Deep Learning-Assisted Nanocavity Sensor for Amphiphilic Biomarker Analysis

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Analytical Chemistry

Background:

• Amphiphilic biomarkers are crucial for diagnosing diseases like cancer and infections.
• Current detection methods may lack the sensitivity and speed required for early diagnosis.
• Nanocavity structures offer potential for enhanced fluorescence detection.

Purpose of the Study:

• To develop and evaluate a silver nanocube-gold mirror nanocavity integrated with a support lipid bilayer (SLB) for enhanced fluorescence detection of amphiphilic biomarkers.
• To demonstrate the platform's capability for sensitive and rapid biomarker detection.
• To assess the potential of this system for clinical diagnostics.

Main Methods:

• Fabrication of a silver nanocube (AgNC)-gold mirror nanocavity.
• Integration of the nanocavity with a support lipid bilayer (SLB) to optimize biomarker spacing and minimize quenching.
• Utilizing a microfluidic platform for precise control of the detection environment.
• Employing a deep learning-based semantic segmentation approach to calculate the fluorescence enhancement factor.

Main Results:

• The integrated nanocavity system significantly enhanced fluorescence signals of amphiphilic biomarkers.
• The support lipid bilayer effectively minimized fluorescence quenching, improving signal-to-noise ratio.
• The deep learning method accurately calculated fluorescence enhancement, reaching a maximum factor of 868.64.
• The system achieved femtomolar-level detection sensitivity for biomarkers.

Conclusions:

• The AgNC-gold mirror nanocavity with SLB provides a highly sensitive and rapid platform for amphiphilic biomarker detection.
• The developed method demonstrates significant potential for advancing clinical diagnostics and healthcare applications.
• The system offers a combination of high sensitivity, rapid detection, and cost-effective preparation.