RNA is expelled from cells via cell death or active release, and can then find its way into blood plasma. A Cornell University-led collaboration has developed machine learning models that use these cell-free molecular RNA dregs to diagnose pediatric inflammatory conditions that are difficult to differentiate. The diagnostic tool can accurately determine if a patient has Kawasaki disease (KD), Multisystem Inflammatory Syndrome in Children (MIS-C), a viral infection or a bacterial infection, while simultaneously monitoring the patient's organ health.
Inflammatory diseases are a particular threat to children because the symptoms – such as fever and rash – are generic, and the patients often get misdiagnosed. If not properly treated, MIS-C can cause swelling in the heart, lungs, brain and other organs. Similarly, KD – the primary cause of acquired heart disease in children – can lead to cardiac aneurysms and heart attack.
A cell-free RNA-based test would be the first molecular diagnostic tool that clinicians can use to catch these inflammatory conditions at the crucial early stage in children. The team's paper published in Proceedings of the National Academy of Sciences . The Cornell team was led by Iwijn De Vlaminck, associate professor of biomedical engineering and co-senior author of the paper.
The lead author is Conor Loy, currently an Ignite Fellow for New Ventures. The findings stem from a previous collaboration that began four years ago and used next-generation seq.