Cerebrospinal fluid-induced stable and reproducible SERS sensing for various meningitis discrimination assisted with machine learning.

Cerebrospinal fluid (CSF)-based pathogen or biochemical testing is the standard approach for clinical diagnosis of various meningitis. However, misdiagnosis and missed diagnosis always occur due to the shortages of unusual clinical manifestations and time-consuming shortcomings, low sensitivity, and poor specificity. Here, for the first time, we propose a simple and reliable CSF-induced SERS platform assisted with machine learning (ML) for the diagnosis and identification of various meningitis. Stable and reproducible SERS spectra are obtained within 30 s by simply mixing the colloidal silver nanoparticles (Ag NPs) with CSF sample, and the relative standard deviation of signal intensity is achieved as low as 2.1%. In contrast to conventional salt agglomeration agent-induced irreversible aggregation for achieving Raman enhancement, a homogeneous and dispersed colloidal solution is observed within 1 h for the mixture of Ag NPs/CSF (containing 110-140 mM chloride), contributing to excellent SERS stability and reproducibility. In addition, the interaction processes and potential enhancement mechanisms of different Ag colloids-based SERS detection induced by CSF sample or conventional NaCl agglomeration agents are studied in detail through in-situ UV-vis absorption spectra, SERS analysis, SEM and optical imaging. Finally, an ML-assisted meningitis classification model is established based on the spectral feature fusion of characteristic peaks and baseline. By using an optimized KNN algorithm, the classification accuracy of autoimmune encephalitis, novel cryptococcal meningitis, viral meningitis, or tuberculous meningitis could be reached 99%, while an accuracy value of 68.74% is achieved for baseline-corrected spectral data. The CSF-induced SERS detection has the potential to provide a new type of liquid biopsy approach in the fields of diagnosis and early detection of various cerebral ailments.