Scientists at Imperial College London are pioneering a project to train dogs to detect harmful bacteria, particularly Pseudomonas, which poses significant health risks, especially for cystic fibrosis patients.

Professor Jane Davies emphasized that dogs can be trained to identify Pseudomonas bacteria in patient samples, which is crucial as this bacterium can lead to severe pneumonia and sepsis.

One of the dogs involved in this initiative, Jodie, a golden labrador, has shown the ability to detect specific bacteria and could play a vital role in identifying infections in cystic fibrosis patients.

The training process utilizes non-invasive samples, such as skin, urine, or clothing from patients, simplifying the detection of infections and making the method more comfortable for patients.

Current detection methods for infections like those caused by Pseudomonas are often invasive, uncomfortable, and costly, highlighting the need for more efficient alternatives.

By employing trained dogs, this innovative approach could provide a more effective and less invasive means of detecting bacteria, addressing the limitations of existing methods.

In the long term, the detection system developed through this research could be applied to other patient groups beyond those with cystic fibrosis, broadening its impact.

Early identification of bacteria through this method will enable precise antibiotic prescriptions, which is essential in combating the growing issue of antibiotic resistance.

Addressing antimicrobial resistance is critical, as it results in approximately one million deaths annually, with increasing mortality rates particularly affecting older populations.

Professor Davies pointed out the necessity for innovative solutions in the fight against antimicrobial resistance, positioning trained dogs like Jodie as valuable allies in this battle.

Funding from LifeArc and the Cystic Fibrosis Trust is supporting the collaboration with Medical Detection Dogs to enhance the capabilities of dogs in detecting Pseudomonas in patient samples.

This detection method not only aims to assist cystic fibrosis patients but could also address challenges in identifying hard-to-detect microbes in various clinical settings.