Integrative protocol for quantifying cholesterol-related sterols in human serum samples and building decision support...

Area of Science:

• Biochemistry
• Analytical Chemistry
• Computational Biology

Background:

• Clinical diagnostics increasingly utilize metabolic biomarkers.
• Cholesterol-related sterols are key indicators in metabolic pathways.
• Accurate quantification of sterols is crucial for disease monitoring.

Purpose of the Study:

• To present a comprehensive protocol for sterol analysis in human blood serum.
• To describe the application of machine learning for novel decision-making systems in disease surveillance.
• To enable precise measurement of 10 sterols using targeted LC-MS/MS.

Main Methods:

• Protocol for sample preparation and sterol extraction.
• Quantification of 10 cholesterol-related sterols via targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS).
• Implementation of machine learning techniques for predictive modeling.

Main Results:

• A validated protocol for sterol extraction and quantification.
• Demonstrated potential of LC-MS/MS for accurate sterol measurement.
• Development of machine learning models for disease monitoring applications.

Conclusions:

• The presented protocol facilitates robust sterol analysis in serum.
• Machine learning integration offers advanced tools for metabolic pathway surveillance.
• This approach supports enhanced clinical diagnostics and disease management.