The National Institutes of Health has awarded eight research grants to refine new technologies for early diagnosis of severe illnesses resulting from SARS-CoV-2 infection in children. The new awards follow grants issued in 2020 to foster methods for diagnosing children at high risk for Multisystem Inflammatory Syndrome in Children (MIS-C), a rare, severe and sometimes fatal after-effect of SARS-CoV-2 infection or exposure in children.
“These highly innovative technologies and tools have the potential to greatly improve the care of children with SARS-CoV-2 infection and other fever-causing illnesses,” said Bill Kapogiannis, MD, of NIH’s Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), which oversees the grants.
The awards are from NIH’s Predicting Viral-Associated Inflammatory Disease Severity in Children with Laboratory Diagnostics and Artificial Intelligence (PreVAIL kIds) initiative. They are part of the Rapid Acceleration of Diagnostics Radical (RADx-rad) program to support new, non-traditional approaches and reimagined uses of existing tools to address gaps in COVID-19 testing and surveillance.
Although some children develop mild or no symptoms from COVID-19, others will develop more severe effects, including MIS-C, which results in inflammation of one or more organs, including the heart, lungs, kidneys, brain, skin, eyes and gastrointestinal tract.
The 2020 awards supported studies involving more than 7,400 research participants in four countries and yielded prototype methods and techniques for potential use in clinics, emergency departments and for hospital inpatients. These PreVAIL kIds studies were supported through NIH’s RADx-rad initiative and were part of an NIH collaborative research effort called CARING for Children with COVID. Results from these studies include a laboratory technique for detecting specific immune cells associated with MIS-C; databases that help diagnose children at risk for MIS-C and severe COVID-19, based on certain blood proteins and genetic biomarkers; and a database that can distinguish between MIS-C, Kawasaki disease (which has similar symptoms) and fever-causing viral and bacterial infections.
The new awards will allow researchers to continue their efforts to develop ways to rapidly diagnose MIS-C and identify those at risk for serious and long-term effects of SARS-CoV-2. Earlier identification of those most at risk will allow for earlier interventions to prevent severe health effects.
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
Data science approach to MIS-C identification and management associated with SARS
Ananth V. Annapragada, Baylor College of Medicine, Houston
AICORE-kids: 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 multineural 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
Source: National Institutes of Health (NIH)