Prolonged activation of nasal immune cell populations and development of tissue-resident SARS-CoV-2-specific CD8(+) T cell responses following COVID-19

Systemic immune cell dynamics during coronavirus disease 2019 (COVID-19) are extensively documented, but these are less well studied in the (upper) respiratory tract, where severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replicates(1-6). Here, we characterized nasal and systemic immune cells in individuals with COVID-19 who were hospitalized or convalescent and compared the immune cells to those seen in healthy donors. We observed increased nasal granulocytes, monocytes, CD11c(+) natural killer (NK) cells and CD4(+) T effector cells during acute COVID-19. The mucosal proinflammatory populations positively associated with peripheral blood human leukocyte antigen (HLA)-DRlow monocytes, CD38(+)PD1(+)CD4(+) T effector (T-eff) cells and plasmablasts. However, there was no general lymphopenia in nasal mucosa, unlike in peripheral blood. Moreover, nasal neutrophils negatively associated with oxygen saturation levels in blood. Following convalescence, nasal immune cells mostly normalized, except for CD127(+) granulocytes and CD38(+)CD8(+) tissue-resident memory T cells (T-RM). SARS-CoV-2-specific CD8(+) T cells persisted at least 2 months after viral clearance in the nasal mucosa, indicating that COVID-19 has both transient and long-term effects on upper respiratory tract immune responses.Perioperative Medicine: Efficacy, Safety and Outcome (Anesthesiology/Intensive Care

Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic

Background The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data 'silos' that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR. Results In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors' research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital. Conclusions Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery

Applying the FAIR principles to data in a hospital: challenges and opportunities in a pandemic

Background The COVID-19 pandemic has challenged healthcare systems and research worldwide. Data is collected all over the world and needs to be integrated and made available to other researchers quickly. However, the various heterogeneous information systems that are used in hospitals can result in fragmentation of health data over multiple data 'silos' that are not interoperable for analysis. Consequently, clinical observations in hospitalised patients are not prepared to be reused efficiently and timely. There is a need to adapt the research data management in hospitals to make COVID-19 observational patient data machine actionable, i.e. more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. We therefore applied the FAIR principles in the hospital to make patient data more FAIR. Results In this paper, we present our FAIR approach to transform COVID-19 observational patient data collected in the hospital into machine actionable digital objects to answer medical doctors' research questions. With this objective, we conducted a coordinated FAIRification among stakeholders based on ontological models for data and metadata, and a FAIR based architecture that complements the existing data management. We applied FAIR Data Points for metadata exposure, turning investigational parameters into a FAIR dataset. We demonstrated that this dataset is machine actionable by means of three different computational activities: federated query of patient data along open existing knowledge sources across the world through the Semantic Web, implementing Web APIs for data query interoperability, and building applications on top of these FAIR patient data for FAIR data analytics in the hospital. Conclusions Our work demonstrates that a FAIR research data management plan based on ontological models for data and metadata, open Science, Semantic Web technologies, and FAIR Data Points is providing data infrastructure in the hospital for machine actionable FAIR Digital Objects. This FAIR data is prepared to be reused for federated analysis, linkable to other FAIR data such as Linked Open Data, and reusable to develop software applications on top of them for hypothesis generation and knowledge discovery.Molecular Technology and Informatics for Personalised Medicine and Healt