Research title: .
IMPROVING FIP DIAGNOSTICS
Feline infectious peritonitis (FIP) is a devastating infectious disease of felids infected with feline coronavirus (FCoV) that, until recently, has been challenging to diagnose, because of the non-specific clinical signs that could be related to many diseases and infections. Both chronic and acute forms of the disease occur, with cats exhibiting general signs such as pyrexia, lethargy, inappetance, anorexia and mood changes; the acute form is commonly accompanied by a build-up of viscous fluid in the chest or abdomen. The pathogenesis remains poorly understood even after decades of research; this lack of knowledge impedes diagnosis, treatment and prevention. I am a highly motivated scientist with a keen interest in virology and specifically viral diagnostics; my interest in FIP has grown from working in a laboratory well known for its involvement in FIP research.
During the course of my PhD project, I will undertake research on a number of themes related to improving FIP diagnostics.
Specifically, I intend to:
1. Evaluate the current range of host parameters used for FIP diagnosis
2. Identify novel biomarkers using established and emerging technologies
3. Develop algorithms to allow quantitative FIP diagnostics
As the project develops, I will investigate the molecular epidemiology of FCoV infection and evaluate whether viral sequence data could improve diagnostic capabilities.
The project is a collaborative effort involving the School of Veterinary Medicine, the Institute of Biodiversity Animal Health and Comparative Medicine and the Centre for Virus Research (CVR) at the University of Glasgow. My primary project supervisor is Dr Willie Weir, the clinical lead in Infectious Disease in the Veterinary Diagnostic Service and a specialist in bioinformatics. My second supervisor is Prof Margaret Hosie, a professor of comparative virology at the CVR with an international reputation in feline virus research. Dr Simon Babayan recently joined my supervisory team. Simon is a member of the Institute of Biodiversity, Animal Health and Comparative Medicine. He has a wealth of experience in the feild of machine learning and disease modelling, as such he will be instrumental in guiding the technical aspects of this project.