The process of determining the drug sensitivity profile of a bacterial infection takes a long time. Scientists at the Nara Institute of Science and Technology and their collaborative partners have just released news of a technology that could potentially save lives by drastically speeding up this currently lengthy process. The US CDC reports that antibiotic-resistant infections are responsible for the deaths of more than a million people worldwide each year. The basis of managing resistant infections is the rapid identification of the appropriate treatment to which the infectious bacteria are sensitive.
“Sensitivity results are often needed much faster than conventional tests can provide,” says Yaxiaer Yalikun, lead author. “To solve this problem, we have developed a technology that can meet this need.” The basis of the group’s work is impedance cytometry, which measures the dielectric properties of individual cells with high throughput – more than a thousand cells per minute.
One has an easy way to tell if an antibiotic is killing the bacteria because the bacteria’s electrical readings correspond to its physical response to the antibiotic. Conventional impedance cytometry requires technicians to perform extensive post-processing on test (antibiotic-treated) and reference (untreated) particles in a single sample prior to two-particle impedance calibration. This was the main limitation that the group was determined to overcome.
The team develops a new impedance cytometry technique in a study appearing in ACS Sensors that simultaneously analyzes test and reference particles in different channels, creating easily analysed, separate data sets. Thanks to the sensitivity of cytometry at the nanoscale, it was possible to detect even the smallest physical changes in bacterial cells. The team built a machine learning tool in a related study, published in Sensors and Actuators B, to analyze the impedance cytometry data.
The reference data set could be automatically labeled as the “learning” data set and used by a machine learning tool to learn the characteristics of the untreated bacteria because the new cytometry method splits the test and reference data sets. The tool can determine whether bacteria are sensitive to drugs and can even determine what percentage of bacterial cells are resistant in a population with mixed resistance by comparing live data with cells that have been treated with antibiotics. Yoichiroh Hosokawa, another lead author on the team, explains that although there was less than 10% misidentification in their research, they were able to distinguish between sensitive and resistant cells within two hours of antibiotic treatment.
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