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Revolutionary Oxford Method Offers Breakthrough in Rapid Antimicrobial Resistance Detection

Oxford

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TL;DR

  • Oxford’s rapid AMR detection: 10x faster with deep learning on bacterial cells.
  • Breakthrough method offers 80% accuracy in 30 mins for precise infectious disease treatment decisions.
  • Oxford researchers aim to scale and speed up their innovation to tackle the global threat of rising antimicrobial resistance.

In a significant stride towards combating antimicrobial resistance (AMR), researchers at the University of Oxford have unveiled a pioneering method that promises to detect AMR at least ten times faster than conventional clinical approaches. 

This breakthrough, hailed as a potential game-changer in the field of public health, addresses a global concern highlighted by the World Health Organization (WHO).

The World Health Organization has identified AMR as one of the foremost global public health threats, underscoring the urgency to develop innovative solutions. The Global Research on Antimicrobial Resistance project revealed that AMR led to approximately 1.3 million deaths in 2019 alone, emphasizing the critical need for swift and accurate detection methods.

Oxford’s deep-learning model: A paradigm shift in AMR testing

The Oxford research team employed a state-of-the-art deep-learning model to analyze bacterial cell images swiftly and accurately. Unlike traditional methods that rely on growing bacterial colonies over several days, this cutting-edge approach delivers results within 30 minutes.

The method exhibited an impressive accuracy rate of at least 80% on a per-cell basis across multiple antibiotics, showcasing its versatility and reliability.

This groundbreaking method, tested on a range of E. coli clinical isolates, has the potential to revolutionize the response to infectious diseases. Dr. Piers Turner, a postdoctoral researcher at the Oxford Martin Programme on AMR Testing, expressed optimism about the innovation’s capacity to enable more precise and timely treatment decisions. 

The efficiency of this method could significantly reduce treatment times, minimize side effects, and prove adaptable to various forms of bacteria and antibiotics.

As the research advances, the Oxford team aims to refine and expedite their method for clinical use. The overarching goal is to enhance scalability, decrease treatment times, and broaden the applicability of the model to different bacteria and antibiotics. This strategic approach aligns with the global mission to curb the rise of AMR by leveraging cutting-edge technology.

World antimicrobial awareness week: Spotlight on collaborative efforts

The announcement of this groundbreaking method coincides with World Antimicrobial Awareness Week (18 to 24 November 2023). This international initiative focuses on the theme “Preventing Antimicrobial Resistance Together,” rallying leaders and communities across various sectors to unite against resistance and promote responsible antimicrobial use.

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Benson Mawira

Benson is a blockchain reporter who has delved into industry news, on-chain analysis, non-fungible tokens (NFTs), Artificial Intelligence (AI), etc.His area of expertise is the cryptocurrency markets, fundamental and technical analysis.With his insightful coverage of everything in Financial Technologies, Benson has garnered a global readership.

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