izmir

[4] Department of Medical Microbiology, Faculty of Medicine, Ege University, Bornova, Izmir

[5] Department of Infectious Diseases and Clinical Microbiology, Faculty of Medicine, Ege University, Bornova, Izmir

[6] Department of Pulmonary Medicine, Faculty of Medicine, Ege University, Bornova, Izmir

[7] EGESAM-Ege University Translational Pulmonary Research Center, Bornova, Izmir

[8] Department of Thoracic Surgery, Faculty of Medicine Ege University, Bornova, Izmir

来源：

Biosensors and Bioelectronics: X | 2022年 / 10卷

关键词：

COVID-19; Laser-scribed graphene; Machine learning; Point-of-care; SARS-CoV-2; Sensor;

D O I：

10.1016/j.biosx.2022.100105

中图分类号：

学科分类号：

摘要：

Point 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. © 2022

引用

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