ABSTRACT
Children with SARS-CoV-2 infection have been consistently described with milder clinical outcomes compared to adults. However, data pertaining to the clinical evolution of SARS-CoV-2 infection in children with cancer remain scarce. In this descriptive cohort study, we report the clinical characteristics and outcomes of 31 pediatric oncology patients with SARS-CoV-2 infection in the province of Quebec, Canada. Most patients were asymptomatic or had mild symptoms, with only 2 COVID-19-related hospitalizations and no COVID-19-related deaths. The favorable outcomes in our cohort may be explained by Quebec’s universal access to health care and regionalization of pediatric oncology care in tertiary centers exclusively.
Subject(s)
COVID-19 , NeoplasmsABSTRACT
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.
Subject(s)
Coronavirus Infections , COVID-19ABSTRACT
Background: COronaVirus Disease 2019 (COVID-19) can be challenging to diagnose, because symptoms are non-specific, clinical presentations are heterogeneous, and false negative tests can occur. Our objective was to assess the utility of lymphocyte count to differentiate COVID-19 from influenza or community-acquired pneumonia (CAP). Methods: We conducted a cohort study of adults hospitalized with COVID-19 or another respiratory infection (i.e., influenza, CAP) at seven hospitals in Ontario, Canada.The first available lymphocyte count during the hospitalization was used. Standard test characteristics for lymphocyte count (x109/L) were calculated (i.e., sensitivity, specificity, area under the receiver operating curve [AUC]). All analyses were conducting using R. Results: There were 869 hospitalizations for COVID-19, 669 for influenza, and 3009 for CAP. The mean age across the three groups was 67 and patients with pneumonia were older than those with influenza or COVID19, and approximately 46% were woman. The median lymphocyte count was nearly identical for the three groups of patients: 1.0 x109/L (interquartile range [IQR]:0.7,2.0) for COVID-19, 0.9 x109/L (IQR 0.6,1.0) for influenza, and 1.0 x109/L (IQR 0.6,2.0) for CAP. At a lymphocyte threshold of less than 2.0 x109/L, the sensitivity was 87% and the specificity was approximately 10%. As the lymphocyte threshold increased, the sensitivity of diagnosing COVID-19 increased while the specificity decreased. The AUC for lymphocyte count was approximately 50%. Interpretation: Lymphocyte count has poor diagnostic discrimination to differentiate between COVID-19 and other respiratory illnesses. The lymphopenia we consistently observed across the three illnesses in our study may reflect a non-specific sign of illness severity. However, lymphocyte count above 2.0 x109/L may be useful in ruling out COVID-19 (sensitivity = 87%).