Suchergebnis: Katalogdaten im Frühjahrssemester 2020

Gesundheitswissenschaften und Technologie Master Information
Vertiefung in Neurowissenschaften
Wahlfächer I
376-0202-00LNeural Control of Movement and Motor LearningW4 KP3GN. Wenderoth
KurzbeschreibungThis course extends the students' knowledge regarding the neural control of movement and motor learning. Particular emphasis will be put on those methods and experimental findings that have shaped current knowledge of this area.
LernzielKnowledge of the physiological and anatomic basis underlying the neural control of movement and motor learning. One central element is that students have first hands-on experience in the lab where small experiments are independently executed, analysed and interpreted.
376-1306-00LClinical Neuroscience Information
More information at:
W3 KP3VG. Schratt, Uni-Dozierende
KurzbeschreibungThe lecture series "Clinical Neuroscience" presents a comprehensive, condensed overview of the most important neurological diseases, their clinical presentation, diagnosis, therapy options and possible causes. Patient demonstrations (Übungen) follow every lecture that is dedicated to a particular disease.
LernzielBy the end of this module students should be able to:
- demonstrate their understanding and deep knowledge concerning the main neurological diseases
- identify and explain the different clinical presentation of these diseases, the methodology of diagnosis and the current therapies available
- summarize and critically review scientific literature efficiently and effectively
376-1430-00LModeling and Methods in Human Behavioural Neuroscience3 KP2GG. Bertolini, F. Romano
KurzbeschreibungThe course presents models in human behavioral neuroscience and methods to:
1) Adapt the models to embed hypotheses;
2) Make model-based predictions;
3) Use models when designing data collections that verify/disprove predictions
LernzielAt the end of this module students should know:
• different types of models used in human behavioral neuroscience, their features and their limits
• how to use models to estimate expected human behavioural outcomes or to interpret behavioural data
• how to implement models and methods via software (Matlab)
Inhalt1. Linear time-invariant model and their practical applications on neuroscience systems (e.g. sensory input, motor control). From equations to block diagram representation.

2. Psychophysical methods to test human perceptual response and statistical models of behaviour (e.g. Bayesian model). Examples from tasks probing perceptual responses.

3. How the brain controls our body through internal models (feedforward and feedback). Examples from motor and balance tasks. The optimal observer as a model of how the human brain interprets inputs, plans and compares actions and finally executes them.

The course will combine theoretical and practical knowledge on how to implement models and techniques via software on datasets (Matlab)
551-0326-00LCell Biology Information W6 KP4VS. Werner, M. Bordoli, W. Kovacs, M. Schäfer, U. Suter, A. Wutz
KurzbeschreibungThis Course introduces principle concepts, techniques, and experimental strategies used in modern Cell Biology. Major topics include: neuron-glia interactions in health and disease; mitochondrial dynamics; stem cell biology; growth factor action in development, tissue repair and disease; cell metabolism, in particular sensing and signaling mechanisms, cell organelles, and lipid metabolism.
Lernziel-To prepare the students for successful and efficient lab work by learning how to ask the right questions and to use the appropriate techniques in a research project.
-To convey knowledge about neuron-glia interactions in health and disease.
- To provide information on different types of stem cells and their function in health and disease
-To provide information on growth factor signaling in development, repair and disease and on the use of growth factors or their receptors as drug targets for major human diseases
-To convey knowledge on the mechanisms underlying repair of injured tissues
-To provide the students with an overview of mitochondrial dynamics.
-Providing an understanding of RNA processing reactions and their regulations.
-To provide a comprehensive understanding of metabolic sensing mechanisms occurring in different cell types and organelles in response to glucose, hormones, oxygen, nutrients as well as lipids, and to discuss downstream signaling pathways and cellular responses.
-To provide models explaining how disturbances in complex metabolic control networks and bioenergetics can lead to disease and to highlight latest experimental approaches to uncover the intricacies of metabolic control at the cellular and organismal level.
-Providing the background and context that foster cross-disciplinary scientific thinking.
Wahlfächer II
227-0390-00LElements of MicroscopyW4 KP3GM. Stampanoni, G. Csúcs, A. Sologubenko
KurzbeschreibungThe lecture reviews the basics of microscopy by discussing wave propagation, diffraction phenomena and aberrations. It gives the basics of light microscopy, introducing fluorescence, wide-field, confocal and multiphoton imaging. It further covers 3D electron microscopy and 3D X-ray tomographic micro and nanoimaging.
LernzielSolid introduction to the basics of microscopy, either with visible light, electrons or X-rays.
InhaltIt would be impossible to imagine any scientific activities without the help of microscopy. Nowadays, scientists can count on very powerful instruments that allow investigating sample down to the atomic level.
The lecture includes a general introduction to the principles of microscopy, from wave physics to image formation. It provides the physical and engineering basics to understand visible light, electron and X-ray microscopy.
During selected exercises in the lab, several sophisticated instrument will be explained and their capabilities demonstrated.
LiteraturAvailable Online.
227-0395-00LNeural SystemsW6 KP2V + 1U + 1AR. Hahnloser, M. F. Yanik, B. Grewe
KurzbeschreibungThis course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experiment techniques used in animal research and methods for inferring neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on artificially intelligent systems.
LernzielThis course introduces
- Basic neurophysiology and mathematical descriptions of neurons
- Methods for dissecting animal behavior
- Neural recordings in intact nervous systems and information decoding principles
- Methods for manipulating the state and activity in selective neuron types
- Neuromodulatory systems and their computational roles
- Reward circuits and reinforcement learning
- Imaging methods for reconstructing the synaptic networks among neurons
- Birdsong and language
- Neurobiological principles for machine learning.
InhaltFrom active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics.
Voraussetzungen / BesonderesBefore taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L).

As part of the exercises for this class, students are expected to complete a programming or literature review project to be defined at the beginning of the semester.
227-1034-00LComputational Vision (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: INI402

Mind the enrolment deadlines at UZH:
W6 KP2V + 1UD. Kiper
KurzbeschreibungThis course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision.
LernzielThis course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed.
The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will
be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
InhaltThis course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed.
The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will
be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
LiteraturBooks: (recommended references, not required)
1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997.
2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995.
227-1046-00LComputer Simulations of Sensory Systems Information
Findet dieses Semester nicht statt.
KurzbeschreibungThis course deals with computer simulations of the human auditory, visual, and balance system. The lecture will cover the physiological and mechanical mechanisms of these sensory systems. And in the exercises, the simulations will be implemented with Python. The simulations will be such that their output could be used as input for actual neuro-sensory prostheses.
LernzielOur sensory systems provide us with information about what is happening in the world surrounding us. Thereby they transform incoming mechanical, electromagnetic, and chemical signals into “action potentials”, the language of the central nervous system.
The main goal of this lecture is to describe how our sensors achieve these transformations, how they can be reproduced with computational tools. For example, our auditory system performs approximately a “Fourier transformation” of the incoming sound waves; our early visual system is optimized for finding edges in images that are projected onto our retina; and our balance system can be well described with a “control system” that transforms linear and rotational movements into nerve impulses.
In the exercises that go with this lecture, we will use Python to reproduce the transformations achieved by our sensory systems. The goal is to write programs whose output could be used as input for actual neurosensory prostheses: such prostheses have become commonplace for the auditory system, and are under development for the visual and the balance system. For the corresponding exercises, at least some basic programing experience is required!!
InhaltThe following topics will be covered:
• Introduction into the signal processing in nerve cells.
• Introduction into Python.
• Simplified simulation of nerve cells (Hodgkins-Huxley model).
• Description of the auditory system, including the application of Fourier transforms on recorded sounds.
• Description of the visual system, including the retina and the information processing in the visual cortex. The corresponding exercises will provide an introduction to digital image processing.
• Description of the mechanics of our balance system, and the “Control System”-language that can be used for an efficient description of the corresponding signal processing (essentially Laplace transforms and control systems).
SkriptFor each module additional material will be provided on the e-learning platform "moodle". The main content of the lecture is also available as a wikibook, under Link
LiteraturOpen source information is available as wikibook Link

For good overviews I recommend:

• Principles of Neural Science (5th Ed, 2012), by Eric Kandel, James Schwartz, Thomas Jessell, Steven Siegelbaum, A.J. Hudspeth
ISBN 0071390111 / 9780071390118
THE standard textbook on neuroscience.

• L. R. Squire, D. Berg, F. E. Bloom, Lac S. du, A. Ghosh, and N. C. Spitzer. Fundamental Neuroscience, Academic Press - Elsevier, 2012 [ISBN: 9780123858702].
This book covers the biological components, from the functioning of an individual ion channels through the various senses, all the way to consciousness. And while it does not cover the computational aspects, it nevertheless provides an excellent overview of the underlying neural processes of sensory systems.

• G. Mather. Foundations of Sensation and Perception, 2nd Ed Psychology Press, 2009 [ISBN: 978-1-84169-698-0 (hardcover), oder 978-1-84169-699-7 (paperback)]
A coherent, up-to-date introduction to the basic facts and theories concerning human sensory perception.

• The best place to get started with Python programming are the Link
Voraussetzungen / Besonderes• Since I have to gravel from Linz, Austria, to Zurich to give this lecture, I plan to hold this lecture in blocks (every 2nd week).
• In addition to the lectures, this course includes external lab visits to institutes actively involved in research on the relevant sensory systems.
327-2125-00LMicroscopy Training SEM I - Introduction to SEM Belegung eingeschränkt - Details anzeigen
Limited number of participants.

Master students will have priority over PhD students. PhD students may still enroll, but will be asked for a fee. (Link).

Registration form: (Link)
W2 KP3PP. Zeng, A. G. Bittermann, S. Gerstl, L. Grafulha Morales, K. Kunze, J. Reuteler
KurzbeschreibungDer Einführungskurs in Rasterelektronenmikroskopie (SEM) betont praktisches Lernen. Die Studierenden haben die Möglichkeit an zwei Elektronenmikroskopen ihre eigenen Proben oder Standard-Testproben zu untersuchen, sowie von ScopeM-Wissenschafler vorbereitete Übungen zu lösen.
Lernziel- Set-up, align and operate a SEM successfully and safely.
- Accomplish imaging tasks successfully and optimize microscope performances.
- Master the operation of a low-vacuum and field-emission SEM and EDX instrument.
- Perform sample preparation with corresponding techniques and equipment for imaging and analysis
- Acquire techniques in obtaining secondary electron and backscatter electron micrographs
- Perform EDX qualitative and semi-quantitative analysis
InhaltDuring the course, students learn through lectures, demonstrations, and hands-on sessions how to setup and operate SEM instruments, including low-vacuum and low-voltage applications.
This course gives basic skills for students new to SEM. At the end of the course, students with no prior experience are able to align a SEM, to obtain secondary electron (SE) and backscatter electron (BSE) micrographs and to perform energy dispersive X-ray spectroscopy (EDX) qualitative and semi-quantitative analysis. The procedures to better utilize SEM to solve practical problems and to optimize SEM analysis for a wide range of materials will be emphasized.

- Discussion of students' sample/interest
- Introduction and discussion on Electron Microscopy and instrumentation
- Lectures on electron sources, electron lenses and probe formation
- Lectures on beam/specimen interaction, image formation, image contrast and imaging modes.
- Lectures on sample preparation techniques for EM
- Brief description and demonstration of the SEM microscope
- Practice on beam/specimen interaction, image formation, image contrast (and image processing)
- Student participation on sample preparation techniques
- Scanning Electron Microscopy lab exercises: setup and operate the instrument under various imaging modalities
- Lecture and demonstrations on X-ray micro-analysis (theory and detection), qualitative and semi-quantitative EDX and point analysis, linescans and spectral mapping
- Practice on real-world samples and report results
Literatur- Detailed course manual
- Williams, Carter: Transmission Electron Microscopy, Plenum Press, 1996
- Hawkes, Valdre: Biophysical Electron Microscopy, Academic Press, 1990
- Egerton: Physical Principles of Electron Microscopy: an introduction to TEM, SEM and AEM, Springer Verlag, 2007
Voraussetzungen / BesonderesNo mandatory prerequisites. Please consider the prior attendance to EM Basic lectures (551- 1618-00V; 227-0390-00L; 327-0703-00L) as suggested prerequisite.
327-2126-00LMicroscopy Training TEM I - Introduction to TEM Belegung eingeschränkt - Details anzeigen
Number of participants limited to 6.
Master students will have priority over PhD students. PhD students may still enroll, but will be asked for a fee (Link).

TEM 1 registration form: (Link)
W2 KP3PP. Zeng, E. J. Barthazy Meier, A. G. Bittermann, F. Gramm, A. Sologubenko, M. Willinger
KurzbeschreibungDer Einführungskurs in Transmissionselektronenmikroskopie (TEM) bietet neuen Nutzern die Möglichkeit theoretisches Wissen und praktische Kenntnisse in TEM zu erwerben
Lernziel- Overview of TEM theory, instrumentation, operation and applications.
- Alignment and operation of a TEM, as well as acquisition and interpretation of images, diffraction patterns, accomplishing basic tasks successfully.
- Knowledge of electron imaging modes (including Scanning Transmission Electron Microscopy), magnification calibration, and image acquisition using CCD cameras.
- To set up the TEM to acquire diffraction patterns, perform camera length calibration, as well as measure and interpret diffraction patterns.
- Overview of techniques for specimen preparation.
InhaltUsing two Transmission Electron Microscopes the students learn how to align a TEM, select parameters for acquisition of images in bright field (BF) and dark field (DF), perform scanning transmission electron microscopy (STEM) imaging, phase contrast imaging, and acquire electron diffraction patterns. The participants will also learn basic and advanced use of digital cameras and digital imaging methods.

- Introduction and discussion on Electron Microscopy and instrumentation.
- Lectures on electron sources, electron lenses and probe formation.
- Lectures on beam/specimen interaction, image formation, image contrast and imaging modes.
- Lectures on sample preparation techniques for EM.
- Brief description and demonstration of the TEM microscope.
- Practice on beam/specimen interaction, image formation, Image contrast (and image processing).
- Demonstration of Transmission Electron Microscopes and imaging modes (Phase contrast, BF, DF, STEM).
- Student participation on sample preparation techniques.
- Transmission Electron Microscopy lab exercises: setup and operate the instrument under various imaging modalities.
- TEM alignment, calibration, correction to improve image contrast and quality.
- Electron diffraction.
- Practice on real-world samples and report results.
Literatur- Detailed course manual
- Williams, Carter: Transmission Electron Microscopy, Plenum Press, 1996
- Hawkes, Valdre: Biophysical Electron Microscopy, Academic Press, 1990
- Egerton: Physical Principles of Electron Microscopy: an introduction to TEM, SEM and AEM, Springer Verlag, 2007
Voraussetzungen / BesonderesNo mandatory prerequisites. Please consider the prior attendance to EM Basic lectures (551- 1618-00V; 227-0390-00L; 327-0703-00L) as suggested prerequisite.
363-1130-00LDigital Health Belegung eingeschränkt - Details anzeigen W3 KP2VT. Kowatsch
KurzbeschreibungToday, we face the challenge of chronic conditions. Personal coaching approaches are neither scalable nor financially sustainable. The question arises therefore to which degree Digital Health applications are appropriate to address this challenge. In this lecture, students will learn about the need, design and assessment of digital health interventions.
LernzielNHS teams up with Amazon to bring Alexa to patients (The Guardian, July 2019), Contactless cardiac arrest detection using smart devices (Nature Digital Medicine, June 2019), Apple Heart Study demonstrates ability of wearable technology to detect atrial fibrillation (Standford Medicine News, March 2019), Digital health companies raised a total of $4.2B across 180 deals through the first half of 2019. If this pace holds steady, the sector is on track for an $8.4B year in 2019 - and may even top 2018's record-breaking annual funding total. Sean Day, Rocket Health, 2019 Midyear Digital Health Market Update

What are the rationale and implications behind the recent developments in the field of digital health?

Digital Health is the use of information and communication technology for the prevention and treatment of diseases in the everyday life of individuals. It is thus linked to topics such as digital health interventions, digital biomarker, digital coaches and healthcare chatbots, telemedicine, mobile and wearable computing, self-tracking, personalized medicine, connected health, smart homes or smart cars.

In the 20th century, healthcare systems specialized in acute care. In the 21st century, we now face the challenge of dealing with the specific characteristics of chronic conditions. These are now responsible for around 70% of all deaths worldwide and 85% of all deaths in Europe and are associated with an estimated economic loss of $7 trillion between 2011 and 2025. Chronic diseases are characterized in particular by the fact that they require an intervention paradigm that focuses on prevention and lifestyle change. Lifestyle (e.g., diet, physical activity, tobacco or alcohol consumption) can reduce the risk of suffering from a chronic condition or, if already present, can reduce its burden. A corresponding change in lifestyle is, however, only implemented by a fraction of those affected, partly because of missing or inadequate interventions or health literacy, partly due to socio-cultural influences. Individual personal coaching of these individuals is neither scalable nor financially sustainable.

Against this background, the question arises on how to develop evidence-based digital health interventions (DHIs) that allow medical doctors and other caregivers to scale and tailor long-term treatments to individuals in need at sustainable costs. At the intersection of health economics, information systems research, computer science, and behavioral medicine, this lecture has the objective to help students and upcoming healthcare executives interested in the multi-disciplinary field of digital health to better understand the need, design and assessment of DHIs.

After the course, students will be able to...

1. understand the importance of DHIs for the management of chronic conditions
2. understand the anatomy of DHIs
3. know frameworks for the design of DHIs
4. know evaluation criteria for DHIs
5. know technologies for DHIs
6. assess DHIs
7. discuss the advantages and disadvantages of DHIs
InhaltTo reach these learning objectives, the following topics are covered in the lecture and will be discussed based on concrete national and international examples including DHIs from the Center for Digital Health Interventions (Link), a joint initiative of the Department of Management, Technology and Economics at ETH Zurich and the Institute of Technology Management at the University of St.Gallen:

1. Motivation for Digital Health
- The rise of chronic diseases in developed countries
- The discrepancy of acute care and care of chronic diseases
- Lifestyle as medicine and prevention
- From excellence of care in healthcare institutions to excellence of care in everyday life

2. Anatomy of Digital Health Interventions
- Just-in-time adaptive interventions
- Digital biomarker for predicting states of vulnerability
- Digital biomarker for predicting states of receptivity
- Digital coaching and healthcare chatbots

3. Design & Evaluation of Digital Health Interventions
- Overview of design frameworks
- Preparation of DHIs
- Optimization of DHIs
- Evaluation of DHIs
- Implementation of DHIs

4. Digital Health Technologies
- Technologies for telemedicine
- Mobile medical devices
- Virtual, augmented and mixed reality applications incl. live demonstrations
- Privacy and regulatory considerations

The Digital Health lecture is structured in two parts and follows the concept of a hybrid therapy consisting of on-site sessions and complementary online lessons. In the first part, students will learn and discuss the topics of the four learning modules in weekly on-site sessions. Complementary learning material (e.g., video and audio clips), multiple-choice questions and exercises are provided online.

In the second part, students work in teams and will use their knowledge from the first part of the lecture to critically assess DHIs. Each team will then present and discuss the findings of the assessment with their fellow students who will provide peer-reviews. Additional on-site coaching sessions are offered to support the teams with the preparation of their presentations.
Literatur1.Chaix, B. (2018) Mobile Sensing in Environmental Health and Neighborhood Research Annual Review of Public Health (39), 367-384.
2.Collins, L. M. (2018) Optimization of Behavioral, Biobehavioral, and Biomedical Interventions: The Multiphase Optimization Strategy (MOST) New York: Springer.
3.Corneta, V. P., and Holden, R. J. (2018) Systematic Review of Smartphone-Based Passive Sensing for Health and Wellbeing Journal of Biomedical Informatics (77:January), 120-132.
4.Coravos, A., Khozin, S., and K. D. Mandl (2019) Developing and Adopting Safe and Effective Digital Biomarkers to Improve Patient Outcomes Nature Digital Medicine 2 Paper 14.
5.Katz, D. L., E. P. Frates, J. P. Bonnet, S. K. Gupta, E. Vartiainen and R. H. Carmona (2018) Lifestyle as Medicine: The Case for a True Health Initiative American Journal of Health Promotion 32 (6), 1452-1458.
6.Kvedar, J. C., A. L. Fogel, E. Elenko and D. Zohar (2016) Digital medicine's march on chronic disease Nature Biotechnology 34 (3), 239-246
7.Nahum-Shani, I., S. N. Smith, B. J. Spring, L. M. Collins, K. Witkiewitz, A. Tewari and S. A. Murphy (2018) Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support Annals of Behavioral Medicine 52 (6), 446-462.
8. Sim, I. (2019). Mobile Devices and Health. The New England Journal of Medicine, 381(10), 956-968.
376-1150-00LClinical Challenges in Musculoskeletal Disorders Belegung eingeschränkt - Details anzeigen W2 KP2GM. Leunig, S. J. Ferguson, A. Müller
KurzbeschreibungThis course reviews musculoskeletal disorders focusing on the clinical presentation, current treatment approaches and future challenges and opportunities to overcome failures.
LernzielAppreciation of the surgical and technical challenges, and future perspectives offered through advances in surgical technique, new biomaterials and advanced medical device construction methods.
InhaltFoot deformities, knee injuries, knee OA, hip disorders in the child and adolescent, hip OA, spine deformities, degenerative spine disease, shoulder in-stability, hand, rheumatoid diseases, neuromuscular diseases, sport injuries and prevention
376-1178-00LHuman Factors IIW3 KP2VM. Menozzi Jäckli, R. Huang, M. Siegrist
KurzbeschreibungStrategies, abilities and needs of human at work as well as properties of products and systems are factors controlling quality and performance in everyday interactions. In Human Factors II (HF II), cognitive aspects are in focus therefore complementing the more physical oriented approach in HF I. A basic scientific approach is adopted and relevant links to practice are illustrated.
LernzielThe goal of the lecture is to empower students in designing products and systems enabling an efficient and qualitatively high standing interaction between human and the environment, considering costs, benefits, health, well-being, and safety as well. The goal is achieved in addressing a broad variety of topics and embedding the discussion in macroscopic factors such as the behavior of consumers and objectives of economy.
InhaltCognitive factors in perception, information processing and action. Experimental techniques in assessing human performance and well-being, human factors and ergonomics in development of products and complex systems, innovation, decision taking, consumer behavior.
LiteraturSalvendy G. (ed), Handbook of Human Factors, Wiley & Sons, 2012
376-1400-00LTransfer of Technologies into Neurorehabilitation Belegung eingeschränkt - Details anzeigen W3 KP2VC. Müller, R. Gassert, R. Riener, H. Van Hedel, N. Wenderoth
KurzbeschreibungThe course focuses on clinical as well as industrial aspects of advanced technologies and their transfer into neurorehabilitation from both theoretical and practical perspectives. The students will learn the basics of neurorehabilitation and the linkage to technologies, gain insight into the development within the medtech field and learn applications of technologies in clinical settings.
LernzielThe students will:
- Learn basics and principles of clinical neuroscience and neurorehabilitation.
- Gain insight into the technical basics of advanced technologies and the transfer into product development processes.
- Gain insight into the application, the development and integration of advanced technologies in clinical settings. This includes the advantages and limitations according to different pathologies and therapy goals.
- Get the opportunity to test advanced technologies in practical settings.
- Learn how to transfer theoretical concepts to actual settings in different working fields.
InhaltMain focus:
- Neurobiological principles applied to the field of neurorehabilitation.
- Clinical applications of advanced rehabilitation technologies.
- Visit medical technology companies, rehabilitation centers and labs to gain deeper insight into the development, application and evaluation of advanced technologie
SkriptTeaching materials will be provided for the individual events and lectures.
- Slides (pdf files)
- Information sheets and flyers of the visited companies, labs and clinics
376-1414-01LCurrent Topics in Brain Research (FS)W1 KP1.5KI. Mansuy, F. Helmchen, weitere Dozierende
KurzbeschreibungEs werden verschiedene wissenschaftliche Gäste aus dem In-und Ausland eingeladen, um ihre aktuellen Forschungsdaten zu präsentieren und diskutieren.
LernzielEs soll der Austausch von wissenschaftlichen Erkenntnissen und Daten sowie die Kommunikation und Zusammenarbeit zwischen den Forschenden gefördert werden. Studierende, welche den Kurs belegen, besuchen während eines Semesters alle Seminare und schreiben einen kritischen Report über ein Seminar ihrer Wahl. Die Anleitung dazu erhalten eingeschriebene Studierende von Prof. Isabelle Mansuy / Dr. Alberto Corcoba 1 Woche vor Semesterbeginn.
InhaltVerschiedene wissenschaftliche Gäste aus den Bereichen Neuroepigenetik, Neurochemie, Neuromorphologie und Neurophysiologie berichten über ihre neuesten wissenschaftlichen Erkenntnisse.
Skriptkein Skript
Literaturkeine Literatur
376-1624-00LPractical Methods in Biofabrication Belegung eingeschränkt - Details anzeigen
Number of participants limited to 12.
W5 KP4PM. Zenobi-Wong, S. J. Ferguson, S. Schürle-Finke
KurzbeschreibungBiofabrication involves the assembly of materials, cells, and biological building blocks into grafts for tissue engineering and in vitro models. The student learns techniques involving the fabrication and characterization of tissue engineered scaffolds and the design of 3D models based on medical imaging data. They apply this knowledge to design, manufacture and evaluate a biofabricated graft.
LernzielThe objective of this course is to give students hands-on experience with the tools required to fabricate tissue engineered grafts. During the first part of this course, students will gain practical knowledge in hydrogel synthesis and characterization, fuse deposition modelling and stereolithography, bioprinting and bioink design, electrospinning, and cell culture and viability testing. They will also learn the properties of common biocompatible materials used in fabrication and how to select materials based on the application requirements. The students learn principles for design of 3D models. Finally the students will apply their knowledge to a problem-based Project in the second half of the Semester. The Project requires significant time outside of class Hours, strong commitment and ability to work independently.
Voraussetzungen / BesonderesNot recommended if passed 376-1622-00 Practical Methods in Tissue Engineering
376-1660-00LScientific Writing, Reporting and Communication Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 30

Nur für Gesundheitswissenschaften und Technologie MSc
W3 KP2VW. R. Taylor, S. H. Hosseini Nasab
KurzbeschreibungThis course aims to teach many of the unwritten rules on how to communicate effectively, from writing reports or manuscripts (or indeed their Master thesis!) through to improving skills in oral presentations, and presenting themselves at interview.
LernzielThis course will teach students to communicate effectively in official environments, including:
- writing manuscripts, theses, CVs, reports etc
- presenting posters
- oral presentations
- critical reviews of literature
376-1724-00LAppropriate Health System Design Information Belegung eingeschränkt - Details anzeigen
Maximale Teilnehmerzahl: 42
W3 KP2VW. Karlen
KurzbeschreibungThis course elaborates upon relevant aspects in the conception, implementation and distribution of health devices and systems that effectively meet peoples and societies' needs in a local context. Four key elements of appropriateness (usage, cost, durability and performance) that are integral to the engineering design process are extensively discussed and applied.
LernzielThe main goals are to
> Evaluate the appropriateness of health systems to the cultural, financial, environmental and medical context in which they will be applied
> Design health systems from a user's perspective for a specific context

At the end of the course, students can
> name, understand and describe the 4 main principles that define appropriate technology
> apply these principles to critically analyze and assess health systems and technology
> project him/herself into a unfamiliar person and context and create hypotheses as to that person's needs, requirements, and priorities
> modify specifications of existing systems to improve appropriateness
> discuss the challenges and illustrate the the ethical and societal consequences of proposed design modifications
> communicate effectively the results of his/her system analysis and implementation strategies to non-specialists
InhaltThe course will be interactive and involve roleplay. Please do not sign up for this course if you are not ready to leave your comfort zone in class. The lectures are divided in two parts:
The first part elaborates upon the important concepts of the design of health care devices and systems, and discusses implementation and dissemination strategies. We focus on communities such as low income households, the elderly, and patients with chronic illnesses that have special needs. Topics covered include point-of-care diagnostics, information and communication technologies, mobile health, user interactions, and also the social-cultural considerations.
The second part consists of elaboration of an appropriate device conducted by student groups. Each group will analyse an existing product or solution, critically assess its appropriateness according to the criteria learned in class, and provide explanations as to why the system succeeds or fails. The students will also present design improvements. Grading will be based on a written case report due in the middle of the semester and a final seminar presentation in form of a poster discussion and demo.
LiteraturWHO, "Medical Devices: Managing the Mismatch", 2010.

PATH, "The IC2030 report. Reimagining Global Health," 2015. Link

R. Malkin and K. Von Oldenburg Beer, "Diffusion of novel healthcare technologies to resource poor settings," Annals of Biomedical Engineering, vol. 41, no. 9, pp. 1841:50, 2013.
Voraussetzungen / BesonderesTarget Group:
Students of higher semesters and doctoral students of
- Biomedical Engineering, Robotics, Systems and Control
- Medical Faculty, University of Zurich
Students of other departments, faculties, courses are also welcome
376-1986-00LBayesian Data Analysis on Models of Behavior (University of Zurich)
Der Kurs muss direkt an der UZH belegt werden.
UZH Modulkürzel: DOEC0829

Beachten Sie die Einschreibungstermine an der UZH: Link
W3 KP2SR. Polania, Uni-Dozierende
KurzbeschreibungMaking sense of the data acquired via experiments is fundamental in many fields of sciences. This course is designed for students/researchers who want to gain practical experience with data analysis based on Bayesian inference. Coursework involves practical demonstrations and discussion of solutions for data analysis problems. No advanced knowledge of statistics and probability is required.
LernzielThe overall goal of this course it that the students are able to develop both analytic and problem-solving skills that will serve to draw reasonable inferences from observations. The first objective is to make the participants familiar with the conceptual framework of Bayesian data analysis. The second goal is to introduce the ideas of modern Bayesian data analysis, including techniques such as Markov chain Monte Carlo (MCMC) techniques, alongside the introduction of programming tools that facilitate the creation of any Bayesian inference model. Throughout the course, this will involve practical demonstrations with example datasets, homework, and discussions that should convince the participants of this course that it is possible to make inference and understand the data acquired from the experiments that they usually obtain in their own research (starting from simple linear regressions all the way up to more complex models with hierarchical structures and dependencies). After working through this course, the participants should be able to build their own inference models in order to interpret meaningfully their own data.
Voraussetzungen / BesonderesThe very basics (or at least intuition) of programming in either Matlab or R
535-0534-00LDrug, Society and Public HealthW1 KP1VJ. Steurer, R. Heusser
KurzbeschreibungEinführung in die Grundkonzepte und Methoden von Public Health, Epidemiologie und Evidence Based Medicine (EBM). Grundlagen und Prinzipien klinischer Studie zur Überprüfung der Wirksamkeit von Medikamenten.
LernzielDie Studierenden kennen die Grundkonzepte und Methoden der Epidemiologie; sie kennen die Grundkonzepte der Evidence Based Medicine (EBM) und wissen, wie nach Evidenz in der Pharmakotherapie zu suchen ist
InhaltEinführung in Epidemiologie / Pharmakoepidemiologie / Evidence-based Medicine: Grundbegriffe, Studiendesigns, object-design, statistische Grundlagen, Kausalität in der Pharmako-Epidemiologie, Methoden und Konzepte, Fallbeispiele.
SkriptWird abgegeben
Literatur- F. Gutzwiller/ F. Paccaud (Hrsg.): Sozial- und Präventivmedizin - Public Health. 4. Aufl. 2011, Verlag Hans Huber, Bern
- R. Beaglehole, R. Bonita, T. Kjellström: Einführung in die Epidemiologie. 1997, Verlag Hans Huber, Bern
- L. Gordis: Epidemiology, 4 th Ed. 2009, W.B. Saunders Comp.
- K.J. Rothman, S. Greenland: Modern Epidemiology, 2. Ed. 1998, Lippincott Williams & Wilkins
- A.G. Hartzema, M. Porta, H.H. Tilson (Eds.): Pharmacoepidemiology - An Introduction. 3. Ed. Harvey Whitney Comp., Cincinnati
- R. Bonita, R. Beaglehole. Einführung in die Epidemiologie, 2. überarbeitete Auflage, 2008 Huber Verlag.
- B.L. Strom (Eds.): Pharmacoepidemiology. 3. Ed. 2000, Wiley & Sons Ltd., Chichester
- S.E. Straus, W.S. Richardson, P.Glasziou, R.B. Haynes: Evidence-based Medicine. 2005, Churchill Livingstone, London
- U. Jaehde, R.Radziwill, S. Mühlebach, W. Schnack (Hrsg): Lehrbuch der Klinischen Pharmazie
- L.M. Bachmann, M.A. Puhan, J.Steurer (Eds.): Patientenorientierte Forschung. EInführung in die Planung und Durchführung einer Studie. Verlag Hans Huber, 2008
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