3D superstructure based metabolite profiling for glaucoma diagnosis.

Area of Science:

• Analytical Chemistry
• Biomedical Engineering
• Ophthalmology

Background:

• Metabolome analysis offers comprehensive disease phenotype information for diagnosis.
• Glaucoma diagnosis can benefit from advanced analytical techniques for early detection and management.

Purpose of the Study:

• To utilize 3D superstructures for metabolome analysis in glaucoma diagnosis.
• To develop a classification model for accurately identifying glaucoma patients using metabolomic data.

Main Methods:

• Fabrication of 3D superstructures via evaporation-induced microprinting.
• Metabolome analysis using the 3D superstructures for signal acquisition.
• Classification of glaucoma patients using a Deep Neural Network (DNN) model.

Main Results:

• The 3D superstructures demonstrated high hotspot density, excellent signal repeatability, and high thermal stability.
• The DNN model achieved high accuracy in classifying glaucoma patients, with 92.05% sensitivity and 93.51% specificity.
• Fabrication and analysis using 3D superstructures significantly reduced measurement time and allowed for smaller sample analysis.

Conclusions:

• 3D superstructures are a versatile and effective platform for metabolome analysis in glaucoma diagnosis.
• The developed method offers a promising approach for accurate and efficient disease detection.
• The adaptability of 3D superstructures suggests potential applications in a broader range of metabolic analyses and disease diagnoses.