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Real-Time Respiration Changes as a Viability Indicator for Rapid Antibiotic Susceptibility Testing in a Microfluidic
Petra Jusková1, Steven Schmitt2, André Kling1
1Department of Biosystems Science and Engineering, Bioanalytics Group, ETH Zürich, Mattenstrasse 26, 4058 Basel, Switzerland.
ACS Sensors
|April 26, 2021
Summary
This study introduces a microfluidic platform for rapid antibiotic susceptibility testing (AST) of bacterial infections. The technology provides pathogen resistance profiles in 2-3 hours, improving diagnostic speed.
Area of Science:
- Biomedical Engineering
- Microfluidics
- Diagnostic Microbiology
Background:
- Rapid identification and antibiotic susceptibility testing (AST) are crucial for managing bacterial infections.
- Microfluidic technologies enable precise handling of small sample volumes and single-cell analysis.
- Downscaled cultivation systems reduce bacterial requirements and shorten time to result for AST.
Purpose of the Study:
- To develop and validate a microfluidic platform for rapid AST.
- To enable pathogen resistance profiling within hours.
- To reduce the time and sample volume needed for AST.
Main Methods:
- A microfluidic platform with hundreds of growth chambers containing oxygen-sensing nanoprobes and various antibiotic concentrations was developed.
- The platform measures changes in dissolved oxygen levels during bacterial cultivation to determine resistance.
- Performance was validated using quality control *Escherichia coli* strains and a clinical isolate.
Main Results:
- The platform achieved pathogen resistance profiles within 2-3 hours.
- Results for quality control strains aligned with established guidelines and independent AST protocols.
- Successful AST was demonstrated for a clinical *E. coli* isolate from a blood culture.
Conclusions:
- The developed microfluidic platform offers a rapid and efficient method for antibiotic susceptibility testing.
- This technology has the potential to significantly improve the diagnosis and treatment of bacterial infections.
- The platform's ability to provide timely resistance data can aid in clinical decision-making.

