Potential impact of individual exposure histories to endemic human coronaviruses on age-dependent severity of COVID-19.

BACKGROUND: Cross-reactivity to SARS-CoV-2 from exposure to endemic human coronaviruses (eHCoV) is gaining increasing attention as a possible driver of both protection against infection and COVID-19 severity. Here we explore the potential role of cross-reactivity induced by eHCoVs on age-specific COVID-19 severity in a mathematical model of eHCoV and SARS-CoV-2 transmission. METHODS: We use an individual-based model, calibrated to prior knowledge of eHCoV dynamics, to fully track individual histories of exposure to eHCoVs. We also model the emergent dynamics of SARS-CoV-2 and the risk of hospitalisation upon infection. RESULTS: We hypothesise that primary exposure with any eHCoV confers temporary cross-protection against severe SARS-CoV-2 infection, while life-long re-exposure to the same eHCoV diminishes cross-protection, and increases the potential for disease severity. We show numerically that our proposed mechanism can explain age patterns of COVID-19 hospitalisation in EU/EEA countries and the UK. We further show that some of the observed variation in health care capacity and testing efforts is compatible with country-specific differences in hospitalisation rates under this model. CONCLUSIONS: This study provides a "proof of possibility" for certain biological and epidemiological mechanisms that could potentially drive COVID-19-related variation across age groups. Our findings call for further research on the role of cross-reactivity to eHCoVs and highlight data interpretation challenges arising from health care capacity and SARS-CoV-2 testing.

The Need for Research-Grade Systems Modeling Technologies for Life Science Education.

The coronavirus disease 2019 (COVID-19) pandemic not only challenged deeply-rooted daily patterns but also put a spotlight on the role of computational modeling in science and society. Amid the impromptu upheaval of in-person education across the world, this article aims to articulate the need to train students in computational and systems biology using research-grade technologies.

Developing new models of care at speed: learning from healthcare redesign for children with COVID-related multisystem inflammation.

This article describes the rapid, system-wide reconfiguration of local and network services in response to the newly described paediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 (PIMS-TS) (also known as multisystem inflammatory syndrome in children). Developing the model of care for this novel disease, whose natural history, characteristics and treatment options were still unclear, presented distinct challenges.We analyse this redesign through the lens of healthcare management science, and outline transferable principles which may be of specific and urgent relevance for paediatricians yet to experience the full impact of the COVID-19 pandemic; and more generally, for those developing a new clinical service or healthcare operating model to manage the sudden emergence of any unanticipated clinical entity. Health service leaders in areas where COVID-19 is, or will soon be, in the ascendancy, and who are anticipating the imminent influx of PIMS-TS, should use these principles and recommendations to plan an agile, responsive and system-wide model of care for these children.

Generation of Complete Multi-Cell Type Lung Organoids From Human Embryonic and Patient-Specific Induced Pluripotent Stem Cells for Infectious Disease Modeling and Therapeutics Validation.

The normal development of the pulmonary system is critical to transitioning from placental-dependent fetal life to alveolar-dependent newborn life. Human lung development and disease have been difficult to study due to the lack of an in vitro model system containing cells from the large airways and distal alveolus. This article describes a system that allows human embryonic stem cells (hESCs) and induced pluripotent stem cells (hiPSCs) to differentiate and form three-dimensional (3D) structures that emulate the development, cytoarchitecture, and function of the lung ("organoids"), containing epithelial and mesenchymal cell populations, and including the production of surfactant and presence of ciliated cells. The organoids can also be invested with mesoderm derivatives, differentiated from the same human pluripotent stem cells, such as alveolar macrophages and vasculature. Such lung organoids may be used to study the impact of environmental modifiers and perturbagens (toxins, microbial or viral pathogens, alterations in microbiome) or the efficacy and safety of drugs, biologics, and gene transfer. © 2020 Wiley Periodicals LLC. Basic Protocol: hESC/hiPSC dissection, definitive endoderm formation, and lung progenitor cell induction.

Virtual Imaging Trials for Coronavirus Disease (COVID-19).

OBJECTIVE. The virtual imaging trial is a unique framework that can greatly facilitate the assessment and optimization of imaging methods by emulating the imaging experiment using representative computational models of patients and validated imaging simulators. The purpose of this study was to show how virtual imaging trials can be adapted for imaging studies of coronavirus disease (COVID-19), enabling effective assessment and optimization of CT and radiography acquisitions and analysis tools for reliable imaging and management of COVID-19. MATERIALS AND METHODS. We developed the first computational models of patients with COVID-19 and as a proof of principle showed how they can be combined with imaging simulators for COVID-19 imaging studies. For the body habitus of the models, we used the 4D extended cardiac-torso (XCAT) model that was developed at Duke University. The morphologic features of COVID-19 abnormalities were segmented from 20 CT images of patients who had been confirmed to have COVID-19 and incorporated into XCAT models. Within a given disease area, the texture and material of the lung parenchyma in the XCAT were modified to match the properties observed in the clinical images. To show the utility, three developed COVID-19 computational phantoms were virtually imaged using a scanner-specific CT and radiography simulator. RESULTS. Subjectively, the simulated abnormalities were realistic in terms of shape and texture. Results showed that the contrast-to-noise ratios in the abnormal regions were 1.6, 3.0, and 3.6 for 5-, 25-, and 50-mAs images, respectively. CONCLUSION. The developed toolsets in this study provide the foundation for use of virtual imaging trials in effective assessment and optimization of CT and radiography acquisitions and analysis tools to help manage the COVID-19 pandemic.

"Virtual" Boot Camp: Orthopaedic Intern Education in the Time of COVID-19 and Beyond.

The emergence of COVID-19 as a viral pandemic in early 2020 resulted in notable changes to the daily practice, workflow, and education of orthopaedic residencies internationally. In particular, social distancing, residency restructuring, and redeployment to other services has increased heterogeneity in schedules and made the in-person gathering of trainees for education increasingly challenging. These changes may last until 2024 based on some mathematical models, resulting in notable disruptions to orthopaedic education, especially for junior residents. Therefore, in this study, we describe how we converted our in-person PGY-1 skills course into a "virtual" boot camp based on validated training modules and existing American Board of Orthopaedic Surgeons guidelines. Lessons learned from the experience and potential areas for improvement in the use of newer technology to teach cognitive knowledge and skills modules are highlighted with the hope that this can be useful to other orthopaedic residency programs, during the pandemic and also beyond.

Towards Contactless Patient Positioning.

The ongoing COVID-19 pandemic, caused by the highly contagious SARS-CoV-2 virus, has overwhelmed healthcare systems worldwide, putting medical professionals at a high risk of getting infected themselves due to a global shortage of personal protective equipment. This has in-turn led to understaffed hospitals unable to handle new patient influx. To help alleviate these problems, we design and develop a contactless patient positioning system that can enable scanning patients in a completely remote and contactless fashion. Our key design objective is to reduce the physical contact time with a patient as much as possible, which we achieve with our contactless workflow. Our system comprises automated calibration, positioning, and multi-view synthesis components that enable patient scan without physical proximity. Our calibration routine ensures system calibration at all times and can be executed without any manual intervention. Our patient positioning routine comprises a novel robust dynamic fusion (RDF) algorithm for accurate 3D patient body modeling. With its multi-modal inference capability, RDF can be trained once and used across different applications (without re-training) having various sensor choices, a key feature to enable system deployment at scale. Our multi-view synthesizer ensures multi-view positioning visualization for the technician to verify positioning accuracy prior to initiating the patient scan. We conduct extensive experiments with publicly available and proprietary datasets to demonstrate efficacy. Our system has already been used, and had a positive impact on, hospitals and technicians on the front lines of the COVID-19 pandemic, and we expect to see its use increase substantially globally.