We’re excited to share that a graduate student within the Center, Anne Gardella, has been featured in Discover Magazine for her recent publication in the Proceedings of the National Academy of Sciences on cell-free RNA biomarkers for ME/CFS. Gardella’s work developed machine-learning models to identify molecular fingerprints in blood samples that achieved 77% accuracy in detecting ME/CFS, representing what she calls “a promising start to a non-invasive test” for this complex condition affecting over 3 million Americans. While acknowledging that clinical diagnostic tools typically require above 90% accuracy, Gardella remains optimistic about the potential for this approach to contribute to both reliable diagnosis and deeper understanding of ME/CFS, stating, “we hope this work not only contributes to a reliable diagnostic tool and a deeper understanding of ME/CFS but also continues to bring understanding of the biological problems that result in the lived experiences of these patients.”