Human infection challenge in the pandemic era and beyond, HIC-Vac annual meeting report, 2022

HIC-Vac is an international network of researchers dedicated to developing human infection challenge studies to accelerate vaccine development against pathogens of high global impact. The HIC-Vac Annual Meeting (3rd and 4th November 2022) brought together stakeholders including researchers, ethicists, volunteers, policymakers, industry partners, and funders with a strong representation from low- and middle-income countries. The network enables sharing of research findings, especially in endemic regions. Discussions included pandemic preparedness and the role of human challenge to accelerate vaccine development during outbreak, with industry speakers emphasising the great utility of human challenge in vaccine development. Public consent, engagement, and participation in human challenge studies were addressed, along with the role of embedded social science and empirical studies to uncover social, ethical, and regulatory issues around human infection challenge studies. Study volunteers shared their experiences and motivations for participating in studies. This report summarises completed and ongoing human challenge studies across a variety of pathogens and demographics, and addresses other key issues discussed at the meeting

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials

Integrated Modelling Frameworks for Environmental Assessment and Decision Support

Modern management of environmental resources defines problems from a holistic and integrated perspective, imposing strong requirements to Environmental Decision Support Systems (EDSSs) and Integrated Assessment Tools (IATs), which tend to be increasingly complex in terms of software architecture and computational power in order to cope with the type of problems they must solve. Such systems need to support methodologies and techniques ranging from agent-based modelling to participatory decision-making. Sometimes EDSSs and IATs are built from scratch, often with limited resources, by non-programmers. The disadvantages of this approach, which can quickly become overly expensive in terms of delivery time and resources required, have been addressed by the development of suites of software engineering tools called Environmental Integrated Modelling Frameworks (EIMFs). EIMFs have typically been designed as a response to the increasing complexity of building and delivering EDSSs and IATs. Modelling and simulation tools and frameworks have been adopted at a large scale in the management science and operations research disciplines, and standards for developing and expanding them have been developed. In contrast, no modelling framework has been universally adopted within the environmental modelling domain, and the number of environmental modelling frameworks is still growing. In this book chapter, we strive to address the above issues and clearly identify the essential characteristics of an EIMF. This book chapter also advocates the development of open standards for the exchange and re-use of modelling knowledge, including data sets, models, and procedures in order to facilitate improved communication among the leading EIMF