AISym4Med aims at developing a platform that will provide healthcare data engineers, practitioners, and researchers access to a trustworthy dataset system augmented with controlled data synthesis for experimentation and modeling purposes. This platform will address data privacy and security by combining new anonymization techniques, attribute-based privacy measures, and trustworthy tracking systems. Moreover, data quality controlling measures, such as unbiased data and respect to ethical norms, context-aware search, and human-centered design for validation purposes will also be implemented to guarantee the representativeness of the synthetic data generated. Indeed, an augmentation module will be responsible for exploring and developing further the techniques of creating synthetic data, also dynamically on demand for specific use cases. Furthermore, this platform will exploit federated technologies for reproducing un-indentifiable data from closed borders, promoting the indirect assessment of a broader number of databases, while respecting the privacy, security, and GDPR-compliant guidelines. The proposed framework will support the development of innovative unbiased AI-based and distributed tools, technologies, and digital solutions for the benefit of researchers, patients, and providers of health services, while maintaining a high level of data privacy and ethical usage. AISym4Med will help in the creation of more robust machine learning (ML) algorithms for real-world readiness, while considering the most effective computation configuration. Furthermore, a machine-learning meta-engine will provide information on the quality of the generalized model by analyzing its limits and breaking points, contributing to the creation of a more robust system by supplying on-demand real and/or synthetic data. This platform will be validated against local, national, and cross-border use-cases for both data engineers, ML developers, and aid for clinicians’ operations.

The list of genes contributing to increased risk of developing Alzheimer’s Disease (AD) grows every year. To date, large case-control Genome-Wide Association Studies have found 98 genetic loci associated with late-onset Alzheimer's Disease (LOAD). However, apart from a small number of genes related to familial cases, it remains largely unknown the direct relationships between genes amyloid-β and tau misfolding protein accumulation (hallmark of AD), and neurodegeneration of the human brain. Even more striking, current knowledge of mutations potentially conferring genetic resilience is sparse. A better characterization of each genetic risk loci and identification of protective genetic variants could lead to development of effective therapeutical targets, actually unavailable for AD. The goal of this project is to characterize associations between the 98 LOAD genetic risk loci, in vivo tau and amyloid-ß PET, and MRI neurodegeneration – including an examination of potential genetic variants conferring disease resilience. Hypotheses: genetic loci will show distinct spatial patterns of tau and amyloid-ß spreading and neurodegeneration, helping us to better understand AD biological heterogeneity. I expect to find different subtypes of AD with different molecular mechanism and different therapeutical targets for each subtype. I will use PET imaging of ~3,000 participants from the Harvard Aging Brain Study, and the A4 and ADNI databases. ~35,000 participants from the UK Biobank with genetic and functional and structural MRI will also be used to study the impact of genetic loci on neurodegeneration. Importantly, as a first analysis, we will search for the effects of each risk loci and protective variants conferring resilience; secondary analyses will also explore the contributions of sex and ethnic minority factors

SOTERIA aims to drive a paradigm shift on data protection and enable active participation of citizens to their own security, privacy and personal data protection. SOTERIA will develop and test in 3 large-scale real-world use cases, a citizen-driven and citizen-centric, cost-effective, marketable service to enable citizens to control their private personal data easily and securely. Led by an SME, this project will develop, using a user-driven and user-centric design, a revolutionary tool, uniquely combining, in a user-friendly manner, a high-level identification tool with a decentralised secured data storage platform, to enable all citizens, whatever their gender, age or ICT skills, to fully protect and control their personal data while also gaining enhanced awareness on potential privacy risks. SOTERIA solution will be tested and validated through 3 real-world large-scale use-cases, involving 6,500 European citizens, targeting 3 applications which usefulness has been highlighted during COVID-19 pandemic: e-learning, e-voting and e-health. This 3-year transdisciplinary project from both SSH and technology angles, will develop an innovative solution based on: a secured access interface relying on high-level identification, a smart platform processing data to transmit only the minimum personal data required, a secured data storage platform (decentralized architecture) under the full control of the citizen, an educational tool to raise awareness of citizens developed using a citizen-driven and citizen-centric approach. The technologies developed will i) empower citizens to monitor and audit their personal data; ii) restore trust on privacy, security and personal data protection of citizens in digital services; iii) be fully compliant to GDPR regulation and apply strictly the data minimization principle; iv) ensure cybersecurity.

The EFFecT Doctoral Training (DN) Network responds to the evolving landscape of RNA-based therapeutics and in particular antisense oligonucleotides (ASO). Over the last 25 years, only 21 antisense nucleic acid-based molecules have been approved by the FDA or/and EMA. However, the field is now at the tipping point of maturity to achieve regular translation from bench to bed side, provided that certain obstacles are successfully addressed. Our consortium involves experts from academia, industry, and non-profit organizations across nine European countries with first-hand experience in the successful translation of this new technology and in-depth knowledge of the remaining hurdles for its success. With leading scientists at different career stages, EFFecT’s core is form by nine academic and three industrial partners. EFFecT's objectives include addressing tissue-specific delivery challenges, imparting knowledge on diverse ASO modalities, and creating a roadmap for ASO therapeutics in Europe. The network's composition leverages over 20 years of experience in nucleic acid drug development, and was born from the successful COST Action "Delivery of Antisense RNA ThERapeutics" (DARTER). The EFFecT training network ensures a comprehensive education for Doctoral Candidates through knowledge exchange, secondments, research workshops and dissemination. This network is expanded with the collaborations of experts in communication, intellectual property, and open science that will train the DCs in good scientific practices. By addressing training gaps and fostering collaborations, EFFecT is poised to deliver the next generation of experts in this up-and coming field, elevating Europe's visibility and competitiveness in antisense RNA therapeutics.