#AI Reads Urine# Molecule-Responsive SERS Sensors for Urine Diagnosis of Kidney Diseases Enhanced by Neural Networks

The early diagnosis of kidney diseases is crucial for treatment and prognosis. Compared with kidney biopsy, a noninvasive urine-based diagnostic method for kidney diseases is more convenient and less painful for patients. Urine is closely related to kidney diseases such as nephritis, kidney failure, and kidney cancer. Since it contains various biomolecules like small-molecule metabolites and proteins, urine is an appropriate sample for diagnosing and monitoring the progression of kidney diseases. This paper develops a liquid biopsy method for diagnosing various kidney diseases, which combines a specific surface-enhanced Raman scattering (SERS) sensing mode with a neural network model. This method allows a single probe to integratively respond to multiple types of targets, facilitating the detection of complex samples from multiple target groups. Compared with label-free SERS, this method relies on changes in probe molecules, which enhances the sensitivity of the assay. Two types of silver nanoparticle-cast films assist in the surface decoration of molecule-responsive Raman reporter molecules, p-mercaptobenzoic acid (MBA) and p-aminothiophenol (PATP). MBA responds to amino components in urine through SERS spectral changes caused by molecular polarizability, while PATP reflects the level of small-molecule metabolites in urine according to SERS changes resulting from the rate of hot hole-catalyzed reactions. These interactions are verified by density functional theory and molecular docking simulations. Through these two SERS sensors, the study obtains SERS datasets of urine samples and establishes a classifier by integrating neural network models, enabling the effective discrimination between healthy and kidney disease samples. This method is helpful for clinical validation and shows promise for application in long-term kidney disease monitoring programs.

Anal Chem. 2025 Jun 22. doi: 10.1021/acs.analchem.5c01785.

Molecule-Responsive SERS Sensors for Urine Diagnosis of Kidney Diseases Enhanced by Neural Networks

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/