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A High Throughput, Multiplexed and Targeted Proteomic CSF Assay to Quantify Neurodegenerative Biomarkers and Apolipoprotein E Isoforms Status
Published on: October 20, 2016
A pH-Calibrated Multi-Analyte MXene Aerogel Electrochemical Platform Assisted by Interpretable Machine Learning for
Yuxian Chen1, Haoyu Xiao1, Qiuyi Deng1
1School of Mechanical Engineering, and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing, Jiangsu Province211189, China.
Abstract:
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.
Insights
This study introduces a rapid, machine-learning-powered electrochemical sensor for early detection of postoperative bacterial meningitis (PNBM) using cerebrospinal fluid (CSF). The point-of-care platform offers quick and reliable risk stratification for neurosurgical patients.
Area of Science:
- Biomedical Engineering
- Nanomaterials Science
- Machine Learning in Healthcare
Background:
- Postoperative meningitis (PNM) diagnosis is challenging due to nonspecific symptoms and slow culture-based methods.
- Early risk stratification of PNM is crucial for timely intervention and improved patient outcomes.
- Current diagnostic tools lack the speed and reliability needed for point-of-care settings.
Purpose of the Study:
- To develop a machine-learning-assisted electrochemical platform for rapid point-of-care (POC) prescreening and triage of postoperative bacterial meningitis (PNBM).
- To enable concurrent quantification of key biomarkers (pH, glucose, lactate) in cerebrospinal fluid (CSF) for PNBM detection.
- To integrate interpretable machine learning models for transparent and reliable risk stratification.
Main Methods:
- Engineered a hierarchically porous MoS2/MXene/rGO (MMG) aerogel as an electrochemical sensor scaffold for multiplex biomarker detection.
- Integrated real-time pH monitoring to correct pH-dependent glucose and lactate measurements, enhancing quantitative accuracy.
- Trained interpretable tree-ensemble machine learning models (Random Forest, XGBoost) on a clinical cohort using cross-validation with noise augmentation.
Main Results:
- The MMG aerogel facilitated stable and reproducible multiplex sensing of pH, glucose, and lactate.
- pH calibration improved the quantitative consistency of glucose and lactate measurements across diverse samples.
- The machine learning framework demonstrated preliminary cross-validated discrimination for PNBM/non-PNBM cases with transparent decision logic.
Conclusions:
- The developed platform integrates pH-calibrated multiplex electrochemical sensing with interpretable machine learning for rapid PNBM prescreening.
- This approach offers a promising, rapid, and transparent strategy for PNBM triage in neurosurgical POC settings.
- The study highlights the potential of advanced nanomaterials and AI in improving infectious disease diagnostics at the point of care.
