Bioprofiling over grid for eHealthcare.

A trend in modern medicine is towards individualization of healthcare and, potentially, grid computing can play an important role in this by allowing sharing of resources and expertise to improve the quality of care. In this paper, we present a new test bed, the BIOPATTERN Grid, which aims to fulfil this role in the long term. The main objectives in this paper are 1) to report the development of the BIOPATTERN Grid, for biopattern analysis and bioprofiling in support of individualization of healthcare. The BIOPATTERN Grid is designed to facilitate secure and seamless sharing of geographically distributed bioprofile databases and to support the analysis of bioprofiles to combat major diseases such as brain diseases and cancer within a major EU project, BIOPATTERN (www.biopattern.org); 2) to illustrate how the BIOPATTERN Grid could be used for biopattern analysis and bioprofiling for early detection of dementia and for brain injury assessment on an individual basis. We highlight important issues that would arise from the mobility of citizens in the EU, such as those associated with access to medical data, ethical and security; and 3) to describe two grid services which aim to integrate BIOPATTERN Grid with existing grid projects on crawling service and remote data acquisition which is necessary to underpin the use of the test bed for biopattern analysis and bioprofiling.

HeartDrive: A Broader Concept of Interoperability to Implement Care Processes for Heart Failure.

This paper originates from the HeartDrive project, a platform of services for a more effective, efficient and integrated management of heart failure and comorbidities. HeartDrive establishes a cooperative approach based on the concepts of continuity of care and extreme, patient oriented, customization of diagnostic, therapeutic and follow-up procedures. Definition and development of evidence based processes, migration from parceled and episode based healthcare provisioning to a workflow oriented model and increased awareness and responsibility of citizens towards their own health and wellness are key objectives of HeartDrive. In two scenarios for rehabilitation and home monitoring we show how the results are achieved by providing a solution that highlights a broader concept of cooperation that goes beyond technical interoperability towards semantic interoperability explicitly sharing process definitions, decision support strategies and information semantics.

[Luigi Guerri, an ordinary pharmacists who became the "first son" of pharmaceutical chemistry]. / Luigi Guerri, da farmacista a ordinario di chimica farmaceutica "Figliola primogenita della medicina".

Luigi Guerri (1823-1892) was one of the most important researchers and teachers in pharmaceutic chemistry in Florence in XIX century. Born and lived in Florence, patriot and colleague of the famous Ugo Schiff, from whom he received much praise, Guerri taught and carried out research into chemistry for about forty years at "Istituto di Studi Superiori" in Florence. This article is a synthesis of his career which started as first assistant to professor Campani, then he became a full professor, teaching pharmaceutic chemistry and eventually became the director of the pharmaceutic laboratory in "Arcispedale Santa Maria Nuova". This article, with the reproduction of some of Guerri's publications, shows the integrity of the man as a chemist and as a teacher. He was commemorated by Schiff and the scientific journal "Orosi" at his death.

A decision support system for the acquisition and elaboration of EEG signals: the AmI-GRID environment.

This paper describes a data-driven Decision Support System for Electroencephalography (EEG) signals acquisition, and parallel elaboration based on the integration of an Ambient Intelligent (AmI) [1] platform and a GRID enabled Infrastructure. The paper explores the analysis and design of the environment, the real-time data acquisition, the integration of the acquired data in dedicated EHR, and the EEG processing through parallel analysis algorithm available on the GRID infrastructure. After an overview of background concepts, the paper presents a brief description of the environment architecture, and a detailed analysis of the EEG algorithm. The challenge of the work presented is to effectively show how medical data can be shared and processed by exploiting the resources and capabilities of both the AmI platform and the GRID infrastructure. This particular Decision Support System, shows how it is possible to improve patient safety, quality of care, and efficiency in healthcare delivery.