Bionic Magnetic Nanorobots Combined with AI-Assisted Microscopic Imaging for Argonaute-Powered Multiplexed Foodborne...

Area of Science:

• Food safety
• Nanotechnology
• Biosensing

Background:

• Conventional foodborne pathogen detection methods face challenges with complex matrices, signal conversion, and sensitivity.
• Ultrasensitive and multiplexed identification is critical for ensuring food safety.

Purpose of the Study:

• To develop a novel platform for multiplexed and ultrasensitive identification of foodborne pathogens.
• To overcome limitations of conventional methods using bionic nanorobots and AI-powered imaging.

Main Methods:

• Constructed bionic nanorobots using magnetic nanoparticles with optimized phenylboronic acid density and polyethylene glycol linkers.
• Developed an AI-powered microscopic imaging strategy employing polystyrene microsphere size-encoding and Clostridium butyricum Argonaute (CbAgo) decoding.
• Utilized CbAgo-activated cyclic cleavage circuits for multiplexed decoding with AI-mediated counting, avoiding DNA amplification.

Main Results:

• Bionic nanorobots achieved 92.21% Gram-positive bacteria capture within 20 min with 25-day stability.
• The AI platform enabled dual-pathogen detection across 5 orders of magnitude (10^2-10^6 CFU/mL) within 1.5 hours.
• Achieved a detection limit of 10^2 CFU/mL, demonstrating 10-fold improved sensitivity compared to quantitative polymerase chain reaction.

Conclusions:

• The developed platform offers a robust and efficient tool for multiplexed pathogen detection in complex food environments.
• This approach significantly enhances sensitivity and speed for food safety applications.
• The combination of nanorobots and AI imaging represents a promising advancement in diagnostic technology.