The SHIELD project seeks to revolutionise early detection of pancreatic cancer, focusing on individuals with high heritable genetic risk. Pancreatic ductal adenocarcinoma (PDAC) has a 5-year survival rate of less than 10%, primarily due to late-stage diagnosis. Consequently, 85% of PDAC cases are identified too late for curative treatment. However, early detection can significantly improve outcomes, increasing the survival rate to 42% with surgical intervention. There is a pressing need for better early detection methods, especially for those with familial or genetic predispositions. The only FDA-approved biomarker, CA19-9, is limited to monitoring treatment response due to its lack of sensitivity and specificity, while imaging methods ofter fail to detect early-stage cancers and cause a strain to the healthcare system due to their cost and limited availability. SHIELD aims to validate a new blood-based diagnostic test designed for early PDAC detection in high-risk individuals and pilot an early detection programme in Greece, Slovenia and Lithuania. Developed by partner Reccan, this test uses a 5-plex multiple immunoassay to analyze protein readouts and provides a probability score for pancreatic cancer. Initial studies with over 450 samples showed excellent performance with >91% sensitivity and >96% specificity. The project will validate the test's clinical performance in a prospective multi-center study across seven EU countries, targeting individuals with familial or genetic predispositions. It will also identify new protein biomarkers for other high-risk indications, such as new-onset diabetes (NOD). Collaboration with national screening authorities will help integrate this test into existing programs, and partnerships with patient organizations will enhance recruitment. SHIELD envisions transforming pancreatic cancer diagnostics by increasing the 5-year survival rate to 30% by 2035 in Europe. This action is part of the Cancer Mission cluster of projects on “Prevention & early detection (early detection heritable cancers)

Aging is an inexorable homeostatic failure of complex but largely unknown aetiology that leads to increased vulnerability to disease with enormous consequences on the quality of individual lives and the overall cost to society. Although, aging is driven by limitations in somatic maintenance, it is also subject to regulation by evolutionarily highly conserved molecular pathways. Indeed, macromolecular damage may drive the functional decline with aging; however, a battery of conserved, longevity assurance mechanisms may set the pace on how rapidly damage builds up and function is lost over time. Human efforts over the last centuries have succeeded in substantially lengthening lifespan, allowing aging to become a common feature of western societies. However, The discouraging complexity of the aging process, the noticeable lack of tools to study it, and a shortage of experimentally tractable model systems have made it significantly challenging to unravel the molecular basis of the processes that cause loss of bodily functions and degeneration of cells and tissues with advancing age. HealthAge was carefully designed to create a joint European program of excellence in training and research with a core intellectual focus on the functional role of “Lifespan Regulation Mechanisms in Health and Disease”. To tackle this, HealthAge combines top-level, state-of-the-art and interdisciplinary research skills that range from basic molecular mechanisms and ‘omics’ level understanding to translational research and clinical applications. This interdisciplinary strategy will allow us to gain functional insight into the fundamental mechanisms regulating longevity as well as to develop a series of rationalized intervention strategies aimed at counteracting age-related diseases.

DIOPTRA aims to introduce a front-line screening tool that will consider risk factors and protein biomarkers for pinpointing individuals at a high risk for colorectal cancer (CRC) incidence. Tissue & blood samples will be examined towards a discriminative set of prognostic proteins that are detectable via standard bloodwork and can indicate a need for further evaluation (i.e. colonoscopy). Other data (e.g. medical, behavioural) will also be considered as potential risk factors. Artificial intelligence (AI) will be leveraged for assessing prognostic power, while personalised behavioural change will be promoted based on modifiable risk factors. Given the low citizen participation on CRC screening across EU, DIOPTRA seeks to broaden the evaluated population, boosting participation rates and bypassing age screening thresholds. This action is part of the Cancer Mission cluster of projects on ‘Prevention, including Screening’.

The European community requires early stage researchers (ESRs) who can work across the boundaries of traditional disciplines, integrating experimental and in silico approaches to understand and manage complex multifactorial disorders. This training network utilises intervertebral disc degeneration (LDD) leading to low back pain (LBP) as a relevant application for data integration and computational simulations in translational medicine. LBP is the largest cause of morbidity worldwide, yet there remains controversy as to the specific cause leading to poor treatment options and prognosis. LDD is reported to account for 50% of LBP in young adults, but the interplay of factors from genetics, environmental, cellular responses and social and psychological factors is poorly understood. Unfortunately, the integration of such data into a holistic and rational map of degenerative processes and risk factors has not been achieved, requiring creation of professional crosscompetencies, which current training programmes fail to address. Disc4All aims to tackle this issue through collaborative expertise of clinicians; computational physicists and biologists; geneticists; computer scientists; cell and molecular biologists; microbiologists; bioinformaticians; and industrial partners. It provides interdisciplinary training in data curation and integration; experimental and theoretical/computational modelling; computer algorithm development; tool generation; and model and simulation platforms to transparently integrate primary data for enhanced clinical interpretations through models and simulations. Complementary training is offered in dissemination; project management; research integrity; ethics; regulation; policy; business strategy; and public and patient engagement. The Disc4All ESRs will provide a new generation of internationally mobile professionals with unique skill sets for the development of thriving careers in translational research applied to multifactorial disorders.

Mathematical, computational models are central in biomedical and biological systems engineering; models enable (i) mechanistically justifying experimental results via current knowledge and (ii) generating new testable hypotheses or novel intervention methods. SyMBioSys is a joint academic/industrial training initiative supporting the convergence of engineering, biological and computational sciences. The consortium's mutual goal is developing a new generation of innovative and entrepreneurial early-stage researchers (ESRs) to develop and exploit cutting-edge dynamic (kinetic) mathematical models for biomedical and biotechnological applications. SyMBioSys integrates: (i) six academic beneficiaries with a strong record in biomedical and biological systems engineering research, these include four universities and two research centres; (ii) four industrial beneficiaries including key players in developing simulation software for process systems engineering, metabolic engineering and industrial biotechnology; (iii) three partner organisations from pharmaceutical, biotechnological and entrepreneurial sectors. SyMBioSys is committed to supporting the establishment of a Biological Systems Engineering research community by stimulating programme coordination via joint activities. The main objectives of this initiative are: * Developing new algorithms and methods for reverse engineering and identifying dynamic models of biosystems and bioprocesses * Developing new model-based optimization algorithms for exploiting dynamic models of biological systems (e.g. predicting behavior in biological networks, identifying design principles and selecting optimal treatment intervention) * Developing software tools, implementing the preceding novel algorithms, using state-of-the-art software engineering practices to ensure usability in biological systems engineering research and practice * Applying the new algorithms and software tools to biomedical and biological test cases.