Liver Transplantation in the Time of COVID19: Barriers and Ethical Considerations for Management and Next Steps.

The recent outbreak of the novel virus severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes the corona virus disease of 2019 (COVID19), has spread globally and affects millions of people. This pandemic has taxed our health care system and disrupted normal operations, even life-saving procedures, such as liver transplants. During these unprecedented times, providers and patients are imperiled and resources for diagnosis and care may be limited. Continuing to perform resource-intense advanced procedures is challenging, as is caring for patients with end-stage liver disease or patients with urgent needs for liver tumor control. Liver transplantation, in particular, requires critical resources, like blood products and critical care beds, which are fairly limited in the COVID19 pandemic. The potential of COVID19 infections in posttransplant recipients on immunosuppression and staff contacts further adds to the complexity. Therefore, transplant programs must reevaluate the ethicality, feasibility, and safety of performing liver transplants during this pandemic. Herein, we discuss the clinical and ethical challenges posed by performing liver transplants and offer guidance for managing patients with end-stage liver disease during the COVID19 pandemic.

Detection of differentially abundant cell subpopulations in scRNA-seq data.

Comprehensive and accurate comparisons of transcriptomic distributions of cells from samples taken from two different biological states, such as healthy versus diseased individuals, are an emerging challenge in single-cell RNA sequencing (scRNA-seq) analysis. Current methods for detecting differentially abundant (DA) subpopulations between samples rely heavily on initial clustering of all cells in both samples. Often, this clustering step is inadequate since the DA subpopulations may not align with a clear cluster structure, and important differences between the two biological states can be missed. Here, we introduce DA-seq, a targeted approach for identifying DA subpopulations not restricted to clusters. DA-seq is a multiscale method that quantifies a local DA measure for each cell, which is computed from its k nearest neighboring cells across a range of k values. Based on this measure, DA-seq delineates contiguous significant DA subpopulations in the transcriptomic space. We apply DA-seq to several scRNA-seq datasets and highlight its improved ability to detect differences between distinct phenotypes in severe versus mildly ill COVID-19 patients, melanomas subjected to immune checkpoint therapy comparing responders to nonresponders, embryonic development at two time points, and young versus aging brain tissue. DA-seq enabled us to detect differences between these phenotypes. Importantly, we find that DA-seq not only recovers the DA cell types as discovered in the original studies but also reveals additional DA subpopulations that were not described before. Analysis of these subpopulations yields biological insights that would otherwise be undetected using conventional computational approaches.

Impact of healthcare worker shift scheduling on workforce preservation during the COVID-19 pandemic.

Reducing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) infections among healthcare workers is critical. We ran Monte Carlo simulations modeling the spread of SARS-CoV-2 in non-COVID-19 wards, and we found that longer nursing shifts and scheduling designs in which teams of nurses and doctors co-rotate no more frequently than every 3 days can lead to fewer infections.