A proposal for selective resuscitation of adult cardiac arrest patients in a pandemic

Allocation of limited resources in pandemics begs for ethical guidance. The issue of ventilator allocation in pandemics has been reviewed by many medical ethicists, but as localities activate crisis standards of care, and health care workers are infected from patient exposure, the decision to pursue cardiopulmonary resuscitation (CPR) must also be examined to better balance the increased risks to healthcare personnel with the very low resuscitation rates of patients infected with coronavirus disease 2019 (COVID‐19). A crisis standard of care that is equitable, transparent, and mindful of both human and physical resources will lessen the impact on society in this era of COVID‐19. This paper builds on previous work of ventilator allocation in pandemic crises to propose a literature‐based, justice‐informed ethical framework for selecting treatment options for CPR. The pandemic affects regions differently over time, so these suggested guidelines may require adaptation to local practice variations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156457/3/emp212096_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156457/2/emp212096.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156457/1/emp212096-sup-0001-Appendix.pd

Molecular Interaction Networks to Select Factors for Cell Conversion

The process of identifying sets of transcription factors that can induce a cell conversion can be time-consuming and expensive. To help alleviate this, a number of computational tools have been developed which integrate gene expression data with molecular interaction networks in order to predict these factors. One such approach is Mogrify, an algorithm which ranks transcriptions factors based on their regulatory influence in different cell types and tissues. These ranks are then used to identify a nonredundant set of transcription factors to promote cell conversion between any two cell types/tissues. Here we summarize the important concepts and data sources that were used in the implementation of this approach. Furthermore, we describe how the associated web resource ( www.mogrify.net ) can be used to tailor predictions to specific experimental scenarios, for instance, limiting the set of possible transcription factors and including domain knowledge. Finally, we describe important considerations for the effective selection of reprogramming factors. We envision that such data-driven approaches will become commonplace in the field, rapidly accelerating the progress in stem cell biology.</p