When bioelectronic devices such as cochlear implants, bionic eyes, brain-machine interfaces, nerve block stimulators and cardiac pacemakers are implanted into the body they induce an inflammatory response that is difficult to control. Metals used historically for these types of devices (for instance platinum/iridium in cardiac pacemakers) are both stiff and inorganic. Consequently these implants are tolerated by the body rather than integrated and the device is often walled off in a scar tissue capsule. As a result high powered and unsafe currents are required to activate tissues and produce a therapeutic response. This limitation has prevented the development of high resolution bionic devices that can improve patient quality of life (for example by enabling improved perception of sound for cochlear implant users). This research programme will bring together concepts from tissue engineering, polymer design and bionic device technologies to develop soft and flexible polymer bioelectronics. A range of novel conductive biomaterials will be used to either coat conventional devices or fabricated as free-standing fully organic electrode arrays from conductive polymers (CPs), hydrogels, elastomers and native proteins. The electrode array stiffness will be matched to that of nerve tissue and the polymer components will be biofunctionalised to improve cell interactions, prevent rejection and minimise scar formation. Coating technologies will be assessed as a pathway to modifying existing commercial devices in collaboration with industry partners, Galvani Bioelectronics and Boston Scientific. Ultimately, the research programme will demonstrate safety and efficacy of polymeric electrode arrays using protocols defined by medical device regulatory bodies. Collaboration with industry partners will ensure that outcomes are relevant to the market and directly translatable while engaging key stakeholders. Polymer bioelectronics will be a ground breaking step towards safer neural cell stimulation, which is more compatible with tissue survival and regeneration. High resolution electrode arrays based on polymer technologies will create a paradigm shift in biomedical electrode design with tremendous impact on healthcare worldwide.

The UK is a global leader in health research and healthcare technology. It is one of the most important sectors in our economy, and the largest in terms of commercial expenditure on research and development. It is also critical to the future of our health service: we need new ways of diagnosing and treating illness, new ways of delivering care, and new ways of planning for and dealing with challenges such as the recent pandemic. To maintain this leading position, the UK needs more healthcare data scientists. It needs data scientists who can advance the state of the art in computer science, statistics, and engineering in support of health and healthcare transformation. These scientists need to have an excellent understanding of the application domain: that is, they need to understand the fundamental features of health data, how to manipulate and model it, draw conclusions from it, and explain the resulting insights to stakeholders. They need also to know how to behave responsibly and ethically. For example, the methods and tools that they produce, and the research that they conduct, should take proper account of the variation and diversity in our population. Above all, they need to know how to work effectively with people from different backgrounds: health professionals, health researchers from academia and industry, patients, and the public. The Oxford EPSRC CDT in Healthcare Data Science will provide the research training that turns talented science graduates into this kind of data scientist. Supporting the EPSRC strategic delivery plan in the research priority area of transforming health and healthcare, it will work in partnership with the NHS, with the UK Health Security Agency, and with a range of research groups and organisations in academia and industry, ensuring that students obtain the essential combination of scientific rigour and real-world experience. The training programme is cohort-based, meaning that students learn how to work together and support one another. This is essential feature: the challenges that we face can only be addressed through trust and collaboration. The programme is designed to be accessible to graduates in different subjects, and we will make efforts to ensure that the cohorts are diverse and representative of the UK. Our experience with the existing EPSRC CDT in Health Data Science shows that this approach works very well. Our students have developed new approaches using real data to solve important problems, and to deliver real benefit in terms of health and healthcare transformation.

Recent advances in surgery have made a significant impact on the management of major acute diseases, prolonging life and continuously pushing the boundaries of survival. Despite increasing sophistication of surgical intervention, complications remain common and poorly understood, contributing significantly to mortality and morbidity. Surgical site infections, catheter related sepsis, wound dehiscence and gastrointestinal anastomotic leakage are recognised complications following surgical interventions or invasive monitoring of critically ill surgical patients. Current methods for detecting these complications rely on episodic clinical examination with 'snap shot' laboratory testing. There is therefore a pressing need to develop new sensing technologies that can be seamlessly integrated with existing surgical appliances to provide continuous sensing and early detection of these adverse events, thus minimising post-operative infection, complication, and readmission. All these will also have a direct impact on healthcare economics, and more importantly the prognosis and quality-of-life of patients after surgery. The proposed project is organised into three research themes: 1) Multimodal Sensing and Miniaturised Embodiment; 2) Active Sensing with Low Power Microelectronics; and 3) Data Inferencing and Stratified Patient Management. These research themes address key technical issues related to sensor design, miniaturisation, and self-calibration, as well as low-power on-node processing, inferencing, and clinical decision support. These research themes are connected by three clinical exemplars in surgical sensing with increasing levels of technical complexity. The vision is to develop smart sensors integrated with surgical appliances and to be inserted in close proximity to the surgical site, encased within surgical drains/catheters, or placed in locations to more seamlessly monitor the systemic inflammatory response. The devices will be implanted during elective surgery or at biopsy, interrogated wirelessly, and eliminated by natural processes, or routine removal of 'hosts' such as the drains or catheters. The research programme is underpinned by extensive experience of the team in body sensor networks and bio-photonics in healthcare. Through an integrated programme of engineering research and development of a novel real-time active sensing paradigm, the project aims to transform the care pathways for surgery with greater consideration on personalised treatment, system level impact, real-time response to complications, patient concordance and quality of life. We expect that the outcome of the research will help improve surgical workflows, support safe discharge and home/community-based recovery, reduce unplanned readmissions, and influence the future of healthcare policy.

The 2019 World Health Organisation (WHO) report on Antimicrobial Resistance (AMR) identifies it as: "one of the greatest threats we face as a global community." The evolution of drug-resistant bacteria, our over-use of antibiotics and failure to develop new methods for tackling infection could leave us without viable treatments for even the most trivial infections within the next 3 decades. There have been significant efforts by the WHO and national agencies to raise awareness of AMR and reduce the use of antibiotics, but there is still an urgent need to intensify these efforts and, crucially, to develop alternatives. The aim of the programme is to address this need. The programme will consist of 4 interlinked work packages focussed on the core research objectives: (1) The development of human organoid models for studying interactions between bacteria and the tissue microenvironment and larger scale interactions with the host microbiome. (2) New microscopy methods to complement the organoid models and to facilitate rapid characterisation of bacteria for improved diagnosis. (3) New antimicrobial therapeutics and targeted delivery techniques to improve the use of existing antibiotics and provide viable alternatives. (4) "Drug-free" methods for treating infections and promoting immune function thereby further reducing the use of antibiotics and providing methods suitable for large scale environmental/industrial use. These will be supported by 2 parallel translational activities to enable development of the translational pathway and wider engagement with clinical and industrial stakeholders, policy-makers and the public.

There have recently been impressive developments in the mathematical modelling of physiological processes. As part of a previously EPSRC-funded research centre (SofTMech), we have developed mathematical models for the mechanical and electrophysiological processes of the heart, and the flow in the blood vessel network. This allows us to gain deeper insight into the state of a variety of serious cardiovascular diseases, like hypoxia (a condition in which a region of the body is deprived of adequate oxygen supply), angina (reduced blood flow to the heart), pulmonary hypertension (high blood pressure in the lungs) and myocardial infarction (heart attack). A more recent extension of this work to modelling blood flow in the eye also provides novel indicators to assess the degree of traumatic brain injury. What all these models have in common is a complex mathematical description of the physiological processes in terms of differential equations that depend on various material parameters, related e.g. to the stiffness of the blood vessels or the contractility of the muscle fibres. While knowledge of these parameters would be of substantial benefit to the clinical practitioner to help them improve their diagnosis of the disease status, most of the parameters cannot be measured in vivo, i.e. in a living patient. For instance, the determination of the stiffness and contractility of the cardiac tissue would require the extraction of the heart from a patient and its inspection in a laboratory, which can only be done in a post mortem autopsy. It is here that our mathematical models reveal their diagnostic potential. Our equations of the mechanical processes in the heart predict the movement of the heart muscle and how its deformations change in time. These movements can also be observed with magnetic resonance image (MRI) scans, and they depend on the physiological parameters. We can thus compare the predictions from our model with the patterns found in the MRI scans, and search for the parameters that provide the best agreement. In a previous proof-of-concept study we have demonstrated that the physiological parameters identified in this way lead to an improved understanding of the cardiac disease status, which is important for deciding on appropriate treatment options. Unfortunately, the calibration procedure described above faces enormous computational costs. We typically have a large number of physiological parameters, and an exhaustive search in a high-dimensional parameter space is a challenging problem. In addition, every time we change the parameters, our mathematical equations need to be solved again. This requires the application of complex numerical procedures, which take several minutes to converge. The consequence is that even with a high-performance computer, it takes several weeks to determine the physiological parameters in the way described above. It therefore appears that despite their enormous potential, state of the art mathematical modelling techniques can never be practically applied in the clinical practice, where diagnosis and decisions on alternative treatment option have to be made in real time. Addressing this difficulty is the objective of our proposed research. The idea is to approximate the computationally expensive mathematical model by a computationally cheap surrogate model called an emulator. To create this emulator, we cover the parameter space with an appropriate design, solve the mathematical equations in parallel numerically for the chosen parameters, and then fit a non-linear statistical regression model to this training set. After this initial computational investment, the emulator thus created gives predictions for new parameter values practically instantaneously, allowing us to carry out the calibration procedure described above in real time. This will open the doors to harnessing the diagnostic potential of state-of-the art mathematical models for improved decision support in the clinic.