Community Challenges

Speeding up the diagnosis and reaction capacity to infectious diseases.

Infectious diseases caused by pathogens (bacteria, viruses, fungi) are undoubtedly a rising threat to public health very much linked to the impact of environmental exposures. The multiple paths ofpathogens transmission can lead to the exponential spread of infectious diseases, leading to apandemic. The current COVID-19 pandemic is already the sixth pandemic in the current century, afterthose originated by HIV-AIDS (starting in 1981 but lasting until today), the H1N1 flu virus (2009), SARS(2002), the ebola virus (2014) and MERS (2015). This, together with the increasing bacterial resistance to antibiotics, suggests that current canonical treatment methods (antibiotics or vaccination) need reinforcement through reliable frontline diagnostic tools to raise an alert of eventual risk to public health. It will only in this way be possible to keep the health system prepared against future pandemics.

Also, the resistance of bacteria to antibiotics increases the risk of foodborne diseases (e.g.Salmonella, Listeria), creating severe concerns in public health worldwide. On top of that, theacceleration at which foodstuffs move through the distribution chain worldwide is another serious concern. Thus, the world health authorities and the global food industry seek to ensure consumers'food safety.

The potential for the application of digitalisation and Artificial Intelligence (AI) technologies in the areaof public health is broad and still not sufficiently exploited. Part of the solution to the challenge ofincreasing the speed and reaction capacity to the current or future infectious diseases, will most probably come from the digitalization, with a special focus on digital simulation to support decision making and AI application to health management.

The collaboration between research groups belonging to the Donostia International Physics Centre (DIPC) on the one hand, and University of the Basque Country (UPV-EHU) on the other, will result onthe development of new research and education programmes that may contribute to tackle this challenge. The key enabling technologies based on photonics may enable, for example, the prevention of infectious diseases through accelerated optimization of advanced diagnostic methods to contribute to tackling the aforemention challenge.

The conventional detection technique relies on expensive equipment, specialized sample preparation,and slow data output. Indeed, the miniaturization of devices allowed for the reduction of sample amount, fasten results outcome, leading to the development of point-of-care devices of facile use andaccessibility to many citizens. Still, the optimization of new diagnostic tools is too slow to face the accelerating rate at which new outbreaks emerge.This is where digitalization can help to tackle the issue. For example, researchers focus on developing. This is where digitalization can help to tackle the issue. For example, researchers focus on developing robotic systems capable of handling liquids, controlling flow, and performing evaluation in real-time,enabling fast and automated readout. The large amount of data needs classification to extract meaningful information and to build a valuable knowledge. Machine learning algorithms are withoutany doubt excellent tools to handle large amount of data, offering today excellent means to predict inreal-time the outcome of the optimization processes.

But having robotic systems and machine learning to analyze data is not sufficient to face today's publichealth issues. These two disciplines need to be linked through the so-called closed-loopexperimentation: an algorithm learns from experimental results and constructs a model to hypothesize about the next experimental step.

To close, the current Edisonian approach to optimize diagnostic tools requires up to ten years of fundamental and applied research to reach the market. The digitalization of health-oriented laboratories can revolutionize the way the diagnostic methods are being optimized, and empowering such optimization with artificial intelligence will enable faster development of new technologies andthus prevent future pandemics.

Expected results of this collaboration can be summarized under two principal action lines:

  • Joint collaborative research and innovation projects aiming at responding to different scientific and technological specific sub-challenges:

As an example, one of the projects currently ongoing (INFECTON) works on the the central hypothesis that known biomarkers for detection of bacterial infectious – enzymes - can be exploited for the detection of viral infection in COVID-19. The general objective of the INFECTON project is to construct a workflow comprising computational chemistry and physics, nanochemistry, robotics and machine learning to demonstrate that colourimetric sensor based on nanoparticles and DNA technology is capable of detecting coronavirus-associated nucleases.The outcome of the INFECTON project will deliver new detection means for corona virus that is orthogonal to the existing tests available in the market. Up to date, there is no feasible tests for COVID-19 based on detection of nucleases. Aiding the biochemical sensor development by computer visionon robotic platform and machine learning model will eradicate the problem with tedious process of sensor optimization (selectivity and sensitivity). The proposed workflow here will not only allow the development a reliable sensor for current coronavirus, but will also serve as a tool in the optimization of sensors for future mutations of corona viruses. Thus, the outcome of the INFECTON project will deliver a new inter disciplinary workflow for fast adaptation to the new outbreaks.

  • New education and training offer for university students.

The ongoing project has already lead the partners to launch new Research and development projects and the development of specialized higher education programmes in the area of Photonics in collaboration with other ENLIGHT partners like, for example, the University of Bordeaux.

Students from the Physics degree of the University of the Basque Country and from master LAPHIA of the University of Bordeaux will have the opportunity to do some visits and internships in the labs of the Basque community of photonics like the DIPC. These internships may provide students at different stages of their specialization curricula (bachelor degree students, master students and pre-doctoral students) with hands on experience in research projects focused on addressing some of the aforementioned scientific objectives. The methodology will combine experiments based learning and challenge based problem solving, based on the scientific methodologies in direct contact with worldclass level researchers.

    Practical Details

    Hosting organization: Donostia International Physics Centre (DIPC)
    Community: Pais Vasco/ Euskadi
    Focus Area: Health, Digitalisation
    Type of activity: Research projects
    Tags: Digitalisation, Health