Plasma Metabolic\nProfiles via p‑p Heterojunction-Assisted\nLaser Desorption/Ionization Mass Spectrometry for Advanced Warning\nand Diagnosis of Epidural-Related Maternal Fever

Epidural-related maternal fever (ERMF) heightens the\nrisk of intrapartum\nfever, whereas effective prevention and treatment in clinical practice\nare currently lacking. Rapid and sensitive screening tools for ERMF\nare urgently needed to advance relevant research. In response to this\nchallenge, we devise and craft porous Co<sub>3</sub>O<sub>4</sub>/CuO\nhollow polyhedral nanocages with p-p heterojunctions derived from\nmetal–organic frameworks. We employ these p-p heterojunctions\nin conjunction with high-throughput mass spectrometry to conduct metabolic\nanalysis of substantial plasma samples, with only about 0.03 μL\nper sample. Leveraging these p-p heterojunctions, metabolic signals\nfrom complex plasma can be amplified, with great reproducibility.\nBy harnessing the power of machine learning on these metabolic signals,\nwe are able to achieve advanced warning of ERMF with an area under\nthe curve (AUC) of 0.887–0.975 by the differentially metabolic\nanalysis of plasma samples collected upon admission. Furthermore,\nwe can accurately diagnose ERMF with an AUC of 0.850–1.000\nby analyzing plasma samples collected at the time of delivery from\nindividuals who have received epidural analgesia. These breakthroughs\noffer invaluable insights for clinical decision making during labor\nand have the potential to significantly reduce the incidence of ERMF.