Due to the ever increase percentage of the global population living in cities, relevant environmental conditions affect people’s quality of life (QoL). In parallel, IoT-powered sensor technologies allow for personalizing environmental pressures, rendering relevant data as appropriate for the development of QoL information services. Such data may include for example physical, chemical and biological weather conditions as well as personalized symptom recordings, which may be used towards symptom modelling. The expected outcome are services that may provide early warnings to patients in relation to environmental conditions, assist them in receiving medical advice and treatment in a more targeted and effective way, and overall improve aspects of their QoL. Course contents include an introduction to basic analysis of environmental data (working example: weather, air pollution, aeroallergens) and the identification of weather, air pollution and pollen types that may trigger symptoms to sensitive parts of the population; qualitative and quantitative mapping of QoL and symptom data; introduction to Citizen science and crowd-sourced powered sensor and personal report collection along with their methods, tools, limitations, ethical and methodological problems and their linkage with the citizen science hub of AUTh/Thessaloniki; design principles, user requirements and functional specifications of electronic information services for QoL support; and finally hands-on practice on getting familiar with some (i) low cost environmental sensors (AQ for indoor as well as outdoor to be used as an example) and (ii) “low-code” development platforms for a “coding without code” approach. Team-work and group-projects are encouraged on the basis of a real world problem solving scenario.

Huge advances in large-scale biology have led to achievements, such as the sequence of the human genome. At the same time, gene expression research using RNA-seq, microarray platforms and other technologies, as well as the generation of big -omics data (genomics, transcriptomics, proteomics, metabolomics) have created a wealth of data, the biological interpretation of which is an important tool in both precision and personalized medicine, as well as prevention, diagnosis and therapeutic approaches. However, the challenge facing scientists is to analyze/combine and extract useful information regarding the biological system under study. Based on the above, the course focuses on familiarizing students with the use of available bioinformatics resources – mainly online programs and databases – to access the wealth of data and their correct biological interpretation, to address problems – questions. Course contents include: 1) sequence alignments and blast, 2) phylogenetics, 3) analysis of gene expression data including information theory, 4) protein interaction networks, 5) interpretation of -omics data (analysis of polymorphisms, toxicogenomic, genomic, epigenomic, transcriptomic, proteomic, metabolomic), 6) signaling, regulatory and metabolic networks, 7) metagenomics, 8) statistical methods in bioinformatics, 9) bioinformatics platforms (R Bioconductor, Galaxy), 10) the role of bioinformatics in systems biology and in the study of adverse outcomes.

Large-scale heterogeneous medical data are commonly acquired in diverse health care centers. The size and complexity of these datasets constitute great challenge in analytics and following application in a practical clinical environment. The field of machine learning offers methodologies that match ideally the task of knowledge extraction from such complex data sets. In the frame of the course technical introduction is given to big data analytics (characteristics of the big data, investigation and visualization of big data, knowledge extraction from big data), pattern classification and identification, with emphasis on biomedical data. The particular classical and modern techniques of machine learning is studied, and the areas and approaches of applications is presented. Topics, such as the quantification and diagnosis of disease as well as patient classification is combined with structural data analysis with methods including nonlinear and connectivity analysis and complex networks, as well as unstructured data and text analysis and image analysis (radiomics). In terms of methodology, machine learning techniques for classification and regression are presented (e.g., linear classification and regression, support vector machines, manifold learning as well as ensemble learning, such as random forests, bagging and boosting), including dimension reduction techniques (penalised regression, variable / feature selection). Projects are given in the application of machine learning techniques in clinical practice. The course focuses on the understanding and application of machine learning techniques commonly used in biomedical applications.

The scope of the course is to introduce the concepts of (a) decision support systems and the basic related methodologies (expert systems, fuzzy systems, learning systems) and (b) automated medical diagnosis. The use of these methodologies will be presented in the context of clinical practice (risk assessment, stratification, medical prognosis, care pathway along with the strategies adopted for their evaluation. The contents of the course include: 1) decision making and optimization 2) knowledge-based decision systems, 3) expert/fuzzy decision making 4) data-learning systems. Specific medical examples will be included. The associated ethical issues will be covered along with the novel concepts of trustworthy and explainable AI as these apply to biomedicine.

New technologies like Virtual Reality and Robotics currently play a major role in health care. Clinically Certified, powerful medical simulators are now available and used all over the world. Advanced general surgery and neurosurgery systems make use of augmented reality and image-guided surgery to improve outcomes and efficiency. Robotics have been used in orthopedics and cardiology, as well as, general practice. In recent years, medical robotics together with advanced extended reality systems are expected to shape the future of mental health, anesthetics, and emergency medicine. So, this course covers the basics aspects of medical robotics, virtual reality and cyber-physical systems and their contemporary applications in healthcare.

The course aims to provide basic knowledge and relevant tools for understanding the basic and practical implications of medical precision, its opportunities and challenges as they arise for accurate diagnosis, treatment choices, genetic counseling, public health interventions and biomedical research. Given the use of sensitive personal data required in personalised medicine and precision prevention, bioethics and data science issues will be an integral part. The contents of the course include: 1) Genomic analysis and genetic counseling, 2) Integration of multiple -omics data (analysis of polymorphisms, gene expression profiles, toxicogenomics, proteomics, metabolomics, microbiome analysis). 3) Pharmacogenomics, 4) Cancer biomarkers, 5) Chronic disease risk assessment, 6) Understanding gene-environment interactions, 7) Basic concepts in modern pharmacology, including drug-drug interactions, personalized medicine and drug development, 8) Basic understanding of the unique facto! rs of pathology and pharmacology that affect different population groups and the disease progression as well as, its utilization in precision medicine, 9) Description of the multidisciplinary nature precision medicine development and application of new tools, 10) Application of modern technologies in improving diagnosis, treatment, prevention of disease and the final outcome of patients, 11) Understand key determinants of individual drug responses, 12) Understand how genetics therapeutic outcomes, 13) The ‘exposome’ and its contribution to accurate diagnosis and therapeutic approaches, 14) The role of nutrition in precision prevention, 15) The role of the environmental exposure in precision prevention.

The aim of the course is to provide students with the basic principles of scientific methodology and research in the field of biomedical engineering through the use of contemporary examples. Τhe course material covers a wide range of methodological approaches in the field of biomedical engineering. Starting with the design, use and control of medical devices and biomedical products, the design and conduct of pilot tests, data collection and analysis, the writing of scientific and technical reports and the organization of “lab-to-market” procedures. In addition, related bioethics issues and concerns are also raised and discussed, along with the concept of innovation and entrepreneurship and the concept of regulatory mechanisms, standardization and patent submission.

The scope of the course is to introduce the basic principles of computational neuroscience and familiarize the students with the associated research methodologies. This scientific area lies at the crossroad of neurophysiology/neuroanatomy from the side of medicine and machine learning / signal analysis from the side of information theory. The following topics are introduced in this course: a) from neurons to systems (recording, processing, analysis and modelling of neural signals), b) applications to cognitive and clinical neuroscience: neuroimaging techniques and interpretation of the acquired data, c) brain activity: spectral analysis, nonlinear dynamics, independent component analysis, connectivity analysis, graph-theoretic description, e) examples of translational neuroscience: brain-computer interfaces, neurofeedback, transcranial brain stimulation, neuromimetic intelligence.

This course is intended to introduce students to basic physics principles pertaining to medical image formation and image processing. The course covers image acquisition by means of ionizing and non-ionizing radiation methods as well as the utilization of magnetic resonance methods, other optical-based and spectroscopic methods but also other newer methods used for imaging living organisms. The course also addresses issues and topics on image processing by means of modern mathematical and algorithmic methodologies.

Τhe class aims at introducing students to the scientific field of nanoscience and nanotechnology in the context of their applications in medicine (nanomedicine). Indicatively it covers topics such as : types, properties and methods of manufacturing of nanoparticles; technological applications of nanoparticles : nanocoatings, nanospheres, nanomagnets, nanomedicine, nanowires, nanotubes, biochips and biosensors, nanodrug delivery, medical devices, biomimetics, minimally invasive cellular and tissue signal transduction, biomarkers, interactions of nanomaterials with cells and tissues, nanodiagnostics, nanotherapeutics. The course will be delivered as a combination of lectures and complementary hands-on learning.

This multi-disciplinary course aims at introducing the students to the field of drug engineering. The course will provide detailed knowledge on: basic engineering of bioresponsive materials for the design and implementation of drug delivery systems, development of SMART delivery processes linked to clinical applications, general principles and applications of lab-to-clinic nano/micro-technology transfer, principles of spray drying and freeze drying technologies, encapsulation techniques, engineering drug delivery systems at the nano and micro level, physicochemical and biological characterization of drug delivery systems and their targeted clinical correlations, the pharmacokinetic and pharmacodynamic principles, analytical methods for validation, film coating technology, oral strip manufacturing technology.

Biofabrication, regenerative medicine and histomechanics. It includes, but is not limited to: I. production, on a small and large scale, of cells, biochemical agents, hybrid biomaterials, biocomposites, scaffolds, 3D printing, II. design and production of tissue substitutes, including soft and hard tissue histomechanics products, use of stem cells, development of 3D tissue models and real-time testing of histomechanical processes, legal issues, bioethics, and case studies.

The course has two objectives: I. Overview of natural biological materials and substitute biomaterials. It includes, but is not limited to: categories of materials, methods of study and characterization, physical, chemical and biological properties, biomaterial interactions with physical structures of the body, principles of biomaterial design, uses in biological and medical applications, case studies. II. Introduction to biomaterial mechanical properties. It includes but is not limited to: general principles of engineering, ways of study, modelling, understanding of the action of mechanical forces at the molecular and cellular level, as well as the level of tissues, organs as well as the whole organism, mechanical properties of biological materials and biomaterials, association of mechanical biology and disease treatment, histomorphology, and mechanical design and orthopedic movement. The course is based on a combination! of theory and corresponding laboratory practice.

The contents of the course are cell physiology, autonomous nervous system, neurophysiology, cardiovascular and respiratory physiology, kidney physiology, gastrointestinal physiology, endocrine system physiology and reproductive system physiology. In addition to the physiology issues covered, issues regarding modeling systems will be raised, presenting: a) in-silico modeling approaches, and b) methods and indicators for quantifying the operation of systems. Applications will be presented and students’ hands-on experience with in-silico approaches will be sought. The course will be adapted to the background of the life sciences students. In parallel with the course, students will have the opportunity to work in small groups with students who attend course 2b (project).

The scope of the course is to introduce the basic principles of digital signal processing and system modelling as practiced in biomedical research and clinical medicine. It covers methodologies and algorithms for the registration and visualization of biosignals, the use of filters and transforms (Fourier, wavelet, PCA), the coding of biomedical data, nonlinear analysis, feature extraction and biomedical systems modelling. It focuses on understanding the theoretical foundation of various biomedical signal processing techniques, as well as their practical advantages and limitations for the purpose of identifying the most promising approach according to the problem at hand. In addition, the implementation of selected signal processing algorithms will be demonstrated in specific tasks that concern real-life biosignals and biomedical systems. The course includes programming projects based on signals from e.g. cardiology, neurology and medical imaging.

The aim of this course is to introduce students to the concept of biomedical technology products and medical devices in specific and emphasise the different prerequisites/stages from their idea conception and design to their use and exploitation in healthcare. The course covers the basic principles of design systems / technology and biomedical research, the basic principles that technological systems must meet in all stages of health care; thus, the course covers elements of specifications and compliance to guidelines and recommendations for equipment procurement, maintenance and equipment management in general (small and large scale), as well as, elements of security, risk management, quality control/assurance. Principles and methodologies of healthcare technology assessment and evaluation are also covered together with the fundamental role they play in decision-making and health policy practice.