Diagnosis of Ischemic Renal Failure Using Surface-Enhanced Raman Spectroscopy and a Machine Learning Algorithm

Area of Science:

• Biomedical Engineering
• Analytical Chemistry
• Renal Physiology

Background:

• Renal ischemia can cause severe kidney dysfunction and injury.
• Early diagnosis of renal dysfunction is crucial for effective treatment.
• Current diagnostic methods may lack sensitivity for early detection.

Purpose of the Study:

• To develop and assess a biochip for diagnosing renal function.
• To evaluate surface-enhanced Raman spectroscopy (SERS) for detecting biomarkers in urine.
• To determine the feasibility of early diagnosis of renal ischemia using Raman measurements.

Main Methods:

• A bilateral renal ischemia rat model was established.
• Blood and urine samples were collected to assess renal injury and function.
• Urine samples were analyzed using a Raman chip with Au-ZnO nanorods for SERS.
• Raman signals were correlated with the severity of renal dysfunction and analyzed using machine learning.

Main Results:

• Raman signals from urine varied significantly with the severity of renal dysfunction.
• Statistically significant differences in Raman signals were observed based on renal function.
• The study confirmed that renal ischemia leads to renal dysfunction.
• SERS biomarkers released into urine were effectively amplified and detected.

Conclusions:

• Surface-enhanced Raman spectroscopy (SERS) combined with a machine learning algorithm can effectively track urinary SERS biomarkers.
• This approach shows promise for the early diagnosis and monitoring of renal dysfunction.
• The developed biochip technology offers a novel method for assessing renal function.