Doctoral Thesis
Next-Generation Robotics for the Surveillance of Livestock Antimicrobial Resistance and Disease
Doctor of Philosophy (PhD), Murdoch University
2024
Abstract
Antimicrobial resistance (AMR) poses a significant global public health threat, projected to cause 10 million deaths annually by 2050. This necessitates a paradigm shift in surveillance methods to handle the scale and complexity of AMR management. This thesis investigates the Robotic Antimicrobial Susceptibility Platform (RASP) as a novel approach to bolster AMR surveillance.
Chapter 1 validates the feasibility and efficiency of RASP for high-throughput antimicrobial susceptibility testing (AST) of Escherichia coli. Adhering to ISO 20776-1:2019 standards, RASP achieves notable gains in testing throughput while preserving genetic context, surpassing conventional methodologies.
Chapter 2 extends RASP’s application to AMR surveillance in Australian poultry, demonstrating its superior data granularity compared to existing programs. Enhanced data granularity enables precise interventions at various levels, from individual animals to national flock or herd management strategies.
Chapter 3 explores RASP’s versatility by examining Pasteurella multocida isolates associated with porcine respiratory disease complex (PRDC). Insights highlight stable AMR patterns and identify persistent strains, informing targeted interventions such as vaccination.
Chapter 4 underscores the importance of integrating phenotypic and genotypic analyses using RASP during a porcine exudative epidermitis outbreak. This approach reveals variable antimicrobial resistance profiles among otherwise clonal isolates from the same lesion and emphasises the importance of comprehensive surveillance in guiding treatment decisions.
In conclusion, RASP emerges as a promising tool for advancing AMR surveillance, offering scalability, precision, and versatility. Recommendations from this thesis include leveraging automation, enhancing data resolution, exploring diverse applications, and maximising the value of genomic data to combat the looming threat of AMR effectively.
Details
- Title
- Next-Generation Robotics for the Surveillance of Livestock Antimicrobial Resistance and Disease
- Authors/Creators
- Alec V Truswell
- Contributors
- Sam Abraham (Supervisor) - Murdoch University, Centre for Biosecurity and One HealthRebecca Abraham (Supervisor) - Murdoch University, Centre for Biosecurity and One HealthDiana Turpin (Supervisor) - Murdoch University, Centre for Animal Production and Health
- Awarding Institution
- Murdoch University; Doctor of Philosophy (PhD)
- Identifiers
- 991005692863607891
- Murdoch Affiliation
- College of Environmental and Life Sciences; School of Medical, Molecular and Forensic Sciences
- Resource Type
- Doctoral Thesis
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