Single-Component Double-Emissive Ratiometric Probe: Toward Machine Learning Driven Detection and Discrimination of...

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Analytical Chemistry

Background:

• Catecholamine neurotransmitters and metabolites are critical biomarkers for neurological diseases.
• Existing detection methods may lack specificity or require complex instrumentation.
• Developing sensitive and selective biosensors is crucial for early disease diagnosis.

Purpose of the Study:

• To develop a single-component ratiometric fluorescent sensor for detecting catecholamine neurotransmitter biomarkers.
• To utilize the oxidation of bovine serum albumin-protected gold nanoclusters (BSA-Au NCs) for sensing applications.
• To enable visual and quantitative detection of biomarkers associated with neurological disorders.

Main Methods:

• Fabrication of a ratiometric fluorescent probe based on BSA-Au NCs.
• Induction of fluorescence changes (red to blue shift) via oxidation with N-Bromosuccinimide (NBS).
• Application of machine learning (LDA and PLS-R) for spectral data analysis and quantification.

Main Results:

• The sensor exhibited distinct spectral and color changes in response to varying concentrations of biomarkers.
• Machine learning algorithms successfully differentiated and quantified the target analytes.
• The sensor demonstrated effective detection and discrimination of biomarkers in human urine samples.

Conclusions:

• The developed single-component ratiometric fluorescent sensor shows high potential for diagnosing neurological diseases.
• This approach offers a sensitive, selective, and visually interpretable method for biomarker detection.
• The sensor's applicability in human urine facilitates clinical diagnostics and disease screening.