Usefulness of a multiplex immunodot in case of discordant results between automated COVID-19 serological assays

Abstract

Background At present, the only reliable test for COVID-19 diagnosis is RT-qPCR. Serological assays have been widely used to increase the detection sensitivity of infected population. Hereby, we report the performance of a new pan-IgG multiplex Enzyme Immunoassay (immunodot) method for exploration of discrepant SARS-COV-2 serological results. Methods A retrospective study on 38 residual serum samples from recovered COVID-19 subjects with discordant serological results on Roche and Snibe platforms, were reanalyzed on a new semi-automated pan-IgG immunodot Enzyme Immunoassay, namely COVIDOT-TEST, in order to find the source of discrepancies and to evaluate the latter method. All samples were analyzed on the BlueDiver® Instrument and all strips were read by the BlueScan® Scanner using Dr DOT® Software. Results: Based on our data, subject samples showed specific IgG reactions on 2 different antigens on immunodot strips. Of these 38 samples, 97.4% of samples showed specific IgG reaction against S1 + S2 antigens, 89.5% showed against RBD antigen, 86.8% against S2 antigen reaction on the COVIDOT-TEST kit. Specific IgG-S1 antigen and IgG-N antigen reactions were detected in 73.7% and 65.8% of the samples, respectively. Conclusion The new semi-automated pan-IgG immunodot Enzyme Immunoassay method appeared to be a reliable assay to confirm suspicious COVID-19 serological screening results.

Techniques for the Analysis of Covid-19 Virus: State-of-the-Art and Future Prospects

“…Artificial intelligence was developed to combat this situation, which could screen out SARS-CoV-2 infected lung abnormalities. Machine learning algorithms based on deep learning were developed to classify and quantify pulmonary diseases [101]. Deep learning models improve early detection, enabling isolation and treatment they relieve pressure from frontline radiologists and increase their efficiency [102].…”

Techniques for the Analysis of Covid-19 Virus: State-of-the-Art and Future Prospects

“…Artificial intelligence was developed to combat this situation, which could screen out SARS-CoV-2 infected lung abnormalities. Machine learning algorithms based on deep learning were developed to classify and quantify pulmonary diseases [101]. Deep learning models improve early detection, enabling isolation and treatment they relieve pressure from frontline radiologists and increase their efficiency [102].…”

Techniques for the Analysis of Covid-19 Virus: State-of-the-Art and Future Prospects

“…Artificial intelligence was developed to combat this situation, which could screen out SARS-CoV-2 infected lung abnormalities. Machine learning algorithms based on deep learning were developed to classify and quantify pulmonary diseases [101]. Deep learning models improve early detection, enabling isolation and treatment they relieve pressure from frontline radiologists and increase their efficiency [102].…”

Techniques for the Analysis of Covid-19 Virus: State-of-the-Art and Future Prospects

“…Artificial intelligence was developed to combat this situation, which could screen out SARS-CoV-2 infected lung abnormalities. Machine learning algorithms based on deep learning were developed to classify and quantify pulmonary diseases [101]. Deep learning models improve early detection, enabling isolation and treatment they relieve pressure from frontline radiologists and increase their efficiency [102].…”