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Directing immune development to curb sky-rocketing diseaseOnce upon a time it was infectious diseases like polio, measles or tuberculosis that most worried parents. With these threats now largely under control, parents face a new challenge – sky-rocketing rates of non-infectious diseases such as asthma, allergies and autism.
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National Hybrid Closed-Loop Outpatient TrialThe national Hybrid Closed-Loop Outpatient Trial will test the use of an automated insulin delivery system to see if it is better at optimising blood glucose levels than standard therapy.
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Western Australian DNA and Longitudinal Serum Bank for Weight RegulationThis resource will allow researchers to carry out studies which will look at the genetic causes of excessive weight gain and identify biomarkers
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Can exercise training Improve health in young people with type 2 diabetes?We are studying exercise in young people with T2DM and obese young people at risk of developing type 2 diabetes
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Closed Loop Study - MulticentreA Closed-Loop System will potentially have a major impact upon acute and chronic complications of diabetes as well as upon their quality of life
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Hypoglycemia alarm enhancement using data fusionThe acceptance of closed-loop blood glucose (BG) control using continuous glucose monitoring systems (CGMS) is likely to improve.
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Analysis of glucose responses to automated insulin suspension with sensor-augmented pump therapyThe advent of sensor-augmented pump therapy with a low-glucose suspend (LGS) function.
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Acute hyperglycaemia does not alter nitric oxide-mediated microvascular function in the skin of adolescents with type 1 diabetesImpact of an acute bout of hyperglycaemia on nitric oxide (NO)-mediated microvascular function in the skin of adolescents with type 1 diabetes
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Machine learning techniques to predict diabetic ketoacidosis and HbA1c above 7% among individuals with type 1 diabetes — A large multi-centre study in Australia and New ZealandType 1 diabetes and diabetic ketoacidosis (DKA) have a significant impact on individuals and society across a wide spectrum. Our objective was to utilize machine learning techniques to predict DKA and HbA1c>7 %.