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Detection of COVID-19: A Smartphone-Based Machine-Learning-Assisted ECL Immunoassay Approach with the Ability of RT-PCR CT Value Prediction.
Firoozbakhtian, Ali; Hosseini, Morteza; Sheikholeslami, Mahsa Naghavi; Salehnia, Foad; Xu, Guobao; Rabbani, Hodjattallah; Sobhanie, Ebtesam.
  • Firoozbakhtian A; Nanobiosensors Lab, Department of Life Science Engineering, Faculty of New Sciences & Technologies, University of Tehran, Tehran1439817435, Iran.
  • Hosseini M; Nanobiosensors Lab, Department of Life Science Engineering, Faculty of New Sciences & Technologies, University of Tehran, Tehran1439817435, Iran.
  • Sheikholeslami MN; National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran1439817435, Iran.
  • Salehnia F; Center of Excellence in Electrochemistry, School of Chemistry, College of Science, University of Tehran, Tehran1439817435, Iran.
  • Xu G; Departament d'Enginyeria Electrònica, Escola Tècnica Superior d'Enginyeria, Universitat Rovira i Virgili, Avinguda dels Països Catalans 26, 43007Tarragona, Spain.
  • Rabbani H; State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun130022, China.
  • Sobhanie E; University of Science and Technology of China, Hefei, Anhui230026, China.
Anal Chem ; 94(47): 16361-16368, 2022 Nov 29.
Article in English | MEDLINE | ID: covidwho-2119248
ABSTRACT
The unstoppable spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has severely threatened public health over the past 2 years. The current ubiquitously accepted method for its diagnosis provides sensitive detection of the virus; however, it is relatively time-consuming and costly, not to mention the need for highly skilled personnel. There is a clear need to develop novel computer-based diagnostic tools to provide rapid, cost-efficient, and time-saving detection in places where massive traditional testing is not practical. Here, we develop an electrochemiluminescence (ECL)-based detection system whose results are quantified as reverse transcriptase polymerase chain reaction (RT-PCR) cyclic threshold (CT) values. A concentration-dependent signal is generated upon the introduction of the virus to the electrode and is recorded with a smartphone camera. The ECL images are used to train machine learning algorithms, and a model using artificial neural networks (ANNs) for 45 samples was developed. The model demonstrated more than 90% accuracy in the diagnosis of 50 unknown real samples, detecting up to a CT value of 32 and a limit of detection (LOD) of 10-12 g mL-1 in the testing of artificial samples.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: Anal Chem Year: 2022 Document Type: Article Affiliation country: Acs.analchem.2c03502

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: Anal Chem Year: 2022 Document Type: Article Affiliation country: Acs.analchem.2c03502