
The U.S. National Institutes of Health (NIH) has announced research funding to identify patients at high risk for developing multisystem inflammatory syndrome in children (MIS-C), a condition thought to be a severe complication of COVID-19. The NIH will award up to $20 million to successful research proposals over four years.
MIS-C is a severe and sometimes fatal inflammation of organs and tissues, including the heart, lungs, kidneys, brain, skin, and eyes. The NIH funding seeks to encourage studies of genetic, immune, viral, environmental, and other factors that influence how severe a case of COVID-19 will be and the chances of it turning into MIS-C.
The National Institute of Child Health and Human Development (NICHD) project, Predicting Viral-Associated Inflammatory Disease Severity in Children with Laboratory Diagnostics and Artificial Intelligence (PreVAIL kIds), is part of NIH's Rapid Acceleration of Diagnostics initiative to spur innovation in the development, commercialization, and implementation of technologies for COVID-19 testing.
Studies funded through PreVAIL kIds will evaluate genes and other biomarkers in COVID-19 pediatric cases, as well as determine how the virus interacts with its host and the immune system response. Researchers will rely on artificial intelligence (AI) and machine learning to sort and categorize data they acquire.
















![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)



