The EU-FRB program aims to identify optimal treatment strategies based on independent robust high quality real-world data taking into account both the chronic nature of the disease and frequent comorbidities associated with retinal diseases. We will track the incidence of local (ocular) and systemic adverse events, which clinical trials have been unable to measure. Intravitreal treatments for retinal disorders are the most significant evolution in ophthalmology in the last decade. For many conditions (including diabetic retinopathy (DR) and age-related macular degeneration (AMD)), the new treatments were shown to be highly effective, resulting in unprecedented improvements in vision and quality of life. Both DR and AMD have common characteristics: 1) They are frequent and incidence increases: in 2013, there were 56 million patients affected by diabetes in Europe. DR is one of the major comorbidities of diabetes. The number of patients affected by AMD in the EU is expected to rise by almost 25% until 2050. 2) Both require long term follow-up and treament: the rate of recurrence of macular disease, and subsequent vision loss, is significant if treatment is withheld. Regular and close follow-up are necessary to achieve best visual outcomes and maintain vision. Most patients with neovascular AMD need to be treated regularly for a lifetime. Similar findings were reported in patients with DR as 50% of the patients still receive treatment after 3 years. Both AMD and DR are chronic conditions requiring lifelong treatment. 3) Patients with AMD and DR have a higher risk for co-morbidities such as cardiovascular disease, myocardial infarction, renal failure and stroke. Particularly in DR, renal failure, peripheral neuropathy, cardial and neurovascular complications are frequent. Similarly, the link between stroke, high blood pressure and AMD is well established. 4) Patients affected by either DME or AMD do greatly benefit from intravitreal antiVEGF injections, as shown in pivotal clinical studies. There is a need to assess the impact of comorbidities on outcomes, as patients with comorbidities are often excluded from clinical trials: these studies were performed in controlled environments with highly selected groups of patients to achieve high internal validity. Few real world data, mostly industry driven, are available. Systemic anti-VEGF, used as chemotherapy, had been shown to increase the risk of stroke, myocardial infarction, renal failure and hyper-blood pressure, but the impact of intra-vitreal injection is still debated. High-quality real-world outcomes data for DR and neovascular AMD treatments are urgently needed. Randomized clinical trials are inadequate to assess effects in life-long chronic diseases. EU-FRB We have formed a collaborative group with the expertise required to lead an international registry: ophthalmologists, epidemiologists, biostatistics, health economists, diabetologists, cardiologists and IT specialists. We aim to collect real-world data on retinal vascular disease, and related comorbidities at a European level. The EU-FRB project will fill a gap in our knowledge about how new and emerging treatments for retinal disorders can be used alone or in combination to produce the best outcomes for patients. This information will benefit to patients, health care professionals, and society. It will be the largest and continuously expanding international registry for ocular outcomes. By demonstrating the benefit of proposing a multi-disciplinary personalized treatment strategy based on observational data and using ICHOM specified outcomes, the EU-FRB project may support the use of similar registries in health. This project will place Europe at the forefront of international research on real-world outcomes of treatment by forming the most consistent registry to improve eye health and life of millions of patients.

Finding a CURE for 35 Million individuals living with HIV/AIDS is one of the great global health challenges of the 21st century. The major obstacle to HIV eradication is the persistence of latent HIV cellular reservoirs, where the integrated viral genome is transcriptionally silenced but replication-competent and can escape both Anti-Retroviral Therapy and Immune Responses. The development of novel strategies aimed at eliminating these reservoirs have become paramount in HIV research, if we want to achieve an HIV/AIDS CURE. To accelerate the State of the Art in HIV CURE research in Europe, our EU4HIVCURE consortium brings together an intersectoral and interdisciplinary collaboration between 3 Universities, 3 Hospitals, 1 International Research Organisation from 4 European countries and 1 University from Canada. Our aim is to dissect the intricate mechanisms controlling HIV-1 latency and identify new druggable targets to develop novel latency-reversing strategies and eradicate persistent viral reservoirs by forcing HIV-1 gene expression. To facilitate continuum for translation to the clinic, we have developed an operational framework, which maximises exchange of knowledge and expertise via secondements, networking and training activities.

In the EU, 1 in 35 women and 1 in 23 men will be diagnosed with colorectal cancer (CRC) in their life span (ca. 340,000 cases and 156,000 deaths in 2020) causing an annual economic burden of ca. 20 billion EUR. Identifying CRC early enables better treatment options. Screening usually entails a quantitative faecal immunological test (FIT) to predict the need of colonoscopy for the detection of colorectal lesions, an expensive and invasive procedure. We aim to predict this need with specificity increased by >20 percentage points by using metagenomic microbiomes. We hypothesise that computational microbiome profiles extracted using artificial intelligence (Al) technology will allow for optimised personal therapy stratification. However, clinicians do not have access to broad microbiome data. With Microb-AI-ome, we will develop a novel kind of computational stratification technology to enable microbiome-enhanced precision medicine of CRC. Metagenomic microbiome data to date is distributed over many national registries, and privacy regulations are hindering its effective integration. With Microb-AI-ome, we will overcome this barrier by establishing the first privacy-preserving federated big data network in CRC research. We will integrate isolated, national databases into one international federated database network - rather than a cloud - covering metagenomes for over 5,000 individuals screened for CRC, and an expected total of 100,000 by 2026. Microb-AI-ome ensures that no sensitive patient data will leave the safe harbours of the local databases while still allowing for the classification of clinical CRC phenotypes, which we will demonstrate in clinical practice allowing regulatory bodies to adopt evidence-based guidelines. Our consortium combines expertise in CRC and its treatment, microbiomics, artificial intelligence, software development, and privacy protection to close the gap between privacy and big data in international medical research.

Developments in robotics allow people with profound neuromuscular deficits after stroke to walk with assistance (during the gait cycle) using an exoskeleton robot. Integrating a robotic device with individualised user electroencephalography (EEG /electrical activity in the motor areas in the brain) and EMG (muscle)feedback would allow more physiological and targeted gait parameters in response to effort, and confer neuroplastic training effects including neuromodulation of temporal and spatial features of gait. Future integration of EEG/EMGsignals with robotic devices will allow patient initiated movement through thought and/or attempted effort, where currently parameters for devices are therapist set and usage is not functionally driven by the patient. Advancement in this regard is stalled primarily because of difficulty in 3D modelling of gait by EEG. This collaborative consortium through secondments and return and built in knowledge sharing strategies will exchange knowledge and expertise across: Design, development and production of exoskeleton gait devices; neuro-rehabilitation; bioelectric EEG/EMG signal capture and interpretation; mathematical modelling and brain computer interface (BCI) platform development can advance the state of the art in gait rehabilitation after stroke rehabilitation. The proposal will allow development of 3D modelling of gait, for gait restoration and explore integration with robotics from multi-stakeholder perspectives. Aims: 1. Define current state of the art in EEG modelling of gait post stroke by systematic review and meta-synthesis 2. Complete 3D modelling of gait as visualised gait, overground gait and robotic walking in healthy individuals and stroke survivors 3. Develop and test a virtual reality BCI gait training device, including end-user feedback 4. Explore integration of this prototype with robotic software platforms

Our overall objectives are to accelerate the diagnosis, and enable personalised management, of inherited metabolic diseases (IMDs). Established academic technology for statistical genomic analysis, deep learning-based prediction of protein structure, and whole-body metabolic network modelling shall be applied to generate personalised computational models, given patient-derived genomic, transcriptomic, proteomic and metabolomic data. To train diagnostic models, a comprehensive clinical team will recruit 1,945 diagnosed patients with a wide variety of IMDs, then validate the clinical utility of personalised computational models on a set of 685 undiagnosed patients. An enhanced human metabolic network reconstruction, especially for lipid metabolism, reaction kinetics and inherited metabolic disease pathways, will increase the predictive capacity of cellular and whole-body metabolic network models. As an exemplar for other IMDs, personalised computational modelling will be used to identify compensatory and aggravating mechanisms that associate with clinical severity in Gaucher disease. The predictive capacity of personalised models will be validated by comparison with additional empirical investigations of protein structure and function as well as metabolomics, tracer-based metabolomics and proteomics of patient-derived in vitro disease models. To maximise the potential for impact, personalised modelling software will be developed to be generally applicable to a broad variety of IMDs, and implemented in a way that is both accessible to clinicians and admissible to regulatory authorities. Sustainability will be promoted by development of a roadmap for a European foundation to aid personalised diagnosis and management of IMDs, informed by broad stakeholder consultation. This is a unique opportunity to realise the potential of personalised computational modelling for a broad set of rare diseases, which is a field where European collaboration is an essential for progress.