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BIG DATA analytics and BRA-NL collaborations in biomedical and biological research

Wednesday 1 April 2020
LUMC Main Building
Albinusdreef 2
2333 ZA Leiden
Room will be sent after registration


09:00-09:30 Coffee and meet. Word of welcome.
Chair: Rodrigo Coutinho de Almeida.  
09:30-10:10 Prof. Annemieke Geluk (LUMC):
Leprosy: Bridging Research between Brazil & the Netherlands.
10:10-10:50 Prof. Fons Verbeek (LIACS):
Tuberculosis in zebrafish: High throughput imaging and computational modeling
10:50-11:40 Dr. Edson Amaro (Albert Einstein Hospital) :
Big Data Analytics applications to guide patient care in Brasil: examples from a tertiary-care Hospital and a National Primary Care program.
11:40-12:00 General discussion, BRA-NL collaborations
12:00 Sandwiches


Dr. Edson Amaro is a neuroradiologist at the Albert Einstein Hospital in São Paulo and Associate Professor of Radiology at the University of São Paulo. He is author / co-author of over 200 peer-reviewed scientific articles. In 2000 he completed a doctorate in functional magnetic resonance imaging and postdoctoral studies at the Institute of Psychiatry, King's College London until 2002 when he joined the Albert Einstein Hospital Radiology Department. From 2008-2012 he headed the Albert Einstein Hospital Brain Institute. Since 2015 he leads the hospitals Big Data Analytics Initiative focused on clinical (home and primary care) and managerial (cost-effectiveness and resource optimization) questions. 
Brazil's Single Health System (denoted as SUS) is the largest public health system in the world. More than three-fourths of roughly 210 million Brazilians rely exclusively on the SUS for health services. In 2017 the National Government launched the e-Health programme - designed to transform SUS in a digital environment. Since January 2017, close to 17,000 (out of 43,612) Primary Care Units are sending all clinical information to the Ministry – this initiative is generating a significant amount of data, beyond the Ministry of Health Analyzing capabilities. Our project aims at helping to transform this data in useful information - a critical step to improve the healthcare system. A few projects and applications on Hospital-based Data Analytics as well as Primary Care programme will be presented. Some of the projects are in line with Dutch initiatives, such as the Dutch Society for Research on Ageing (DuSRA).

Prof. Annemieke Geluk is a chemist and immunologist with a background in cellular mechanisms of host immunity in mycobacterial infectious diseases including the identification of immune-, metabolic- and transcriptomic host biomarkers.
Currently her research focuses on immunodiagnostics of leprosy and tuberculosis including basic, translational, applied and field research with the main goal to design, develop and validate host biomarker-based, POC assays for low resource settings and personalized diagnostics. She has designed and coordinated multiple large-scale, multi-center studies in Bangladesh, Brazil, Ethiopia, India and Nepal. The Geluk Immunodiagnostics group at the Dept. of Infectious Diseases (LUMC) functions as reference center for routine serological diagnosis of leprosy and provides this service also for other European countries and the Antilles. 
Prof. Geluk will discuss the translation of basic immunological research on leprosy for the purpose of novel diagnostic tests including large scale vaccination field trials and public health aspects of leprosy.
Professor Fons Verbeek (Leiden Institute of Advanced Computer Science) is a computer scientist with an interest in biology. His focus is on image (data) analysis and computational imaging in the field of bioinformatics. He is specifically focusing on dealing with large datasets from high-throughput experiments in the domain of bio-imaging. The study of disease mechanisms in the pre-clinical phase involves model systems that are typically useful for high-throughput applications. The zebrafish is a popular model system that has been used successfully in the past years to further understand diseases like tuberculosis and cancer. Analysis of high throughput data provides patterns that can be included in computational predictive models. These computational models are useful in the further analysis of the experimental material. Computer science brings machine learning techniques to the daily practice of data analysis but also provides tools for modelling of complex networks that are used to understand the analysis.
Professor Verbeek will discuss the study of Tuberculosis in zebrafish and how image analysis, high-throughput imaging and computational modelling is used to understand mechanisms in tuberculosis. Recent findings from testing of potential drug compounds from natural products from the Brazilian littoral fit well in that framework.
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