e-OpenDay

This chapter presents the feasibility study of a virtual platform for medical related technology transfer, continuing medical education and e-conference. The concept extends the idea of live events (e.g. conferences, open day events) in one physical location. It exploits the creation of a virtual platform where the research world in the area of biomedicine, can showcase their success, interact and co-operate with the business community and collaborate on potentially valuable outcomes and learn without time or place restrictions. The main objective of the project was to offer a pilot service that can showcase the e-OpenDay market potential and technical feasibility. By developing a prototype and through user feedback and evaluation processes, a set of services was identified, developed and validated. The e-OpenDay project made clear that health information services are facing rapid development and expansion to wider markets and user groups. Based on the project results, a business plan was developed that showcased potential in commercial exploitation.

Computer Aided Risk Estimation of Breast Cancer

In this chapter the authors report about their experiences in designing, implementing, prototyping and evaluating a system for computer aided risk estimation of breast cancer. The strategy and architecture of “Hippocrates-mst” along with its functionalities are going to be presented. Also, the evaluation results in the clinical practice concerning the performance of “Hippocrates-mst” in the “Ippokrateio” University Hospital of Athens will be presented. The feedback from medical experts along with the new features of the system that are under development will be discussed.

E-Health Project Implementation

Privacy includes the right of individuals and organizations to determine for themselves when, how and to what extent information about them is communicated to others. The growing need of managing large amounts of medical data raises important legal and ethical challenges. E-Health systems must be capable of adhering to clearly defined security policies based upon legal requirements, regulations and standards while catering for dynamic healthcare and professional needs. Such security policies, incorporating enterprise level principles of privacy, integrity and availability, coupled with appropriate audit and control processes, must be able to be clearly defined by enterprise management with the understanding that such policy will be reliably and continuously enforced. This chapter addresses the issue of identifying and fulfilling security requirements for critical applications in the e-health domain. In this chapter the authors describe the main privacy and security measures that may be taken by the implementation of e-health projects.

Explorative Data Analysis of In-Vitro Neuronal Network Behavior Based on an Unsupervised Learning Approach

In the present chapter authors want to expose new insights in the field of Computational Neuroscience at regard to the study of neuronal networks grown in vitro. Such kind of analyses can exploit the availability of a huge amount of data thanks to the use of Multi Electrode Arrays (MEA), a multi-channel technology which allows capturing the activity of several different neuronal cells for long time recordings. Given the possibility of simultaneous targeting of various sites, neuroscientists are so applying such recent technology for various researches. The chapter begins by giving a brief presentation of MEA technology and of the data produced in output, punctuating some of the pros and cons of MEA recordings. Then we present an overview of the analytical techniques applied in order to extrapolate the hidden information from available data. Then we shall explain the approach we developed and applied on MEAs prepared in our cell culture laboratory, consisting of statistical methods capturing the main features of the spiking, in particular bursting, activity of various neuron, and performing data dimensionality reduction and clustering, in order to classify neurons according to their spiking properties having showed correlated features. Finally the chapter wants to furnish to neuroscientists an overview about the quantitative analysis of in-vitro spiking activity data recorded via MEA technology and to give an example of explorative analysis applied on MEA data. Such study is based on methods from Statistics and Machine Learning or Computer Science but at the same time strictly related to neurophysiological interpretations of the putative pharmacological manipulation of synaptic connections and mode of firing, with the final aim to extract new information and knowledge about neuronal networks behavior and organization.

Modelling and Simulation in Biomedical Research

In the following chapter, the authors will discuss the development of medical imaging and, through specific case studies, its application in elucidating the role of fluid mechanical forces in cardiovascular disease development and therapy (namely the connection between flow patterns and circulatory system disease - atherosclerosis and aneurysms) by means of computational fluid dynamics (CFD). The research carried in the Biomedical Simulation Laboratory can be described as a multi-step process through which, from the reality of the human body through the generation of a mathematical model that is then translated into a visual representation, a refined visual representation easily understandable and used in the clinic is generated. Thus, the authors’ daily research generates virtual representations of blood flow that can serve two purposes: a) that of a model for a phenomenon or disease or b) that of a model for an experiment (non-invasive way of determining the best treatment option).

Computational Analysis and Characterization of Marfan Syndrome Associated Human Proteins

Novel computational procedures and methods have been used to analyze, characterize and to provide more detailed definition of some Marfan syndrome associated human Fibrillin 1 proteins retrieved from NCBI Entrez protein database. Primary structure analysis reveals that the Marfan syndrome associated proteins are rich in cysteine and glycine residues. Extinction Coefficients of Marfan syndrome associated proteins at 280nm is ranging from 1490 to 259165 M-1 cm-1. Expasy’s ProtParam classifies most of the Marfan syndrome associated human Fibrillin 1 proteins as unstable on the basis of Instability index (II>40) and few proteins (AAB25244.1, 1EMO_A, Q504W9) as stable (II