Enhanced detection of HBV and HCV using Cas13a-FLAP and FGoAI platforms

Area of Science:

• Biotechnology
• Molecular Diagnostics
• Infectious Diseases

Background:

• Hepatitis viruses (HBV, HCV) present a significant global health challenge.
• Early diagnosis is crucial for effective hepatitis virus disease control and management.
• Existing diagnostic methods may lack the required sensitivity or speed for widespread application.

Purpose of the Study:

• To develop a highly efficient and sensitive ratiometric fluorescence biosensor for detecting hepatitis viruses.
• To integrate Cas13a technology with fluorescent RNA aptamers for enhanced detection capabilities.
• To validate the diagnostic accuracy of the developed biosensor using clinical samples.

Main Methods:

• Development of an integrated biosensor system utilizing duplex-specific nuclease (DSN) and Cas13a.
• Employing fluorescent RNA aptamers for ratiometric signal generation.
• Combining classical image processing with AI algorithms for enhanced image analysis and stability.
• Utilizing a portable fluorescence imaging device for detection.

Main Results:

• Achieved limits of detection of 7.4 copies/µL for HBV gene and 2.9 copies/µL for HCV gene.
• Demonstrated high diagnostic accuracy in clinical samples with 100% specificity and >96.3% sensitivity.
• Enhanced stability and anti-interference capability through AI-integrated image analysis.

Conclusions:

• The developed Cas13a-based ratiometric fluorescence biosensor provides a sensitive and highly specific platform for hepatitis virus detection.
• This innovative strategy shows significant potential for accurate and rapid diagnosis of HBV and HCV infections.
• The integration of AI enhances the robustness and applicability of the biosensor in diagnostic settings.