CAMO welcomes Will Bolton as he starts his AI4Health CDT-funded PhD studentship, supervised by Professor Alison Holmes and Dr. Pantelis Georgiou.
As part of the programme, Will’s PhD will combine artificial intelligence and machine learning to support healthcare diagnosis, monitoring and improvements in efficiency of care delivery.
The project aims to develop intelligent, personalised clinical decision support systems in order to optimise antibiotic therapy in obesity patients.
When an individual has two or more chronic healthcare conditions, this is known as multi-morbidity and this presents a significant challenge in healthcare. Obese patients are at higher risk of acquiring infections and being prescribed complex antibiotic treatments. They often fail to be dosed appropriately due to a lack of evidence to support decision making. This leads to a major health equality in management and outcomes of infections within obese cohorts, including risk of antimicrobial resistance. Will’s project will develop a novel decision support system, linking artificial intelligence with data from electronic health records to improve the diagnosis and management of infections in obese patients.
Professor Holmes and Dr Georgiou were successfully awarded a UKRI Centre for Doctoral Training in Artificial Intelligence (AI) PhD studentship. The studentship focuses on the development of intelligent, AI-based personalised clinical decision support systems to support and optimise antimicrobial therapy in obese patients with multi-morbidities.
Whilst antimicrobial therapy is often prescribed according to a ‘one dose fits all’ model, individual responses to antimicrobials vary widely, particularly in certain patient groups such as those with multi-morbidities (e.g. obesity or sepsis). The project will build upon our previous NIHR i4i Product Development Award: Enhanced, Personalised, Integrated, Care for Infection Management at the Point-of-Care award which led to the development of a clinical decision support system to support evidence based antimicrobial prescribing.
Patients with multi-morbidities are often inappropriately treated with antimicrobials due to a lack of evidence to support prescribing decisions. This project will improve patient outcomes through the development a novel decision support system, linking artificial intelligence with data from electronic health records to improve the diagnosis and management of infections in obese patients.
Investigators from the Centre for Antimicorbial Optimisation (CAMO) have developed a handheld point-of-care diagnostic system for the rapid detection of SARS-CoV-2 and Aspergillus fumigatus. Led by Dr. Pantelis Georgiou from the Department of Electrical and Electronic Engineering in collaboration with Dr. Jesus Rodriguez-Manzano and Professor Alison Holmes from the Department of Infectious Disease, this novel technology performs nucleic acid amplification (DNA and RNA) in under 30 minutes in the palm of your hand. The system consists of (i) a portable Lab-on-Chip device called Lacewing, (ii) a disposable cartridge which combines state-of-the art microchip technology and microfluidics, and (iii) a smartphone application to monitor the reaction in real-time, visualize the results and perform geo-tagging using a smartphone GPS. Nucleic acid amplification reactions are performed within the single-use cartridge using loop-mediated isothermal amplification (LAMP), a rapid alternative to conventional PCR which does not require thermal-cycling.
Supported by the Imperial College COVID-19 Response Fund, Rodriguez-Manzano et al. (2020) adapted this system for rapid (<20 miniutes) and sensitive detection of SARS-CoV-2. A novel LAMP assay with a limit of detection of 10 copies was developed based on phylogenetic analysis and validated on 183 clinical samples. A subset of samples was further validated on Lacewing, demonstrating a comparable performance to a benchtop commercial instrument. More details can be found in the recent MedRxiv publication.
In response to the emergence of antimicrobial resistance, Yu et al. (2020) repurposed Lacewing for the rapid detection of azole-resistance Aspergillus fumigatus. This work was carried out in close collaboration with Professor Matthew Fisher from the School of Public Health at Imperial Colleg LAMP assays targeting the two most relevant genetic markers linked to azole resistance, tandem-repeat 34 (TR-34) and TR-46 were developed. The assay showed a limit of detection of 10 copies/reaction under 30 minutes, and it was validated with isolates from clinical and environmental samples from 6 countries across 5 continents. More details can be found in the recent publication on the Journal of Clinical Microbiology.
Article contribution: Pantelis Georgiou and Jesus Rodriguez-Manzano
Researchers from the Centre for Antimicrobial Optimisation (CAMO) and the Centre for Bio-Inspired Technology at Imperial College London (ICL) have developed a new data-driven method to increase multiplexing capabilities of widely used PCR instrumentation.
In two studies published last month, the team at ICL, demonstrated the method using single-molecule real-time PCR. This increases the throughput of molecular diagnostic platforms and reduces the cost of tests, without any changes to instrument hardware, by virtue of smarter data analytics.
“There is plenty of room to maximise the value of existing data using sophisticated machine learning methods.”
-Dr Jesus Rodriguez-Manzano
CAMO Chief Scientist
In the first study, the team explored ways to enhance multiplexing capabilities by training machine learning models using the kinetic information in DNA/RNA amplification curves. As a proof-of-concept study, this was shown using a 3-plex assay targeting common carbapenemase genes (KPC, NDM and VIM). In the second study, the group incorporated thermodynamic information via melting curves, to enhance the method to high-level multiplexing applications, such as detecting nine variants of mobilised colistin resistance (mcr-1 to mcr-9).
For more information, read media coverage of the new methods from GenomeWeb.