tetano
Editor, Senior Moderator
Anal Chem
. 2022 Nov 16.
doi: 10.1021/acs.analchem.2c03502. Online ahead of print.
Detection of COVID-19: A Smartphone-Based Machine-Learning-Assisted ECL Immunoassay Approach with the Ability of RT-PCR CT Value Prediction
Ali Firoozbakhtian[SUP] 1 [/SUP], Morteza Hosseini[SUP] 1 2 [/SUP], Mahsa Naghavi Sheikholeslami[SUP] 3 [/SUP], Foad Salehnia[SUP] 4 [/SUP], Guobao Xu[SUP] 5 6 [/SUP], Hodjattallah Rabbani[SUP] 7 [/SUP], Ebtesam Sobhanie[SUP] 1 [/SUP]
Affiliations
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[SUP]-12[/SUP] g mL[SUP]-1[/SUP] in the testing of artificial samples.
. 2022 Nov 16.
doi: 10.1021/acs.analchem.2c03502. Online ahead of print.
Detection of COVID-19: A Smartphone-Based Machine-Learning-Assisted ECL Immunoassay Approach with the Ability of RT-PCR CT Value Prediction
Ali Firoozbakhtian[SUP] 1 [/SUP], Morteza Hosseini[SUP] 1 2 [/SUP], Mahsa Naghavi Sheikholeslami[SUP] 3 [/SUP], Foad Salehnia[SUP] 4 [/SUP], Guobao Xu[SUP] 5 6 [/SUP], Hodjattallah Rabbani[SUP] 7 [/SUP], Ebtesam Sobhanie[SUP] 1 [/SUP]
Affiliations
- PMID: 36383743
- DOI: 10.1021/acs.analchem.2c03502
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[SUP]-12[/SUP] g mL[SUP]-1[/SUP] in the testing of artificial samples.