SARS-CoV-2 Neutralization Assay System using Pseudo-lentivirus

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects humans' lower respiratory tracts and causes coronavirus disease-2019 (COVID-19). Neutralizing antibodies is one of the adaptive immune system responses that can reduce SARS-CoV-2 infection. This study aimed to develop a SARS-CoV-2 neutralization assay system using pseudo-lentivirus. METHODS: The plasmid used for pseudo-lentivirus production was characterized using restriction analysis. The gene encoding for SARS-CoV-2 spike protein was confirmed using sequencing. The transfection pseudolentivirus optimal condition was determined by choosing the transfection reagents and adding centrifugation step. Optimal pseudo-lentivirus infection was analysed using fluorescent assay and luciferase assay. The optimal condition of pseudo-lentivirus infection was determined by the target cell type and the number of pseudo-lentiviruses used for neutralization test. SARS-CoV-2 pseudo-lentivirus was used to detect neutralizing antibodies from serum samples. RESULTS: The plasmid used for pseudo-lentivirus production was characterized and confirmed to have no mutations. Lipofectamine 2000 reagent generated pseudolentivirus with a higher ability to infect target cells, as indicated by a percentage green fluorescent protein (GFP) of 12.68%. Pseudo-lentivirus centrifuged obtained more stable results in luciferase expression. Optimal pseudo-lentivirus infection conditions were obtained using puromycinselected HEK 293T-ACE2 cells as target cells. The number of pseudo-lentiviruses used in the neutralization assay system was multiplicity of infection (MOI) 0.075. Serum A samples with a 1:10 dilution had the highest neutralizing antibody activity. CONCLUSION: This study shows that SARS-CoV-2 neutralization assay system using pseudo-lentivirus successfully detected neutralizing antibodies in human serum, which were indicated by a decrease in the percentage of pseudo-lentivirus infections. © 2023 The Prodia Education and Research Institute

Vehicle detection using YOLO and mobility tracking during COVID-19 pandemic lockdowns

Data-driven approaches to traffic monitoring have important applications in tracking vehicle mobility in the COVID-19 pandemic lockdowns. We report preliminary results of a pipeline that uses the You Only Look Once (YOLOv3) and the Simple Online and Realtime Tracking (SORT) algorithms to count and classify vehicles in traffic videos. We correlate vehicle counts in Katipunan Avenue, Metro Manila and Google COVID-19 Community Mobility Reports from May to August 2020 and we show that vehicle detection data may be considered for monitoring community response to changes in COVID-19 lockdown stringency levels.