Department of Medical and Clinical Biochemistry, Faculty of Medicine, Pavol Jozef Šafárik in Košice, Trieda SNP 1, 040 11 Košice, Slovakia.
Sensors (Basel). 2022 Jul 25;22(15):5534. doi: 10.3390/s22155534.
Sensitive and rapid diagnosis of the early stages of glaucoma from tear fluid is a great challenge for researchers.
Tear fluid was analyzed using three-dimensional synchronous fluorescence spectroscopy (3D-SFS). Our previously published results briefly describe the main methods which applied the second derivative to a selected synchronous spectrum Δλ = 110 nm in distinguishing between healthy subjects (CTRL) and patients with glaucoma (POAG).
In this paper, a novel strategy was used to evaluate three-dimensional spectra from the tear fluid database of our patients. A series of synchronous excitation spectra were processed as a front view and presented as a single curve showcasing the overall fluorescence profile of the tear fluid. The second derivative spectrum provides two parameters that can enhance the distinction between CTRL and POAG tear fluid.
Combining different types of 3D-SFS data can offer interesting and useful diagnostic tools and it can be used as input for machine learning and process automation.
从泪液中敏感、快速地诊断青光眼的早期阶段是研究人员面临的一大挑战。
使用三维同步荧光光谱法(3D-SFS)分析泪液。我们之前发表的结果简要描述了主要方法,即应用二阶导数对选定的同步谱Δλ=110nm 进行区分,以区分健康受试者(CTRL)和青光眼患者(POAG)。
在本文中,使用了一种新策略来评估我们患者的泪液数据库中的三维光谱。一系列同步激发光谱被处理为前视图,并呈现为一条单一的曲线,展示了泪液的整体荧光特征。二阶导数光谱提供了两个参数,可以增强对 CTRL 和 POAG 泪液的区分。
结合不同类型的 3D-SFS 数据可以提供有趣和有用的诊断工具,并可作为机器学习和过程自动化的输入。