Introduction Dissemination benefits come from the outputs integration and implementation by the key audience, who will also determine the relevance and usability of the disseminated content. Aim One of the CrowdHEALTH project's objectives is the transition from patient health records towards the Holistic Health Records (HHRs) and Social HHR. The CrowdHEALTH project aims at integrating high volumes of health-related data collected from various sources to support policy-making decisions. Methods The European Federation for Medical Informatics (EFMI) supports the development of an effective Communication and Collaboration Plan identifying the messages, the tools and channels in disseminating the project and its outcomes to the target audience based on the McGuire approach. Results The process for defining the dissemination strategy is a cyclic one as shown in the following figure involving review of each step periodically The next step was to define the four dimension dissemination approach based on McGuire attributes of persuasive communication. The objectives, target groups, key messages, the tools and channels where defined at this stage. Conclusion The CrowdHEALTH project and its outcomes were disseminated with a variety of tools and channels such as scientific journals, conferences, exhibitions and social media communication. © 2019 Andriana Magdalinoua, John Mantas, Lydia Montandon, Patrick Weber, Parisis Gallos.Introduction With With the proliferation of available ICT services, several sensors and health applications have become ubiquitous, while many applications have been developed to detect certain health conditions and early signs of disease.  https://www.selleckchem.com/products/nf-kb-activator-1.html  Currently, all these services operate independently, and the available data is heterogeneous with limited value gained from its exploitation. Aim The Data Sources and Gateways component aims at providing an abstracted and unified API to support the data accumulation from various sources including healthcare organisations, biosensors, laboratories and mobile applications. Meanwhile it tackles connectivity and communication issues with such information sources. Methods The CrowdHEALTH Data Sources and Gateways Service incorporates four main services The Configuration Service, The DB Connection Handling Service, The File Parsing Service and The RESTful Client Service. Results The initial version of the component design has built upon the requirements collected from the use case participants acting also as data providers. Conclusion These four services presented in this paper guide the implementation of the first version of the Data Sources and Gateways component software prototype. The Data Sources and Gateways component remains to be evaluated within the context of the project and be enriched in order to meet additional end user needs. © 2019 Konstantinos Perakis, Dimitris Miltiadou, Antonio De Nigro, Francesco Torelli, Lydia Montandon, Andriana Magdalinou, Argyro Mavrogiorgou, Dimosthenis Kyriazis.Introduction Individuals and healthcare providers need to trust that the EHRs are protected and that the confidentiality of their personal information is not at stake. Aim Within CrowdHEALTH project, a security and privacy framework that ensures confidentiality, integrity, and availability of the data was developed. Methods The CrowdHEALTH Security and Privacy framework includes Privacy Enhancing Technologies (PETs) in order to comply with the GDPR EU laws of data protection. CrowdHEALTH deploys OpenID Connect, an authentication protocol to provide flexibility, scalability, and lightweight user authentication as well as the attribute-base access control (ABAC) mechanism which supports creating efficient access control policies. Results CrowdHEALTH integrates ABAC with OpenID Connect to build an effective and scalable base for end-users' authorization. CrowdHEALTH's security and privacy framework interacts with other CrowdHEALTH's components, for instance the Big Data Platform, that depends on user authentication and authorization. CrowdHEALTH users are able to access the CrowdHEALTH's database based on the result of an ABAC request. Moreover, due to the fact that the CrowdHEALTH system requires proofs during the interactions with data producers of low trust or low reputation level, the requirements for the Trust and Reputation Model have been identified. Conclusion The CrowdHEALTH Integrated Holistic Security and Privacy framework meets the security criteria for an e-health cross-border system, due to the adoption of security mechanisms, such as user authentication, user authorization, access control, data anonymization, trust management and reputation modelling. The implemented framework remains to be tested to ensure its robustness and to evaluate its performance. The holistic security and privacy framework might be adapted during the project's life circle according to new legislations. © 2019 Stefanos Malliaros, Christos Xenakis, George Moldovan, John Mantas, Andriana Magdalinou, Lydia Montandon.Introduction Diabetic retinopathy (DR) is the most common diabetic eye disease worldwide and a leading cause of blindness. The number of diabetic patients will increase to 552 million by 2034, as per the International Diabetes Federation (IDF). Aim With advances in computer science techniques, such as artificial intelligence (AI) and deep learning (DL), opportunities for the detection of DR at the early stages have increased. This increase means that the chances of recovery will increase and the possibility of vision loss in patients will be reduced in the future. Methods In this paper, deep transfer learning models for medical DR detection were investigated. The DL models were trained and tested over the Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset. According to literature surveys, this research is considered one the first studies to use of the APTOS 2019 dataset, as it was freshly published in the second quarter of 2019. The selected deep transfer models in this research were AlexNet, Res-Net18, SqueezeNet, GoogleNet, VGG16, and VGG19. These models were selected, as they consist of a small number of layers when compared to larger models, such as DenseNet and InceptionResNet. Data augmentation techniques were used to render the models more robust and to overcome the overfitting problem. Results The testing accuracy and performance metrics, such as the precision, recall, and F1 score, were calculated to prove the robustness of the selected models. The AlexNet model achieved the highest testing accuracy at 97.9%. In addition, the achieved performance metrics strengthened our achieved results. Moreover, AlexNet has a minimum number of layers, which decreases the training time and the computational complexity. © 2019 Nour Eldeen M. Khalifa, Mohamed Loey, Mohamed Hamed N. Taha, Hamed Nasr Eldin T. Mohamed.