Twinning to skyrocket scientific excellence towards individual radiosensitivity...
Twinning to skyrocket scientific excellence towards individual radiosensitivity prediction by raising the bar in knowledge transfer, networking, and technological innovation in radiobiology
Prostate cancer patients receiving radiotherapy (RT) may develop radiation-induced side effects, which significantly impact quality of life. One of the main challenges in radiobiology is to predict individual patient's normal tiss...
Prostate cancer patients receiving radiotherapy (RT) may develop radiation-induced side effects, which significantly impact quality of life. One of the main challenges in radiobiology is to predict individual patient's normal tissue radiosensitivity to tailor personalized RT. Although the Institute for Oncology and Radiology of Serbia (IORS) is a highly specialized health, scientific, and educational institution it is necessary to develop a strategic action plan and sustainable knowledge transfer networking with top-class leading European institutions. Through the 6 Specific Objectives, RadExIORSBoost project aims to develop and implement institutional Scientific Strategy on radiobiology, to investigate individual radiosensitivity by transcriptome profiling, radiation-induced lymphocyte apoptosis, and measurement of DNA damage levels, to design a high-quality clinical study, and to raise the research capacity of IORS and partner institutions through 5 international workshops, 8 short-term visits, and 6 trainings. RadExIORSBoost project aims to strengthen the administrative and research management skills of IORS. RadExIORSBoost project realization will increase the scientific excellence of IORS to approach high-ranking partner institutions, Medical Faculty Mannheim, Heidelberg University (UHEI), Germany, the University of Leicester (ULEIC), United Kingdom, Medical University of Vienna, Center for Cancer Research, (MUW) Austria, and the Institute of Oncology Ljubljana (IOL) from Slovenia. Artificial intelligence-based models, such as machine learning may give directions towards the clinical application of peripheral blood mononuclear cell transcriptome, and list potential biomarkers predicting radiotoxicity, not only in cancer patients, but also in healthy individuals at risk. RadExIORSBoost project may blaze the trail for technological innovations in modern radiation oncology to significantly reduce the side effects of RT and its harmful effects on the environment.ver más
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