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Quantification of Plasmid-Mediated Antibiotic Resistance in an Experimental Evolution Approach
Published on: December 14, 2019
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3× multiplexed detection of antibiotic resistant plasmids with single molecule sensitivity
G G Meena1, R L Hanson, R L Wood
1School of Engineering, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA. hschmidt@soe.ucsc.edu.
Lab on a Chip
|October 13, 2020
Summary
A novel optofluidic system enables rapid, amplification-free detection of antibiotic resistance genes in bacteria. This technology offers a faster and more accurate alternative to current diagnostic methods for carbapenem-resistant pathogens.
Area of Science:
- Biomedical Engineering
- Molecular Biology
- Clinical Diagnostics
Background:
- Antibiotic-resistant bacterial pathogens pose a significant global health threat.
- Carbapenem resistance in bacteria is particularly lethal due to limited treatment options.
- Current diagnostic methods for antibiotic resistance are often slow and error-prone.
Purpose of the Study:
- To develop a rapid, amplification-free, and multiplexed diagnostic system for detecting antibiotic resistance genes.
- To demonstrate a chip-based optofluidic platform for single-molecule sensitivity detection.
- To enable timely identification of carbapenem-resistant pathogens.
Main Methods:
- Utilized a chip-based optofluidic system with rotating disks and microfluidic chips for plasmid extraction from blood.
- Employed functionalized polymer monoliths for selective plasmid isolation.
- Implemented waveguide-based spatial multiplexing for parallel detection of carbapenem resistance genes.
Main Results:
- Achieved amplification-free, multiplexed detection of antibiotic resistance genes within one hour.
- Demonstrated single-molecule sensitivity in the optofluidic system.
- Successfully detected three different carbapenem resistance genes in spiked blood samples.
Conclusions:
- The developed optofluidic system offers a rapid and accurate solution for detecting antibiotic resistance.
- This technology has the potential to significantly improve the diagnosis of antibiotic-resistant infections.
- Paves the way for faster clinical decision-making in managing bacterial infections.

