Ventricular tachycardia (VT) is an unpredictable and potentially deadly condition and should be promptly treated with catheter ablation and medication, before irreversible and potentially fatal organ damage follows. Unfortunately, this combination of treatments does not prevent VT reoccurrence in 30-50% of VT patients and while they can undergo multiple invasive ablations, technical difficulties or refusal of the patient can lead to a lack of effective treatment options. A promising novel, non-invasive treatment option for VT is stereotactic arrhythmia radioablation (STAR). Besides being non-invasive, STAR can also be used to reach locations that are inaccessible for catheter ablation, which may potentially improve effectiveness of overall VT treatment. Small scale first in men/early phase trials have been performed for STAR, providing proof-of-concept for clinical safety and efficacy. However, patients with recurrent VT are not a homogenous group and each center deals with different inclusion criteria, imaging and/or target definition. Many questions remain and the available studies lack the power to clinically validate the approach and prepare for late stage phase III trials. The STOPSTORM consortium sets out to consolidate all current and future European efforts to clinically validate STAR treatment by merging all data in a validation cohort study, standardising pre-treatment and follow-up, in order to collect the data sets and statistical power needed to unanimously establish clinical safety, efficacy and benefit for STAR. The STOPSTORM consortium also sets out to refine protocols and guidelines, determine volumes of interest, define and model the optimal target region and target dose, also in relation to surrounding healthy tissues (i.e. organs at risk) and determine which patient population and underlying cardiopathies respond best to STAR. By doing so the STOPSTORM consortium paves the way to consensus and future late stage clinical trials for STAR.

A new generation of diagnostic systems available at the point of care (POC) could save lives and reduce the spread of infectious diseases worldwide through early detection and treatment. Optical microscopy remains the gold standard for the diagnosis of many parasitological diseases; however, its accuracy is dependent on the availability and expertise of the analyst at the POC. This limitation is increased by the dependence on labour-intensive examination processes, lack of standardization, high interobserver variability, insufficient precision in sample quantification and, as a consequence, a high misdiagnosis rate. This project introduces an AI diagnostic system leveraging existing microscopes and mobile technology providing a comprehensive and holistic sample analysis rather than just detecting individual pathogens. MultiplexAI is a scalable, low-cost, autonomous AI diagnostic system for the POC that upgrades any optical microscope into an AI agent able to accurately identify any parasite in a sample. We will collect data, train, deploy and evaluate the integrated system to detect multiple diseases including malaria and parasitic Neglected Tropical Diseases. The project will pursue the following objectives and methodological steps: 1) To design a trustworthy AI system, ensuring technical and social robustness, and adherence to WHO AI ethical principles of safety, transparency, explainability, accountability, equity, and sustainability; 2) To develop AI foundational models for microscopy analysis capable of automating the detection, differentiation and quantification of multiple parasites causing disease and integrate them into an automatic mobile microscopy system; 3) To validate the system in laboratory settings; 4) To undertake a performance evaluation study in clinical workflows of four countries in SSA; 5) To assess usability, acceptability and feasibility with end-users and evaluate the cost-effectiveness of its implementation; 6) To model and evaluate the health impact of introducing our system to improve diagnosis and surveillance at both local and national level; and 7) To execute a regulatory roadmap for compliance in EU and SSA, and determine a path to market. Overall, this project aims to unleash the AI revolution leveraging mobile technologies and upgrading millions of optical microscopes into a network of intelligent POC devices, capable of performing high-throughput sample analysis to provide reliable and ubiquitous diagnostics and medical knowledge for everyone, everywhere.

This project is based on previous experience developed at our institution in the framework of a scientific study about the design of an education program to promote the quality of life of adults with multiple disabilities, validated by international experts. We now feel the need to bring together the best practices developed at European level in this area, to promote the qualification and fairness of the services available to these citizens. Regarding education for the quality of life of people with disabilities, it is aimed at focusing on an educational system of multidimensional and multidisciplinary nature that respects diversity, individuality and development, aiming at a culture of cooperation and collaboration for the disabled. problem solving, maximizing the potential of each individual with a disability and providing an improvement in the educational response. This project intends to develop an education program to promote the quality of life of adults with severe and profound disabilities, defining strategies to be implemented with these people and their families, as well as identifying areas of training for employees and guidelines of organizational policies and practices. In this context, the main objective of the project is to improve the education of adults with disabilities through the provision of educational strategies that promote their quality of life and foster the qualification and professionalism of service providers in the scope of that issue. The project involves about 270 participants from the following target groups: clients, professionals, managers and stakeholders of centers for the education of adults with severe and profound disabilities or similar organizations; researchers from a research center on education for the quality of life of people with disabilities; persons concerned with the quality of life of people with disabilities; and policy makers. It will be determined the profile of the quality of life of adults persons with severe and profound disabilities and identified individual variables, services and community that are predictors of personal quality of life outcomes (quantitative methodology and benchmarking). We intend to identify good practices and successful experiences developed by partner in terms of adequacy, quality, force and relevance for improving the quality of life of adults with severe and profound disabilities (case study and benchlearning). And design an education program for the quality of life of adults with severe and profound disabilities In terms of expected results, it is expected that the application of the new paradigms through best practices based on conceptual models and frameworks of human functioning and the provision of individual support among people with disabilities, centered on change through innovation, reorganization of services and institutions and its effectiveness, contribute to a successful and effective impact in improving their quality of life. Thus, it is intended to promote a positive vision in the way these people are seen and lead to the change of attitudes towards the disabled population, reflecting the quality and the equity of the services made available during their life.

unCoVer is a functional network of research institutions collecting data derived from the provision of care to COVID-19 patients by health systems across Europe and internationally. These real-world data allow for studies into patient’s characteristics, risk factors, safety and effectiveness of treatments and potential strategies against COVID-19 in real settings, and complement findings from efficacy/safety clinical trials where vulnerable groups, and patients with comorbidities are often excluded. The network will facilitate access to otherwise scattered datasets, and build computational and analytical platforms to streamline studies on risk characterisation, and prediction modelling using standardised pooled data derived from real life practices. It will fill data gaps, unify current initiatives and create downstream exploitation opportunities for researchers and public health strategies to optimise COVID-19 strategies and minimise the impacts of future outbreaks