DUBLIN CITY UNIVERSITY

Safe and Sustainable by Design approach requires an entire life cycle monitoring of toxicity of chemicals. However, current testing systems cannot mimic the exposure conditions related to each step and are not compatible with downstream in silico analyses. New sets of instrumentation that enables modular testing capacities with integrated data bridging and progressive in silico model development systems are needed. TOXBOX will provide a device based on a prototype developed in a H2020 project, PANBioRA, with a flexible microfluidic and instrument architecture to provide a plug and play testing platform to ease accessibility and interlaboratory validation. The system will incorporate the following tests, with modifications for each step of life cycle: automated cytotoxicity and genotoxicity tests, connected barrier/metabolic tissue couples with cytokine and real-time electro-chemical read-outs, flow cycle modules with environment mimicking conditions, a testing module based on zebrafish embryo with mechanical stimuli. The system will be validated using metallic 2D structures and nanoparticles, biocides, and known endocrine disruptors. Custom made functional polypeptides (novel biocides) will be used to cover the design phase. Progressive in silico models for long term effects will be iteratively developed and used to predict each new group to be tested until good predictability is achieved with new chemical formulations (comprehensive risk assessment). A data management platform that enables interfacing with the available databases will be developed. After interlaboratory validation of the device by 4 partners, a standardization folder will be prepared, to make the device available for testing with accessibility at all stages of material life cycle assessment to different stakeholders. TOXBOX aspires to bring forth an instrument that will provide reliable toxicity data in relevant conditions for each chemical and enable reliable in silico model development.

EC contribution:

EUR 751k

Start:

2024-01-01

End:

2027-12-31

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-CL4-2023-RESILIENCE-01

View on CORDIS (DOI 10.3030/101138387)

Women’s cardiovascular health is an urgent clinical unmet need as reported by the European Society of Cardiology (ESC), as cardiovascular disease (CVD) risk in women still tends to be underestimated by clinicians and women themselves. CVD is under-diagnosed, under-treated and poorly understood, more so in women in the 40-60 age group, when personalised risk assessment and prevention can have a positive impact on their health. In this context, CARAMEL will deliver an innovative personalised prevention model aimed at women 40-60yrs based upon a risk assessment stratification model considering sex and gender specific risk factors and a self-assessment and self-management approach using innovative digital technologies, empowering women to optimise their cardiovascular health. The proposed CVD-Risk Assessment and Stratification scheme will only be possible by the cumulative risk factors analysis, fueled by AI, emerging from a wide number of different data sources, including clinical data from EHR, medical imaging, biomarkers, metabolomics, lifestyle information (sleep, physical activity, diet) from large cohorts and biobanks. A consortium composed by 25 partners and Affiliated Entities coming from 11 countries, composed by 9 clinical entities, 6 research organisations and 10 industry and SMEs will develop, test and validate the personalised prevention program in observational and interventional studies in clinical sites in Colombia, Croatia, Greece, Lithuania and Spain. To that end, engagement of women aged 40-60, will take place from the onset on the co-design and co-creation of the studies and the self-management app ecosystem to be developed. Likewise, Gender in research will be mainstreamed all along the intervention. CARAMEL will also deliver policy recommendation and clinical guidelines supporting the design and update of CVD Plans by health authorities and healthcare providers, considering novel AI based risk models, applicable to women aged 40-60 yrs.

EC contribution:

EUR 395k

Start:

2024-12-01

End:

2029-11-30

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-HLTH-2024-STAYHLTH-01-two-stage

View on CORDIS (DOI 10.3030/101156210)

The objective of HARNESS is to train a multidisciplinary cohort of 13 PhD students in an international Doctoral Network, specialising in ethics, law, and technology. The network will offer comprehensive training, supervision, and secondments, allowing individuals to become experts in their specific disciplines while gaining practical knowledge across various fields. The program is supported by an excellent team of partners who are world leaders in their respective domains. The ethical and socio-technical impacts of the current and forthcoming AI and data-intensive information systems (e.g. Common European Data Spaces) require careful multidisciplinary consideration and foresight techniques regarding their alignment with EU's values, sovereignty and economic and societal goals. The new European regulations on data and AI should be supported by robust methods and tools to empower citizens and civil society to swiftly comprehend, navigate, and address emerging threats to fundamental rights. HARNESS will analyse the joint impact of new AI and data-intensive technologies and their regulation, considering legal entanglements, new legal compliance tools and the consequences that can be anticipated, from ethical, societal and economic perspectives. AI and data legislation will be formalised and operationalised under the 'Law as Code' paradigm using international standards and semantic web technologies. Specific cases such as AI Foundational models, impact of new regulations on competitiveness and the interplay of self-sovereign identity in Common European Data Spaces will be analysed in detail. HARNESS will utilise and build upon the state of the art in open information systems and knowledge engineering to develop appropriate methodologies, tools, and taxonomies to assist with regulatory compliance, deliberation and agreement on ethical norms. This will involve charting the constellation of possible systems, anticipating new risks and developing effective mitigations.

EC contribution:

EUR 286k

Start:

2025-01-01

End:

2028-12-31

Status:

SIGNED

Scheme:

HORIZON-TMA-MSCA-DN

Call:

HORIZON-MSCA-2023-DN-01

View on CORDIS (DOI 10.3030/101169409)

ONE-BLUE will provide an integrated assessment of contaminants of emerging concern (CECs) and their impacts, will develop new monitoring tools, and will provide an advanced understanding of the combined effects of CECs and climate change (CC) on the different marine ecosystems and their biodiversity. The following objectives are defined: • Improve the current knowledge of the concentrations, profiles, fate, behaviour, and effects of CECs, in the different marine compartments and ecosystems through three case studies (Atlantic and Arctic oceans and the Mediterranean Sea) and develop safety guidelines and protocols for future CECs monitoring in marine ecosystems. • Provide an advanced understanding of possible interaction between CC and CECs in marine ecosystems with studies under controlled conditions in marine mesocosms. • Develop a database (DB), the CECsMarineDB, following FAIR principles, with a database management system (DBMS) to collect the data generated in ONE-BLUE and capitalize data from other projects and existing DBs. A graphical user interface (GUI) will be developed and used for data exploration and demonstration of the fate and behaviour of CECs in a changing environment. • Provide new solutions in support of the implementation of relevant EU policies; (i) a series of new approach methodologies (NAMs) to improve the ecotoxicity assessment of CECs in marine ecosystems; (ii) a tiered effect-directed approaches (EDA) combining toxicological assessment and chemical analysis; (iii) an advanced ultrasonic system for sampling and enrichment micro/nanoplastics from seawater; (iv) a remote autonomous sensor to assess CECs in marine waters in quasi-real-time; a decision support system (DSS) based on machine and deep learning strategies to assess and forecast combined effects of CC and CECs in marine ecosystems. • Disseminate the project results and establish the exploitation plan for the developed technologies.

EC contribution:

EUR 206k

Start:

2024-01-01

End:

2027-06-30

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-CL6-2023-ZEROPOLLUTION-01

View on CORDIS (DOI 10.3030/101134929)

Artificial Intelligence (AI) is one of the most significant pillars for the digital transformation of modern healthcare systems which will leverage the growing volume of real-world data collected through wearables and sensors, and consider multitude of complex interactions between diseases and individual/population. While AI-enabled digital health services and products are rapidly expanding in volume and variety, most of the AI innovations remain in the form of proof-of-concept. There is a continuous debate regarding whether AI is worthy of trust. The EU AI HLEG has defined that trustworthy AI systems should be lawful, ethical and robust. To translate it into actionable practices, provision of explainability, fairness and privacy is crucial. A considerable volume of research has been conducted in the areas of explainable AI, fair AI and privacy-preserving AI. However, the current research efforts to tackle the three challenges are fragmented and have culminated in a variety of solutions with heterogeneous, non-interoperable, or even conflicting capabilities. The ambitious vision of HarmonicAI is to build a human-machine collaborative multi-objective design framework to foster coherently explainable, fair and privacy-preserving AI for digital health. HarmonicAI draws together proven experts in AI, health care, IoT, data science, privacy, cyber security, software engineering, HCI and industrial design with an underlying common aim to develop concrete technical and operational guidelines for AI practitioners to design human-centered, domain-specific, requirement-oriented trustworthy AI solutions, accelerating the scalable deployment of AI-powered digital health services and offering assurance to the public that AI in digital health is being developed and used in an ethical and trustworthy manner.

EC contribution:

EUR 55k

Start:

2024-01-01

End:

2027-12-31

Status:

SIGNED

Scheme:

HORIZON-TMA-MSCA-SE

Call:

HORIZON-MSCA-2022-SE-01

View on CORDIS (DOI 10.3030/101131117)