Extensive Nonsegmental Pulmonary Perfusion Defects on SPECT/CT as an Early Sign of COVID-19 Infection.

We describe a hospitalized patient with confirmed coronavirus disease 2019 in whom the initial chest computed tomography (CT) was negative, while subsequent perfusion single-photon emission computed tomography/computed tomography imaging revealed extensive nonsegmental perfusion defects in addition to newly developing parenchymal densities. Possible reasons for these findings and their relationship to the multisystem severe acute respiratory syndrome coronavirus 2 infection are discussed in this article.

Reverse-Bayes methods for evidence assessment and research synthesis

It is now widely accepted that the standard inferential toolkit used by the scientific research community-null-hypothesis significance testing (NHST)-is not fit for purpose. Yet despite the threat posed to the scientific enterprise, there is no agreement concerning alternative approaches for evidence assessment. This lack of consensus reflects long-standing issues concerning Bayesian methods, the principal alternative to NHST. We report on recent work that builds on an approach to inference put forward over 70 years ago to address the well-known "Problem of Priors" in Bayesian analysis, by reversing the conventional prior-likelihood-posterior ("forward") use of Bayes' theorem. Such Reverse-Bayes analysis allows priors to be deduced from the likelihood by requiring that the posterior achieve a specified level of credibility. We summarise the technical underpinning of this approach, and show how it opens up new approaches to common inferential challenges, such as assessing the credibility of scientific findings, setting them in appropriate context, estimating the probability of successful replications, and extracting more insight from NHST while reducing the risk of misinterpretation. We argue that Reverse-Bayes methods have a key role to play in making Bayesian methods more accessible and attractive for evidence assessment and research synthesis. As a running example we consider a recently published meta-analysis from several randomised controlled trials (RCTs) investigating the association between corticosteroids and mortality in hospitalised patients with COVID-19. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Reverse-Bayes methods for evidence assessment and research synthesis.

It is now widely accepted that the standard inferential toolkit used by the scientific research community-null-hypothesis significance testing (NHST)-is not fit for purpose. Yet despite the threat posed to the scientific enterprise, there is no agreement concerning alternative approaches for evidence assessment. This lack of consensus reflects long-standing issues concerning Bayesian methods, the principal alternative to NHST. We report on recent work that builds on an approach to inference put forward over 70 years ago to address the well-known "Problem of Priors" in Bayesian analysis, by reversing the conventional prior-likelihood-posterior ("forward") use of Bayes' theorem. Such Reverse-Bayes analysis allows priors to be deduced from the likelihood by requiring that the posterior achieve a specified level of credibility. We summarise the technical underpinning of this approach, and show how it opens up new approaches to common inferential challenges, such as assessing the credibility of scientific findings, setting them in appropriate context, estimating the probability of successful replications, and extracting more insight from NHST while reducing the risk of misinterpretation. We argue that Reverse-Bayes methods have a key role to play in making Bayesian methods more accessible and attractive for evidence assessment and research synthesis. As a running example we consider a recently published meta-analysis from several randomised controlled trials (RCTs) investigating the association between corticosteroids and mortality in hospitalised patients with COVID-19.

Bioeconomy and green recovery in a post-COVID-19 era.

The spread of the COVID-19 pandemic has generated a health crisis and repetitive lockdowns that disrupted different economic and societal segments. As the world has placed hope on the vaccination progress to bring back the socio-economic "normal," this article explores how the bioeconomy can enhance the resilience and sustainability of bio-based, food, and energy systems in the post-COVID-19 era. The proposed recovery approach integrates technological innovations, environment, ecosystem services, "biocities," food, rural economies, and tourism. The importance of integrating culture, arts, and the fashion industry as part of the recovery is underlined towards building a better bioeconomy that, together with environmental safeguards, promotes socio-cultural and economic innovations. This integration could be achieved supporting communities and stakeholders to diversify their activities by combining sustainable production with decarbonization, stimulating private investments in this direction and monitoring the resulting impact of mitigation measures. Food systems should become more resilient in order to allow adapting rapidly to severe crises and future shocks, while it is important to increase circularity towards the valorization of waste, the integration of different processes within the biorefinery concept and the production of bio-based products and biofuels.

Incidental PET/CT Findings of Suspected COVID-19 in a Region of High Prevalence.

We describe a case of suspected COVID-19 pneumonia in a 61-year-old male with known primary central nervous system diffuse large B-cell lymphoma (DLBCL) who underwent restaging PET/CT during the initial peak of infection of COVID-19 pneumonia within the New York region. At the time of his routine PET-CT to assess for disease progression, typical CT imaging features of COVID-19 pneumonia were identified. Upon further investigation, the patient was asymptomatic, and his infection status remained unknown. He was subsequently lost to follow-up with his COVID-19 status pending.