ARTreat Project: Three-Dimensional Numerical Simulation of Plaque Formation and Development in the Arteries

Atherosclerosis is a progressive disease characterized by the accumulation of lipids and fibrous elements in arteries. It is characterized by dysfunction of endothelium and vasculitis, and accumulation of lipid, cholesterol, and cell elements inside blood vessel wall. In this study, a continuum-based approach for plaque formation and development in 3-D is presented. The blood flow is simulated by the 3-D Navier-Stokes equations, together with the continuity equation while low-density lipoprotein (LDL) transport in lumen of the vessel is coupled with Kedem-Katchalsky equations. The inflammatory process was solved using three additional reaction-diffusion partial differential equations. Transport of labeled LDL was fitted with our experiment on the rabbit animal model. Matching with histological data for LDL localization was achieved. Also, 3-D model of the straight artery with initial mild constriction of 30% plaque for formation and development is presented

Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.

Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions

Techniques for DiffServ - based QoS in Hierarchically Federated MAN Networks - the GRNET Case

Abstract—DiffServ is the basis of contemporary QoS-enabled networks. Setting up DiffServ QoS requires extensive engineering effort in dimensioning and provisioning, especially for adjacent networks under different administrations linked in a “federated” hierarchy. In this paper we present a case study for QoS techniques employed in the GRNET MAN networks of Athens and Crete. After introducing the supported QoS mechanisms and service types, we discuss our dimensioning methodology and present two algorithms for worst-case dimensioning. We explain the provisioning mechanisms of GRNET and we present in brief our new automated provisioning ANS tool. Finally, we deal with the extension of our mechanisms and tools in hierarchically federated networks and give some future directions of our work. Index Terms—QoS dimensioning, provisioning, federated QoS

Large-scale deep learning analysis to identify adult patients at risk for combined and common variable immunodeficiencies

Abstract Background Primary immunodeficiency (PI) is a group of heterogeneous disorders resulting from immune system defects. Over 70% of PI is undiagnosed, leading to increased mortality, co-morbidity and healthcare costs. Among PI disorders, combined immunodeficiencies (CID) are characterized by complex immune defects. Common variable immunodeficiency (CVID) is among the most common types of PI. In light of available treatments, it is critical to identify adult patients at risk for CID and CVID, before the development of serious morbidity and mortality. Methods We developed a deep learning-based method (named “TabMLPNet”) to analyze clinical history from nationally representative medical claims from electronic health records (Optum® data, covering all US), evaluated in the setting of identifying CID/CVID in adults. Further, we revealed the most important CID/CVID-associated antecedent phenotype combinations. Four large cohorts were generated: a total of 47,660 PI cases and (1:1 matched) controls. Results The sensitivity/specificity of TabMLPNet modeling ranges from 0.82-0.88/0.82-0.85 across cohorts. Distinctive combinations of antecedent phenotypes associated with CID/CVID are identified, consisting of respiratory infections/conditions, genetic anomalies, cardiac defects, autoimmune diseases, blood disorders and malignancies, which can possibly be useful to systematize the identification of CID and CVID. Conclusions We demonstrated an accurate method in terms of CID and CVID detection evaluated on large-scale medical claims data. Our predictive scheme can potentially lead to the development of new clinical insights and expanded guidelines for identification of adult patients at risk for CID and CVID as well as be used to improve patient outcomes on population level

Designing interoperable telehealth platforms: bridging IoT devices with cloud infrastructures

© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. A platform offering web technologies and interoperable components is proposed, allowing integration of different technologies into a robust system. Key modules are provided in home, to support integration of IoT devices, and in the cloud, offering centralised services and storage. Communication between the two is performed using the open FIWARE-Orion protocol. Data are not tied to methods and resources, so the platform can handle multiple types of requests and data formats. The platform is deployed in HOLOBALANCE, a tele-rehabilitation system for balance disorders, providing surrogate holographic physiotherapists, real time evaluations of task performance and cloud-based data analytics for personalised coaching