Diagnosis of Ischemic Renal Failure Using Surface-Enhanced Raman Spectroscopy and a Machine Learning Algorithm
Sanghwa Lee, Jeongmin Oh, Kwan-Hee Lee, Minju Cho, Bjorn Paulson, Jun Ki Kim
Abstract
To diagnose renal function using a biochip capable of detecting SERS and to assess Raman measurements taken from a bilateral renal ischemia model and the feasibility of early diagnosis was done. After generating a bilateral renal ischemia rat model, blood and urine were collected. After confirming the presence of renal injury and function, liquid drops were placed onto a Raman chip whose surface had been enhanced with Au-ZnO nanorods. SERS biomarkers that diffused into the nanogaps were selectively amplified. Raman signals varied based on the severity of the renal function, and these differences were confirmed statistically. These results confirm that renal ischemia leads to renal dysfunction and that surface-enhanced Raman spectroscopy and a machine learning algorithm can be used to track signals in the urine from the release of SERS biomarkers.