Ikoma [Japan], October 21 (ANI): The process of figuring out a bacterial infection's drug susceptibility profile takes a long time. Researchers from the Nara Institute of Science and Technology and their cooperating partners have just published reports on a technology that could potentially save lives by drastically speeding up this currently tedious process.

The U.S. CDC states that antibiotic-resistant infections are responsible for killing over a million people worldwide every year. Central to managing resistant infections are quickly identifying an appropriate treatment to which the infective bacteria are susceptible. "Oftentimes susceptibility results are needed much faster than conventional tests can deliver them," says Yaxiaer Yalikun, senior author. "To address this, we developed a technology that can meet this need."

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Impedance cytometry, which measures the dielectric properties of individual cells with high throughput--more than a thousand cells per minute--is the foundation of the group's work. One has an easy way to tell if an antibiotic kills the bacteria because a bacterium's electrical readout matches up with its physical reaction to an antibiotic. Conventional impedance cytometry requires technical experts to perform extensive post-processing on the test (antibiotic-treated) and reference (untreated) particles in one sample before calibrating the impedance of the two particles. This was a major limitation the group was determined to overcome.

The team creates a novel impedance cytometry technique in a study that appears in ACS Sensors that simultaneously analyses the test and reference particles in different channels, producing easily analysed separate datasets. Due to the cytometry's nanoscale sensitivity, even the smallest physical alterations in bacterial cells could be detected. The team created a machine learning tool in a related study that was published in Sensors and Actuators B to analyse the impedance cytometry data. The reference dataset could be automatically labelled as the "learning" dataset and used by the machine learning tool to learn the characteristics of an untreated bacterium because the new cytometry method divides the test and reference datasets.

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The tool can determine whether the bacteria are susceptible to the medication 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 senior author in the team, explains that even though there was a misidentification error of less than 10% in their research, they were able to distinguish between susceptible and resistant cells within two hours of antibiotic treatment. (ANI)

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