Aptamer-functionalized graphene quantum dots combined with artificial intelligence detect bacteria for urinary tract...

Area of Science:

• Biotechnology
• Nanotechnology
• Artificial Intelligence

Background:

• Urinary tract infections (UTIs) are common bacterial infections requiring prompt pathogen identification.
• Accurate and rapid detection of bacteria like Escherichia coli (E. coli) is crucial for effective UTI management.

Purpose of the Study:

• To develop a novel aptamer-functionalized graphene quantum dot integrated with an artificial intelligence (AG-AI) detection system.
• To achieve rapid and highly sensitive detection of E. coli in UTI diagnostics.

Main Methods:

• Graphene quantum dots were modified with E. coli-specific aptamers, creating a fluorescence-based detection mechanism.
• The fluorescence intensity correlated with E. coli concentration, with AI processing for quantification.
• The AG-AI system's performance was validated against MALDI-TOF MS.

Main Results:

• The AG-AI detection system demonstrated wide linearity from 10^3 to 10^9 CFU/mL.
• A low detection limit of 3.38x10^2 CFU/mL was achieved for E. coli.
• The system effectively differentiated E. coli from other UTI-causing bacteria.

Conclusions:

• The developed AG-AI detection system provides an accurate and effective method for bacterial detection in UTIs.
• This approach offers a promising tool for rapid and sensitive diagnosis of E. coli infections.