Applied Mathematics and Statistics are routinely seen as separate disciplines. However, many of the methodological challenges in image analysis, particularly from the types of multiscale multimodal images available from Neurological, Cardiovascular and Oncology imaging, illustrate that a combined approach, dissolving intradisciplinary mathematical boundaries, is the only possible way forward if a step-change in image analysis of combined data is to occur. This Centre will foster links between applied maths and statistics, particularly high dimensional and functional statistical analysis with applied and computational numerical analysis, through the focus on multimodal imaging data. The Centre proposes to provide a research focus on bringing state-of-the-art mathematical tools to clinical end users through collaborations both within mathematics and between mathematics and healthcare professionals, particularly those in oncology, cardiovascular medicine and neurology. This will ultimately lead to new mathematical frontiers, joining statistics and computational mathematics, as well as a move away from individual image analysis to a holistic approach to all available imaging, from the cellular to systems scale, for clinical diagnosis, prognosis and treatment planning.

This project brings together unique expertise in Computational and Experimental Fluid Dynamics, Model Reduction and Artificial Intelligence, to identify solutions for the management of people and spaces in the current pandemic and post lockdown. A new interactive tool is proposed that evaluates the risk of infection in the indoor environment from droplets and aerosols generated when breathing, talking, coughing and sneezing. This capability will become more critical as winter approaches and building ventilation will need to be limited for comfort considerations. The fluid dynamic behaviour of droplets and aerosols, the effect of using face masks as well as other parameters such as room volume, ventilation and number of occupants are considered. A datahub capable of storing, curating and managing heterogeneous data from sources internal and external to the project will be created. A synergetic experimental and numerical approach will be undertaken. These will complement the existing literature and data from other EPSRC-funded projects providing suitable datasets with adequate resolution in time and space for all the relevant features. To support experiments and numerical simulations, reduced order models capable of interpolating and extrapolating the scenarios collected in the database will be used. This will permit the estimation of droplet and aerosol concentrations and distributions in unknown scenarios at low-computational cost, in near real-time. A state-of-the-art AI-based framework, incorporating descriptive, predictive and prescriptive techniques will extract the knowledge from the data and drive the decision-making process and provide in near real-time the assessment of risk levels.

One in 17 people have a rare disease. Rare diseases can be extremely difficult to diagnose, but they often have an unidentified genetic cause. Recent advances in clinical imaging, pathology, and genomic technologies have led to remarkable progress in understanding disease - particularly rare diseases. However, the power of these technologies cannot be fully realised until the immense volume of data generated can be integrated with NHS data, then analysed by researchers in a secure environment that protects the privacy of individuals. Working across the NHS, academia and industry we will use existing tools to transfer data from NHS Trusts to a secure environment that interfaces with the NHS network and shares data with Public Health England. NHS information will then be combined with research data in a cloud-based platform. Initially, we will involve patients with rare diseases recruited to the NIHR BioResource; a national resource of volunteers who have already provided consent that information retrieved from their health records can be used for medical research. This will create a rich research resource with the potential to transform our understanding of rare genetic disorders, drive improvements in diagnosis and management, and provide proof of principle for use in other diseases.

Osteoarthritis (OA) is an incurable disease that has evaded pharmacological interference, biologic therapy or surgical intervention to prevent disease progression. Currently, OA is designated the 11th highest contributor (of 291 diseases) of global disability. In the absence of effective treatment options, cellular therapies using mesenchymal stem/stromal cells (MSCs) have emerged as potential candidates to overcome this clinical short-coming. Autologous adipose-derived mesenchymal stromal cells (ASCs) are attractive for cellular therapy given the abundance of tissue, high frequency of MSCs and minimally invasive harvest procedure. The EU consortium ADIPOA has shown in a ‘first in man’ 2-centre Phase I safety study that intraarticular injection of a single dose of autologous ASCs to the knee (18 patients, 12 month follow-up) was well-tolerated, had no adverse effects, and resulted in an improvement in pain score and functional outcome. ADIPOA2 will deliver a large-scale clinical trial in regenerative medicine for OA. The purpose of the project is to design and implement a phase IIb study to assess the safety and efficacy of autologous (patient-derived) ACSs in the treatment of advanced OA of the knee. The cells will be prepared from samples of adipose tissue harvested from patients by lipoaspiration. ADIPOA2 will comprise a multi-centre, randomized clinical trial comparing culture-expanded, autologous adult ASCs in subjects with knee OA with another widely used therapeutic approach for knee degeneration (injection of Hyaluronan). There are two large elements of the study: (1) the production of consistent batches of high-quality autologous ASCs under GMP-compliant conditions and (2) delivery of these cell doses to patients in a trial which will meet all national and European regulatory and ethical standards and which will have sufficient statistical power to provide an unambiguous and definitive assessment of safety and efficacy.