Self-Assembled Plasmonic Magnifier: A New Platform for Ultra-Sensitive Detection of Respiratory Viruses Using...

Area of Science:

• Nanotechnology
• Biotechnology
• Spectroscopy

Background:

• Respiratory viral infections pose significant global health threats, especially to vulnerable populations.
• Accurate and sensitive pathogen detection is crucial for effective prevention and control.
• Existing detection methods often face limitations in speed, sensitivity, or specificity.

Purpose of the Study:

• To develop a novel, highly sensitive, and specific platform for the rapid detection and quantification of respiratory viruses.
• To integrate Surface-Enhanced Raman Scattering (SERS) with Artificial Intelligence (AI) and a unique self-assembled plasmonic magnifier (SPM) substrate.
• To demonstrate the platform's efficacy in complex biological samples like serum and saliva.

Main Methods:

• Development of a virus-triggered self-assembled plasmonic magnifier (SPM) substrate using C12 DNA and calcium ions.
• Integration of the SPM substrate with SERS for label-free viral detection.
• Application of AI-driven spectral analysis for rapid identification and quantification of multiple respiratory viruses.
• Validation of the platform using respiratory syncytial virus, adenovirus type 5, influenza B virus, and H1N1 virus in serum and saliva.

Main Results:

• The SERS-AI-SPM platform achieved high sensitivity with detection limits as low as 5 × 10^-5 copies/mL.
• Unique SERS fingerprints showed strong linear correlations with viral concentrations, demonstrating quantitative accuracy.
• The AI analysis enabled accurate differentiation of multiple viruses within 2 minutes.
• The platform exhibited excellent reproducibility and robustness in complex biological matrices.

Conclusions:

• The SERS-AI-SPM platform represents a significant advancement in SERS technology for respiratory virus detection.
• The integration of nanomaterial engineering and AI offers a transformative tool for clinical diagnostics and public health.
• This technology holds potential for real-time infectious disease monitoring, epidemic prevention, and personalized medicine.