Dimitra Iliopoulou

SMARTDIAB: A Communication and Information Technology Approach for the Intelligent Monitoring, Management and Follow-up of Type 1 Diabetes Patients

SMARTDIAB is a platform designed to support the monitoring, management, and treatment of patients with type 1 diabetes mellitus (T1DM), by combining state-of-the-art approaches in the fields of database (DB) technologies, communications, simulation algorithms, and data mining. SMARTDIAB consists mainly of two units: 1) the patient unit (PU); and 2) the patient management unit (PMU), which communicate with each other for data exchange. The PMU can be accessed by the PU through the internet using devices, such as PCs/laptops with direct internet access or mobile phones via a Wi-Fi/General Packet Radio Service access network. The PU consists of an insulin pump for subcutaneous insulin infusion to the patient and a continuous glucose measurement system. The aforementioned devices running a user-friendly application gather patients related information and transmit it to the PMU. The PMU consists of a diabetes data management system (DDMS), a decision support system (DSS) that provides risk assessment for long-term diabetes complications, and an insulin infusion advisory system (IIAS), which reside on a Web server. The DDMS can be accessed from both medical personnel and patients, with appropriate security access rights and front-end interfaces. The DDMS, apart from being used for data storage/retrieval, provides also advanced tools for the intelligent processing of the patients data, supporting the physician in decision making, regarding the patients treatment. The IIAS is used to close the loop between the insulin pump and the continuous glucose monitoring system, by providing the pump with the appropriate insulin infusion rate in order to keep the patients glucose levels within predefined limits. The pilot version of the SMARTDIAB has already been implemented, while the platforms evaluation in clinical environment is being in progress.

C15 Acetogenins from the red alga Laurencia obtusa

Four C(15) acetogenins, 13-epilaurencienyne (3Z) (1), 13-epipinnatifidenyne (3E) (2), (3E, 6S(*), 7R(*), 9S(*), 10S(*), 12R(*))-9-chloro-13-bromo-6:12-epoxy-7, 10-diacetoxypentadec-3-en-1-yne (3), (3Z, 6S(*), 7R(*), 9S(*), 10S(*), 12R(*))-9-chloro-13-bromo-6:12-epoxy-7, 10-diacetoxypentadec-3-en-1-yne (4), along with the known 13-epilaurencienyne (3E) (5), have been isolated from the organic extract of the red alga Laurencia obtusa, collected in the Aegean Sea, Greece. The structures of the new natural products, as well as their relative stereochemistry, were established by means of spectral data analysis, including 2D NMR spectroscopic experiments. Some of the new metabolites exhibited significant insecticidal activity.

Neural Network based Glucose - Insulin Metabolism Models for Children with Type 1 Diabetes

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN

Chemical Composition and Biological Activity of Nepeta parnassica Oils and Isolated Nepetalactones

Abstract Essential oils of Nepeta parnassica, collected at different developmental stages, were analyzed by means of GC/MS. From the fifty-five identified constituents in samples A and B, representing 94.8% and 98.7% of the oils respectively, 4aα,7α,7aβ-nepetalactone (22.0%), 1,8-cineole (21.1%), α-pinene (9.5%) and 4aα,7β,7aβ-nepetalactone (7.9%) were the major components of sample A (vegetative stage), whereas in sample B (flowering stage) the main contributors were 1,8-cineole (34.6%), 4aα,7α,7aα-nepetalactone (17.3%), α-pinene (11.4%) and 4aα,7α,7aβ-nepetalactone (8.9%). The oils were tested on human health important insects such as the Pogonomyrmex sp. ants and the Culex pipiens molestus mosquitoes with promising results on insect repellency/toxicity.

Mobile phone technologies and advanced data analysis towards the enhancement of diabetes self-management

Advances in the area of mobile and wireless communication for healthcare (m-Health) along with the improvements in information science allow the design and development of new patient-centric models for the provision of personalised healthcare services, increase of patient independence and improvement of patients self-control and self-management capabilities. This paper comprises a brief overview of the m-Health applications towards the self-management of individuals with diabetes mellitus and the enhancement of their quality of life. Furthermore, the design and development of a mobile phone application for Type 1 Diabetes Mellitus (T1DM) self-management is presented. The technical evaluation of the application, which permits the management of blood glucose measurements, blood pressure measurements, insulin dosage, food/drink intake and physical activity, has shown that the use of the mobile phone technologies along with data analysis methods might improve the self-management of T1DM.

Mobile technology to empower people with Diabetes Mellitus: Design and development of a mobile application

Recent advances in information and communication technologies permitted the design and development of new patient-centric models for the provision of better health care services and enhancement of patients self-management. This paper presents a prototype mobile phone application which is being designed to improve the self-management of individuals with Type 1 Diabetes Mellitus (T1DM). The developed application using the Microsoft .Net framework runs on 3G mobile phones. The application consists of five major interfaces for the management of: blood glucose measurements, blood pressure measurements, insulin dosage, food/drink intake and physical activity. Furthermore, the user has the following capabilities i) to keep notes, and ii) in case of an emergency to press a button, in order to transmit immediately his/her position to both an emergency call center, and the attendant physician. It has to be noted, that the above mentioned data are stored locally to the mobile phone, and regularly transmitted via the mobile network to a dedicated hospital web-server. Technical evaluation of the prototype indicates that the use of the mobile network makes feasible the self-management of T1DM.

E-Health towards ecumenical framework for personalized medicine via Decision Support System

The purpose of the present manuscript is to present the advances performed in medicine using a Personalized Decision Support System (PDSS). The models used in Decision Support Systems (DSS) are examined in combination with Genome Information and Biomarkers to produce personalized result for each individual. The concept of personalize medicine is described in depth and application of PDSS for Cardiovascular Diseases (CVD) and Type-1 Diabetes Mellitus (T1DM) are analyzed. Parameters extracted from genes, biomarkers, nutrition habits, lifestyle and biological measurements feed DSSs, incorporating Artificial Intelligence Modules (AIM), to provide personalized advice, medication and treatment.

A Communication Platform for Tele-monitoring and Tele-management of Type 1 Diabetes

Type 1 diabetes mellitus is a chronic disease characterized by blood glucose levels out of normal range due to inability of insulin production. This dysfunction leads to many short- and long-term complications. In this paper, a system for tele-monitoring and tele-management of type 1 diabetes patients is proposed, aiming at reducing the risk of diabetes complications and improving quality of life. The system integrates wireless personal area networks (WPAN), mobile infrastructure, and Internet technology along with commercially available and novel glucose measurement devices, advanced modeling techniques, and tools for the intelligent processing of the available diabetes patients information. The integration of the above technologies enables intensive monitoring of blood glucose levels, treatment optimisation, continuous medical care, and improvement of quality of life for type 1 diabetes patients, without restrictions in everyday life activities

Udoteal B, A New Linear Diterpenoid from the Green Alga Udotea petiolata

Abstract Udoteal B (1), a geometrical isomer of udoteal, was isolated, along with udoteal (2), clionasterol and 28-isofucosterol, from the organic extract of the green alga Udotea petiolata. The structure of natural product 1 was proposed on the basis of its spectral data including 2D NMR experiments. The identification of the known metabolites was based on comparison of their spectral data with literature values.

Smart adaptable system for older adults' Daily Life Activities Management - The ABLE platform.

In this paper we propose a system (ABLE) that will act as the main platform for a number of low-cost, mature technologies that will be integrated in order to create a dynamically adaptive Daily Life Activities Management environment in order to facilitate the everyday life of senior (but not exclusively) citizens at home. While the main target group of ABLEs users is the ageing population its use can be extended to all people that are vulnerable or atypical in body, intellect or emotions and are categorized by society as disabled. The classes of assistive products that are well defined in the international standard, ISO9999 such as assistive products for personal medical treatment, personal care and protection, communication, information and reaction and for personal mobility, will be easily incorporated in our proposed platform. Furthermore, our platform could integrate and implement the above classes under several service models that will be analyzed further.