The award will support the Center for Data Science and Artificial Intelligence in developing an AI-powered system to help identify personalized treatment options for children with cancer.

Children with idiopathic short stature respond differently to growth hormone therapy, making treatment outcomes difficult to predict. Researchers from South Korea developed a mathematical model to better capture these individual differences in growth response over time. By analyzing treatment and patient characteristics, the study offers new insights into why some children may experience greater catch-up growth than others—and points toward a more personalized approach to predicting outcomes before therapy begins.

Fungal bloodstream infections can be life-threatening, and accurately identifying the causative fungal species is important for clinicians to administer appropriate treatment. In a recent study, researchers from Japan developed an innovative workflow that uses various genomic and sequence-analysis techniques to identify fungal species in blood samples much faster than conventional methods. Their approach could help patients receive the most effective treatment for their specific fungal infection as soon as possible.