Courses


First Semester – Core Courses

1a. Systems biology (for students of life science education) [6 ECTS]  read more ...

Short Description

Systems Biology involves the mathematical and computational modeling of complex biological systems. The field is the result of convergence and synergy of three scientific areas: 1) Rapid accumulation of detailed biological data at the submolecular, molecular, cellular and physiological levels, 2) Technological development that permits analysis of biological systems in vivo using sensors, imaging techniques, and biomarker expression profiles, 3) Combined evolution of mathematical, physical and computational techniques that are more powerful and available to most of the scientific community than ever before. It represents an interdisciplinary scientific field that focuses on complex interactions within biological systems using a holistic approach to biological research. The contents of the course include: 1) description of complex biological systems, 2) mathematical modeling of biological systems, 3) stochastic processes in Biology (with emphasis on the simulation and analysis of stochastic phenomena in biological systems), 4) static network models, 5) cellular response models, gene and protein systems/networks, 6) metabolic systems  structural analysis of metabolic networks, 8) dynamic analysis of metabolic network flows, 9) analysis of signaling pathways/networks, 10) population systems, 13) systems biology in medicine/physiology and drug development, 14) systems biology in personalized medicine approaches prevention and therapy, 15) new horizons in systems biology, 16) from neurons to the brain 17) multi-step models of carcinogenesis, 18 ) multifactorial diseases, inflammation and trauma, 19) interactions between environment and health. The course will be adapted to the background of the life sciences students. Students will have the opportunity to work in small groups with students who attend course 1b (project).

Timetable:

Assessment:

Coordinator: Dimosthenis Sarigiannis

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Section 1: Introduction to Systems Biology - an overview Lecture D. Sarigiannis
Week 2 Section 2: Introduction to mathematical modeling of biological systems Lecture V. Rothos
Week 3 Section 2: Static models of networks Lecture D. Sarigiannis
Week 4 Section 2: The mathematics of biological systems - I Lecture V. Rothos
Week 5 Section 2: The mathematics of biological systems  - II  Lecture V. Rothos
Week 6 Section 4: Gene systems Lecture A. Triantafyllidis
Week 7 Section 3: Cellular reaction models - model parameterization Lecture D. Sarigiannis
Week 8 Section 5: Protein systems Lecture A. Aggeli
Week 9 Section 6: Signalling systems Lecture D. Dafou
Week 10 Section 7: Metabolic systems Lecture D. Sarigiannis
Week 11 Section 7: Structural analysis of metabolic networks and fluxes Lecture D. Sarigiannis
Week 12 Section 8: Case studies: High dimension biological systems analysis Computer Lab D. Sarigiannis, N. Papaioannou, A. Triantafyllidis, A. Aggeli, A. Salifoglou
Week 13 Section 8: Case studies: Systems biology applied in human physiology and pathology (multi-stage cancer models) Computer Lab S. Karakitsios, A. Karakoltzidis


1b. Systems biology (for students of science / engineering education) [6 ECTS]  read more ...

Short Description

Systems Biology involves the mathematical and computational modeling of complex biological systems. The field is the result of convergence and synergy of three scientific areas: 1) Rapid accumulation of detailed biological data at the submolecular, molecular, cellular and physiological levels, 2) Technological development that permits analysis of biological systems in vivo using sensors, imaging techniques, and biomarker expression profiles, 3) Combined evolution of mathematical, physical and computational techniques that are more powerful and available to most of the scientific community than ever before. It represents an interdisciplinary scientific field that focuses on complex interactions within biological systems using a holistic approach to biological research. The contents of the course include: 1) description of complex biological systems, 2) mathematical modeling of biological systems, 3) stochastic processes in Biology (with emphasis on the simulation and analysis of stochastic phenomena in biological systems), 4) static network models, 5) cellular response models, gene and protein systems/networks, 6) metabolic systems  structural analysis of metabolic networks, 8) dynamic analysis of metabolic network flows, 9) analysis of signaling pathways/networks, 10) population systems, 13) systems biology in medicine/physiology and drug development, 14) systems biology in personalized medicine approaches prevention and therapy, 15) new horizons in systems biology, 16) from neurons to the brain 17) multi-step models of carcinogenesis, 18 ) multifactorial diseases, inflammation and trauma, 19) interactions between environment and health. The course will be adapted to the background of the students of sciences or engineering. Students will have the opportunity to work in small groups with students who attend course 1a (project).

Timetable:

Assessment:

Coordinator: Dimosthenis Sarigiannis

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Section 1: Introduction to Systems Biology - an overview Lecture D. Sarigiannis
Week 2 Section 2: Introduction to mathematical modeling of biological systems Lecture V. Rothos
Week 3 Section 2: Static models of networks Lecture D. Sarigiannis
Week 4 Section 2: The mathematics of biological systems - I Lecture V. Rothos
Week 5 Section 2: The mathematics of biological systems  - II  Lecture V. Rothos
Week 6 Section 4: Gene systems Lecture A. Triantafyllidis
Week 7 Section 3: Cellular reaction models - model parameterization Lecture D. Sarigiannis
Week 8 Section 5: Protein systems Lecture A. Aggeli
Week 9 Section 6: Signalling systems Lecture D. Dafou
Week 10 Section 7: Metabolic systems Lecture D. Sarigiannis
Week 11 Section 7: Structural analysis of metabolic networks and fluxes Lecture D. Sarigiannis
Week 12 Section 8: Case studies: High dimension biological systems analysis Computer Lab D. Sarigiannis, N. Papaioannou, A. Triantafyllidis, A. Aggeli, A. Salifoglou
Week 13 Section 8: Case studies: Systems biology applied in human physiology and pathology (multi-stage cancer models) Computer Lab S. Karakitsios, A. Karakoltzidis


2a. Physiological and anatomical modeling (for students of life science education) [6 ECTS]  read more ...

Short Description

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).

Timetable:

Assessment:

Coordinator: Dimitra Dafou

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Modelling in physiology: a historical  and future  perspective Lecture E. Kosmidis
Week 2 Introduction to Physiology and homeostasis mechanisms Lecture A. Lazou
Week 3 Biochemical Reactions (Thermodynamics, enzyme kinetics) Lecture D. Sarigiannis
Week 4 Cellular Physiology Lecture T. Samaras
Week 5 Electrophysiology Ι: The Hodgkin-Huxley Model and Excitability (physiological  description) Lecture E. Kosmidis
Week 6 Nervous system (Description of basic structure and function) Lecture D. Dafou
Week 7 Muscle/skeletal physiological system description Simulation example (1hr) /3hr  Lecture P. Givisis/T. Samaras
Week 8 Cardiovascular system (Description, blood flow, laminar flow, arrythmia) Lecture A. Lazou
Week 9 In silico Modelling of Physiology I Lecture I. Chouvarda / A. Delopoulos
Week 10 In silico Modelling of Physiology II: Overview of Differential Equations in Physiological Systems Lecture I. Chouvarda / A. Delopoulos
Week 11 In silico Modelling of Physiology III: Non linear Dynamics (bifurcation) Lecture I. Chouvarda / A. Delopoulos
Week 12 Electrophysiology ΙΙ: The Hodgkin-Huxley Model and Excitability II (mathematical simulation) Lab (Lab of informatics, Engineering) I. Chouvarda / A. Delopoulos
Week 13 Example: Simulation of a system  Lab (Lab of Informatics, Engineering)


2b. Physiological and anatomical modeling (for students of science / engineering education) [6 ECTS]  read more ...

Short Description

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 students of sciences or engineering. In parallel with the course, students will have the opportunity to work in small groups with students who attend course 2a (project).

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Modelling in physiology: a historical  and future  perspective Lecture E. Kosmidis
Week 2 Introduction to Physiology and homeostasis mechanisms Lecture A. Lazou
Week 3 Biochemical Reactions (Thermodynamics, enzyme kinetics) Lecture D. Sarigiannis
Week 4 Cellular Physiology Lecture T. Samaras
Week 5 Electrophysiology Ι: The Hodgkin-Huxley Model and Excitability (physiological  description) Lecture E. Kosmidis
Week 6 Nervous system (Description of basic structure and function) Lecture D. Dafou
Week 7 Muscle/skeletal physiological system description Simulation example (1hr) /3hr  Lecture P. Givisis/T. Samaras
Week 8 Cardiovascular system (Description, blood flow, laminar flow, arrythmia) Lecture A. Lazou
Week 9 In silico Modelling of Physiology I Lecture I. Chouvarda / A. Delopoulos
Week 10 In silico Modelling of Physiology II: Overview of Differential Equations in Physiological Systems Lecture I. Chouvarda / A. Delopoulos
Week 11 In silico Modelling of Physiology III: Non linear Dynamics (bifurcation) Lecture I. Chouvarda / A. Delopoulos
Week 12 Electrophysiology ΙΙ: The Hodgkin-Huxley Model and Excitability II (mathematical simulation) Lab (Lab of informatics, Engineering) I. Chouvarda / A. Delopoulos
Week 13 Example: Simulation of a system  Lab (Lab of Informatics, Engineering)


3. Mechanical properties of biomaterials [5 ECTS]  read more ...

Short Description

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.

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Biomaterials - The medical perspective II - Hands-on on implants - Application of biomaterials in surgery Class Nikolaos Michailidis
Week 2 Βiomaterial mechanical properties ΙΙ - Hardness - Fracture strength - Fracture toughness - Fatigue - Creep - Micro-testing - Introduction to Μodelling Class Nikolaos Michailidis
Week 3 Hands-on mechanical testing - Tension - Compression - Bending Lab Nikolaos Michailidis, Alexandros Prospathopoulos, Apostolos Argyros
Week 4 • Materials in medicine (e.g. natural and hybrid composite polymers, hydrogels, resorbable materials, bioglasses, ceramics, etc.). • Analysis techniques of biomaterials (e.g. XPS, AFM, FT-IR, NMR, ESI-MS, XRD, SEM, TEM, etc.). Physicochemical properties • Biology of materials (e.g. adsorbed proteins in biomaterials, cell (patho)physiology, tissues, the extracellular matrix, and cell–biomaterial interactions). Host reactions to biomaterials and their evaluation (e.g. inflammation, wound healing, foreign-body response, innate and adaptive immunity with immune response to foreign materials, tumorigenesis and biomaterials, biofilms, biomaterials, and device-related infections, testing of biomaterials). Class A. Salifoglou
Week 5 • Techniques in biomaterials • NMR spectroscopy • ESI-MS spectrometry in biomaterials testing Molecular biology and informatics in biomaterials (from genes to databases) Lab Thanos Salifoglou, Sevasti Matsia
Week 6 “Biocompatibility of biomaterials’, including: - Biocompatibility mechanisms of biomaterials and their cellular mechanisms - Systemic biocompatibility - Remote-site biocompatibility - Clinical examples Class Amalia Aggeli
Week 7 Rheology of biomaterials - Basic theory - Rheology of biological materials - Rheology of biomaterials - Clιnical examples Class/Lab Amalia Aggeli, Eleftherios Rizos
Week 8 Corrosion and wear aspects of biomaterials Class Fani Stergioudi
Week 9 Biomaterials - The medical perspective I - Implants in orthopaedic surgery - Biomechanical properties Class Panagiotis Givissis
Week 10 Biomaterials - The medical perspective II - Hands-on on implants - Application of biomaterials in surgery Clinic Panagiotis Givissis, Dr. Β. Chalidis
Week 11 Case study: Failure Analysis of a biomaterials Lab/Clinic P. Vareltzis, N. Sidirokastritis
Week 12 Case study: Collagen and Hyaluronic acid Isolation and quantitation of biopolymers (UV, HPLC, LC-MS) Class/Lab P. Vareltzis, A. Aggeli, Eleftherios Rizos, Nikolaos Sidirokastritis
Week 13 Case study: Viscosupplamentation therapies in osteorthritis Lab/Clinic A. Aggeli / P. Givissis, Dr. Β. Chalidis, Dr. E. Rizos


4. Biomedical data acquisition and signal processing [5 ECTS]  read more ...

Short Description

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.

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Section 1: Data acquisition. Introduction to data acquisition, devices, sensors, registration of 1D signals Lecture E. Kitsas
Week 2 Section 1: Data acquisition. Devices, sensors, registration of 2D signals Lecture E. Kitsas
Week 3 Section 1: Data acquisition. Representation and visualization of biosignals, spectra and Fourier transform Lecture A. Aletras
Week 4 Data acquisition and registration of EEG Lab (AXEPA) V. Kimiskidis
Week 5 Data acquisition and registration of ECG and Wearables Lab (Seminar room) D. Filos / V. Papanagiotou
Week 6 Section 2: Stochastic processes and noise Lecture D. Kugiumtzis
Week 7 Section 3: Filters and transforms (Fourier, wavelet, PCA): theory Lecture I. Chouvarda
Week 8 Section 3: Filters and transforms (Fourier, wavelet, PCA): implementation Lecture N. Pitsianis
Week 9 Section 4: Feature extraction, statistics, linear and nonlinear features - 1 Lecture D. Kugiumtzis
Week 10 Section 4: Feature extraction, statistics, linear and nonlinear features - 2 Lecture D. kugiumtzis
Week 11 Processing of ECG and wearable-based signals Lab (Lab of informatics, Engineering) D. Filos / V. Papanagiotou
Week 12 Processing of EEG  Lab (Lab of informatics, Engineering) D. Kugiumtzis
Week 13 Summary Discussion


5. Health technology design and clinical engineering [5 ECTS]  read more ...

Short Description

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.

Timetable:

Assessment:

Coordinator: Panagiotis Bamidis

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Welcome, Introduction to the course, The Health Care Environment Lecture P.D. Bamidis
Week 2 Section 1: Healthcare technology design. Software engineering aspects of healthcare technology systems Lecture A. Symeonidis
Week 3 Co-designing healthcare technology systems: the Living Lab methodology Lecture/demo E. Konstantinidis
Week 4 Healthcare technology design. Overall aspects Lecture P.D. Bamidis
Week 5 A Biomedical Technology System case study: the Viter system for COVID-19 Lecture/demo P. Givissis / C. Antonopoulos
Week 6 Health Technology Design - ThermoFluid Measurement Technology for Biomedical Applications Lecture A. Kalfas
Week 7 Section 2: Medical Sensors/Devices/Equipment. Gaining Access to physiological signals Lecture E. Papanastasiou/P.D.Bamidis
Week 8 Development and Prototyping Implantable Devices. Case study on the application of remote and implantable devices in cardio vascular clinical practice Lecture A. Kalfas
Week 9 Section 3: Clinical Engineering Introduction. Visits to Hospital sites Lecture/Clinic P.D. Bamidis / V. Papadopoulos / A.Athanasiou
Week 10 Managing Biomedical Technology: regulation, management and assessment of medical devices Lecture A. Dermitzakis / E. Valchinov / INBIT
Week 11 Section 4: Safety and quality control Lecture P.D. Bamidis / E. Papanastasiou / E. Valchinov
Week 12 Safety and quality control  Lab P.D. Bamidis / E. Papanastasiou / E. Valchinov / INBIT / ELEVIT
Week 13 Section 5: Healthcare technology assessment frameworks Lecture and Discussion P.D. Bamidis
Week 14 Semester group project/lab presentations/other assessment Assessment P.D. Bamidis


6. Seminar series on topics in biomedical engineering [3 ECTS]  read more ...

Short Description

The course comprises a series of seminars given by the affiliated lecturers and invited lecturers on timely topics of biomedical engineering. Students are required to deliver a specific report in a topic relevant to the topics of the seminar.


Second Semester – Core Courses

7. Medical physics, imaging and image processing [5 ECTS]  read more ...

Short Description

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.

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Basic Image Principles Lecture A. Delopoulos
Week 2 2D Systems and filtering Lecture A. Delopoulos
Week 3 X-Rays, Fluoroscopy, Computed tomography part 1 Lecture E. Papanastasiou
Week 4 X-Rays, Fluoroscopy, Computed tomography part 2 Lecture E. Papanastasiou
Week 5 Magnetic Resonance Imaging basics Lecture A. Aletras
Week 6 Back-projection reconstruction and density compensation Lecture I. Kitsas
Week 7 Nuclear imaging Lecture I. Kitsas
Week 8 2D US, Doppler, Electrical Impedance Tomography and other non-ionizing methods Lecture I. Kitsas
Week 9 fMRI, MEG, EEG Lecture P. Bamidis
Week 10 Magnetic Resonance Imaging advanced topics Lecture A. Aletras
Week 11 Image restoration and enhancement Lecture A. Delopoulos
Week 12 Radon transform, NUFFT, Under-sampled imaging Lecture N. Pitsianis
Week 13 Image Analysis, Pattern recognition Lecture A. Delopoulos
Tutorials (to be scheduled) Lecture A. Delopoulos

8. Seminar series on research methodology and practice [5 ECTS]  read more ...

Short Description

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.

Timetable:

Assessment:

Coordinator: Panagiotis Bamidis

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Welcome, Introduction to the course, The BME research environment/ecosystem, Basics of research reports-plagiarism Lecture P. Bamidis
Week 2 Basic elements of BME research I - how to read a paper Lecture/Hands-on P. Bamidis
Week 3 Setting the scene: how to approach the study design Workshop P. Bamidis
Week 4 Types of reviews. Scoping reviews, Quantitative and Qualitative research Lecture P. Bamidis
Week 5 Basic elements of BME research II -how to write a paper Lecture A. Kalfas
Week 6 Conducting clinical research studies examples; other general concerns and processes for conducting BME research (design and regulatory) Lecture P. Givissis / P. Bamidis
Week 7 Biomedical Research Ethics Lecture C. Sardeli
Week 8 Ethical aspects of ΑΙ applcations in biomedicine Lecture C. Sardeli
Week 9 Patient stratification strategies - research choices in using large data form reach but little utilised medical records Lecture A. Kalfas
Week 10 BME research case studies in Computational Fluid Dynamics in internal flows; other emerging research topics in BME: developing the digital twin principals for prediction of heart and other organ performance in the future of patient specific treatment Lecture A. Kalfas
Week 11 Writing the MSc Thesis - preparing the MSc Thesis proposal Lecture/Hands-on P. Bamidis/A. Kalfas
Week 12 Visits to Research Centres, Digital Innovation Hubs and/or other sites? Site visit P. Bamidis
Week 13 Project presentations/other assessment Assessment P. Bamidis


Second Semester – Elective Courses

9. Information and communication technologies in medicine and healthcare [5 ECTS]  read more ...

Short Description

The course introduces the field of biomedical informatics and topics of medical technology. In particular, it focuses on the use of information and communication technology (ICT) on application areas, such as the health care, preventive care, care for the elderly and home. On the practical side, the topics of the course cover processing of physiological signals and development of software systems, wireless sensors and applications to smart phones in health. Tools of signal processing and machine learning are combined with artificial intelligence systems in applications in medical engineering and life and health care. The course extends also to the introduction to molecular and nanoscale communications: a) nanomachines and nanonetworks, b) communication by molecular diffusion, c) applications of these. 

Timetable:

Assessment:

Coordinator: Nikolaos Maglaveras

Teaching Staff : Nikolaos Maglaveras, V. Kilintzis, L. Stefanopoulos, P. Diamantoulakis

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 eHealth/mHealth/uHealth/pHealth - Principles and definitions Lecture N. Maglaveras
Week 2 Data sources, sensors and wearables Lecture N. Maglaveras
Week 3 Health Data Modelling (SQL, noSQL, RDF) Lecture V. Kilintzis
Week 4 International terminologies, ontologies, FHIR Lecture V. Kilintzis
Week 5 mHealth Systems Design and Implementation Methodology Lecture N. Maglaveras / L. Stefanopoulos
Week 6 mHealth Applications Development Lab N. Maglaveras / L. Stefanopoulos
Week 7 mHealth Applications Development Lab N. Maglaveras / L. Stefanopoulos
Week 8 Internet of Things Lecture P. Diamantoulakis
Week 9 Internet of Things Lecture P. Diamantoulakis
Week 10 Molecular and NanoScale Communications Lecture N. Maglaveras
Week 11 Molecular and NanoScale Communications Lecture N. Maglaveras
Week 12 Digital Twin Lecture V. Kilintzis / P. Diamantoulakis
Week 13 Demos of eHealth/ICT Systems Lecture N. Maglaveras

10. Artificial intelligence and medical diagnosis & decision support systems [5 ECTS]  read more ...

Short Description

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.

Timetable:

Assessment: Evaluation is based on 1 homework assignment and the final examination

Coordinator: Anastasios Delopoulos

Teaching Staff : Anastasios Delopoulos, Ioanna Chouvarda, Panagiotis Givissis

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Medical diagnosis, prognosis, risk assessment and their role in medical practice and public health. The basic modules of a medical Decision Support System Lecture P. Givissis
Week 2 Expert systems and their formalism – Logic, description logic, ontologies, if-then rules Lecture A. Delopoulos
Week 3 Use of expert systems in medicine – How to design, worked examples Lecture/Demo A. Delopoulos
Week 4 Learning from data: statistical knowledge – Probabilistic decisions, tests, statistical significance limitations, causality vs correlation Lecture A. Delopoulos
Week 5 Use of statistical knowledge in medical decisions and in risk assessment – Worked examples Demo A. Delopoulos
Week 6 Ethical and legal issues in DSS - the human factor Lecture P. Givissis
Week 7 Learning from data: machine learning – The concepts of ML classifiers and ML regression models Lecture A. Delopoulos
Week 8 Learning from data: machine learning – Main types of ML models (Neural Networks, Support Vector Machines, Random Forests, etc), training procedure, evaluation metrics, limitations Lecture I. Chouvarda
Week 9 Learning from data: machine learning – Main types of ML models (Neural Networks, Support Vector Machines, Random Forests, etc), training procedure, evaluation metrics, limitations Lecture I. Chouvarda
Week 10 Learning from data: deep learning – advances w.r.t shallow ML, main types of DL models (Convolutional NN, Recurrent Networks incl LSTM), training options, evaluation, limitations Lecture I. Chouvarda
Week 11 Learning from data: trustwοrthiness of AI – explainability, fairness, uncertainty, privacy Lecture I. Chouvarda
Week 12 Use of ML and DL in medical decision and risk assessment – Worked examples Demo A. Delopoulos, I. Chouvarda
Week 13 Use of ML and DL in medical decision and risk assessment – Worked examples Demo A. Delopoulos, I. Chouvarda

11. Biomanufacturing – tissue engineering [5 ECTS]  read more ...

Short Description

The course aims at introducing the students in 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 is based on a combination of theory and corresponding laboratory practice.

Week Topic Lecture / Lab Teacher
Week 1 Trends in Biomaterials and biomanufacturing Class A. Salifoglou
Week 2 3D-bioprinting in PLA scaffolds for pharmaceutical and hard tissue engineering applications Lab A. Salifoglou
Week 3 Additive manufacturing of metallic and ceramic biomaterials Class N. Michailidis
Week 4 3D bioprinting of PCL scaffolds and bioinspired structures Lab N. Michailidis
Week 5 Mechanical testing of Scaffolds and bioinspired structures Lab N. Michailidis
Week 6 Extraction, purification, and processing of biomaterials for biomedical application Class P. Vareltzis
Week 7 Hands on experiment in extracting collagen from eggshell membrane by chemical and/or enzymic treatment Lab P. Vareltzis
Week 8 • Mechano-biology • Extra Cellular Matrix • Bone tissue engineering • Cartilage tissue engineering • Bioactive scaffolds Class T. Choli-Papadopoulou
Week 9 Stem Cells and Tissue Engineering Class K. Chatzistergos
Week 10 Scaffold morphology and tissue reaction Lab A. Cheva
Week 11 From biofrabrication processes to regenerative medicine Class A. Aggeli
Week 12 Tissue engineering as an alternative to disease treatment Class A. Aggeli
Week 13 Project presentations Class A. Aggeli

12. Medical robotics, cyber physical engineering and virtual reality [5 ECTS]  read more ...

Short Description

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.

Timetable:

Assessment:

Coordinator: Ioannis Papaefstathiou

Teaching Staff : Ioannis Papaefstathiou, Panagiotis Bamidis, Nikolaos Tampouratzis, Alkinoos Athanasiou, Panagiotis Antoniou

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Section 1: Designing of Cyber Physical Medical Systems (CPMS) Lecture Y. Papaefstathiou
Week 2 Section 1: Designing of Interconnected IoT systems Lecture Y. Papaefstathiou
Week 3 Section 1: Lab on CPMS/IoT Lab N. Tampouratzis
Week 4 Section 2: Security of Medical Systems Lecture Y. Papaefstathiou
Week 5 Section 2 : Security of Medical Systems Lecture Y. Papaefstathiou
Week 6 Section 2 : Security of Medical Systems Lecture Y. Papaefstathiou
Week 7 Section 3: Extended/virtual/mixed/augmented reality in healthcare Lecture P. Bamidis
Week 8 Section 3: Applications of XR/VR/MR/AR in Medical Education, General surgery and Neurosurgery and image guided surgery, mental health Lecture P. Bamidis, A. Athanasiou, P. Antoniou
Week 9 Section 3: Lab on XR/VR/MR/AR Lab P. Bamidis, A. Athanasiou, P. Antoniou
Week 10 Section 4: BCI / BMI systems Lecture P. Bamidis, A. Athanasiou
Week 11 Section 4: Medical robotics Lecture P. Bamidis and Guest Lecturer (TBC)
Week 12 Section 4: Medical robotics practice Hospital Visit A. Athanasiou
Week 13 Summary/Coursework presentations Discussion/Assessment

13. Nanomaterials – nanomedicine [5 ECTS]  read more ...

Short Description

Τhe module 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.

14. Precision medicine and prevention [5 ECTS]  read more ...

Short Description

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 factors 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.

Timetable:

Assessment:

Coordinator: Dimitra Dafou

Teaching Staff: Dimitra Dafou, Maria Milapidou, Marina Boziki, Chrysanthi Ainali, Dimosthenis Sarigiannis, Eirini Kanata, Nikoleta Psatha

Teaching program:

Topic Lecture / Lab Teacher
Week 1 Ongoing revolution of genetic technologies Lecture D. Dafou
Week 2 Ethics-Risk Assessment-Genetic Counselling Lecture M. Milapidou
Week 3 Monogenic Diseases Lecture D. Dafou
Week 4 Complex Diseases/Degenerative Diseases Lecture M. Boziki
Week 5 Precision Health /Bioinformatics Lecture C. Ainali
Week 6 Pharmacogenomics/Drugs Development Lecture C. Ainali
Week 7 Genome-exposome interactions I Lecture D. Sarigiannis
Week 8 Genome-exposome interactions II Lecture D. Sarigiannis
Week 9 Biomarkers/Genomic Lecture D. Dafou
Week 10 Biomarkers/Proteomics Lecture E. Kanata
Week 11 Biomarkers/Imaging Lecture M. Boziki
Week 12 Prevention methods Lecture N. Psatha
Week 13 Therapeutic methods/cellular Lecture N. Psatha

15. Computational neuroscience – neuroengineering [5 ECTS]  read more ...

Short Description

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.

Timetable:

Assessment:

Coordinator: Vasilios Kimiskidis

Teaching Staff : Dimitris Kugiumtzis, Panagiotis Bamidis, Theodoros Samaras, Evangelos Paraskevopoulos, Alkinoos Athanasiou, Christos Papadelis

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Section 1: From neurons to systems (recording and modelling of neural signals) Lab T. Samaras
Week 2 Section 1: From neurons to systems (recording and modelling of neural signals) Lab T. Samaras
Week 3 Section 1: From neurons to systems (processing and analysis of neural signals) Lab D. Kugiumtzis
Week 4 Section 1: From neurons to systems (interpretation and modelling of the acquired data) Lab E. Paraskevopoulos
Week 5 Section 2: Applications to cognitive and clinical neuroscience: neuroimaging techniques Lecture P. Bamidis
Week 6 Section 2: applications to cognitive and clinical neuroscience: TMS Lab AHEPA
Week 7 Section 3: Brain activity and connectivity (spectral analysis, nonlinear dynamics, independent component analysis) Lecture D. Kugiumtzis
Week 8 Section 3: Brain activity and connectivity ( connectivity analysis, graph-theoretic description, machine learning) Lecture D. Kugiumtzis
Week 9 Section 3: Brain activity and connectivity - Clinical example (Epilepsy) Lab AHEPA
Week 10 Section 3: Brain activity and connectivity – case studies Lab I. Kugiumtzis
Week 11 Section 4: Examples of translational neuroscience: brain-computer interface, neurofeedback Lab A. Athanasiou
Week 12 Section 4: Examples of translational neuroscience: application in pediatric neurological diseases Lecture / LabGuest Lecture P. Bamidis - C. Papadelis
Week 13 Discussion

16. Bioinformatics [5 ECTS]  read more ...

Short Description

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.

Timetable:

Assessment: Written Assignment, Presentation, MCQs, Hands-on Lab exercises

Coordinator: Michalis Aivaliotis

Teaching Staff : Michalis Aivaliotis, Ilias Kappas, Nikos Pitsianis, Andigoni Malousi, Ilias Kitsas, George Tzimagiorgis

Week Topic Lecture / Lab Teacher
Week 1 Introduction to Bioinformatics - Overview Lecture M. Aivaliotis - I. Kappas
Week 2 Databases Lecture / Lab I. Kappas
Week 3 Sequence alignment - Blast Lecture / Lab I. Kappas
Week 4 Statistical methods in bioinformatics Lecture I. Kitsas
Week 5 Bioinformatics platforms (R Bioconductor, Galaxy) Lecture / Lab A. Malousi
Week 6 High-throughput technologies: NGS and microarrays Lecture G. Tzimagiorgis
Week 7 Bioinformatics in Genomics Lecture / Lab A. Malousi
Week 8 Bioinformatics in Transcriptomics Lecture / Lab M. Aivaliotis
Week 9 Bioinformatics in Epigenomics Lecture / Lab A. Malousi
Week 10 Bioinformatics in Proteomics Lecture / Lab M. Aivaliotis
Week 11 Bioinformatics in Metabolomics Lecture / Lab M. Aivaliotis
Week 12 Clustering, visualization and network analysis Lecture / Lab N. Pitsianis
Week 13 The role of bioinformatics in systems biology - Data integration Lecture M. Aivaliotis

17. Microscopy, lasers, nano-testing and reverse engineering [5 ECTS]  read more ...

Short Description

The aim is to theoretically and experimentally acquaint the students with specialized knowledge and skills related to microscopy, lasers, nano-testing and reverse engineering. A detailed presentation of the various microscopy techniques will be carried out, such as optical microscopy of transmitted and reflected light, fluorescence microscopy, confocal microscopy, scanning electron microscopy (SEM) and transmission electron microscopy (TEM), lasers for characterisation, nano-tests such as nano-indentation and atomic force microscopy (AFM), as well as reverse engineering techniques for 3D geometry reproduction. Understanding their operating principles and using intelligent practices to obtain useful information about the materials-biomaterials, tissues and living organisms is a central objective of the course. In addition, the theory will be coupled with laboratory practice in the above techniques in order to acquire relevant practical experiences and skills in their use.

Timetable:

Assessment:

Coordinator: Fani Stergioudi

Teaching Staff : Fani Stergioudi, Nikolaos Michalidis, Georgios Skordaris, Alexandros Prospathopoulos, Ioannis Arvanitidis, Georgios Vourlias, Evangelia Delli, Philomela Komninou, Nikoleta Florini

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Optical microscopy for biomaterials Class/Lab N. Michailidis / A. Prospathopoulos
Week 2 IR and Raman spectroscopy Class I. Arvanitidis
Week 3 IR and Raman spectroscopy Class I. Arvanitidis
Week 4 X-ray diffraction for materials characterization Class G. Vourlias / E. Delli
Week 5 X-ray diffraction for materials characterization Class/Lab G. Vourlias / E. Delli
Week 6 Electron microscopy for materials characterization Class P. Komninou/ N. Florini
Week 7 Electron microscopy for materials characterization Class/Lab P. Komninou/ N. Florini
Week 8 Laboratory for IR, Raman, XRD, and Electron microscopy Lab I. Arvanitidis, P. Komninou, N. Florini, G. Vourlias, E. Delli
Week 9 Nanoindentatons as well as nano- and micro-impacts for materials characterization Class G. Skordaris
Week 10 Confocal microscopy, nanoindentatons as well as nano- and micro-impacts for materials characterization Lab G. Skordaris
Week 11 Introduction to numerical methods for materials characterization Class/Lab G. Skordaris
Week 12 Reverse engineering techniques for 3D geometry reproduction Class G. Skordaris
Week 13 SEM for biomaterials characterization Lab F. Stergioudi

18. Drug engineering [5 ECTS]  read more ...

Short Description

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.

Timetable:

Assessment:

Coordinator: Athanasios Salifoglou

Teaching Staff : Athanasios Salifoglou, Sevasti Matsia

Teaching Program:

Topic Lecture / Lab Teacher
Week 1 Smart nanocarrier systems in drug delivery Class A. Salifoglou
Week 2 Design of drug delivery systems. From materials to assembly and target loci Class A. Salifoglou
Week 3 Drug delivery: from Lab to clinical trials Class A. Salifoglou
Week 4 Nano and micro-particles as drug delivery hosts Class A. Salifoglou
Week 5 Implantable drug delivery carriers Class A. Salifoglou
Week 6 3D-bioprinting in drug delivery systems Class A. Salifoglou
Week 7 Polymeric materials in hydrogels: two sides of the same coin. That is the other side? Class A. Salifoglou
Week 8 Drug delivery in diabetic patients Class A. Salifoglou
Week 9 Microneedles in vaccination Class A. Salifoglou
Week 10 Drug delivery and infectious diseases Class A. Salifoglou
Week 11 Cancer stem cell drug delivery Class A. Salifoglou
Week 12 Preparation of nanoparticulate drug delivery systems Lab A. Salifoglou, S. Matsia
Week 13 Drug release from a nanoparticle system Lab A. Salifoglou, S. Matsia

19. Biomedical engineering and global (environmental) challenges [5 ECTS]  read more ...

Short Description

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.

Timetable:

Assessment:

Coordinator: Kostas Karatzas

Teaching Staff : Kostas Karatzas, Panagiotis Bamidis, Andreas Symeonidis

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Introduction to global environmental challenges: the example of the atmospheric environment (physical, chemical, biological weather). Climate change aspects Lecture K. Karatzas
Week 2 Sources of environmental information (EI): human estimations/recordings, instrument/device-oriented observations, computations, citizen science. Environmental Information available via the internet. Pervasive EI, the smart city and the IoT Lecture / Lab K. Karatzas
Week 3 BME and City Science challenges Lecture P. Bamidis
Week 4 BME in low resource settings Lecture P. Bamidis
Week 5 Citizen science and crowd-sourced environmental and health data collection. Tools, limitations, and methodological problems Lecture / Lab K. Karatzas
Week 6 Understanding and analyzing environmental data. Basic descriptive statistics, symmetry measures and graphical representations. Data science, data analytics and modelling Lecture K. Karatzas
Week 7 Environmental criteria for triggering symptoms and causing health effects- the aeroallergens example: pollen season definition for the environmental scientist and the health practitioner Lecture K. Karatzas
Week 8 Low cost environmental (AQ) sensors. Basic technologies, advantages and limitations. Opportunities for smart everyday environmental monitoring and quality of life support Lab K. Karatzas
Week 9 Low-code” development platforms for a “coding without code” objective Lecture A. Symeonidis
Week 10 Low-code” development platforms for a “coding without code” objective Lab A. Symeonidis
Week 11 Presentation of environmental information: making sense out of environmental data towards quality-of-life support. Perception and use of EI Lecture K. Karatzas
Week 12 Design principles, user requirements, functional specifications, and project examples of environmental information services with health orientation. The example of the atmospheric environment Lecture K. Karatzas
Week 13 Final project presentation. Synopsis Lecture K. Karatzas

20. Machine learning in biomedical data analysis [5 ECTS]  read more ...

Short Description

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.

Timetable:

Assessment:

Coordinator: Petrantonakis Panagiotis

Teaching Staff : Anastasios Delopoulos, Stelios Hadjidimitriou, Ioanna Chouvarda, Andreas Symeonidis, Panagiotis Petrantonakis, Nikolaos Maglaveras, Chrysanthi Sardeli

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Introduction to Exploratory Data Analysis, Classification and Regression in Biomedicine Lecture A. Delopoulos
Week 2 Data clustering: Methods and Challenges Lecture S. Hadjidimitriou
Week 3 Linear/Logistic Regression. Support Vector Machines. Random Forests Lecture I. Chouvarda
Week 4 Hyperparameter selection, validation, evaluation. Demonstration on the use of SVM/SVR on a real dataset Lecture / Demo I. Chouvarda
Week 5 Feed Forward Neural Networks Lecture P. Petrantonakis
Week 6 Convolutional Neural Networks Lecture P. Petrantonakis
Week 7 Recurrent Networks, LSTMs Lecture P. Petrantonakis
Week 8 Introduction to Big Data analytics Lecture A. Symeonidis
Week 9 Big Data Engineering Lecture A. Symeonidis
Week 10 Learning patterns of time-series: Hidden Markov Models vs LSTM on time-series datasets Lecture N. Maglaveras
Week 11 Worked example on a real dataset Demo N. Maglaveras
Week 12 Learning from partially labeled data: Semi-supervised and Self-supervised learning Lecture A. Delopoulos
Week 13 Ethical constraints and challenges of ML and DL in biomedicine Lecture C. Sardeli

21. Physiology-based biokinetics and biodynamics [5 ECTS]  read more ...

Short Description

Physiology-based biokinetics and biodynamics describe the interaction of human organism with exogenous chemical substances. These may include :  a) industrial substances  which we are exposed to passively in our daily lives through environmental exposure, our nutrition and the use of consumer products, b) medicinal substances that we are purposefully exposed to for therapeutic purposes. Biokinetics describes the process of absorption, distribution, metabolism and excretion (ADME) of a chemical substance in the body, ie what the body does to the chemical substance, whilst biodynamics describes the impact the chemical substance has on the physiology of the body, ie what the substance does to the body. The module syllabus includes : 1) understanding the basic concepts and principles of biokinetics and biodynamics. 2) understanding the mathematical framework of biokinetic models. 3) Generalised biokinetic models. 4) Development and applications of quantitative structure-activity relationship models (QSARs) for the configuration of biokinetic models and the prediction of biodynamic interactions. 5) Biokinetic models that describe the gestational process and the interaction between mother and embryo (transport through placenta, breast-feeding). 6) Biokinetic models that describe changes in physiology from conception all the way through to adulthood. 7) Impact of genetic polymorphism on biokinetics and biodynamics. 8) Influence of blood-brain barrier on the transport of chemical substances in the brain. 9) Cumulative effects of chemicals and medicinal substances. 10) Interactions with respect to biodynamics. 11) Understanding the process and principles of the design of a biokinetics model with applications in a wide range of therapeutic settings (small molecules, proteinaceous drugs and nanoparticles). 12) Data interpretation of human biomonitoring via exposure reconstruction with the use of biokinetic models. 13) Applications of biokinetics and biodynamics models on risk analysis – connection with relevant systems biology models. 14) Applications of biokinetics and biodynamics models on precision medicine. 15) Applications of biokinetics and biodynamics models on real data and chemicals that attract a lot of attention.

Timetable:

Assessment:

Coordinator: Dimosthenis Sarigiannis

Teaching Staff : Dimosthenis Sarigiannis, Spyros Karakitsios

Teaching Program:

Week Topic Lecture / Lab Teacher
Week 1 Pharmacokinetic Concepts Lecture D. Sarigiannis / S. Karakitsios
Week 2 PBPK model compartments basis / s/w demo Lecture D. Sarigiannis / S. Karakitsios
Week 3 Lab 1: PBPK for BPA / organic compounds/ phthalates Lab D. Sarigiannis / S. Karakitsios
Week 4 Lab 2: PBPK for metals Lab D. Sarigiannis / S. Karakitsios
Week 5 Building a PBPK model / model parameterization / validation Lecture D. Sarigiannis / S. Karakitsios
Week 6 Application of modeling to interpret biomonitoring data – dose reconstruction Lecture D. Sarigiannis / S. Karakitsios
Week 7 Lab 3: Dose reconstruction in organic compounds Lab D. Sarigiannis / S. Karakitsios
Week 8 Risk estimation using reverse dosimetry Lecture D. Sarigiannis / S. Karakitsios
Week 9 PBPK modeling for various classes of compounds Lecture D. Sarigiannis / S. Karakitsios
Week 10 Developing generic PBPK models Lecture D. Sarigiannis / S. Karakitsios
Week 11 Modelling interactions of chemical and pharmaceutical mixtures Lecture D. Sarigiannis / S. Karakitsios
Week 12 Integrative health risk assessment models based on PBPK modelling Lecture D. Sarigiannis / S. Karakitsios
Week 13 Lab 4: PBPK models of pharmaceuticals and models for blood-brain-barrier (e.g. caffeine, neurological drugs) Lab D. Sarigiannis / S. Karakitsios