In the current world, there is a clear need of advanced multi-sensing systems capable of providing fast and quantitative detection of a huge range of hazards which could affect human health in our daily life. Sectors such as healthcare, food safety or environment control, among others, will require these tools to take fast and effective actions and prevent potential crisis impact. In this context, PHOTONGATE aims to develop an adaptable diagnostics solution, comprising Photonic cartridges and read-out platform, which allow to quantify multiple analytes of the same or different nature (biomolecules, chemicals, metals, bacteria, etc.) in a single test with levels of sensitivity and selectivity at/or over those offered by current commercial solutions. PHOTONGATE technology relies on a new sensing concept which combines two core technologies: a bio-chemical technology (molecular gates) which will confer the specificity and increased sensitivity to the system, and, on the other hand, a photonic technology (light interaction with Local Surface Plasmonic Resonance (LSPR) structures) working as transducers and allowing the quantification. PHOTONGATE consortium has been specifically designed for maximizing the project success since all the actors of the value chain are enrolled. In addition, the development and integration of the different PHOTONGATE components have been designed searching for the European autonomy by using European research, knowledge and fabrication networks as well as favoring European providers. PHOTONGATE goals will involve a significant progress beyond the State-of-the-Art in multi-sensing systems achieving faster and high sensitivity detection of multiples targets. A final validation of PHOTONGATE technology in relevant scenarios for health and food safety (TRL5) will be performed to demonstrate the system capabilities.

Influenza is a major public health problem. In a conservative estimate, influenza infects annually 60 of the 500 million inhabitants of the EU. Vaccines are the cornerstone for preventing influenza and its consequences. Current influenza vaccines have a moderate variable effect, given the mismatch between vaccine and circulating strains, waning immunity and interference from previous vaccination, among others. The single most important challenge in achieving VE studies for the various influenza vaccines put every year on the European market is the ability of the different stakeholders to work in collaboration. To enable a sustainable network of influenza vaccine VE studies, the Development of Robust and Innovative Vaccines Effectiveness (DRIVE) main goal will be the development of a governance model between public and private entities. This model will ensure scientific independence in the studies and full transparency, allowing different stakeholders to fulfill their needs taking into account their respective obligations and statutes. A second challenge will be to reach the capacity to perform vaccine brand- specific effectiveness studies, which is agile enough to deliver the needed outputs in timely manner, and robust enough to provide results by different age and risk groups and flexible enough to utilize novel tools while at the same time aims to be sustainable. Combining these outputs, DRIVE will establish a sustainable platform for joint influenza vaccine effectiveness evaluation which will have a positive impact on European citizens public health.

The public-private partnership, READI, seeks to help clinical studies (CS) to finally serve the complete general population, and therefore more patients. To date CS have struggled to recruit and retain participants from diverse backgrounds and communities, such as marginalized or disadvantaged groups (e.g., sexual, gender, age, cultural, and socioeconomic cohorts). The resulting knowledge gaps entrench or increase health disparities. The READI consortium strives to tackle these challenges by fostering a more cohesive and integrated CS ecosystem for underserved (US) and underrepresented (UR) communities. It will actively connect all key stakeholders who can facilitate access to a wide range of patient populations. It will provide these stakeholders with the necessary tools, training programs, and approaches essential for the recruitment and retention of US/UR patients in CS. In addition, it will design, build and implement a digital platform which is patient-centred, sustainable, open and innovative. This will foster improved access to CS information and READI tools, while also supporting patient connections with the created communities. Finally, at least 4 CS will be used for testing the effectiveness of the developed tools and approaches. READI has a three-fold objective: to help US/UR communities overcome CS participation barriers (e.g., lack of information/awareness, mistrust, poor communication, geographic limitations, prejudice), which in turn will improve research of many diseases and conditions, preventative care and treatment effectiveness in different demographic groups, and better serve society. READI’s success will draw from its interdisciplinary, multi-stakeholder, consortium composition of 73 organizations from 18 countries, with key expertise in drug development and CS (design and operations), engagement strategies for US/UR populations, digital platform development, training and capability building initiatives, effective communication and dissemination, long-term sustainability, ethics and regulatory affairs.

Billions of people suffer from oral diseases world-wide while oral health treatments are not affordable for everyone. Goal 3 of the United Nations Sustainable Development challenge/priority programs, to be reached by 2030, is to promote “access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines [..] for all”. The WHO points therefore towards cost-effective oral disease prevention as a key strategy to reach a universal health care access. The main scientific objective of SMILES is identifying, characterizing, and harnessing antimicrobial peptides (AMPs) produced by Streptococcus dentisani and select cocktails with biomedical application in the treatment of caries and periodontitis. S. dentisani is a probiotic bacterium which produces AMPs, and which is naturally found in oral disease-free individuals maintaining the balance for a healthy oral microbiota. Caries and periodontitis are two oral diseases resulting from microbial dysbiosis. These cocktails could be incorporated in the future as additives in conventional oral hygiene products (ex. toothpaste), as oral disease preventive agents accessible for the public independently of their monetary resources. Dr. Revilla-Guarinos has expertise in antimicrobial resistance and will lead SMILES. The research and training at the academic host institution will be complemented with an intersectoral secondment in DENTAID (an oral health care products leading company), international networking & collaborations, specialized training courses & complementary skills workshops. Dissemination & exploitation activities, as well as communication & outreach events will promote the action and its results. The overarching ambition of this action is training and preparing Dr. Revilla-Guarinos for her to start her own group in academia or, alternatively, to develop a new path by changing to a career in industry, working at the forefront of Applied Microbiology in Europe.

Health scientific discovery and innovation are expected to quickly move forward under the so called “fourth paradigm of science”, which relies on unifying the traditionally separated and heterogeneous high-performance computing and big data analytics environments. Under this paradigm, the DeepHealth project will provide HPC computing power at the service of biomedical applications; and apply Deep Learning (DL) techniques on large and complex biomedical datasets to support new and more efficient ways of diagnosis, monitoring and treatment of diseases. DeepHealth will develop a flexible and scalable framework for the HPC + Big Data environment, based on two new libraries: the European Distributed Deep Learning Library (EDDLL) and the European Computer Vision Library (ECVL). The framework will be validated in 14 use cases which will allow to train models and provide training data from different medical areas (migraine, dementia, depression, etc.). The resulting trained models, and the libraries, will be integrated and validated in 7 existing biomedical software platforms, which include: a) commercial platforms (e.g. PHILIPS Clinical Decision Support System from or THALES SIX PIAF; and b) research oriented platforms (e.g. CEA`s ExpressIF™ or CRS4`s Digital Pathology). Impact is measured by tracking the time-to-model-in-production (ttmip). Through this approach, DeepHealth will also standardise HPC resources to the needs of DL applications, and underpin the compatibility and uniformity on the set of tools used by medical staff and expert users. The final DeepHealth solution will be compatible with HPC infrastructures ranging from the ones in supercomputing centers to the ones in hospitals. DeepHealth involves 21 partners from 9 European Countries, gathering a multidisciplinary group from research organisations (9), health organisations (4) as well as (4) large and (4) SME industrial partners, with strong commitment towards innovation, exploitation and sustainability.