A pH-Calibrated Multi-Analyte MXene Aerogel Electrochemical Platform Assisted by Interpretable Machine Learning for...

Rapid and reliable prescreening and early risk stratification of postoperative meningitis (PNM) remain challenging because clinical symptoms are nonspecific and culture-based testing is time-consuming. Here, we report a machine-learning-assisted electrochemical platform for point-of-care (POC) prescreening and triage of PNM using cerebrospinal fluid (CSF). A MoS2/MXene/reduced graphene oxide (rGO)-based multiplex sensor array enables concurrent quantification of pH, glucose, and lactate-three clinically relevant biomarkers for postoperative bacterial meningitis (PNBM). To ensure stable and reproducible multiplex sensing, a hierarchically porous, ant-nest-like (macropore-mesopore) MoS2/MXene/rGO (MMG) aerogel is engineered as the electrochemical transduction scaffold, providing interconnected transport pathways while mitigating 2D nanosheet restacking. In addition, real-time pH monitoring is integrated to correct the intrinsically pH-dependent glucose and lactate responses, improving quantitative consistency across physiologically variable samples. Based on a clinically relevant PNBM/non-PNBM cohort, interpretable tree-ensemble models (Random Forest and XGBoost) are trained using Gaussian-noise augmentation within cross-validation folds to avoid information leakage. The resulting framework demonstrates preliminary internal cross-validated discrimination, while maintaining transparent decision logic. Overall, this work integrates pH-calibrated multiplex electrochemical sensing with interpretable machine learning, offering a rapid and transparent strategy for PNBM prescreening in neurosurgical POC settings.