In silico drug repurposing for the identification of potential candidate molecules against arboviruses infection.

Arboviral diseases caused by dengue (DENV), Zika (ZIKV) and chikungunya (CHIKV) viruses represent a major public health problem worldwide, especially in tropical areas where millions of infections occur every year. The aim of this research was to identify candidate molecules for the treatment of these diseases among the drugs currently available in the market, through in silico screening and subsequent in vitro evaluation with cell culture models of DENV and ZIKV infections. Numerous pharmaceutical compounds from antibiotics to chemotherapeutic agents presented high in silico binding affinity for the viral proteins, including ergotamine, antrafenine, natamycin, pranlukast, nilotinib, itraconazole, conivaptan and novobiocin.

ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU.

To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes. However, they have been shown by several studies to offer sub-optimal performance.

Features of Dengue and Chikungunya Infections of Colombian Children under 24 Months of Age Admitted to the Emergency Department.

We aimed to assess clinical and laboratory differences between dengue and chikungunya in children <24 months of age in a comparative study. We collected retrospective clinical and laboratory data confirmed by NS1/IgM for dengue for 19 months (1 January 2013 to 17 August 2014). Prospective data for chikungunya confirmed by real-time polymerase chain reaction were collected for 4 months (22 September 2014-14 December 2014).