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A Program Of Methodological And Collaborative Research In Biostatistics And Population Health
Funder
National Health and Medical Research Council
Funding Amount
$264,081.00
Summary
Biostatistics is a critical component of health and medical research, especially for studies in population health. However, there is an increasing gap between supply and demand for high-level biostatistical input. This proposal combines novel methodological research into methods for analysing incomplete data, with collaborative research applying new ideas and complex analyses to important health problems. The fellowship will facilitate my development as a future leader in this key area.
Finally New Tools Are Available To Combat Drug Resistant Tuberculosis, But How Do We Make Them Work? Models To Determine Effective Implementation Strategies In Australia And Our Region
Funder
National Health and Medical Research Council
Funding Amount
$1,562,250.00
Summary
The risk of drug resistance in tuberculosis (MDR TB) is thwarting control efforts and must be addressed. Potential strategies being considered are; treating MDR TB in the latent phase, using new short-course treatment for active TB and using new vaccines. My work develops simulation models to test strategies before they are implemented, to maximise impact and avoid costly or low-impact interventions. I will examine strategies in Australia and in high burden countries in Australia’s region.
I am investigating several areas relating to Paediatric surgery in particular the quality and coverage of paediatric surgical trials. The context for this study is to evaluate the current state of paediatric surgical research for methodological rigour, aiming to establish an evidence base and agenda for future trials to improve outcomes in paediatric surgery. The project will involve epidemiological methodology studies and systematic reviews, data linkage and analysis of existing study datasets.
Developing Interpretable Machine Learning Models For Clinical Imaging And Single-cell Genomics
Funder
National Health and Medical Research Council
Funding Amount
$1,312,250.00
Summary
Machine learning methods will be vital to make best use of the deluge of data generated by high-throughput technologies in biomedical science. To get the most out of these models, however, we need to be able to unpack the 'black box'. I will use curated clinical and public research data to benchmark and develop interpretable deep learning models and software tools. These models will be used for breast cancer screening programs and for analysis of complex, large-scale single-cell genomics data.
Harnessing The Power Of Single Cell Sequencing To Advance Biomedical Research
Funder
National Health and Medical Research Council
Funding Amount
$2,013,220.00
Summary
Our ability to sequence the DNA code has advanced rapidly in the last 10 years but this data is large and complex and we need advanced computational and statistical methods to analyse these data. Now we are able to generate this data from individual cells. I will provide the analysis approached that are required in order to use this data to understand the complexity of tissues at a cellular resolution. This will provide deep molecular insights into development and disease.
Cannabis is the most widely used illicit drug in the world. Delta-9-tetrahydrocannabinol (THC), the main psychoactive constituent of cannabis, is known to be preferentially taken up into fat tissue where it can be stored for weeks, months and possibly years. Boy fat has the capacity to store large quantities of THC and the slow passive release of THC from fat cells into the blood accounts for why THC remains at detectable levels in the blood or urine for weeks after exposure to cannabis. Under n ....Cannabis is the most widely used illicit drug in the world. Delta-9-tetrahydrocannabinol (THC), the main psychoactive constituent of cannabis, is known to be preferentially taken up into fat tissue where it can be stored for weeks, months and possibly years. Boy fat has the capacity to store large quantities of THC and the slow passive release of THC from fat cells into the blood accounts for why THC remains at detectable levels in the blood or urine for weeks after exposure to cannabis. Under normal conditions the slow passive release of THC from fat cells has negligible effects on the user as the amounts involved are so small. However, we have recent preliminary evidence to show that conditions associated with increased fat metabolism (e.g. dieting, exercise or stress) cause a greatly enhanced release of THC from fat stores into the blood supply. Further, we have demonstrated that such levels attained promote significant behavioural and physiological changes The current proposal aims to further characterise this phenomenon. We aim to: (1) determine the length of time that THC can be stored in fat before being released into blood, (2) establish that the release of THC stored in fat tissue may be promoted by fat breakdown associated with food deprivation, stress or exercise, (3) characterise the physiological and behavioural effects of THC released from fat, (4) determine the mechanisms responsible for THC release from fat, and (5) determine if THC released from fat can cross from the bloodstream into saliva. This proposal has far reaching consequences for our understanding of the long-term effects on cannabis use on physical health and behaviour. Further, it may have major implications for the correct interpretation of analytical data from road-side saliva testing and forensic and criminal cases involving cannabis use.Read moreRead less
New genomic technologies are revolutionizing biological research. RNA-seq is a recently developed high-throughput sequencing technology that provides scientists with much more detail how genes are regulated and expressed than any earlier technology. New tools developed by Professor Gordon Smyth are allowing researchers to use RNA-Seq technology to more accurately determine which genes are genuinely changing in the development of cancers and in response to cancer treatments.
Understanding Gene Regulation In Disease Using High Throughput Sequencing
Funder
National Health and Medical Research Council
Funding Amount
$415,218.00
Summary
While genetics refers to the gene sequence, or DNA code, epigenetics refers to all the other factors that control how and when each gene is expressed. New technologies with the ability to sequencing billions of bases of DNA are now being used to study epigenetics. However the data sets are vast and complex. I use statistical and computational approaches in the emerging field of bioinformatics to make sense of this data and relate genome wide disruption of epigenetic marks to diseases.