Protein profile analysis of tear fluid with hyphenated HPLC-UV LED-induced fluorescence detection for the diagnosis of dry eye syndrome

Tear fluid contains organic and inorganic constituents, variations in their relative concentrations could provide valuable information and can be useful for the detection of several ophthalmological diseases. This report describes the application of the lab-assembled light-emitting diode (LED)-based...

Full description

Saved in:
Bibliographic Details
Published in:RSC advances 2023-07, Vol.13 (32), p.22559-22568
Main Authors: Adigal, Sphurti S, Bhandary, Sulatha V, Hegde, Nagaraj, Nidheesh, V. R, John, Reena V, Rizvi, Alisha, George, Sajan D, Kartha, V. B, Chidangil, Santhosh
Format: Article
Language:English
Subjects:
Artificial neural networks
Chemistry
Diagnosis
Eye diseases
High performance liquid chromatography
Light emitting diodes
Principal components analysis
Proteins
Sensitivity
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Tear fluid contains organic and inorganic constituents, variations in their relative concentrations could provide valuable information and can be useful for the detection of several ophthalmological diseases. This report describes the application of the lab-assembled light-emitting diode (LED)-based high-performance liquid chromatography system for protein profiling of tear fluids to diagnose dry eye disease. Principal Component Analysis (PCA), match/no-match, and Artificial Neural Network (ANN) based binary classification of protein profile data were performed for disease diagnosis. Results from the match/no-match test of the protein profile data showed 94.4% sensitivity and 87.8% specificity. ANN with the leaving one out procedure has given 91.6% sensitivity and 93.9% specificity. Tear fluid contains organic and inorganic constituents, variations in their relative concentrations could provide valuable information and can be useful for the detection of several ophthalmological diseases.
ISSN:2046-2069
2046-2069
DOI:10.1039/d3ra04389d