#AI reads Urine# Fabricating a SMVF@GO@Ag Composite Aerogel SERS Sensor for the Enrichment and Label-Free Detection of Urinary VOCs

To address the demand for early screening of chronic kidney disease (CKD), this study developed a surface-enhanced Raman scattering (SERS) sensor based on SMVF@GO@Ag composite aerogel. The aerogel substrate was integrated into a centrifuge tube to enable the enrichment of urinary volatile organic compounds (VOCs) and label-free Raman spectral acquisition. Four machine learning algorithms, namely Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM), were employed to construct a predictive model for CKD staging. Among these models, the RF model demonstrated the best performance, achieving a training accuracy of 100% and a testing accuracy of 96%, with all models showing an AUC value greater than 0.95 on the ROC curve. The aerogel substrate exhibited excellent performance, with a limit of detection as low as \(9.55 ×10^{-11}\) M for 4-MBN and a BET specific surface area of \(5.57 m^{2}/g\). It also possessed favorable homogeneity, reproducibility, and stability, providing a highly sensitive, low-cost, and clinically translatable innovative solution for large-scale non-invasive screening of CKD.

Anal Chem. 2026 Jan 29. doi: 10.1021/acs.analchem.5c05672.

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/