Teste molecular com dupla estratégia de detecção para o diagnóstico diferencial de SARS-CoV-2 e Influenza A por RT-LAMP no ponto de atendimento

Abstract

COVID-19 and influenza are acute infections with overlapping symptoms such as cough, fever, sore throat, and myalgia, which can lead to misdiagnoses and hinder epidemiological control. This highlights the need for reliable and accessible testing methodologies to accurately identify these diseases and control the spread of their causative viruses. This study proposes a molecular detection strategy for the simultaneous discrimination of SARS-CoV-2 and Influenza A pathogens using RT-LAMP (Reverse Transcription Loop-Mediated Isothermal Amplification). The developed assay adopts a more selective approach with two sequential detection steps: a colorimetric stage, which confirms the presence of at least one of the viruses, and a lateral flow assay, which discriminates between them. In negative cases, the second step is omitted, saving resources and streamlining the diagnostic process. Reactions were initially optimized in a singleplex format and subsequently adjusted to duplex, performed at 65 °C for 40 minutes. Regarding the test specificity, no cross-reactivity with other respiratory viruses was observed, and the assay showed a detection limit of 150 copies of viral RNA/μL in both detection steps. When testing a panel of 60 clinical samples containing various SARS-CoV-2 variants and Influenza A subtypes, the assay showed an accuracy of 86.7% (colorimetric stage), 85.0% (lateral flow for SARS-CoV-2), and 88.3% (lateral flow for Influenza A) compared to the gold standard method RT-qPCR (Reverse Transcription followed by quantitative Polymerase Chain Reaction). The test can be completed in under one hour, is visually interpretable, and requires minimal laboratory infrastructure. The combination of molecular accuracy with operational simplicity makes it a promising tool for point-of-care applications, contributing to the control of COVID-19 and influenza, especially in resource-limited settings.