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Using human pluripotent stem cells to model obesity in vitro
Obesity and neurodegeneration lead to millions of premature deaths each year and lack broadly effective treatments. Obesity is largely caused by the abnormal function of cell populations in the hypothalamus that regulate appetite. We have developed methods generate human hypothalamic neurons from hPSCs to study how they respond to nutrients and hormones (e.g. leptin) and how disease-associated mutations alter their function. Since human hypothalamic neurons can be produced in large numbers, are functionally responsive, have a human genome that can be readily edited, and are in culture environment that can be readily controlled, there is an unprecedented opportunity to study the genetic and environmental factors underlying obesity. In addition, we are fascinated by the fact that mid-life obesity is a risk factor for dementia later in life, and caloric restriction, exercise, and certain anti-obesity drugs are neuroprotective, suggesting that there are shared mechanisms between obesity and neurodegeneration. Studies of HPSC-derived hypothalamic neurons may help bridge the mechanistic gulf between human genetic data and organismic phenotypes, revealing new therapeutic targets.
Biomedical Image and Genetic Data Analysis with machine learning; applications in neurology and oncology
In this presentation I will show the opportunities and challenges of big data analytics with AI techniques in medical imaging, also in combination with genetic and clinical data. Both conventional machine learning techniques, such as radiomics for tumor characterization, and deep learning techniques for studying brain ageing and prognosis in dementia, will be addressed. Also the concept of deep imaging, a full integration of medical imaging and machine learning, will be discussed. Finally, I will address the challenges of how to successfully integrate these technologies in daily clinical workflow.
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