CNN-assisted SERS enables ultra-sensitive and simultaneous detection of Scr and BUN for rapid kidney function assessment. Issue 3 (3rd January 2023)

• Record Type: Journal Article
• Authors: Lu, Ping; Lin, Dajun; Chen, Ning; Wang, Luyao; Zhang, Xuedian; Chen, Hui; Ma, Pei
• Is Part Of: Analytical methods. Volume 15:Issue 3(2023)
• Page Start: 322
• Page End: 332
• Publication Date: 2023-01-03
• Subjects: Chemistry, Analytic -- Periodicals; Analytical biochemistry -- Periodicals; Chemical laboratories -- Standards -- Periodicals; 543.1905
• DOI: 10.1039/d2ay01573k
• Languages: English
• ISSNs: 1759-9660

Abstract

Abstract : An ultra-sensitive and real-time kidney function assessment system based on a deep-learning assisted spectroscopy method. Abstract : Kidney disease is highly prevalent and may result in severe clinical outcomes. Serum creatinine (Scr) and blood urea nitrogen (BUN) are the most widely used biomarkers for kidney function assessment, yet when measured alone, the result can be affected by a variety of parameters such as age, gender, protein consumption, etc. Measuring Scr and BUN simultaneously can eliminate most of the external influences and greatly improve the assessment of kidney function. In this study, a real-time kidney function assessment system based on dual biomarker detection was proposed. Scr and BUN were determined using surface-enhanced Raman scattering (SERS) within the concentration range of 10 −1 to 10 −6 M and 0.28 to 100 mg dl −1, respectively. A one-dimensional convolutional neural network (1D-CNN) model was employed to quantitatively analyze the concentration of biomarkers from the SERS spectral measurements. Moreover, we simulated a variety of kidney health conditions with 16 groups of mixed Scr and BUN in serum. The proposed CNN-assisted SERS method was used to quantify both biomarkers and provide diagnostic results. The Au core-Ag shell nanoprobes provided ultra-sensitive SERS detection and the CNN model achieved excellent regression results with an R 2 of 0.9871 in the testing dataset. The system demonstrated a rapid and robust evaluation for the assessment of kidney function, providing a promising idea for medical diagnosis with the help of spectroscopy and deep learning methods.