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Introduction: Coronavirus disease-2019 (COVID-19) outbreak has spread around the world. However, the dynamic course of critically ill COVID-19 has not been described thoroughly. Patients and Methods: We retrospectively analyzed 195 critically ill COVID-19 patients in Hubei province, China, between January 5, 2020 and April 3, 2020. Epidemiologic data, clinical features, treatments, and outcomes were collected and analyzed. Results: Most critically ill patients were older with higher Acute Physiology and Chronic Health Evaluation II scores. After critical illness onset, a total of 181 (92.8%) patients received ventilation support, of which 84 (43.1%) received noninvasive and 97 (49.7%) received invasive mechanic ventilation (IMV). Among the 97 patients with IMV, 28 (28.9%) received prone ventilation, 57 (58.8%) received neuromuscular blocked therapy, and 22 (11.3%) received tracheostomy due to prolonged ventilator use. Early hypoxemia, subsequent hypercapnia, pulmonary hypertension, and finally pulmonary fibrosis were notable in the clinical course of acute respiratory distress syndrome (ARDS). Eighty-nine (45.6%) patients presented with shock. Acute kidney injury (29.7%) and secondary infection (28.2%) were also notable. The overall mortality of critically ill patients at day 28 was 42.1%. Intensive care unit (ICU) mortality was around 33%, as 16 patients died prior to ICU admission. A low PaO2/FiO2 ratio was an independent risk factor for death. High viral load was observed in most non-survivors. Conclusion: ARDS and shock were notable in the critical illness of COVID-19. Ventilation support and hemodynamic support were the cornerstones for critical care. High viral load was associated with death of critically ill COVID-19 patients.

BackgroundThe pandemic of Coronavirus Disease 2019 (COVID-19) brings new challenges for pediatricians, especially in the differentiation with non-COVID-19 pneumonia in the peak season of pneumonia. We aimed to compare the clinical characteristics of pediatric patients with COVID-19 and other respiratory pathogens infected pneumonias.MethodsWe conducted a multi-center, cross-sectional study of pediatric inpatients in China. Based on pathogenic test results, pediatric patients were divided into three groups, including COVID-19 pneumonia group, Non-COVID-19 viral (NCV) pneumonia group and Non-viral (NV) pneumonia group. Their clinical characteristics were compared by Kruskal-Wallis H test or chi-square test.ResultsA total of 636 pediatric pneumonia inpatients, among which 87 in COVID-19 group, 194 in NCV group, and 355 in NV group, were included in analysis. Compared with NCV and NV patients, COVID-19 patients were older (median age 6.33, IQR 2.00-12.00 years), and relatively fewer COVID-19 patients presented fever (63.2%), cough (60.9%), shortness of breath (1.1%), and abnormal pulmonary auscultation (18.4%). The results were verified by the comparison of COVID-19, respiratory syncytial virus (RSV) and influenza A (IFA) pneumonia patients. Approximately 42.5%, 44.8%, and 12.6% of the COVID-19 patients presented simply ground-glass opacity (GGO), simply consolidation, and the both changes on computed tomography (CT) scans, respectively; the proportions were similar as those in NCV and NV group (p>0.05). Only 47.1% of COVID-19 patients had both lungs pneumonia, which was significantly lower than that proportion of nearly 80% in the other two groups. COVID-19 patients presented lower proportions of increased white blood cell count (16.5%) and abnormal procalcitonin (PCT) (10.7%), and a higher proportion of decreased lymphocyte count (44.0%) compared with the other two groups.ConclusionMajority clinical characteristics of pediatric COVID-19 pneumonia patients were milder than non-COVID-19 patients. However, lymphocytopenia remained a prominent feature of COVID-19 pediatric pneumonia.

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BLASTZ is a sequence alignment tool designed mainly for aligning neutrally evolved bio-sequences and has been the choice for aligning noncoding sequences. However, its running time is impractical for high throughput alignment of long sequences, for example, for the alignment of human and mouse genomes. In order to improve the performance and efficiency for alignment at genome scale, BLASTZ was implemented using the GLOBUS toolkit on a computing grid. A dynamic load balancing technique was introduced to achieve enhanced performance on a grid which consists of sources of heterogeneous characteristics, such as resources of different computational powers. The robustness of the implementation to disturbances due to other processes on the grid is demonstrated.