Biomedical Engineering – From Theory to Applications

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60 Biomedical Engineering – From Theory to Applications possible, if we have to satisfy the task to formulate a model suitable for use in feedback control. Simulation results were coherent with the medical findings: the comments of clinical researchers expert in HIV therapies were essential in testing the model and for evaluating the effectiveness of the proposed control methods. Obtaining reliable models is relevant from a diagnostic and prognostic point of view, because it allows the physician to prove the therapeutic action using the model for testing the treatment in terms of optimal dosage and administration of drugs. In 2008, the FDA approved an in silico model of diabetes as a pre-clinical testing tool for closed loop research at the seven JDRF Artificial Pancreas Consortium sites. The overall goal of the Artificial Pancreas project was to accelerate the development, regulatory approval, health insurance coverage, and clinical acceptance of continuous glucose monitoring and artificial pancreas technology (Juvenile Diabetes Research Foundation, 2008). We strongly believe that also a simple but reliable in silico model of HIV can lead to an acceleration of the experimental tests for a clinical acceptance of new drugs in HIV disease. Future activity will be devoted to develop models of HIV infection, able to include the issues of drug resistance and viral mutation, key issues for the HIV studies, and the interest of many clinical researchers in our work is encouraging in the upcoming research. 4. Conclusion The Physiological Cybernetics course represents an example of integration between different disciplines, in order to produce a common language between students in biomedical engineer and physicians. It offers students an opportunity to verify in practice how to move theoretical lectures, based on the development of mathematical models, to a practical interaction with physicians. This fact seems obvious from an educational viewpoint, but it isn't so usual in practice, because it requires a preliminary long period for preparing a common language between researchers in different fields. Judging from the students’ excellent results, if compared to students attending under-graduated courses in previous years, the example proposed was very successful. In this chapter we presented two examples of research applications, derived from this educational experience, demonstrating that the old-novel vision of Wiener was not a utopia, and that a synergic cooperation between biomedical engineers and physicians can lead to interesting results. 5. Acknowledgment The authors wish to thank all people cooperating with the activities of the Physiological Cybernetics course over many years, the physicians for their support and clinical supervision and the undergraduate active students for their enthusiasm. 6. References Abbas, A.K, Lichtman, A.H. & Pober, J.S. (2000). Cellular and Molecular Immunology, IV ed., WB Saunders Company, ISBN 07216823323, Philadelphia, US Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Clarendon Press, ISBN 0198538642, Oxford, UK
Physiological Cybernetics: An Old-Novel Approach for Students in Biomedical Systems Cobelli, C. & Carson, E. (2008). Introduction to Modelling in Physiology and Medicine, Elsevier/Academic Press, ISBN 978-0-12-160240-62008, New York Di Stefano III, J.J., Wilson, K.C, Jang, M. & Mak, P.H. (1975). Identification of the Dynamics of Thyroid Hormone Metabolism. Automatica, Vol. 11, No. 2, (March 1975) 149-159, ISSN 0005-1098 Edelstein-Keshet, L. (2005). Mathematival Models in Biology, SIAM, Classics in Applied Mathematics, ISBN 13-978-0898715-54-5, Philadelphia, PA Hoppensteadt, F.C. & Peskin, C.S. (2002). Modeling and Simulation in Medicine and the Life Sciences, Springer Science series, II edition, ISBN 0-387-95072-9, New York Ionescu, C. M., Keyser, R. D., Torrico, B. C., De Smet, T., Struys, M. M. & Normey-Rico, J. E. (2008). Robust Predictive Control Strategy Applied for Propofol Dosing Using Bis as a Controlled Variable During Anesthesia. IEEE Transactions on Biomedical Engineering, Vol. 55, No. 9, (September 2008), pp. 2161–2170, ISSN 0018-929 Jolliffe, I.T. (2002). Principal Component Analysis, Springer Verlag, II edition, ISBN 978-0-387- 95442-4, New York. Jones, D.S., Planck, M.J. & Sleeman, B.D. (2008). Differential Equations and Mathematical Biology, CRC Press, Taylor & Francis Group, II edition, ISBN 978-1-4200-8357-6, London, UK Juvenile Diabetes Research Foundation (2008). Emerging Technologies in Diabetes Research JDRF Emerging Technologies E-Newsletter, No. 7, May 2008. Khoo, M.C.K. (2001). Physiological Control Systems, Prentice Hall of India Pvt. Ltd., ISBN 81- 203-18-447, New Delhi. Kutner, M., Neter, J., Nachtsheim, C., & Wasserman, W (2005). Applied Linear Statistical Models, V edition, McGraw-Hill/Irwin, ISBN 0071122214, Boston, MA. Models, McGraw Hill, Boston, MA. 1985 Landi, A., Pacini, L., Piaggi, P., Lippi, C., Santini, F. & Pinchera, A. (2007). Obefix: a Friendly Toolbox for Obesity Classification, Proceedings of 2007 International Conference on Cybernetics and Information Technologies, Systems and Applications, pp.1-6, ISBN 1- 934272-07-8, Orlando, Florida, 12-15 July 2007 Landi, A., Mazzoldi, A., Andreoni, C., Bianchi, M., Cavallini, A., Laurino M., Ricotti L., Iuliano, R., Matteoli B. & Ceccherini Nelli, L. (2008). Modelling and Control of HIV Dynamics, Journal Computer Methods and Programs in Biomedicine, Vol. 89, No. 2, (February 2008), pp. 162–168, ISSN 0169-2607 Landi, A., Piaggi, P., Lippi, C., Santini, F. & Pinchera, A. (2010). Statistical Toolbox in Medicine for Predicting Effects of Therapies in Obesity, Proceedings of 2010 IEEE Workshop on Health Care Management, pp.1-6, ISBN 978-1-4244-4998-9, Venice, Italy, 18-20 February 2010 Ljung. L. (1987). System Identification: Theory for the User, Prentice Hall, Inc., ISBN 0-13- 881640-9, Englewood Cliffs, N. J. Mardia, K.V., Kent, J.T. & Bibby, J.M. (1979) Multivariate Analysis, Academic Press, ISBN : 0124712509, London, UK Marmarelis, V.Z. (2004). Nonlinear Dynamic Modeling of Physiological Systems, John Wiley & Sons, Inc, ISBN 0-471-46960-2, Hoboken, NJ Mayne, D. Q., Rawlings, J. B., Rao, C. V. & Scokaert, P. O. M. (2000). Constrained Model Predictive Control: Stability and Optimality. Automatica, Vol. 36, No. 6, (June 2000), pp. 789–814, ISSN 0005-1098 61
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BIOMEDICAL ENGINEERING - FROM THEOR
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VI Contents Chapter 9 Targeted Magn
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120 6. Conclusion Biomedical Engine
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180 16. Acknowledgments Biomedical
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190 R* M M M M MR*M O O O O M O R*
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