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Ehre, a member of the UNC Marsico Lung Institute and the UNC Children's Research Institute, captured these images to illustrate how intense the SARS-CoV-2 infection of the airways can be in very graphic and easily understood images. Her lab conducted this research in collaboration with the labs of Ralph Baric, Ph.D., the William R. Kenan Distinguished Professor of Epidemiology at the UNC Gillings School of Public Health, who holds a joint faculty appointment at the UNC Department of Microbiology and Immunology, and Richard Boucher, MD, the James C. Moeser Eminent Distinguished Professor of Medicine and Director of the Marsico Lung Institute at the UNC School of Medicine Covid-19

"There is a system of checks and balances that make sure the craziest of us are put back in line. That's true in human societies, in ecosystems, and inside organisms."

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.

In December 2019, the Coronavirus disease 2019 (COVID-19), caused by a novel coronavirus SARS-CoV-2, emerged in Wuhan, Hubei province, China1 and soon spread across the world. In this ongoing pandemic, public health concerns and the urgent need for effective therapeutic measures require a deep understanding of its epidemiology, transmissibility and pathogenesis. Here we analyzed the clinical, molecular and immunological data from 326 confirmed cases of COVID-19 in Shanghai. Genomic sequences of SARS-CoV-2 assembled from 112 quality samples together with sequences in the Global Initiative on Sharing All Influenza Data (GISAID) showed a stable evolution and suggested two major lineages with differential exposure history during the early phase of the outbreak in Wuhan. Nevertheless, they exhibited similar virulence and clinical outcomes. Lymphocytopenia, especially the reduced CD4+ and CD8+ T cell counts upon admission, was predictive of disease progression. High levels of IL-6 and IL-8 during treatment were observed in patients with severe or critical disease and correlated with decreased lymphocyte count. The determinants of disease severity seemed to stem mostly from host factors such as age, lymphocytopenia, and its associated cytokine storm, whereas viral genetic variation did not significantly affect the outcomes.

Over the past few decades, researchers have gathered evidence suggesting that tensegrity is a key design principle in nature, as it applies to a number of biological systems, including bodies, organs, cells and molecules. Tensegrity structures could thus also prove valuable for the development of bio-inspired robots, as it may enable the creation of systems that closely resemble those observed in living organisms.