Purpose: The study showcased the application of the lab-assembled HPLC-LED-IF system to analyze proteins in tear fluid samples collected from individuals diagnosed with primary open-angle glaucoma (POAG).
Methods: Clinical application of the said technique was evaluated by recording chromatograms of tear fluid samples from control and POAG subjects and by analyzing the protein profile using multivariate analysis. The data analysis methods involved are principal component analysis (PCA), Match/No-Match, and artificial neural network (ANN) based binary classification for disease diagnosis.
Results: Mahalanobis distance and spectral residual values calculated using a standard calibration set of clinically confirmed POAG samples for the Match/No-Match test gave 86.9% sensitivity and 81.8% specificity. ANN with leaving one out procedure has given 87.1% sensitivity and 81.8% specificity.
Conclusion: The results of the study revealed that the utilization of a 278 nm LED excitation in the HPLC system offers good sensitivity for detecting proteins at low concentrations allowing to obtain reliable protein profiles for the diagnosis of POAG.
Keywords: HPLC-LED-IF; machine learning; multivariate analysis; primary open-angle glaucoma; tear protein profiling.