Combination of deep learning reconstruction and quantification for dynamic contrast-enhanced (DCE) MRI.

Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity that can lead to improved characterization of tumor extent and heterogeneity, and for early assessment of treatment response. However, clinical adoption of quantitative DCE-MRI remains limited due to challenges in acquisition and quantification performance, and lack of automated tools. This study presents an end-to-end deep learning pipeline that exploits a novel deep reconstruction network called DCE-Movienet with a previously developed deep quantification network called DCE-Qnet for fast and quantitative DCE-MRI.

Transient anti-interferon autoantibodies in the airways are associated with recovery from COVID-19.

Preexisting anti-interferon-α (anti-IFN-α) autoantibodies in blood are associated with susceptibility to life-threatening COVID-19. However, it is unclear whether anti-IFN-α autoantibodies in the airways, the initial site of infection, can also determine disease outcomes. In this study, we developed a multiparameter technology, FlowBEAT, to quantify and profile the isotypes of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and anti-IFN-α antibodies in longitudinal samples collected over 20 months from the airways and blood of 129 donors spanning mild to severe COVID-19.

Mapping vulnerability to climate-related hazards to inform local authority action in adaptation: A feasibility study.

Background: Local authorities have a crucial role in building community resilience to the health effects of a changing climate. Support in achieving local action can be provided through improving available public health intelligence to inform decision making. We aimed to co-develop with a local authority a tool mapping vulnerability to climate related hazards.