The doctoral program in Innovative Biomedical Technologies in Clinical Medicine aims to implement a translational research training program.
Thus, the specific aims of the doctoral program are to:
(a) achieve better interaction between the contents of biological, clinical and computer sciences;
(b) to create a new professional figure in the biomedical and technological field who is an expert in translational medicine and is able to merge basic and clinical skills.
The training objectives are:
(a) learning elements of epidemiology to interpret genetic susceptibility to multifactorial diseases
(b) learning genome study methodologies including through bioinformatics methodologies
(c) learning methodologies for studying epigenetic modifications
(d) learning methodologies for studying gene expression
(e) learning techniques for cell, stem cell and organoid culture
(f) learning techniques for in vitro and ex vivo study of cells of innate and adaptive immunity
(g) learning the main methods of generating constitutive and conditional genetically modified animals
(h) learning of proteomics and metabolomics techniques for the identification of predictive markers of disease
(i) learning the principles and applications of network medicine
(l) learning and application of innovative imaging techniques
(m) learning the principles of pharmacology with special reference to knowledge of pharmacogenetics and pharmaco-genomics, pharmaco-prevention
(n) learning the principles and applications of BigData Science
(o) learning the basics of clinical trials
(p) learning the basics of in silico clinical trials (development of simulation models for human physiology, pathophysiology, pharmacokinetics, and pharmacodynamics; development of methods and software for evaluating the efficacy of drugs, diagnostic tools, or personalized medical treatments)
(q) learning the principles of animal experimentation including elements of biomedical ethics;
(r) evaluation of entrepreneurial and industrial aspects of advanced biotechnology.
(s) design and implementation of predictive algorithms (machine learning) to analyse and extract useful knowledge from the large variety and quantity of available medical data in the form of networks (biological and social), sequences, and unstructured data (text and images, medical records, and social data).
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