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联用高效液相色谱-紫外光发光二极管诱导荧光检测法对泪液进行蛋白质谱分析以诊断干眼综合征

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

作者信息

Adigal Sphurti S, Bhandary Sulatha V, Hegde Nagaraj, Nidheesh V R, John Reena V, Rizvi Alisha, George Sajan D, Kartha V B, Chidangil Santhosh

机构信息

Centre of Excellence for Biophotonics, Department of Atomic and Molecular Physics, Manipal Academy of Higher Education Manipal Karnataka India 576104

Department of Ophthalmology, Kasturba Medical College Manipal Karnataka India 576104.

出版信息

RSC Adv. 2023 Jul 26;13(32):22559-22568. doi: 10.1039/d3ra04389d. eCollection 2023 Jul 19.

DOI:10.1039/d3ra04389d
PMID:37501778
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10369224/
Abstract

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.

摘要

泪液含有有机和无机成分，它们相对浓度的变化可以提供有价值的信息，并且有助于检测多种眼科疾病。本报告描述了实验室组装的基于发光二极管（LED）的高效液相色谱系统在泪液蛋白质谱分析以诊断干眼病中的应用。为了疾病诊断，对蛋白质谱数据进行了主成分分析（PCA）、匹配/不匹配以及基于人工神经网络（ANN）的二元分类。蛋白质谱数据的匹配/不匹配测试结果显示敏感性为94.4%，特异性为87.8%。采用留一法的人工神经网络给出的敏感性为91.6%，特异性为93.9%。

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