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Epidural-related maternal fever (ERMF) heightens the risk of intrapartum fever, whereas effective prevention and treatment in clinical practice are currently lacking. Rapid and sensitive screening tools for ERMF are urgently needed to advance relevant research. In response to this challenge, we devise and craft porous Co 3 O 4 /CuO hollow polyhedral nanocages with p-p heterojunctions derived from metal–organic frameworks. We employ these p-p heterojunctions in conjunction with high-throughput mass spectrometry to conduct metabolic analysis of substantial plasma samples, with only about 0.03 μL per sample. Leveraging these p-p heterojunctions, metabolic signals from complex plasma can be amplified, with great reproducibility. By harnessing the power of machine learning on these metabolic signals, we are able to achieve advanced warning of ERMF with an area under the curve (AUC) of 0.887–0.975 by the differentially metabolic analysis of plasma samples collected upon admission. Furthermore, we can accurately diagnose ERMF with an AUC of 0.850–1.000 by analyzing plasma samples collected at the time of delivery from individuals who have received epidural analgesia. These breakthroughs offer invaluable insights for clinical decision making during labor and have the potential to significantly reduce the incidence of ERMF.
Zhang et al. (Thu,) studied this question.