Viewing posts tagged Covid-19

COVID-19: A lesson in community working for the public good

In a new longform story, the Morgride Institute of Research scientists and researchers reflect on what collectively happened in late 2019, as the novel coronavirus began spreading and along with it deep uncertainty and unprecedented challenges.

This is a science and also a story about how people and communities came together to work for the public good. It is about the lessons learned and those that still remain. It features the experiences of researchers Tim Grant, Josh Coon, Tony Gitter, Melissa Skala, and Paul Alhquist, and many others.

Read about it here: Resilience: How COVID-19 challenged the scientific world

Data suggest a unique inflammatory signature associated with severe COVID19

The COVID19 pandemic will cause more than a million of deaths worldwide, primarily due to complications from acute respiratory distress syndrome (ARDS). Controversy surrounds the current cytokine/chemokine profile of COVID19-associated ARDS, with some groups suggesting that it is similar to non-COVID19 ARDS patients and others observing substantial differences. Balnis et. al. conducted a study of 41 mechanically ventilated patients with COVID19 infection using highly calibrated methods to define the levels of plasma cytokines/chemokines. Plasma IL1RA and IL8 were found positively associated with mortality, while RANTES and EGF negatively associated with that outcome. However, the leukocyte gene expression of these proteins had no significant correlation with mortality. Their data suggest a unique inflammatory signature associated with severe COVID19.

Read the article: Unique inflammatory profile is associated with higher SARS-CoV-2 acute respiratory distress syndrome (ARDS) mortality

Multi-Omics of COVID-19 Collaboration with Albany Medical College

A collaboration with the lab of Dr. Ariel Jaitovich at Albany Medical College studied a Large-scale Multi-omic Analysis of COVID-19 Severity (in preprint). Over 17,000 transcripts, proteins, metabolites, and lipids were quantified and associated with clinical outcomes in a curated relational database, uniquely enabling systems analysis and cross-ome correlations to molecules and patient prognoses. A web-based tool ( enables interactive exploration and illustrates its utility through a comparative analysis with published data and a machine learning prediction of COVID-19 severity.