BIOENGINEERING

The teaching unit aims to provide students with a general understanding of the essential tools and operational skills for the quantitative analysis of data and signals of interest in medicine and biology, from a probabilistic perspective.

The course intends to provide the basic notions for data and signal analysis with emphasis on application to biology and medicine.

Written exam (weight 50%)
Project work (individuals or couples, weight 50%)
Solution of a real problem of biomedical data analysis/processing, chosen from a list of proposed projects
Development of software for calculation/analysis/processing
Interactive application (MATLAB Livescript) reporting the results
Fixed deadline for submission (early February)
Oral discussion of activity and results

Project work will be assessed in terms of:
1) Documentation (correctess, clarity, synthesis, terminology)
2) Implementation (code structure and organization, efficiency)
3) Data Visualization (technical quality of figures, adequacy of display technique, efficacy, clarity)
4) Bonus (max 2 pts) if the report provides additional analysis (in addition to those required). Bonus is only awarded if maximum score is obtained in the other three criteria.

There is no course textbook; the course material draws from a variety of sources. Recommended references:
1. Murphy, KP (2022) Probabilistic Machine Learning: An Introduction. MIT Press.
2. Bishop, CM (2006) Pattern Recognition and Machine Learning. Springer
3. Barber D (2006) Machine Learning A Probabilistic Approach.

The aim of the course is to introduce the fundamental principles of artificial intelligence applied to medicine, providing an overview of the main machine learning techniques, with a particular focus on classification, regression, and interpretability problems. The objective is to offer students a general understanding of the potential, challenges, and application domains of AI in clinical and biomedical contexts, allowing for a critical and interdisciplinary approach in relation to the other modules of the Degree Programme.

This course provides a comprehensive introduction to the fundamentals of Artificial Intelligence for biomedical and clinical applications. It is designed for students in Bioengineering and covers core machine learning and deep learning methods, along with practical guidance on building experimental pipelines that ensure robustness and reproducibility. Emphasis is placed on both theoretical foundations and hands-on practice, enabling students to apply data-driven approaches to real-world medical problems. The course integrates algorithmic principles with clinical relevance, fostering critical thinking about the opportunities, limitations, and ethical implications of AI in healthcare. Each class combines theoretical lectures with practical sessions to consolidate learning through direct experimentation.

The final exam consists of two components:
Quiz: A series of multiple-choice and short-answer questions assessing understanding of theoretical concepts. This quiz can be taken either in a single session or progressively throughout the course period, depending on student preference and class scheduling.
Written Test: A practical, open-ended exam where students are required to design and critically discuss machine learning pipelines applied to biomedical problems. Emphasis is placed on the ability to justify methodological choices, address issues of bias and generalization, and ensure interpretability and reproducibility.

The evaluation process is composed of three components:
Test (Entry Quiz) A preliminary quiz on theoretical and practical course content. The test can be taken progressively during the teaching period or in a single session. Passing the test is mandatory to access the written exam.
Written Exam The main assessment consists of a written test where students are required to design and justify machine learning pipelines in response to applied biomedical problems. The written exam grade constitutes the base of the final grade.
Optional Project Students who achieve a grade of 28/30 or higher in the written exam and wish to improve their final grade may complete an individual project. The project involves solving a data analysis task (coding required) and can add or subtract up to 3 points from the written exam grade. The maximum final grade, including the project, is 30 e lode.

Eric Topol — Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again (2019)
Anil Ananthaswamy — Why Machines Learn: The Elegant Math Behind Modern AI (2024)
Hastie, Tibshirani & Friedman — The Elements of Statistical Learning (2009) https://hastie.su.domains/ElemStatLearn/printings/ESLII_print12_toc.pdf.download.html
Ian Goodfellow, Yoshua Bengio & Aaron Courville — Deep Learning (2016) https://www.deeplearningbook.org/

The course covers the technologies, the analytical methods, the modeling approaches used for the analysis and quantification of human movement and its neural correlates. Specific topics include three-dimensional analysis of movements, muscle and body mechanics, physiology and physiological signals in motor control, computational motor control.

The course provides the essential physiological background and knowledge of experimental methods and computational tools for understanding human movements and their neural control.

Written exam (weight 60%)
​Lab Activities (weight 40%)

The written exam will consist of questions and problems on lecture topics.
There will be four lab activities which will be carried out as teams. Each activity will involve recording a specific movement, analyzing the recorded data and preparing and submitting a technical report.

Uchida TK, Delp SL (2021) Biomechanics of Movement. MIT Press.

The teaching unit aims to provide students with the basic knowledge to use open-source tools for managing -omic biological data, understanding and consulting databases (such as Genebank, NCBI, Uniprot, etc.), also accessing them through dedicated R packages. The goal of the course is for participants to acquire knowledge and understand the biological processes underlying celular transcription and translation, in order to appreciate the utility of analyzing genomic, transcriptomic, and proteomic data using bioinformatics tools.

Nowadays, solving biological problems is heavily linked to the necessity of managing large amounts of biological data. This is made possible thanks to advancements in technologies that allow for the study of molecular changes in cells. In this course, students will have the opportunity to learn how to analyze and interpret this data using computational tools, which are available in free libraries such as Bioconductor and CRAN. Additionally, students will learn how to effectively reuse existing data deposited in biological databases.

Project work and oral exam

The practical test aims to verify students’ acquired knowledge of basic bioinformatics notions, and their ability to face up a bio-chemical problems by consulting databases and using open source informatic tools available in scientific bibliography.
The oral exam will mainly focus on the topics covered during the lectures and must confirm that the student has acquired the basic notions (biological and IT) provided by the course.

Paul M. Selzer, Richard J. Marhöfer, Oliver Koch. Applied Bioinformatics. Springer International Publishing AG. Available online at the link: https://link.springer.com/book/10.1007/978-3-319-68301-0
Slides provided by teachers

The course aims to provide students with an interdisciplinary and applied understanding of materials used in the biomedical field, highlighting the relationships between structure, properties, biological interactions, and clinical applications. The course integrates chemical, physical, biological, and engineering knowledge to prepare students to contribute to the development, characterization, and evaluation of advanced biomaterials for medical devices and therapies. Laboratory activities enable students to apply theoretical knowledge to experimental practice through exercises focused on the preparation, modification, and analysis of biomaterials.

The Biomaterials course offers an interdisciplinary overview of materials used in the biomedical field, analyzing the relationships between structure, properties, biological interactions, and clinical applications. The course integrates knowledge from chemistry, physics, biology, and engineering to prepare students to contribute to the development and evaluation of advanced biomaterials.

The final exam consists of an oral examination.
During the oral exam, students will be asked to discuss key concepts covered in the course, demonstrate their understanding of biomaterials’ properties, biological interactions, and clinical applications, and explain experimental procedures performed in the lab. The exam will also assess the student’s ability to integrate knowledge from multiple disciplines and use appropriate scientific terminology.

The oral exam is designed to verify the achievement of the expected learning outcomes by evaluating the student's ability to:
Clearly explain the classification, properties, and modifications of biomaterials;
Describe biological responses to materials and relate them to clinical applications;
Interpret laboratory experiences and experimental results;
Use correct technical language and communicate concepts effectively;
Demonstrate critical thinking and interdisciplinary integration.
The evaluation will be based on:
Accuracy and completeness of the answers;
Logical structure and clarity of explanation;
Use of appropriate scientific vocabulary;
Depth of understanding and ability to connect theory with practice.
Students with certification of Specific Learning Disorders (SLD), disabilities, or other special educational needs are advised to contact the instructor at the beginning of the course in order to identify teaching and examination methods that, while respecting the learning objectives of the course, take into account individual learning styles and provide appropriate compensatory tools.

Provided materials:
Lecture slides
Selected scientific papers
Lab manuals
Recommended textbooks:
J. D. Bronzino, D. R. Peterson, Biomaterials: Principles and Practices, CRC Press (latest edition)
B. D. Ratner et al., Biomaterials Science: An Introduction to Materials in Medicine, Academic Press
Additional materials and updates will be made available on AulaWeb.

The aim of the teaching unit is to provide students with theoretical and practical tools to analyze, understand, and extract information from biomedical and biological images. The teaching unit is intended to introduce the fundamental principles of the main diagnostic imaging modalities and to develop students' skills in digital image processing through theoretical lessons and practical laboratory activities. In particular, the teaching aims to provide basic knowledge related to: a) image quality and pre/post-processing techniques; b) segmentation, pattern recognition, and classification; c) three-dimensional visualization. The development of group projects using Matlab and open-source platforms also contributes to enhancing practical and collaborative skills, useful for future professional opportunities in healthcare, engineering, and information technology sectors.

The influence and impact of digital images on modern society is tremendous, and image processing is now a critical component in science and technology. The rapid progress in computerized medical image reconstruction, and the associated developments in analysis methods and computer-aided diagnosis, has propelled medical imaging into one of the most important sub-fields in scientific imaging. The course takes its motivations from medical applications and uses real medical images and situations to clarify concepts and to build intuition and understanding. Designed for students who will become end users of digital image processing, the course emphasizes the effective use of image processing tools

Exam consists of
eight imaging elaboration projects to be completed before the written test. This activity will be carried out in group of max 4 students, assisted by the teachers and tutors
a written test .

For the imaging elaboration projects, the student's level of autonomy in developing activities will be evaluated. For the written test, the mastery of the theoretical topics covered during the lectures and behind the practical activities will be assessed.
At the middle of the year, students must prepare a short presentation with the results obtained from the experimental activity. The organisation and dates of the laboratory activities will be communicated directly by the lecturers at the beginning of class. The examination is passed if the marks for both activities (practical exercises and written test) are sufficient (grade > 18).

All slides used during lessons and other teaching materials will be available on aula@web. The books listed below are strongly suggested as supporting texts to complete the student's preparation, but students can also use other university level texts as long as they are published in the last 5 years.
J.G. Webster. Medical Instrumentation - Application and Design 4th Edition. Wiley.
Rafael C. Gonzales, Richard E. Woods - Elaborazione delle immagini digitali
Zhi - Pei Liang, Paul C. Lauterbur - Principles of Magnetic Resonance Imaging
Scott A. Huettel, Allen W. Song - Functional Magnetic Resonance Imaging
Geoff Dougherty - Digital image processing for medical applications
Paul Suetens - Foudamentals of medical imaging
Nadine Barrie Smith and Andrew Webb - Introduction to biomedical imaging

The purpose of this teaching unit is to provide a perspective on robotic technologies applied to (and inspired by) themes of biomedical research and practice. Robotics is a multidisciplinary field, involving elements from computer, electrical, and mechanical engineering, and characterized by a growing range of biomedical applications. The first part of the course aims to provide a set of general notions for the control of robotic devices. The second part is devoted to the in-depth analysis of specific applications. These include basic research on the sensorimotor system, advanced surgical and diagnostic techniques, human-machine interfaces, robots for assistance and rehabilitation, and biorobotics.

The purpose of this course is to provide an overview of robotic technologies applied to—and inspired by—biomedical research and clinical practice. Robotics is a multidisciplinary field, integrating computer, electrical, and mechanical engineering, with an increasingly broad range of biomedical applications. These include basic research on sensorimotor systems, advanced surgical and diagnostic techniques, human–machine interfaces, and robotic systems for assistance and rehabilitation. In addition to learning theoretical concepts, students will engage in practical activities such as software development and scientific discussions, both individually and in teams.

The assessment methods refer to the components described above:
a) Assignments These are designed to evaluate the student's ability to understand, explain, and solve problems related to the fundamental concepts of robotics presented in the first part of the course.
b) Active participation This component assesses the ability to explore in depth, communicate, and critically discuss the topics covered in the second part of the course, with a focus on biomedical applications of robotics.
c) Final exam The exam aims to verify that students have assimilated the course contents, and can use them critically and explain them clearly.
In addition to subject-specific knowledge, students will be assessed on:
the quality of their presentations
the correct use of technical and specialized vocabulary
their ability to reason critically
the capacity to apply acquired knowledge to specific exercises or problems
transferable skills such as teamwork, problem-solving, critical thinking, and scientific communication, which are developed through collaborative projects and classroom interactions.

Main references:
Siciliano, B., & Khatib, O. (Eds.). Springer Handbook of Robotics. Springer.
Spong, M. W., Hutchinson, S., & Vidyasagar, M. (2005). Robot Modeling and Control. Wiley.
Additional materials, including selected research articles and specialized readings, will be provided throughout the course via the AulaWeb platform and/or Teams.

The aim of the teaching unit is to provide students with a solid foundation in the principles and components of biosensors, including the sensing element and the transducer, along with essential criteria for their design and selection according to different application contexts. In the second part, the teaching unit focuses on microsystems for medical and biotechnological applications, highlighting the impact of miniaturization and introducing key nano- and microfabrication techniques. A further objective is to develop the students’ ability to critically assess available technologies and commercial solutions in relation to their suitability for specific biomedical and biotechnological needs.

The course provides an overview of the enabling technologies, working principles, and biomedical applications of biosensors and microsystems. Key biochemical sensing elements and mechanisms are presented, along with the main physical transduction principles as well as the main features introduced by miniaturization of a physical/chemical system. The course also covers relevant microfabrication techniques and processes for the development of biosensors and microsystems. Particular emphasis is placed on the unique opportunities offered by these technologies in medical diagnostics, disease treatment, and therapy.

During the lecture period, students will be assigned homework, which may consist of Wooclap questions or short problems to be solved individually. Assignments must be submitted within the given deadlines; failure to do so will negatively affect the final evaluation. On a voluntary basis, students may also give a classroom presentation of a scientific paper previously agreed upon with the instructor; this activity will be positively considered in the final assessment.
The final examination consists of an oral interview structured into four questions:
two questions on different topics covered during the lectures,
one question related to the laboratory reports submitted during the lectures
one question concerning the homework assignments.

The oral exam is designed to verify
the achievement of the expected learning outcomes
the capability to interpret laboratory experiences and experimental results;
the correct use of technical/sceitnific language and the capability to communicate concepts effectively;
the capability of critical thinking and interdisciplinary integration.
The evaluation will be based on:
accuracy and completeness of the answers;
logical structure and clarity of explanation;
use of appropriate scientific vocabulary;
depth of understanding and ability to connect theory with practice.
Students with certification of Specific Learning Disorders (SLD), disabilities, or other special educational needs are advised to contact the instructor at the beginning of the course in order to identify teaching and examination methods that, while respecting the learning objectives of the course, take into account individual learning styles and provide appropriate compensatory tools.

All the slides used in the classroom and other material (videos, additional readings) will be avaiable on Aualweb/Teams
Specific indications on reference bibliography will be provided by the professor at the beginning of the lectures

The course aims to provide a comprehensive understanding of the fundamental and technological aspects of tissue engineering and regenerative medicine, with a particular focus on cell biology, stem cells, biocompatibility, cell culture systems, innovative substrates for cell growth, and advanced in vitro models. The primary objective is to highlight the importance of understanding biological phenomena at the cellular and tissue levels to develop therapeutic strategies capable of overcoming the limitations of conventional treatments. Practical examples will be presented to stimulate and enhance students' ability to apply theoretical knowledge to real-world challenges in the field of tissue engineering.

The Cellular and Tissue Engineering course offers an interdisciplinary overview of the fabrication and engineering of structures suitable for mimicking/replacing/regenerating of human body tissues. The course integrates knowledge from understanding biological phenomena at the cellular and tissue level to prepare students to contribute to the development of therapeutic strategies that can overcome the limitations of conventional therapies

The final exam consists of an oral examination.
During the oral exam, students will be asked to discuss key concepts covered in the course, demonstrate their understanding of the approaches and techniques of tissue engineering both for tissue regeneration and modeling. The exam will also assess the student’s ability to integrate knowledge from multiple disciplines and use appropriate scientific terminology.

The oral exam is designed to verify the achievement of the expected learning outcomes by evaluating the student's ability to:
Clearly explain the classification, properties, and modifications of biomaterials;
Describe biological responses to materials and relate them to clinical applications;
Use correct technical language and communicate concepts effectively;
Demonstrate critical thinking and interdisciplinary integration.
The evaluation will be based on:
Accuracy and completeness of the answers;
Logical structure and clarity of explanation;
Use of appropriate scientific vocabulary;
Depth of understanding and ability to connect theory with practice.
Students with certification of Specific Learning Disorders (SLD), disabilities, or other special educational needs are advised to contact the instructor at the beginning of the course in order to identify teaching and examination methods that, while respecting the learning objectives of the course, take into account individual learning styles and provide appropriate compensatory tools.

Provided materials:
Lecture slides
Selected scientific papers
Lab manuals
Recommended textbooks:
Textbook of Personalized Medicine Third Edition 2021 by Kewal K. Jain (Author)
Additional materials and updates will be made available on AulaWeb.

This teaching unit consists of two modules: Chemistry and Organic Chemistry, and Biochemistry. The course aims to provide students with a solid foundation in chemistry, organic chemistry, as well as biochemistry and clinical biochemistry. Students will develop an understanding of the structure and reactivity of organic compounds and macromolecules, metabolic pathways, and enzyme regulation, as well as the biochemical principles underlying diagnostic tools. Additionally, the course seeks to equip students with the language and the conceptual and technical knowledge required to collaborate effectively with professionals in the fields of medicine and biotechnology, particularly in the context of biomedical research and diagnostics.

The course is divided in two modules. In module 1, the main topics of chemistry are explored through the study of functional groups and reactions for the main classes of organic compounds, with a focus on (bio)macromolecules. In module 2, the functional and regulatory properties of these biological macromolecules are then discussed in relation to the main metabolic pathways in humans and to their diagnostic value.

Oral exam covering all the topics of the program, or two separate partial oral tests, corresponding to the topics of Chemistry (Module 1), first, and Biochemistry (Module 2). The exam related to the first module should be taken before the exam of the second module. The final exam result is the weighted average of the marks obtained in each module.

At the end of the course, the student is required to demonstrate that the learning outcomes have been reached, having acquired operational skills and methodology for approaching problems related to the properties, structure and reactivity of complex biomolecules, especially in relation to applications of technological and medical-biological interest. The student will be able to connect different metabolic pathways, to discuss their cross-regulation and to describe the correlation among metabolic pathways in different organs. Finally, the student will be able to recognize the main metabolites with a diagnostic value and to describe methods of analysis currently in use.

The main purpose of the Course is to introduce students to a pragmatic examination of the Italian National Health Service (NHS ) and its structures (Local Health Authorities, Hospitals, IRCCS, etc) including the study of their various areas and departments. Economic and financial aspects of the NHS are also studied. The primary objectives are : a) to provide future clinical engineers with the necessary communication skills, to interact effectively with physicians in order to understand their needs and guide the proper application of clinical engineering principles. b) to teach an appropriate approach to hospital design, with particular attention to functional and economic aspects. c) to equip students with the ability to design and operate using modern principle of Health Technology Assessment ( HTA).

The classes include theoretical instructions and practical applications (based on the long operative experience of the Professor) in the field of design and management in both public and private healthcare sectors. Therefore, it does not present merely theories, formulas, etc., but rather practical applications: in just 48 hours of lessons, extensive knowledge of clinical engineering is imparted, based on solid foundations of healthcare engineering, which are also provided.

Only oral exams. Schedule in accordance with agreements with the Professor.
During the course, as mentioned above, there will be a written exercise/test whose result will certainly contribute to the student's evaluation.
Participation in the written class test exercise is mandatory and a prerequisite for the oral exam.
The grade obtained in the written test will not be averaged with the oral examination grade to determine the final grade, but it will give the Professor an initial assessment of the student’s preparation.

During the exam, the student will need to:
-Clearly demonstrate an understanding of the concepts presented during the teaching course.
-Use terminology appropriate to the subject matter.
-Ensure clarity in the presentation of concepts.
-Demonstrate the ability to interpret and discuss drawings of various hospital areas covered during the lessons.
And/or
-Demonstrate an understanding of the distinction between the profit and losses statements and the assets and liabilities in the context of managing HC Companies
-Demonstrate an understanding of the structure and functioning of the NHS
-Demonstrate an understanding of the hospital waste program.
-The capability to calculate the necessary area quickly and preliminarily for building a hospital , based on certain simple criteria.
-The capability to preliminarily estimate and calculate the construction costs of a hospital and relevant annual management.
-Demonstrate an understanding of the project financing principles.

The course textbook is: "Ingegneria clinica e ingegneria per la Sanita, Clinical and Health care engineering" by Prof. Ing. G. Stefano Scillieri ( published by Editor Flaccovio, latest edition September 2024). The book is written in both Italian and English. The textbook will be supplemented by slides in English, which will be projected during classroom lessons and distributed online to attending students.

Provide to students the capability to manage new material items based upon an engineering point of view focused on the device need and constraints when created with advanced innovative materials. Carbon fiber as well as ceramic based composites and the technologies to produce, treat, stabilize them will be presented and studied in their specific features. Application methodologies with their main advantages and possible problems will be discussed with the students. Regulatory frameworks, specifically devoted to bring a new product on the market and to take care of the strict biomedical need, will be also highlighted by a specific 8 hrs short course. Comparison between European and Usa normative will be illustrated. Continuous comparative analysis between the scientific and industrial viewpoints will be performed in classroom.

Composite materials are very useful in biomedical engineering but their use requires special care. The course focuses on the study of the characteristics of composite materials and their design methodologies. The materials for the construction of exoprostheses as well as those for endoprostheses will be examined. Definition of matrix and reinforcement. Study of the anisotropic aspects of composites. Characteristics and construction of the main long fiber reinforcements (carbon fibers) and particulate(ceramic-based) composite. Interaction and adhesion between reinforcement and matrix, defects and problems inherent to technology and materials. Prepregs and separate component systems. Woven and non woven materials. Fracture of fibers and laminates, scaffolds and hybrid systems. Reinforcement sizing and layering treatments. Ceramic matrix fibers and composites. Manufacturing techniques. The course will be completed by a module on regulatory aspects in designing with innovative materials.

Oral exam with possibility for the student to introduce an item of her/his choice among those discussed in the course. This option, if applied, will make the item to be discussed in a detailed fashion.

The student will be evaluated by her/his capability to manage the introduced problems and propose a technical solution based and supported by what has been learned in the course

Will be indicated by the teacher

The aim of the course is to provide students with methods and techniques for modeling the nervous system, from single neurons and synapses to large-scale neural networks. A specific objective is to highlight the relationship between the patterns of action potential generation by neurons and the modeling approach used.

The course provides an overview of the tools, methods, and strategies used in the field of computational neuroscience for modeling the nervous system at different scales, from single neurons to complex neuronal networks. Particular emphasis is placed on the different approaches that can be followed to mathematically describe nervous structures on the basis of the experimental insights.

The exam will consist in a written exam about all the topics presented during the lectures. An optional project is a plus. Exams will be during the months of January, February, June, July, and September. No others exams will be provided during the year.

The aim of the exam will be to evaluate the level of the learning outcomes of the course reached by the student.

Slide and other teaching materials will be provided
Additional materials will be made avaiable on the course aualweb page

The course aims to meet society's demand for people who can design, develop, and maintain information systems that support current and future digital health care needs due to the increasing introduction of computational techniques and tools into biomedical environments. There is a wide range of context-dependent computing problems that people can only appreciate by working on problems defined by the health care environment and its constraints. To this end, the course will present the typical applications of information science to medical issues (such as electronic health record systems, public health informatics, personal health informatics, health information infrastructure, telemedicine) by considering some basic technologies such as: database systems, standard definition at both technical and semantic levels, Internet-based communication, natural language processing, and decision support systems.

Digital Health, or Digital Healthcare, is a broad, multidisciplinary subject that includes concepts from the intersection of technology and healthcare. Digital Health applies digital transformation to the healthcare sector, incorporating software, hardware and services. Under its umbrella, Digital Health includes mobile health technologies, electronic health records (EHR), electronic health record tools, telemedicine, and personalised medicine technologies. Building on the introductory content presented in the previous Technologies for Personalised Medicine course, this course aims to provide the theoretical, technological and practical foundations for addressing the topics described in the previous section

Discussion on the theoretical topics listed in the syllabus to assess the student's knowledge of the most important applications of information technology in the field of health that can enable the development of Digital Health and the major standards that enable the exchange of data of medical interest and clinical research
Discussion of the practical applications developed during the lessons to assess the student's ability to analyse and design simple systems for processing medical and clinical data based on database technology and simple user interfaces for processing medical and clinical data addressed to users who may not have adequate digital literacy
Presentation of a recent scientific article on the subjects covered in the course to assess the student's ability to discuss the latest innovations in the field of Digital Health

In the final practical interview, all the components of the system developed during the lessons relating to a specific topic will be analyzed. The analysis skills of the developed codes and the ability to change the same codes as the requirements change will be evaluated.
In the theoretical interview, the ability to connect between the various topics studied will be assessed, especially in order to test the propensity for complex design in the medical IT field.

E. H. Shortliffe, J. J. Cimino (Editors), M. F. Chiang (Co-Editor) “Biomedical Informatics: Computer Applications in Health Care and Biomedicine” Fifth Edition, 2021, Cham (Switzerland), Springer
Silberschatz, H. Korth, S. Sudarshan “Database Systems Concept” Seventh Edition, 2020, New York, Mc Grow Hill
N. Harrison “SQL Queries Succinctly”, 2017, Syncfusion
D. Roth, J. Fritz, T. Southwick “Blazor for ASP.NET Web Forms Developers” 2021, Microsoft

The aim of the teaching unit is to introduce the students to a set of laboratory techniques relevant to personalized medicine and to enable them to carry out an individual project in this field.

The purpose of this course is first to provide an overview of the scientific activities performed within the department, related to the research field of engineering for personalized medicine. Secondly, the students will be offered an internship in a lab of their choice, where they will learn to conduct a research project and evaluate new scientific/technical problems and questions. This ‘training through research’ approach will allow them to acquire new skills and techniques, which can be exploited for analyzing, evaluating and solving specific problems arising in a research Lab.

The exam consists in the presentation of the performed project, in the form of a ppt presentation (or similar format). The presentation will be given by the student at the presence of the course teacher and the Lab head and of all the other interested students.

The work performed in the hosting lab and the final presentation of the conducted project will allow to evaluate the achievements of Aim1 and Aim2. More specifically, we expect the student will demonstrate knowledge of the research topic, acquisition of new skills/expertise, independence, proactivity, critical thinking, contextualization of the work within the research community.
Students with SLD, disability or other special educational needs certification are advised to contact the teacher at the beginning of the course to agree on teaching and exam methods that, in compliance with the teaching objectives, take into account the modalities of individual learning and provide suitable tools compensatory.

Recommended reading and specific bibliography will be provided to the students by the Lab head of reference, since they are strongly related to the chosen internship project.

The teaching unit aims to develop the learners’ communicative competence in English at the B2 level of the CEFR, with particular regard to receptive language skills (reading and listening comprehension) and metalinguistic competence. Furthermore, the course focuses on expanding the vocabulary relating to the technical-scientific area with particular regard to the biomedical field, including robotics, artificial intelligence, automation, nanotechnologies and 3D printing.

The course, comprising 36 hours of lessons, aims to develop and consolidate the learners’ communicative competence in English at the B2 level of the CEFR, focusing in particular on receptive (reading and listening comprehension) and productive (oral) language skills, metalinguistic competence and skills in English for Specific Purposes. The topics covered relate to the technical-scientific area with particular regard to the biomedical field, including robotics, artificial intelligence, automation, nanotechnologies and 3D printing.

Written exam (90 minutes): reading comprehension, stylistic, formal and lexcial analysis of academic writing, text production.
If the lecturer considers it necessary, an oral examination may be scheduled after the written examination. If necessary, students will be informed in good time by e-mail.

Written exam that will ascertain receptive, analytic and productive language skills (reading comprehension; formal, stylistic and lexical analysis; text production) developed during the course. The pass mark is 60%.
Active participation in class.
If the lecturer considers it necessary, an oral examination may be scheduled after the written examination. If necessary, students will be informed in good time by e-mail.

Materials provided by the instructor (on Aulaweb)
R. Murphy (2019), English Grammar in Use, Cambridge University Press.
Online English language course level B2 on the https://clim.aulaweb.unige.it/ platform (link and enrollment key will be provided by the lecturer)

The course aims at providing appropriate conceptual tools for understanding healthcare markets and the role played by information asymmetry in influencing market equilibria. Students will acquire the skills necessary to analyze and compare different healthcare systems, exploring issues related to financing, organization, and insurance models. Furthermore, they will learn to apply economic analysis to assess public and private health policies.

Health Economics provides an advanced understanding of the economic principles and analytical tools applied to healthcare systems, policies and markets. Through theoretical lectures, case studies and empirical exercises, students learn to evaluate the allocation of healthcare resources, financing mechanisms and the impact of policy interventions on health outcomes.

Written exam: multiple-choice questions, open-ended questions, and exercises

Continuous assessment: Two written mid-term assessment — one mid-course and one at the end — provide ongoing feedback on students’ understanding and progress
Final examination (alternative): A single written exam may be taken instead of the intermediate tests

The course is structured around teacher’s handouts and original papers, which will be made available by aulaweb.

The aim of the course is provide students with skills concerning hospital systems useful for job placement in public and private structures and for active participation in teams for the management of complex structures systems and biomedical equipment. Knowledge will cover electricity security and safety (power systems, fault protection, supply continuity, energy efficiency) and subjects related to thermal energy (mass and energy balance, air conditioning, heating and refrigeration).

The course unit aims at presenting the technological aspects for the management of hospital facilities. The student will be provided with basic knowledge on the electrical plant engineering part relating to the description of medium and low voltage systems and with basic knowledge of thermal plant engineering part relating to heating, refrigerating and air conditioning devices and systems.

Oral examination on two topics of the course, with an average duration of 45 minutes. Either theoretical open questions or exercises will be posed.
Students having disabilities or Specific Learning Disorders can request suitable aids for the examinations. Such aids will be defined according to specific needs, together with the Referent for the Polytechnic School of the Committe for the inclusion of Students with Disabilites and with SLD. Students in such conditions are invited to get in touch (via e-mail) with the Teacher a sufficient time before the examination, inserting in copy the Referent for the Polytechninc School (https://unige.it/en/commissioni/comitatoperlinclusionedeglistudenticondisabilita.html), without sending any document about their disabilites.

In order to assess the effective achievement of the aims and learning outcomes of the course, the oral examination will evaluate the ability of the students in correctly intepret design data, use standard clauses and apply energy engineering principles in hospital plants. Evaluation parameters will be the quality of oral presentation, the correct use of specific terminology and the appropriate use of standards clauses and analytical relationships

Specific indications on reference bibliography will be provided by the professor at the beginning of the lectures

The unit aims to provide students with the basics of developing applications for the Internet of Medical Things, with a focus on server-side software development frameworks, data transmission protocols, both traditional (UDP, TCP, HTTPS) and IoT-oriented (CoAP and MQTT), and IoT platforms, both open source and proprietary.

The Internet of things is the network of physical objects that contain embedded technology to communicate, sense, and interact with their internal state or an external environment. The term "Internet of things" reflects the growing number of smart, connected products and highlights the new opportunities they can represent in the health sector, the Internet of Medical Things (IoMT).

Projectwork. Working in teams, students will be required to design, develop, and demonstrate a wearable device for monitoring some form of motor/perceptual/cognitive performance of the user, with internet connectivity.

The work will involve two phases with different deadlines:
1. Project idea development. Teams will prepare a short public presentation (elevator pitch) of their project idea, identifying target users, main functions (mid-December)
2. Submission of the project with presentation and demonstration of the working device (mid-February)
Assessment will be based on originality, functionality, and ease of use.

Published on the course web site of the Unige Aulaweb portal.

L'insegnamento si propone di fornire conoscenze di base della lingua italiana parlata e scritta e una introduzione alla cultura e al patrimonio culturale italiano.

Il corso, della durata di 36 ore lettive, si propone di sviluppare la competenza comunicativa in lingua italiana privilegiando in particolare le abilità linguistiche ricettive (comprensione scritta e orale) e la competenza metalinguistica.

Esame scritto e orale

Esame scritto e orale

Durante il corso si userà questo manuale:
Fumagalli, L., Rota, D. & Bozzone Costa, R., C. Ghezzi & M. Piantoni (2024), Italiano da zero, Loescher Editore
Si farà riferimento a questo libro di grammatica:
Morano, L. (2024), Alfa Beta Gramma, Nina edizioni

The final exam consists of the discussion in front of a specific commission of a written paper (degree thesis), related to design activities, conduct of experiments, development of methodologies or operational tools of bioengineering interest, with the aim of ascertaining the level of preparation technical-scientific and professional candidate, as well as his / her innovative capacity The candidate must also demonstrate that they have acquired analysis, processing and communication skills.

Students who hold valid certificates relating to Specific Learning Difficulties (SLD), disabilities or other educational needs are invited to contact the lecturer and the school’s disability liaison officer at the start of the course to agree on any teaching arrangements which, whilst respecting the course objectives, take into account individual learning styles.
The contact details for the university’s disability liaison officer are available at the following link: https://unige.it/commissioni/comitatoperlinclusionedeglistudenticondisabilita.

The teaching unit aims at introducing some of the main mathematical tools that are largely used in applications, and experimenting with them using the PC. Specific topics include: iterative algorithms and gradient methods, interpolation, regularization and inverse problems.

The course will introduce and analyze fundamental tools for the numerical solution of mathematical problems and for the mathematical modeling of complex systems, with application in the field of Bioengineering.

Oral exam

During the oral test the candidate may be required to
address and solve a given problem;
introduce and address a general topic, such as ‘Lagrange interpolation’ or ‘Iterative algorithms for zeros of functions’;
graphically represent one of the problems studied;
discuss the operation of one of the Matlab codes developed during the exercises.
Laboratory exercises carried out during the year are also handed in, in order to test the ability to use complex mathematical models on the computer.

Lecture notes provided by the teacher

The course consists of two modules: Continuous Models for Biological Tissues and Mechanics of Sensory Systems. The aim of the first module is to introduce the fundamental principles of the mechanics of biological tissues, with a focus on developing mathematical models to describe their behaviour. Students will also gain knowledge of numerical analysis techniques for solving these mathematical models. A key objective is to encourage independent work on new projects and to promote the ability to explore and critically analyse scientific literature. The second module is designed to focus students' attention on the biomechanics of sensory systems, with particular emphasis on a single sensory system. Students will learn how to build mathematical models to study how organisms gather and process sensory information. This module also emphasises teamwork, critical reading of the literature, and the development and evaluation of simple mathematical models to address open problems, taking into account their potential, limitations, and possible improvements.

The course is divided into two modules and aims to provide students with knowledge of the mechanical behaviour of biological tissues. In each module, the instructors will propose a hands-on project for students to work on during class hours. For each project, students will be required to formulate a mathematical model and solve it using analytical and/or numerical techniques.

Students will have to prepare individual reports on their work on the two projects. The exam will be an oral discussion of such reports.

The exam will consist in an oral discussion of the student's reports. The reports themselves will be evaluated on the basis of the results obtained and the clarity and quality of presentation.

The course aims to provide basic knowledge of various methods used to investigate the nervous system for the study of the human brain in vivo, focusing on its structure and function in relation to pathology and/or behavioral data (cognitive or motor). Medical technologies will be presented, primarily related to advanced quantitative magnetic resonance imaging techniques, as applied in both clinical practice and research involving healthy subjects and neurological patients.

The course deals with topics related to the development and application of biomedical technologies in the field of neuroscience and rehabilitation, with specific attention to methodological and translational aspects. In particular, neuroimaging plays a major role in the medical sciences, supporting, for instance, the morphological study of lesions following brain damage and the localization of the brain structures involved in the pathology under investigation.
The following topics will be mainly addressed:
1. Innovative methods and technologies in the field of neuroimaging,
2. Basic research aimed at studying the neural correlates of motor and cognitive aspects in humans,
3. Applied research and clinical and rehabilitative perspectives.

Oral presentation of a scientific paper; multiple choice test

The written exam (multiple choice quiz) will focus on the topics covered during the lessons and will aim to evaluate not only the achievement of an adequate level of basic knowledge, but also the acquisition of the ability to critically analyze the questions that will be asked.
The following elements will contribute to the final graduation mark (out of 30, cum laude):

• knowledge of the basic principles, applications and possible problems of the technologies presented during the lessons
• basic knowledge of neurological pathology and of the tools to critically understand different diagnostic and therapeutic techniques, based on the most recent developments in the field of applied medical technologies
• understanding and ability to critically discuss a scientific article (teaching mode: Flipped Classroom)

Slides shown during the lectures and other material, including review articles (made available in Aulaweb).

This module aims to provide students with an in-depth knowledge on topics of chemistry and organic chemistry, focusing on the identification of the main classes of organic compounds, on explanation of the main reaction mechanisms in organic chemistry and on the discussion of structure/properties relationship of synthetic and natural macromolecules.

Review of main functional groups in Organic Chemistry (hydrocarbons, alkyl halides, alcohols, ethers, amines, aldeydes, ketons, carboxylic acids and derivatives, benzene), IUPAC nomenclature and classifications. Main organic reaction mechanisms: homolytic and heterolytic bond cleavage, nucleophilic substitution, electrophilic addition, elimination, nucleophilic addition, nucleophilic acyl substitution, chemical reactivity of benzene. Oxidation-reduction reactions. Synthetic polymers and polymerisation reactions. Molecules of biological interest (carbohydrates, amino acids and proteins, triacylglycerols, nuclei acids) and their reactivity.

Oral (partial) exam. The exam related to the first module should be taken before the exam of the second module. The final exam result is the weighted average of the marks obtained in each module.

After completing this course, the student will be able to manage skills and operating method for the approach of problems related to reactivity, properties and structure of organic molecules and biomolecules. The oral exam will verify the ability of the student to analyze and compare properties and structure of the main classes of compounds in organic chemistry including synthetic polymers and biomacromolecules. During the exam, the student should be able to discuss reaction mechanisms of different classes of compounds and to predict reaction products, describing them in general and critically analyzing specific problems. The correctness and completeness of the answers and also the quality of the exposure, with the use of the appropriate specialized lexicon, will be evaluated.

Lecture notes, Aulaweb, Chemistry reference textbooks: J.McMurry, Fundamentals of Organic Chemistry; W.Brown, T.Poon, Introduction to Organic Chemistry, J.Wiley and Sons; J.G.Smith, Organic Chemistry, McGraw Hill

The aim of the module is to provide the basic notions of mechanics of biological tissue and the knowledge necessary to formulate appropriate mathematical models. It will also provide notions on numerical analysis to find numerical solutions of the mathematical problems. One specific aim of the course is to teach the students how to work independently on a new project and how to find and study the existing scientific literature.

The module will provide
the fundamental concepts of the use of continuum mechanics to solve problems related to biological tissues
provide information about how to formulate appropriate mathematical models
cover elements of numerical analysis and analytical techniques

The exam is the same for both modules. For the exam modalities, please refer to the description in the corresponding form common to both modules.

The exam is the same for both modules. For the assessment methods, please refer to the description in the corresponding form common to both modules

For each of the proposed projects the instructors will provide material to study (typically scientific articles or text books).

The general objective of the module is to provide concepts of biochemistry and clinical biochemistry to understand the meaning of metabolite evaluations, as indicators of (dys)metabolism, and to acquire a language that allows the bioengineer to interact with physician/biotechnologists in the design/implementation of equipment for biomedical diagnosis and/or research. Specific objectives include: 1. To recognize and describe the main biomolecules (lipids, proteins, carbohydrates, nucleotides); 2. To understand and discuss the role of enzymatic regulation (also through knowledge of signaling and kinetics) in the different metabolic pathways; 3. To distinguish the main anabolic and catabolic pathways, connecting them in different metabolic or dysmetabolic conditions, with a focus on the (dys)functional integration between the different human organs. 4. To get knowledge of the main biochemical diagnostic techniques currently in use.

Structure, properties and functions of aminoacids and proteins.
Enzymology. Enzymatic kinetics. Michaelis-Menten's equation. Enzyme inhibition. Allosteric enzymes. Properties and classification of enzymes. Vitamins as coenzymes.
Proteins that bind and transport oxygen; myoglobin and hemoglobin: structural properties and saturation curves. Cooperativeness.
Principles of bioenergetics and biosignalling.
Role of ATP and other phosphorous compounds. General principles of metabolism, catabolism, anabolism.
Carbohydrate metabolism: Glycolysis, gluconeogenesis and regulation. Ketone bodies. Glycogen synthesis, glycogen lysis and their reciprocal regulation in muscle and liver. Pentose phosphate pathway: oxidative and non-oxidative phase, metabolic meaning and regulation. Glutathione.
The citric acid cycle. Pyruvate in mitochondria. Pyruvate dehydrogenase. AcetilCoA. Cycle regulation.
Oxidative phosphorylation. Flow of electrons on respiratory chain complexes. Structures and functions of cofactors involved in the transport of electrons. Proton gradient formation. Chemiosmotic coupling. ATP synthase. Structure and function. Coupling of ATP synthesis with the proton gradient and oxygen consumption.
Lipid metabolism. Lipases and regulation of the metabolism of triacylglycerol. Fatty acids: activation, transport mechanism, β-oxidation. Regulation. Energy yield for palmitate oxidation. Biosynthesis of fatty acids and triacylglycerol.
Protein catabolism. Transamination and deamination of glutamate. Ketogenic and glucogenic amino acids. Urea cycle and its regulation.
Metabolic correlations between organs.
Main methods of assaying metabolites and enzymes of diagnostic interest. Use of spectrofluorimetric analysis, chromatography (HPLC) and immunometric techniques. Tests to evaluate glucose and lipid metabolisms in blood and urine.

Oral exam covering all the topics of the program within Module 2 Biochemistry. The exam related to the first module (Chemistry) should be taken before or in the same session of the exam of the second module. The final exam result is the weighted average of the marks obtained in each module.

The level of aim achievements will be verified with a discussion by the student, that will start from a given subject on a specific metabolic pathway (with knowledge of the involved metabolites and enzymes) from which connections to other metabolic pathways, regulation systems and/or correlation among different organs will be developed. Finally, the discussion will focus on the recognition of metabolites useful for diagnostic purpose and the description of the appropriate test for their assessment.

Lehninger Principles of Biochemistry
International Edition
Cox and Nelson

The aim of the module is to introduce senses and sensory systems in living organisms with special focus on the biomechanics of one sensory system. We will build a mathematical model to study how organisms collect and process specfici sensory information from their surroundings. A main aim of the module is to develop the skills necessary for group work, learn how to critically read the literature, develop simple mathematical models that capture the fundamental ingredients of an open problem and assess its potential, limitations and potential improvements.

Every year we will analyze a different sensory system and how it functions physically. The content will be an introduction of the function of the sensory system; an introduction on the physical laws that dictate how it behaves; a simplified model that analyzes a specific aspect of the sensory system and the methods to run simple numerical simulations regarding the model. More specifically:
Critical reading of literature regarding the sensory system
Mechanics of the tissue and its relation to its sensing function
Simplified mathematical model
Numerical methods for solving pdes in Matlab

The exam is the same for both modules. For the exam modalities, please refer to the description in the corresponding form common to both modules.

The exam is the same for both modules. For the assessment methods, please refer to the description in the corresponding form common to both modules.

Each year a different sensory system will be analyzed, and the teacher will therefore provide research papers and book relevant to the topic at the beginning of the module.

The course aims to provide the theoretical foundations for the study of neural interfaces and electronic devices designed to interface with the nervous system, both in vitro and in vivo. It introduces and explores topics related to the encoding and decoding of information in neural interfaces and data analysis. Students will learn the theoretical basics of electrode-neuron interfaces, device fabrication technologies, neural signal recording techniques, signal analysis, and the encoding and decoding of neural signals in the context of brain-machine interfaces, neuroprosthetics, and, more generally, devices capable of interacting with the nervous system. Deeper insights will be provided through Journal Club sessions featuring student presentations. Laboratory experiences are also planned to familiarize students with experimental activities

The course is focused on the description and understanding of the functioning principles of the neural and brain machine interfaces. It will start with the definition of neural interface and state of the art in the field of neuro-electronic systems. The techniques for measuring the electrophysiological activity of excitable cells and tissues will be presented and explained. A brief introduction of advances signal processing for neural interfaces will be then provided, as a basis for the understanding of coding and decoding of information in neural interfaces. The state of the art in current neural interfaces, including invasive and non-invasive Brain Machine/Computer Interfaces, Neural Prostheses and Neuromodulators will be analyzed and discussed, by focusing on their materials, methods and current translational and clinical applications.

The exam is constituted by a written and an oral examination (exercises and theoretical questions on the topics presented during the frontal lessons and the supervised exercises) and by a presentation of a scientific paper as a Journal Club. The evaluation of the presentation, by the Professor and by two reviewers chosen among the students participating to the course, gives a bonus of up to 3 points.
The experimental lab training do not concur to the final exam evaluation, but serve for the achievement of Aim5.
The exam dates will be decided by the professor and communicated to the students with a proper timing.

The written and the oral test will allow to evaluate the achievements of Aim1, Aim2 and Aim3.
The Journal Club will assess the achievement of Aim4.
The experimental lab training activities will allow to reach Aim5.
All the activities described above will be necessary for the attribution of the Open Badges.

Bioelectronics: MOSFETs, biosensors, neurons, M. Grattarola e G. Massobrio, McGraw -Hill, (1998)
Principle of applied Biomedical instrumentation, Geddes and Baker, Wiley Interscience ed., 1989.
SPIKES: exploring the neural code, Riecke, Warland, de Reuyter van Steveninck, Bialek, MIT press, 1997.
Toward Replacement Parts For The Brain: Implantable Biomimetic Electronics As Neural Prostheses, T. Berger, D. Glanzman eds, MIT press 2005
Analyzing Neural time series data: Theory and practice, Michael X. Cohen, MIT Press, (2014)
Niedermayers’ Electroencephalography: Basic Principles, Clinical Applications and Related Fields, Schomer and Lopes da Silva, sixth ed. Lippincot Williams and wilkins, (2011)

The teaching unit aims to provide a critical analysis of methods for analyzing neuronal signals, starting from the characterization of individual spikes, to the activity of multiple cells, and up to the analysis of electroencephalographic signals. It will provide the theoretical and practical foundations needed to manipulate, analyze, and critically interpret the most common electrophysiological data.

Brain signals play a central role in the field of neuroengineering. This course will cover fundamental methods for treating and interpreting brain signals at different scales (from the microscale of the single neuron up the macroscale of the whole brain). The students will understand how brain signals are generated, will be able to distinguish between different acquisition methodologies and will be provided all the tools to analyze, interpret and discuss brain data

The exam is composed of Project assignments (group work) and an Oral exam. Students will self-organise in small groups (max 3) and these groups will participate in several activities during the semester. Each assignment will be evaluated for its completeness and overall quality.

Aim 1. Will be primarily assessed during oral examination where the students will be asked to critically discuss about the different methods presented
Aim 2. and Aim 3. Will be assessed in the group-based assignments.

Analyzing neural time series - Cohen

The teaching unit covers the technologies, the analytical methods, the modeling approaches used for the analysis and quantification of human movement and its neural correlates. Specific topics include three-dimensional analysis of movements, muscle and body mechanics, physiology and physiological signals in motor control, computational motor control.

The primary goal of the course is to provide an overview of the scientific activities conducted within the department, with a particular focus on Neuroengineering. Students will have the opportunity to participate in an internship in one of the available laboratories, where they will learn how to conduct a small research project and address new technical-scientific challenges. This 'learning through research' approach will enable them to acquire innovative methodologies and skills useful for analyzing, evaluating, and solving complex technical problems, applicable both within the field of research and beyond.

The exam involves presenting the completed project, either as a PowerPoint presentation (or similar format) or by submitting the deliverable agreed upon with the lab supervisor, such as a repository containing the project code. The student will present or discuss the deliverable in the presence of course instructors, the lab supervisor, and any interested students.

The work performed in the hosting lab and the final presentation of the project will allow for the evaluation of Aim 1 and Aim 2. Specifically, we expect the student to demonstrate knowledge of the research topic, acquisition of new skills and expertise, independence, proactivity, critical thinking, and the ability to contextualize their work within the research community.
The activities carried out in the course are necessary for the attribution of the Open Badges.

Recommended reading and specific bibliography will be provided to the students by the Lab head of reference, since they are strongly related to the chosen internship project.

The course provides a design guideline of neuromorphic models for the representation and distributed processing of multidimensional signals. The emphasis is on both computational primitives and architectural schemes. Applications to the development of perceptual engines to enable autonomous behaviors in complex systems and natural environments are presented as case studies.

Several real-world applications already rely upon modules based on neural paradigms as the computational engines of AI algorithms (e.g., deep/convolutional neural networks). The course aims at overcoming the formal framework of artificial neural networks and to relate more decisively to models derived from Neurosciences. A multidisciplinary approach, which links bidirectionally with the Brain Sciences is crucial: from one side, it fosters the transfer towards artificial systems of the knowledge gained from the study of biological systems (i.e., models specified in hardware, software and eventually in wetware that embody in an essential form their principles, architectures and functionalities), and, from the other side, it demonstrates the usefulness of the “artificial” approach as a method for the investigation of the nervous systems.

Discussion on an example/application/deepening of a topic seen in class proposed in the Journal Clubs (weight 30%). A 20 minute presentation to summarize the main take-home messages of a scientific paper chosen from a list that will be available in Aulaweb. The assignment is to analyze only those parts that mostly relate to the topics presented during classes. An open discussion will follow the presentation, and everyone is invited to participate. The discussion will be also a chance to introduce some supplementary topics, which will be part of the exam’s program.
Oral examination. The exam consists in appropriately framing and discussing two assigned topics (typically, a "theoretical model" and a "computational solution of a perceptual problem"). The oral exam is aimed at verifying the acquisition of the notions presented in the teaching, and at evaluating the ability to analyze and plan a specific problem (weight 70%).

The student should eventually demonstrate the ability to analyze and synthesize neuromorphic processing paradigms at the cellular, circuit and system level.
In general, in addition to the correctness and completeness of the answer, the evaluation criteria comprise: the relevance to the question, the clarity of the answer, and the ability to synthesize.

Slides and other distributed material (available through Aulaweb).
Further reading, for consultation only:
P. Dayan and L.F. Abbott. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. The MIT Press, 2001.
H.A. Mallot. Computational Vision: Information Processing in Perception and Visual Behavior. The MIT Press, 2000.

The course explores how sensory interaction works, how it can be used to monitor brain health, and how our sensory abilities can be rehabilitated or augmented. The course will show the students how the current technologies and the knowledge about the sensory mechanisms could 1) help, empower, educate the correct development of sensory faculties, 2) rehabilitate sensory deficits, 3) assist the diagnosis of sensory disfunctions, and 4) lead/promote early diagnosis.

Our senses are the main means through which we perceive, understand, interact with and react to our environment. Consequently, sensory disabilities are a serious challenge that impacts all levels of physical and social functioning. The impaired brain is often difficult to restore, owing to our limited knowledge of the complex nervous system. Accumulating knowledge in systems neuroscience, combined with innovative technologies, may enable brain restoration in patients with nervous system disorders currently untreatable. This course explores how bioengineering techniques and principles are applied to understand and model sensory systems. It addresses technologies for the analysis, evaluation, and rehabilitation of sensory disorders. Major sensory systems, diagnostic and rehabilitation technologies, sensory prostheses, and sensory substitution or augmentation strategies are covered. The course includes both theoretical lectures and laboratory activities.

The final exam consists of an oral examination in which the student will discuss theoretical content covered during lectures and labs. The assessment may include critical analysis of case studies or technologies introduced during the course. Group presentations of the projects developed in laboratory sessions will also be part of the final evaluation.

The final grade will be determined through a combination of the oral examination and the practical activities carried out during the laboratory sessions. Evaluation will consider the accuracy and depth of content, critical and analytical thinking, clarity and effectiveness of communication, and the originality of the proposed solutions. All the assignements evaluations will be carried out using a shared and transparent grading rubric to ensure consistency and fairness in the assessment process.

Specific indications on reference bibliography will be provided by the professor at the beginning of the lectures.

The teaching unit of the course is to provide students with a comprehensive overview of human in vitro models used in personalized medicine. In particular, the course will introduce the fundamentals of organ-on-chip technologies - including cell cultures, devices, and key characterization techniques - with focus on their applications in disease modelling and drug testing. Special attention will be given to the characterization of electrophysiological signals generated by brain-on-chip and heart-on-chip models.

This course provides a comprehensive introduction to organ-on-chip technology, an innovative platform that combines microfluidics and cell biology to model human organ functions in vitro. Students will explore the design and application of the organ-on-chip systems for drug testing, disease modeling, and personalized medicine. The course also addresses current challenges and the translational potential of organ-on-chip models in pharmaceutical and biomedical research.

The final assessment will consist of an oral examination covering the theoretical and conceptual aspects of the course.
In addition, students will be required to perform a Journal Club on a specific topic (i.e., placenta on chip, cancer on chip, immune system on chip).

Student performance will be evaluated through a combination of the following:
Oral Examination (80%) To assess understanding of key concepts, theoretical foundations, and the ability to discuss applications of organ-on-chip technology.
Journal club (20%) Students will present a journal club on a specific topic. Question and discussion during the entire journal club sessions (also during presentation from collegues) are considered.
Active participation during lecture sections may also be considered for borderline evaluations.
All the topics discussed during the journal clubs are part of the oral examination.

Lecture slides and selected scientific articles from peer-reviewed journals.

The teaching unit aims at presenting a consistent and up-to-date introduction to basic facts and theories regarding human sensory perception. The teaching covers the physical and physiological aspects of each sensory modality and its perceptual characteristics. Emphasis is given on how perceptual experience relates to the physical properties of the external world, the characteristics of sensory signals, and the architectural principles of the nervous system.

The course, compulsory for all curricula, provides a coherent, up-to-date introduction to the basic facts and theories concerning (human) sensory perception. The physical and physiological aspects of each sensory modality are considered before its perceptual characteristics. Emphasis is placed on how perceptual experience relates to the physical properties of the world and to physiological constraints in the brain.

Oral exam (max score 27/30) and evaluation of Psychophysics practical (max score 3/30).
Oral exam consists of two short-answer questions followed by two in-depth questions.
Psychophysics practical consists of two guided assignments:
Level 1 (a total score of 1/30 is gained when the code works and the data were collected and analyzed, 0 otherwise).
Level 2 (a total score of 1/30 is gained if the code is complete and syntactically correct, a total score of 2/30 is gained if the code is complete and syntactically correct, and the data were collected and plotted).
Level 2 can be tackled after completion of the first level’s objectives (i.e., delivery and evaluation of Level 1 assignment).
Final delivery deadline: last day of the semenster.
Note: Psychophysics practical is part of the oral exams program with sample checking of the knowledge of the developed code.
The student who fails or withdraws from the exam cannot retake the exam on the immediately subsequent round.
Students can refuse the score just once, but in that case they will be allowed to retake the exam on any of the subsequent exam dates, without jumps.

The student should eventually demonstrate:
Basic knowledge of biology, anatomy and physiology of neurosensorial systems.
Capacity of distinguishing and analysing the correlations among perceptual sensations, characteristics of the sensory signals, and biological structure.
Mastery of methods, techniques and instruments to experimentally characterize perceptual phemomenology.
Experience of possible application domains.
The oral exam is aimed at (1) verifying the acquisition of the concepts presented in the course, and (2) evaluating the ability of analysis and modeling on specific problems. In general, in addition to the correctness and completeness of the answer, the evaluation criteria comprise: the relevance to the question, the clarity of the answer, and the ability to synthesise.

Slides and other distributed material (available through Aulaweb).
Recommended textbook: G. Mather. “Foundations of sensation and perception” (2nd edition) Psychology Press 2009

The aim of the course is to provide students with the fundamentals needed to understand the dynamics of biological fluid flows, with particular emphasis on fluid motion in the human body. The following topics will be covered: blood rheology; fluid flow in the heart, arteries, capillaries, and veins; and ocular fluid mechanics.

The course provides the student with understanding of the dynamics of physiological fluid flows. The focus will be on the mechanical aspects of such flows.

Oral exam.

The exam will consist in an oral discussion of the topics covered during the course. Great attention will be devoted to asses the student understanding of the physical processes underlying the physiological flow considered.

Lecture notes of the course.

The course aims to provide an overview of the technical knowledge and transversal skills essential for pursuing a professional career in the field of bioengineering. It is structured as a series of seminars delivered by experts from national and international companies, offering students direct exposure to the professional world. Through these sessions, students will have the opportunity to explore the various roles that biomedical engineers can undertake in the industrial sector, including research and development, regulatory affairs, quality control, project management, intellectual property rights and clinical trials. The course is designed to support students in identifying potential career paths and in developing the skills required for a well-informed and qualified entry into the job market.

This professional development course will provide an overview of technical knowledge and soft skills that are useful/necessary to pursue a career in the bioengineering field. The course is organized as a seminar series by experts from national and international companies, who will also introduce the various professional roles that biomedical engineers can have in companies/hospitals.

Active participation in classes: the student must attend all classes in person as single/group exercises can be done during the lectures. Additionally, it is possible to request a project on the topics covered in the lectures to discuss with the instructor.

Discussion of the presented activities

None

The course aims to provide students with a general understanding of the methods and technologies developed to support individuals with disabilities in recovering cognitive, sensory, and/or motor functions. Specifically, the course offers foundational knowledge of technological solutions designed to (i) assess and monitor impairments, (ii) assist individuals with disabilities, and (iii) replace missing limbs and/or promote the recovery of lost functions. The course provides an overview of the most advanced techniques in functional assessment, prosthetics, sensory substitution, neurorehabilitation, and assistive technologies.

This is an introductory course to methods and technologies for helping people with disabilities to regain lost cognitive, sensory and/or motor functions.
The course is intended to provide the basic knowledge behind technological solutions (i) to evaluate and monitor the impairment, (ii) to assist individuals with disabilities, (iii) to promote the recovery of missing limbs or lost functions.
In addition to learning theoretical concepts, students will engage in practical activities such as software development and scientific discussions.

Students will be evaluated based on the following components:
a) Assignments / Project work Students will analyze and discuss data related to individuals with disabilities or rehabilitation technologies. The goal is to assess their ability to apply course concepts to real-world scenarios.
b) Active participation Students are expected to actively participate in class by presenting research articles on specific applications and/or engaging in discussions with invited experts in the field.
c) Final exam The final exam will assess the overall understanding and critical use of the course contents.
Components a) and b) are considered ongoing assessments (continuous evaluation), while c) is the final summative evaluation. For students who do not attend classes, both a) and b) must still be completed before taking the final exam. Participation in b) may also be included as part of the final exam.

The assessment methods refer to the components described above:
a) Assignments / Project work These are intended to evaluate the student’s ability to understand, explain, and solve problems related to Rehabilitation Engineering topics.
b) Active participation This assesses the student’s ability to learn, explore in depth, and explain course topics, particularly through presentations and discussions based on scientific literature or expert seminars.
c) Final exam The final exam evaluates whether the student has assimilated the course content, can use it critically, and is able to communicate it effectively.
In addition to content knowledge, the following aspects will be assessed throughout the course:
the quality and clarity of presentations
the correct use of specialized terminology
the ability to think critically and apply acquired knowledge
participation in group activities and the ability to collaborate effectively.

Materials and readings will be made available on the course website (AulaWeb). The course will include the review and discussion of selected research articles recently published in international peer-reviewed journals.

The purpose of the teaching unit is for participants to acquire knowledge and understand the concepts of Human-Computer Interaction (HCI) that enable the design of effective systems for user needs, both in terms of ease of interaction and the naturalness of system usage as a whole. Attendance and active participation in the proposed learning activities, together with individual study, will enable the student to learn the theoretical tools for designing advanced interaction systems, use the theoretical and practical tools for their implementation, and develop the ability to apply the acquired concepts in the context of virtual and augmented reality.

The course introduces the concepts of Human-Computer Interaction that allow designing effective systems for users' needs both from the point of view of the simplicity of interaction and the naturalness of the use (fundamental aspects in the biomedical field). Programming and computer vision techniques are presented for the realization of advanced interaction systems. Furthermore, interaction evaluation is introduced: design of experimental sessions and data analysis.

Final assignment (optional)
Oral exam on the topics of the course

Details on how to prepare for the exam and the degree of depth of each topic will be given during the lessons. Through the monographic project, the students must demonstrate that they are able to develop simple systems of interaction in virtual and augmented reality environments. The oral exam will verify the actual knowledge of the theoretical topics learned during the semester.

Notes and slides available on AulaWeb.
Specific indications on reference bibliography will be provided by the professors at the beginning of the lectures

The teaching unit aims to provide knowledge of experimental methods as well as analytical and numerical techniques for the study of human movement, with particular focus on sports activities. A specific objective is to equip the biomedical engineering student with the ability to analyze simple motor tasks through the integration of models and measurements. Elements of ergonomics will also be introduced.

In recent years, the analysis of human movement in sport and exercise has become increasingly important for optimizing athletic performance, preventing injuries, supporting rehabilitation, and promoting safe and effective physical activity across diverse populations. The availability of advanced biomechanical measurement systems, motion analysis technologies, and computational modeling tools makes it crucial for bioengineers to acquire solid theoretical foundations and practical skills in this multidisciplinary field. This course introduces the scientific principles and practical applications of biomechanics in sports, integrating musculoskeletal biomechanics, ergonomics, movement science, and neuromuscular function. Students will learn how mechanical loads, joint kinematics, muscle activations, and motor control influence sports performance and injury risk. The course combines theoretical knowledge with hands-on experience in experimental techniques widely used in sports biomechanics.

The final assessment consists of an oral examination on the theoretical topics covered throughout the course and a project-based discussion related to the laboratory work and data analysis performed by each student or group.

Assessment will be based on both the final oral exam and the laboratory activities. Each component will be evaluated for completeness, originality, and quality, according to a shared and transparent grading rubric. Evaluation criteria include mastery of content, analytical skills, clarity of presentation, and innovation in data analysis or project development.

Specific indications on reference bibliography will be provided by the professor at the beginning of the lectures.

The teaching unit aims, in the first part, to provide basic knowledge of the main aspects of personalized medicine and bioengineering as an enabling strategy, starting from the impact that individual molecular variability, lifestyle, and environmental factors have on the development, progression, and treatment of diseases. In particular, the course will cover omics principles that allow for greater precision in diagnosis and therapy compared to conventional approaches. Students will therefore explore the foundations of molecular diagnostics, the role of biomarkers, and genomic and non-genomic factors underlying personalized medicine. The second part of the teaching unit aims to provide the technological foundations of digital health applied to the care of individual patients and the influence of specific components of the IT infrastructure (such as operating systems, communication tools, and security) on the performance and applicability of personal digital health.

The main purpose of the teaching is to provide students with basic knowledge related to the principles of personalized medicine and its engineering applications in the fields of diagnostics and therapy and the fundamentals for the design of related materials and devices, both at nano and micro-scale. Since all these aspects are based on an intense and conscious use of information systems both in terms of operations and communication, basic contents of: operating systems, communication networks and IT security will also be presented.

The exam consists of an oral exam divided into four main topics, one of which is chosen by the student.
For the IT part, a written exam with closed answers is foreseen for access to the oral interview

The oral exam will focus on the topics covered during the lectures and will have the purpose of evaluating not only if the student has reached an adequate level of knowledge, but if he has acquired the ability to critically analyze the engineering problems that will be posed during the course.
Students with SLD, disability or other special educational needs certification are advised to contact the teacher at the beginning of the course to agree on teaching and exam methods that, in compliance with the teaching objectives, take into account the modalities of individual learning and provide suitable tools compensatory.

Slides provided in class and scientific articles on specific topics.
For further information:
Andrew S. Tanenbaum, Herbert Bos “Modern Operating System” IV Ed. Pearson
Andrew S. Tanenbaum, David J. Wetherall “Computer Networks” V Ed. Pearson
Jain, Kewal K. Textbook of personalized medicine. Springer Nature, 2020.

The teaching unit aims to provide students with both theoretical and practical skills to design, implement, and integrate wearable devices with IoT platforms for health monitoring and diagnostics. This teaching unit consists of two modules: Wearable Devices for Biomedical Applications (module 1) and the Internet of Things for Healthcare (module 2). The first module aims to introduce the principles underlying the design and development of wearable devices for biomedical applications, with a focus on sensors, actuators, and microcontroller programming for monitoring physiological functions. The second unit aims to students with the basics of developing applications for the Internet of Medical Things, with a focus on server-side software development frameworks, data transmission protocols, both traditional (UDP, TCP, HTTPS) and IoT-oriented (CoAP and MQTT), and IoT platforms, both open source and proprietary.

The course covers the principles, the technologies and the design tools for wearable devices and the Internet of Things and their application in the biomedical field. INTERNET OF HEALTHCARE THINGS MOD. 2: The Internet of things is the network of physical objects that contain embedded technology to communicate, sense, and interact with their internal state or an external environment. The term "Internet of things" reflects the growing number of smart, connected products and highlights the new opportunities they can represent in the health sector, the Internet of Medical Things (IoMT).

Projectwork. Working in teams, students will be required to design, develop, and demonstrate a wearable device for monitoring some form of motor/perceptual/cognitive performance of the user, with internet connectivity.

The work will involve two phases with different deadlines:
1. Project idea development. Teams will prepare a short public presentation (elevator pitch) of their project idea, identifying target users, main functions (mid-December)
2. Submission of the project with presentation and demonstration of the working device (mid-February)
Assessment will be based on originality, functionality, and ease of use.

This unit will cover the general principles for design and development of wearable devices for biomedical applications (diagnosis and monitoring of functions). The main topics include micro-controller programming and the use of sensors and actuators.

General course introduction. Wearable devices in medicine. Basics of microcontroller programming. Introduction to the Arduino platform (Arduino IDE and simulator) Lab activity 1: A Hello world MCU program
Actuators. Digital, analog. PWM control. Transistors as switches Lab activity 2: LED dimmer, DC motor
Sensors. Digital, analog. A/D conversion. Lab activity 3: LED dimmer/DC motor controlled by a potentiometer
Communication protocols. Sensors and actuators based on serial protocols (synchronous/synchronous) Lab activity 4: IMU sensor, OLED screen
Timed acquisition/control. Timers, interrupts Lab activity 5: timed acquisition

Common to both units

Common to both units

Datasheets for microcontrollers, sensors and actuators