FRIDAY, JUNE 15, 2018

New test may detect resistant TB faster

New test may detect resistant TB faster
New test may detect resistant TB faster | Courtesy of
A new testing technique created by a team at UCL can genetically sequence tuberculosis bacteria (Mtb) from samples, reducing testing time to mere days and improving treatments, control of the disease, and management of outbreaks.

Experts report that some regions of London has had a drastic rise in tuberculosis (TB) rates, bringing London’s TB rates equal to Sub-Saharan Africa’s rates. One factor contributing to rising rates is drug-resistant TB. Drug-resistant TB strains have grown more common during the last few years, and these strains require a specialized treatment.

The new test will enable doctors to start this specialized treatment sooner than before, as they will know in just a few days whether the strain is drug resistant. Sequencing whole genomes enables researchers to recognize the complete genetic (DNA) sequence of samples. This enables health professionals to immediately recognize and treat drug-resistant Mtb strains.

"Using the conventional methods, patients with resistant TB would need to wait for up to six weeks for antibiotic resistance testing," Professor Judith Breuer UCL Infection & Immunity and senior author of the study, said. "In that time, they may be taking drugs that are suboptimal or suffer unnecessary and unpleasant treatment side effects. Our technique and the associated software could reduce testing for antimicrobial resistance to a few days, allowing doctors to give precise antimicrobial treatment earlier than is currently possible."

"As well as delivering personalised treatments to patients, the tests could also be used to precisely track the spread of TB," Dr. Josephine Bryant, UCL Infection & Immunity and co-lead author, said. "With rapid sequencing available it would be possible to trace TB infections in communities, or to identify a few highly infectious people, sometimes called 'super-spreaders'. If public health officials can identify these individuals faster and stop them from spreading the disease, control and prevention of future outbreaks could be improved."

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University College London

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