Predicting Neonatal Sepsis

This episode explores a groundbreaking study published inb The Lancet that identified gene expression biomarkers capable of predicting neonatal sepsis before symptoms appear. Conducted in The Gambia, researchers analysed RNA sequencing data from healthy newborns to distinguish those who later developed early- or late-onset sepsis. Using machine learning, they identified a four-gene signature — HSPH1, BORA, NCAPG2, and PRIM1 — that accurately predicted early-onset sepsis at birth. The findings also revealed that early sepsis profoundly alters immune and metabolic development in the first week of life. This research highlights how genomic tools could transform neonatal care, especially in low- and middle-income settings. Research paper: An AY, Acton E, Idoko OT, Shannon CP, Blimkie TM, Falsafi R,Wariri O, Imam A, Dibbasey T, Bennike TB, Smolen KK, Diray Arce J, Ben-Othman R, Montante S, Angelidou A, Odumade OA, Martino D, Tebbutt SJ, Levy O, Steen H, Kollmann TR, Kampmann B, Hancock REW, Lee AH; EPIC Consortium. Predictive gene expression signature diagnoses neonatal sepsis before clinical presentation. EBioMedicine. 2024 Dec;110:105411 Click here to read the article Voiced by NotebookLM Disclaimer