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https://www.selleckchem.com/products/GSK1059615.html and young surgeons to perform the puncture operation with increased accuracy. Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome. We develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A Firoozgar Hospital, Iran, 105 patients; site B Massachusetts General Hospital, USA, 88 patients). AI-based severity scores are strongly associated with those evaluated by radiologists (Spearman's rank correlation 0.837, [Formula see text]). Using AI-nd the current pandemic. Proteinuria has been commonly reported in patients with COVID-19. However, onlydipstick tests have been frequently used thus far. Here, the quantification and characterization of proteinuria were investigated and their association with mortality was assessed. This retrospective, observational, single center study included 153 patients, hospitalized with COVID-19 between March 28th and April 30th, 2020, in whom total proteinuria and urinaryα -microglobulin (a marker of tubular injury) were measured. Association with mortality was evaluated, with a follow-up until May 7th, 2020. According to the Kidney Disease Improving Global Outcomes staging, 14% (n = 21) of the patientshad category 1 proteinuria (< 150mg/g of urine creatinine), 42% (n = 64) had category 2 (between 150 and 500mg/g) and 44% (n = 68) had category 3 p

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