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A new approach to diagnosing bacterial infections and detecting antimicrobial-resistant bacteria could be on the horizon, as engineers, microbiologists and machine learning experts propose the development of sensors that “sniff out” bacteria. Published in Cell Biomaterials on July 2, an opinion paper outlines the potential for sensors to detect bacterial infections in bodily fluids, providing a quick, affordable alternative to traditional diagnostic methods.
Bypassing laboratory analysis for faster results
One of the major challenges in combating antimicrobial resistance is the lack of rapid diagnostic tools, says senior author Andreas Güntner, a mechanical and process engineer at ETH Zurich. His team proposes a solution to this challenge: a device that could offer results in just seconds or minutes, bypassing the lengthy, multi-step laboratory processes that usually take hours or even days.
Antimicrobial resistance
Antimicrobial resistance (AMR) occurs when bacteria evolve to resist the effects of drugs designed to kill or inhibit them. This resistance can make infections harder to treat and increase the risk of spreading resistant strains.
“Our idea is to bypass laboratory analysis, which is multi-step process that usually takes hours to days, and sometimes even weeks, with a simple test that gives results within seconds to minutes.”
Dr. Andreas Güntner.
The science behind bacterial detection
Historically, doctors relied on their sense of smell to diagnose certain bacterial infections. For example, Pseudomonas aeruginosa infections emit a sweet, grape-like odor, while Clostridium infections produce a foul, putrid smell. These odors are linked to volatile organic compounds (VOCs) – small molecules emitted by bacteria that carry distinct smells.
Volatile organic compounds (VOCs)
VOCs are organic chemicals that can easily evaporate into the air at room temperature. Many microbes produce specific VOCs that can be used to identify them, making VOCs a useful tool for detecting bacterial infections.
Rather than using human noses, the team envisions developing chemical sensors that can detect VOCs in bodily fluids like blood, urine and sputum. This technology is similar to devices used in alcohol breathalyzers or air-quality monitoring systems.
“We have already developed and commercialized something similar for detecting contaminations like methanol in alcoholic beverages,” says Güntner. “Now, we are trying to transfer this technology to more complex situations.”
Identifying antimicrobial resistance through VOCs
One of the most promising aspects of the technology is its potential to detect antimicrobial-resistant bacteria. VOCs vary not only by bacterial species but also by strain. This means the sensors could potentially differentiate between antibiotic-resistant and non-resistant strains of bacteria. A previous study demonstrated that VOCs could distinguish between methicillin-resistant Staphylococcus aureus (MRSA) and non-resistant strains, showing that the concept is feasible in a laboratory setting.
However, bringing this technology to clinical practice is no small feat. VOC concentrations are extremely low, which makes sensor development a challenge. Güntner likens the task to finding a single red ball in a room full of one billion blue balls, emphasizing the need for highly sensitive and precise sensors.
Overcoming technical challenges
The sensors must be able to detect and differentiate thousands of VOCs emitted by bacteria. To achieve this, the devices will require a combination of sensors with varying binding capacities. These sensors could be made from materials such as metal oxides, polymers, graphene derivatives, and carbon nanotubes. Recent advances in nanoengineering will help optimize sensor performance, but additional challenges remain, such as filtering out VOCs produced by human cells or common to all bacteria.
Machine learning algorithms will play a critical role in optimizing sensor design, according to the researchers. These algorithms will help identify the key VOC combinations needed to distinguish between bacterial types, as well as provide insights into antimicrobial resistance and virulence.
A future of rapid, reliable diagnostics
Once developed, the sensors could provide a rapid, portable method for diagnosing infections, offering a solution that requires minimal training to operate. This breakthrough could pave the way for real-time infection detection and more informed treatment decisions.
“The overall goal is to translate scientific advances in VOC analysis into practical, reliable tools that can be used in everyday medical practice,” says Güntner. “Ultimately, we hope this will improve patient outcomes and support antibiotic stewardship.”
Reference: Bilgin MB, Shin H, Jutzeler CR, et al. Microbial and antimicrobial resistance diagnostics by gas sensors and machine learning. Cell Biomater. doi: 10.1016/j.celbio.2025.100125
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