Exploring the Role of Innate Lymphocytes in the Immune System of Bats and Virus-Host Interactions.

Bats are reservoirs of a large number of viruses of global public health significance, including the ancestral virus for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the causative agent of coronavirus disease 2019 (COVID-19). Although bats are natural carriers of multiple pathogenic viruses, they rarely display signs of disease. Recent insights suggest that bats have a more balanced host defense and tolerance system to viral infections that may be linked to the evolutionary adaptation to powered flight. Therefore, a deeper understanding of bat immune system may provide intervention strategies to prevent zoonotic disease transmission and to identify new therapeutic targets. Similar to other eutherian mammals, bats have both innate and adaptive immune systems that have evolved to detect and respond to invading pathogens. Bridging these two systems are innate lymphocytes, which are highly abundant within circulation and barrier tissues. These cells share the characteristics of both innate and adaptive immune cells and are poised to mount rapid effector responses. They are ideally suited as the first line of defense against early stages of viral infections. Here, we will focus on the current knowledge of innate lymphocytes in bats, their function, and their potential role in host-pathogen interactions. Moreover, given that studies into bat immune systems are often hindered by a lack of bat-specific research tools, we will discuss strategies that may aid future research in bat immunity, including the potential use of organoid models to delineate the interplay between innate lymphocytes, bat viruses, and host tolerance.

Erratum: A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics.

[This corrects the article DOI: 10.1016/j.patter.2020.100092.].

A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics.

The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics.