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PI: Kapanidis, Achillefs

Department: Physics (DE)

Widespread use of antibiotics has led to the emergence of antibiotic-resistant bacteria that evade antibiotic treatment and cause more than a million deaths/year worldwide, threatening a return to the pre-antibiotic era. The problem is compounded by the fact that existing tests for antibiotic resistance are slow, requiring days to complete; during that time, and especially when urgent action is needed because a patient is unwell, doctors prescribe broad-spectrum antibiotics, which may further fuel antibiotic resistance.

Our team of experts in physics, medicine, microbiology and computing has recently developed rapid methods to detect antibiotic resistance in strains of bacteria causing infection in patients through microscopic imaging of bacterial cell structures and image analysis via computer algorithms known as ""deep learning"". We have also recently developed a highly novel, rapid and scalable way to produce devices that filter tiny amounts of fluid (""microfluidic"" devices) that can enrich certain bacterial cells. These devices rely on structures known as ""monoliths"", which are made up of a crosslinked network of polymer fibres with tiny holes; the size of holes can be tuned to meet the demands of the fluid being filtered.

The aim of the project is to integrate monolith microfluidics in a diagnostic workflow for bacterial imaging and antibiotic resistance detection, first for common bacteria causing infections that are grown in the laboratory, and then for samples taken from patients (including urine, blood). This will enable us to purify and concentrate bacteria from clinical samples for antibiotic resistance testing. These developments have the potential to improve the clinical detection of antibiotic resistance and improve healthcare, cutting the time-to-result, simplifying workflows and reducing costs. The project will also offer supporting evidence for a patent application for the novel monolith filtration technology and the clinical tests it enables.

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