Urine collection by sodium alginate/CMC composite self-calibrating aerogel SERS platform for accurate screening and staging of chronic kidney disease.

Chronic kidney disease (CKD), characterized by structural or functional renal abnormalities, poses substantial health risks. With a global prevalence nearing 13 %, fewer than 10 % of affected individuals are aware of their condition. Early-stage CKD frequently goes undetected due to the lack of specific symptoms and the high costs and logistical challenges associated with traditional blood testing. This study introduces an anionic, ultrasensitive aerogel SERS platform (CS-Ag@4-mbn), constructed from sodium alginate and carboxymethyl cellulose, optimized for efficient biomarker capture and rapid urinary analysis. The CS-Ag@4-mbn platform demonstrates an exceptional R6G adsorption capacity of 2.67 × 10-6 mol/g and a detection limit of 8.81 × 10-11 M, underscoring its advanced SERS recognition performance. Furthermore, the integration of an internal standard signal within the CS-Ag@4-mbn system reduces potential errors and facilitates dataset correction. To interpret the acquired SERS data, five machine learning algorithms-PCA, SVM, DT, KNN, and RF-were employed, successfully constructing models to differentiate CKD stages (healthy and stages IV). These models achieved accuracy rates of up to 100 % in training and 97.58 % in testing, even without urine pre-processing, highlighting the platform's potential for non-invasive, early-stage CKD screening and diagnosis.