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Researchers Introduce new method for identifying bacteria and antibiotic susceptibility



Test tubes ina. row with a dropper placing liquid in one for a bacteria test on urine to test for a UTI

Researchers have developed an innovative method for rapid identification and determining antibiotic sensitivity of bacterial pathogens in urinary tract infections (UTI) in patients.

The method enables the detection of bacterial pathogens directly from urine samples in 30 to 40 minutes. The technology combines measurements of the infrared spectrum of the infecting bacteria with machine learning algorithms, to enable the simultaneous determination of both bacterial type at the species level and bacterial sensitivity to antibiotics.


The method has been tested on over 1,000 urine samples and was able to discriminate between bacteria species with approximately 97 per cent accuracy. It also determined bacterial susceptibility to various antibiotics to approximately 85 per cent accuracy.

The current identification of bacterial pathogens in UTI patients is labour intensive and can take up to three days. It is also potentially susceptible to treatment delays and potential complications delaying the results further.

The new method would allow medical staff to obtain results within 1 hour after they collect a urine sample with high accuracy and minimal effort. The researchers are confident that it has the potential to become a mainstay in hospitals and outpatient clinics.

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UTI bacteria

Urinary tract infection is a common infection affecting any part of the urinary tract but usually the lower tract. It can cause a patient to experience pain and discomfort but left untreated it can develop into permanent kidney damage and blood contamination.

These infections can affect over 150 million people annually around the globe and are the most common outpatient infection in the US. In hospitals. UTIs account for 40 per cent of all hospital-acquired infections.


The inventors include researchers at the Ben-Gurion University of the Negev (BGU)Shamoon College of Engineering and Afeka Tel Aviv Academic College of Engineering.

Professors Mahmoud Huleihel from the Department of Microbiology, Immunology and Genetics, said: “The new technology offers a novel clinical decision-support tool for early and precise antibiotic recommendations, that will result in ineffective treatment. More broadly, our invention is timely, given the global emerging threat of antimicrobial resistance.”

Josh Peleg, CEO of BGN Technologies, said: “This method for the identification of bacterial pathogens in UTI patients is an important and long-awaited solution for the management of UTI. Currently, identification of the bacterial pathogen and its antibiotics sensitivity is labour-intensive and can take up to three days, leading to treatment delays and potential complications.”

He added: “This novel solution can supply medical staff with results within 1 hour after collecting a urine sample, with very high accuracy and minimal effort. We are confident that this method has the potential to become a mainstay in hospitals and outpatient clinics alike.”

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  1. Pingback: New machine learning approach can predict risk of antibiotic resistance - Health Tech World

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