Label-free SERS for the detection of renal failure biomarkers in blood serum using gold nanoparticles as a substrate.

BACKGROUND

Renal failure (RF) requires rapid and accurate diagnosis to improve patient outcomes and quality of life. Conventional diagnostic methods are often invasive and time-consuming, highlighting the need for sensitive analytical approaches.

RESEARCH DESIGN AND

METHODS

Gold nanoparticles (AuNPs) were used as surface-enhanced Raman spectroscopy (SERS) substrates to identify biomarkers in serum samples from healthy individuals and RF patients with varying creatinine levels. Raman spectral fingerprints of proteins, lipids, carbohydrates, nucleic acids, creatinine, urea, and uric acid were analyzed. Principal component analysis (PCA) was used for data reduction, partial least squares regression (PLSR) for creatinine prediction, and support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) for sample classification. No treatment regimen or adverse-event assessment was involved.

RESULTS

AuNP-assisted SERS enhanced Raman signals and enabled discrimination between healthy and RF serum samples. PLSR supported quantitative prediction of creatinine concentration, while SVM and PLS-DA classified healthy and diseased samples with high accuracy.

CONCLUSIONS

SERS combined with AuNPs and multivariate analysis provides a sensitive platform for RF biomarker detection in blood serum. However, spectral variability, reproducible AuNPs substrate preparation, and validation using larger clinical datasets remain important challenges.