A Rapid and Low-Cost Pathogen Detection Platform by Using a Molecular Agglutination Assay

Area of Science:

• Molecular diagnostics
• Microfluidic cytometry
• Machine learning applications in healthcare

Background:

• Current bacterial identification methods are time-consuming, delaying clinical decisions and contributing to antibiotic misuse.
• Existing molecular techniques, while accurate, are often too complex for resource-limited settings due to specialized equipment and labeling processes.

Purpose of the Study:

• To develop a simplified, rapid, and low-cost molecular diagnostic assay for pathogen identification and antimicrobial susceptibility testing.
• To adapt the assay for use with readily available imaging technology, such as smartphone cameras, enhancing accessibility.

Main Methods:

• A wash-free molecular agglutination assay targeting the 16S rRNA gene was developed.
• Pathogen identification was performed within 30 minutes using a dark-field imaging microfluidic cytometry platform.
• A machine learning algorithm analyzed dark-field images to quantify bacterial abundance.
• Antimicrobial susceptibility testing was achieved within 3 hours.

Main Results:

• The assay successfully identified *Escherichia coli* in 50 clinical samples with 96% sensitivity and 100% specificity.
• Dark-field images were acquired using a narrow beam scanning technique and CMOS imagers, similar to smartphone cameras.
• The machine learning algorithm effectively deconvoluted agglutinated cluster features for bacterial quantification.

Conclusions:

• The developed molecular agglutination assay offers a user-friendly, sensitive, and rapid alternative for pathogen detection and antimicrobial susceptibility testing.
• This technology has significant potential for point-of-care applications, especially in resource-limited environments.
• Simplified molecular testing protocols can improve clinical decision-making and optimize antibiotic use.