RESUMEN
Novel coronavirus (SARS-CoV-2) has caused more than 100 million confirmed cases of human infectious disease (COVID-19) since December 2019 to paralyze our global community. However, only limited access has been allowed to COVID-19 vaccines and antiviral treatment options. Here, we report the efficacy of the anticancer drug pralatrexate against SARS-CoV-2. In Vero and human lung epithelial Calu-3 cells, pralatrexate reduced viral RNA copies of SARS-CoV-2 without detectable cytotoxicity, and viral replication was successfully inhibited in a dose-dependent manner. In a time-to-addition assay, pralatrexate treatment at almost half a day after infection also exhibited inhibitory effects on the replication of SARS-CoV-2 in Calu-3 cells. Taken together, these results suggest the potential of pralatrexate as a drug repurposing COVID-19 remedy.
RESUMEN
Novel coronavirus (SARS-CoV-2) has caused more than 100 million confirmed cases of human infectious disease (COVID-19) since December 2019 to paralyze our global community. However, only limited access has been allowed to COVID-19 vaccines and antiviral treatment options. Here, we report the efficacy of the anticancer drug pralatrexate against SARS-CoV-2. In Vero and human lung epithelial Calu-3 cells, pralatrexate reduced viral RNA copies of SARS-CoV-2 without detectable cytotoxicity, and viral replication was successfully inhibited in a dose-dependent manner. In a time-to-addition assay, pralatrexate treatment at almost half a day after infection also exhibited inhibitory effects on the replication of SARS-CoV-2 in Calu-3 cells. Taken together, these results suggest the potential of pralatrexate as a drug repurposing COVID-19 remedy.
RESUMEN
OBJECTIVES: Mobile healthcare applications are becoming a growing trend. Also, the prevalence of dementia in modern society is showing a steady growing trend. Among degenerative brain diseases that cause dementia, Alzheimer disease (AD) is the most common. The purpose of this study was to identify AD patients using magnetic resonance imaging in the mobile environment. METHODS: We propose an incremental classification for mobile healthcare systems. Our classification method is based on incremental learning for AD diagnosis and AD prediction using the cortical thickness data and hippocampus shape. We constructed a classifier based on principal component analysis and linear discriminant analysis. We performed initial learning and mobile subject classification. Initial learning is the group learning part in our server. Our smartphone agent implements the mobile classification and shows various results. RESULTS: With use of cortical thickness data analysis alone, the discrimination accuracy was 87.33% (sensitivity 96.49% and specificity 64.33%). When cortical thickness data and hippocampal shape were analyzed together, the achieved accuracy was 87.52% (sensitivity 96.79% and specificity 63.24%). CONCLUSIONS: In this paper, we presented a classification method based on online learning for AD diagnosis by employing both cortical thickness data and hippocampal shape analysis data. Our method was implemented on smartphone devices and discriminated AD patients for normal group.