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Abstract The 2019 global coronavirus (COVID-19) pandemic has brought the world to a grinding halt, highlighting the urgent need for therapeutic and preventive solutions to slow the spread of emerging viruses. The objective of this study was to assess the anti-SARS-CoV-2 effectiveness of 8 FDA-approved cationic amphiphilic drugs (CADs). SARS-CoV-2-infected Vero cells, Calu-3 cells and primary Human Nasal Epithelial Cells (HNEC) were used to investigate the effects of CADs and revealed their antiviral mode of action. Among the CADs tested, desloratadine, a commonly used antiallergic, well-tolerated with no major side effects, potently reduced the production of SARS-CoV-2 RNA in Vero-E6 cells. Interestingly, desloratadine was also effective against HCoV-229E and HCoV-OC43 showing that it possessed broad-spectrum anti-coronavirus activity. Investigation of its mode of action revealed that it targeted an early step of virus lifecycle and blocked SARS-CoV-2 entry through the endosomal pathway. Finally, the ex vivo kinetic of the antiviral effect of desloratadine was evaluated on primary Human Nasal Epithelial Cells (HNEC), showing a significant delay of viral RNA production with a maximal reduction reached after 72 h of treatment. Thus, this treatment could provide a substantial contribution to prophylaxis and systemic therapy of COVID-19 or other coronaviruses infections and requires further studies.

Abstract Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA.