Using machine learning to improve the characterisation and quantification of fibrotic lung.

Research Opportunity
PhD students, Honours students, Master of Biomedical Science
Number of Honour Places Available
1
Number of Master Places Available
1
Primary Supervisor Email Number Webpage
Dr Robert O'Donoghue orjj@unimelb.edu.au Personal web page
Co-supervisor Email Number Webpage
Prof Gary Anderson gpa@unimelb.edu.au
Dr Andrew Jarnicki

Summary Our research is focused on understanding the molecular basis of chronic degenerative lung diseases, in particular severe refractory asthma, Chronic Obstructive Lung Disease (COPD), Asthma-COPD Overlap, the COPD-lung cancer interface and fibrotic lung diseases. We are interested in understanding the reasons why lung disease becomes chronic and resists the normal processes that help resolve tissue damage, as well as why the damaged lung is so susceptible to subsequent infections. Our research also focuses on developing and testing experimental medicines in preclinical models. We work with leading clinicians/researchers at the RMH and internationally to translate our basic findings into useful medicines.

Project Details

Pulmonary fibrosis is severe untreatable feature of some acute and chronic lung diseases, it has recently been recognised as a consequence of severe COVID-19-induced pneumonia. Fibrosis is essentially scar tissue that replaces functional lung tissue and is a feature of Idiopathic pulmonary fibrosis (IPF) where it is progressive in nature and always causes the demise of the patient. Fibrosis is also observed in Acute Respiratory Distress Syndrome (ARDS) where it develops rapidly, does not usually progress but can leave patients with permanent lung damage and lifelong reduced physical capacity.  The current pre-clinical models of pulmonary fibrosis have a poor record in translating successful treatments from the laboratory to clinic, in part, due to their inability to mimic molecular or cellular mechanisms that occur during tobacco smoking, viral infections and the development of fibrotic lung diseases such as IPF. We are interested in developing pre-clinical models that better represent  these molecular and cellular mechanisms to improve and develop fibrosis treatments that will be successful in the laboratory and the clinic.
The aim of this project is to combine ‘wet lab’ methods with machine learning to improve the characterisation and         quantification of fibrosis in mouse models with real world relevance to IPF and ARDS. You will use a number of laboratory techniques including in vivo disease modelling, tissue culture, QPCR, western blotting and FACs as well as machine learning methodologies using the facilities located within the Biological Optical Microscopy Platform (BOMP).




Research Opportunities

PhD students, Honours students, Master of Biomedical Science
Students who are interested in joining this project will need to consider their elegibility as well as other requirements before contacting the supervisor of this research

Graduate Research application

Honours application

Key Contact

For further information about this research, please contact a supervisor.


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