National grants

PRIME – Programme for Research Innovation and Market Excellence

PRIME is the first comprehensive acceleration programme in Poland dedicated to supporting the commercialization of scientific research results. It is implemented by the Foundation for Polish Science in partnership with the UK-based consultancy Oxentia, and funded by the European Funds for a Modern Economy (FENG). The programme aims to shift the perception of science’s role in society—emphasizing the practical application of research and its impact on the market and the economy.

Project title: Digital Twin of Blood Flow: CFD for Personalized Treatment Application in Optimizing Deep Vein Thrombosis (DVT) Therapy

Abstract: We have developed a Digital Twin (DT)-based simulation tool that leverages Computational Fluid Dynamics (CFD) to model patient-specific blood flow dynamics. Using medical imaging and clinical data, the tool constructs a virtual model of the patient’s vascular system, providing haemodynamic insights. This enables vascular surgeons to make data-driven, personalized treatment decisions, optimizing DVT management, reducing complications, and improving patient outcomes.

Scientific Leader: Magdalena Otta 

Business Leader: Katarzyna Baliga-Nicholson 

Technology Transfer Supervisor: Dominik Czaplicki  

Duration of Phase I: 01.06.2025 – 30.11.2025

Amount of European Funds Contribution in Phase I is 69 849,00 PLN

Amount of European Funds Contribution 313 904,00 PLN

“The PRIME project – science commercialization support, is implemented by the Foundation for Polish Science (FNP) and co-financed by the European Union under the European Funds for Smart Economy 2021-2027 (FENG)

Grants for Grants – NeoSolution

Type of Budgetary Grant: Initiative of the Minister of Education and Science titled “Grants for Grants – Promotion of Quality IV (Horizon Europe),” supporting activities related to the preparation of a project proposal for a European Union research programme, carried out by the Institution and described in application no. ID 627458 (acronym: NEU SOLUTION).

Project Proposal Title: “European Centre of Excellence in the Longitudinal Approach to Prognosis, Treatment, and Rehabilitation of Non-Traumatic Brain Disorders (Work Package WP2),” submitted under the European Union Research Programme – Horizon Europe Framework Programme.

Granted Financial Support: PLN 15,000

Date of Contract Signature: June 3, 2025

Contract Number: AGREEMENT No. 6067/GGPJ6–22/HEUROPA/01

Abstract:  NEU SOLUTION aims to establish a network of excellence hubs across Europe, focusing on personalised diagnosis and treatment of non communicable neurological disorders (nNCDs) based on a cognitive neuroscience approach. The project will use stroke as a case study, capitalising on the potential for transferring diagnostic and therapeutic advances from one disease context to another. This effort will initially utilise existing scientific groundwork and extensive databases to enhance diagnostic and treatment protocols for stroke survivors, intending to extend these methodologies to other nervous system ailments, including neurodegenerative diseases and consciousness disorders from traumatic brain injuries. At its core, the project will form a quadruple helix consortium to integrate efforts across research, clinical practice, industry, and community stakeholders, driving innovation in nNCDs management. Building on multiple large scale grants already realised, the consortium aims to develop analytical tools for predicting individualised disease trajectories for personalised therapy and to establish remotely-controlled, neuroscientific-based intervention protocols via telemedicine. Strategically, the project will develop excellence hubs in Poland, Portugal, Malta, and a mentoring scheme in Serbia. Each of these with a designated focus ranging from clinical products to national data stewardship and emerging research capacitation. These hubs will work collaboratively to formulate a unified long-term research and innovation (R&I) strategy that encompasses data stewardship and analysis for utilising large biomedical datasets effectively in clinical and research settings. The overarching goal is to foster knowledge transfer between academia and industry, enhancing treatment and rehabilitation processes for brain disorders, and to create resources that facilitate the translation of knowledge into practical value through skill development, networking, and the creation of startups

Grants for Grants – BioTwinXR

Type of budget subsidy: Initiative of the Minister of Education and Science titled “Grants for Grants – Promotion of Quality IV (Horizon Europe)”, related to the preparation of a project proposal for the European Union research programme, carried out by the Entity and described in application no. ID 627440 (acronym: BioTwinXR).

Project proposal titled “Biomedical Digital Twins Enhanced with Extended Reality (Work Package 5: Applications of Extended Reality – Enhanced Virtual Human Twins)”, submitted under the European Union research programme – Horizon Europe Framework Programme, Pillar: Excellent Science.

Awarded funding: 12,361.23 PLN

Date of agreement signing: April 15, 2025

Agreement number: AGREEMENT no. 6024/GGPJ6-22/HEUROPA/01

English Summary: Combining digital twins (DTs) with extended reality visualisation has the potential to revolutionise many working practices, particularly in biomedicine. The global DT market is expected to grow to nearly $50 billion by 2026. To stay competitive, European Research Infrastructures (RIs) need support to be able to integrate the deployment of DTs into the services they provide their communities, irrespective of their domains of interest. Our strategy in BioTwinXR is to empower RIs to do so, providing flexible DT tools for extended reality, validation, verification and uncertainty quantification, and infrastructure integration. BioTwinXR particularly targets the biomedical domain where an individualised DT may be used as a clinical decision support tool for that person in a specific healthcare scenario. We will develop a set of exemplars addressing disease cases pertaining to 75% of all deaths in Europe including cardiovascular disease, cancer, and respiratory infections. Successful deployment of these DTs is expected to lead to significant healthcare cost savings by diminution of ineffective treatments, improved therapies and lives saved through personalised interventions. Extended reality has a crucial role to play in enhancing the impact of these DTs by permitting doctors and patients to experience interactive and immersive engagement with the complex numerical models underpinning them. It will substantially increase the scope for education, training and therapy planning in the use of DTs. Our exemplars, being built on generic platforms, will highlight how DTs can be deployed by the far wider range of communities supported by RIs. Our efforts in dissemination and outreach will be aimed at a broad range of end-users, from RIs who wish to develop DT capabilities through industry, regulatory authorities and healthcare providers to patients and the general public to ensure human DTs meet user requirements and address concerns about reliability, usability and privacy.

Preludium 2024/53/N/NZ4/03513 

Title: Diffusion magnetic resonance imaging for accurate white matter tracking in peritumoral tissue to enhance surgical planning, precision, and prediction in neuro-oncology  

This project leverages diffusion MRI (dMRI) and tractography to map glioma-induced structural and connectivity changes in the brain. By combining imaging data with molecular markers, and applying advanced techniques such as spherical deconvolution, the project aims to improve fiber tracking in tumor-adjacent regions and provide deeper insights into tumor behavior. The ultimate goal is to develop reliable imaging biomarkers to support diagnosis, surgical planning, and understanding of glioma biology. 

Project Goals: 

  • Optimize preprocessing pipelines for large-scale dMRI datasets of glioma patients. 
  • Apply advanced tractography techniques, including spherical deconvolution, to improve reconstruction of white matter in peritumoral regions. 
  • Integrate imaging and molecular data to explore the relationship between tumor biology and structural brain connectivity. 
  • Support the development of imaging biomarkers to enhance glioma diagnosis, treatment planning, and research, especially when individual dMRI scans are not available. 

Duration:  16 January 2025 – 15 January 2027

Project lead: Joan Falco Roget (PhD Student in Computational Neuroscience)

Partners/ List of collaborators: 

  • The University of Messina 

Sonata 2023/51/D/NZ7/02596 

Title: “One step to game changer”: combined antimicrobial therapy based on AI and nanotechnology to combat challenging diabetes foot infections 

Antimicrobial resistance (AMR) poses a growing threat to public health, particularly in diabetic patients prone to severe infections such as diabetic foot infections (DFIs). DFIs can escalate rapidly, often leading to amputation or death, and are increasingly resistant to standard therapies. This interdisciplinary project aims to design and develop biocompatible antimicrobial nanoparticles (BANs) as an innovative solution for treating DFIs and combating AMR. Using AI-guided drug development, green-chemistry-based nanomaterials, and cutting-edge biological models—including patient-derived clinical isolates and human skin organoids—the project seeks to deliver effective and safe therapies. 

The work is a collaboration between the Łukasiewicz Research Network – Krakow Institute of Technology and Sano – Centre for Computational Personalised Medicine. 

Project Objectives: 

  1. Develop AI-powered drug selection tools: 
  • ComBiotic: Identifies synergistic antimicrobial combinations. 
  • CombiGuard: Predicts and evaluates potential side effects of drug interactions. 
  1. Create biocompatible nanocarriers: 
  • Design biopolysaccharide-based inclusion matrices (BIMs) to encapsulate AI-selected antimicrobials. 
  • Engineer self-organizing “Trojan horse” nanoparticles (BANs) that exploit bacteria’s increased sugar uptake in diabetic environments. 
  1. Evaluate biological effectiveness of BANs: 
  • Test antimicrobial efficacy in vitro against DFI-relevant pathogens (e.g., S. aureus, E. coli, P. aeruginosa). 
  • Assess cytotoxicity and selectivity using human keratinocyte models. 
  • Validate findings in vivo using mouse infection models and advanced human skin organoids to support personalized treatment development. 

Duration: 10 July 2024 –– 9 Jul 2027 

Project lead: Barbara Pucelik (Łukasiewicz Research Network – Krakow Institute of Technology)

Sano’s project mentor: Tomasz Kościółek (Research Team Leader of Structural and Functional Genomics Group) 

Partners/ List of collaborators: 

Łukasiewicz KIT 

Preludium 2023/49/N/ST6/04252

Title: Understanding the influence of variation in venous anatomy on local haemodynamics in patients with deep vein thrombosis of the lower limb: statistical shape modelling approach. 

This project aims to improve the understanding and prediction of post-thrombotic syndrome (PTS) following deep vein thrombosis (DVT) in the lower limbs by investigating how local venous anatomy influences blood flow patterns associated with thrombus formation. Using a combination of Computational Fluid Dynamics (CFD) and Statistical Shape Models (SSM), the research will analyze both idealized and patient-specific venous geometries derived from medical imaging (CT/MRI) to identify key anatomical features linked to abnormal haemodynamics, such as low wall shear stress and retrograde flow. 

The core objective is to determine whether SSMs can efficiently characterize shape-related risk factors and reduce the need for computationally expensive CFD simulations in clinical workflows. By applying sensitivity analysis and uncertainty quantification methods, the project will establish a framework to assess how anatomical variability influences haemodynamic metrics, ultimately supporting more accurate and cost-effective patient-specific diagnosis and intervention planning. This novel application of SSM-CFD integration to venous disease has strong potential to enhance clinical decision-making in vascular medicine. 

Project Goals:  

The influence of the diversity of vein anatomy on the local anatomy hemodynamics in patients with venous thrombosis  deep lower limb: statistical technician shape modeling. 

Duration:  29 January 2024 – 28 August 2025 

Project lead: Magdalena Otta (PhD Student in Extreme-scale Data and Computing)

Partners/ List of collaborators: 

  • Royal Free Hospital 
  • University of Sheffield 

Preludium 2023/49/N/ST6/01841 

Title: Multimodal Deep Learning for Noninvasive Pulmonary Hypertension Diagnosis from Magnetic Resonance Imaging 

Pulmonary hypertension (PH) is characterized by elevated pressure in the pulmonary artery and affects approximately 1% of adults. The standard diagnostic method—cardiac catheterization—is invasive, expensive, and carries clinical risk. The project focuses on developing non-invasive deep learning techniques for estimating mean pulmonary artery pressure (mPAP) using cardiac MRI data.A dataset comprising multimodal MRI recordings and corresponding clinical information, collected by the University of Sheffield and Sheffield Teaching Hospitals, will be used to train and evaluate predictive models. Initial efforts will involve convolutional and transformer-based architectures applied to single-modality data. In subsequent stages, multimodal learning strategies will be implemented by integrating diverse imaging data and structured patient information. To ensure clinical interpretability, existing explainability tools will be applied and extended.  

Project Objectives: 

  • Development of benchmark deep learning models for mPAP prediction based on MRI. 
  • Design of multimodal architectures combining various imaging modalities. 
  • Integration of tabular clinical data to enhance prediction accuracy. 
  • Implementation of explainability methods to support clinical interpretability. 
  • Establishment of foundations for fully non-invasive cardiac diagnostics. 

Project Goals: 

The project aims to enhance current approaches to PH diagnosis through the utilization of deep learning techniques. 

The work is a collaboration between the Warsaw University of Technology  and Sano – Centre for Computational Personalised Medicine. 

Duration:  17 January 2024 – 16 January 2027 

Project lead: Michał Grzeszczyk (PhD Student in Medical Imaging and Robotics)

Partners/ List of collaborators: 

  • Massachusetts General Hospital, Harvard Medical School 
  • Warsaw University of Technology  
  • University of Sheffield 
  • Sheffield Teaching Hospitals NHS Foundation Trust