Publication: A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis

Summary: Sepsis, a dysfunctional immune response to infection that results in multi-organ failure, is a leading cause of death, with mortality rates that skyrocket alongside delay in diagnosis and treatment. Using machine learning to analyze blood samples from suspected sepsis patients, the authors identified a six-gene expression signature that reflects the immune cell reprogramming characteristic of the disease, providing a biomarker of early-stage sepsis. The team also developed a portable blood testing device for this "Sepset" signature, enabling faster detection of sepsis at the bedside.