NIH funds 8 new studies on COVID-19 related MIS-C in children

Contemporary PEDS Journal, Vol 37 No 7, Volume 37, Issue 7

The National Institutes of Health award 8 research grants to develop ways to identify children at high risk for multi system inflammatory syndrome I children (MIS-C).

The National Institutes of Health (NIH) has awarded 8 new research grants to help identify children who are at high risk of contracting multisystem inflammatory syndrome in children (MIS-C), a severe result of COVID-19 in some children.

The studies will include how genetic, immune, viral, environmental, and other conditions influence the level of severity of COVID-19, and the chance of it progressing to MIS-C in certain cases. The awards are a part of the Rapid Acceleration of Diagnostics (RADx) Radical (RADx-rad) program that supporrs non-traditional approaches and new uses of existing technologies to address gaps in COVID-19 testing and surveillance.

The studies, which will be using artificial intelligence and machine learning, will be studying genes, immune system proteins, and other biomarkers, examine how the virus interacts with the body and how the immune system responds to it. The awardees and their project names are:

Jane C Burns, University of California, San Diego
Diagnosing and predicting risk in children with SARS-CoV-2-related illness

Cedric Manlhiot, Johns Hopkins University, Baltimore
A data science approach to identify and manage MIS-C associated with SARS-CoV-2 infection and Kawasaki disease in pediatric patients

Ananth V Annapragada, Baylor College of Medicine, Houston
Artificial intelligence COVID-19 risk assessment for kids

Audrey R Odom John, Children's Hospital of Philadelphia
Diagnosis of MIS-C in febrile children

Usha Sethuraman, Central Michigan University, Mount Pleasant
Severity predictors integrating salivary transcriptomics and proteomics with multi neural network intelligence in SARS-CoV2 infection in children

Juan C Salazar, Connecticut Children's Medical Center, Hartford
Identifying biomarker signatures of prognostic value for MIS-C

Charles Yen Chiu, University of California, San Francisco
Discovery and clinical validation of host biomarkers of disease severity and MIS-C with COVID-19

Lawrence Kleinman, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey
COVID-19 Network of networks expanding clincial and translational approaches to predict severe illness in children