This is an interdisciplinary project that looks at the past, present, and future of the senses in healthcare settings. It is concerned with sensory experience, sensory design and how the two have come together over time. Ultimately the project aims better to understand sensory experiences in hospitals in order to improve them for everyone who spends time there. It also aims to raise awareness of the importance and value of thinking in multi-sensory terms - beyond the visual - for everyone from medical historians to hospital architects. This project still has the NHS hospital at its centre, but in the continuation period it will also think more expansively about how its findings and methods can be used in international contexts. The first stage of 'Sensing Spaces of Healthcare: Rethinking the NHS Hospital' combined historical and creative research to better understand NHS hospitals from a multi-sensory angle (see 'Objectives' for the original objectives of this phase). This work included identifying challenges and opportunities in NHS hospitals. The PI's work has focused on the 'big picture' of particular challenges in the NHS and how they have been addressed over time, for example through publications on the history of 'noise' in modern British hospitals. The project RA has focused on site-specific creative research to understand current-day sensory experiences; she is working with a children's hospital outpatient's unit for young people with sight and/or hearing loss, and a 'maternity' department. Together, the PI and RA have developed new methodologies for exploring sensory memory and sensory experience in healthcare settings. These have included working with methods from line-drawing to explore sound in hospital 'atmospheres', to mapping and clay-based activities to explore questions around spatiality and healthcare touch respectively. In addition to traditional academic outputs, we developed two toolkits: (1) A creative research toolkit (by the project RA) and (2) a 'how to' guide for good sensory design (as part of a working group including architects, academics, and hospital arts coordinators). In 2023 we will hold a project exhibition and develop prototype responses to our research findings, working with designers to respond to key themes identified in the research. From 2024 onwards, as part of the three-year renewal period, we will maximise the impact potential of our work to date, continue to develop new, novel methodologies, and generate new knowledge. Firstly, we will evaluate the work to date, including reflecting on the impact of our work, and the success or potential of our prototypes, at the two partner sites (Great Ormond Street Hospital and Southmead Hospital). We will also explore ways to 'scale up' the above research methods so that they can be used across the UK and internationally. The two draft 'toolkits' we have written will be tested with different potential beneficiaries - including internationally - and developed with a focus on inclusivity and accessibility. We also pose new research questions at this stage, which have emerged from and build on the work to date. We will seek newly to understand sensory memories of hospitals (led by the PI), adapting the creative methods used in years 1-4 to explore sensory memory. We will also ask new questions about neurodivergent experiences of healthcare settings, through a co-produced film with autistic staff, patients and visitors at East London Foundation Trust. The RA will newly focus on mechanisms for improving sensory design in healthcare settings, a process that involves not only sharing and adapting the toolkit, but also conducting research with potential beneficiaries to understand design processes and potential 'roadblocks' to good sensory design in hospitals. Throughout this renewal period, the PI and RA will continue to develop their leadership skills and independent research skills in order to meet the career goals of the FLF scheme.

Bacterial infections have great public health and economic impact. While at present most can be treated with antibiotics, doing so requires cases of bacterial infections to be recognised early so that they can be treated with the right drugs, while ensuring that antibiotics are not given unnecessarily. With the growth in antibiotic resistance, it is becoming essential that we use these drugs appropriately. At present growth of organisms from patient samples (e.g. urine), a process which takes 18 hours or more, is usually required before specific infecting bacteria can identified. A device able to rapidly detect the presence of bacteria in such samples, and identify which species are present, without this growth step would enable doctors to make rapid and informed decisions about when antibiotic treatment is necessary and which drug should be used. Here we propose to develop and evaluate a technology for identifying bacteria in patient samples. We will combine a novel series of chemical probes (fluorescent carbon dots, FCDs) that can attach to bacteria to make them fluorescent, with an ultra-sensitive quantum photonic sensor (QPS) developed by our industrial partner, FluoretiQ Ltd., that is able to detect these fluorescent bacteria in patient samples. In order to identify individual species of bacteria we will attach specific sugars (glycans) to the surface of FCDs, exploiting the fact that different bacteria recognise particular sugar molecules as part of the process of binding to the cells of their host. We base our trials around E coli bacteria causing urinary tract infections as these are common conditions that create high workloads for NHS laboratories (our clinical partner processes up to 1000 urine samples per day) and if improperly treated can lead to severe conditions such as sepsis. We will test this methodology by assessing in the laboratory whether specific bacteria can bind to specific glycan-FCDs. A second series of laboratory experiments will then seek to replicate patient samples by suspending bacteria derived from patients, and cultured human cells, in liquid media designed to mimic the composition of human urine and testing whether glycan-FCDs bind bacteria under these conditions. Finally, with support from clinical microbiologists, we will test whether the glycan-FCD/QPS method can detect and identify bacteria in urine samples from human patients and evaluate its effectiveness compared to methods currently in use. As future users they will also help us to optimise the method and associated instrumentation to ensure that this can be used easily in the clinical laboratory, and provide guidance on how to ensure that our method can be validated against appropriate comparators and demonstrated to comply with NHS quality management systems. In parallel we will test whether glycan-FCDs can be used as the basis for new treatments for bacterial infections. We have already demonstrated that FCDs can bind to and enter bacteria; preliminary experiments show that they can also kill bacteria, in a light-dependent process. Hence we will investigate whether our modified glycan-FCDs retain the ability to kill bacteria, and whether this killing is specific to the species targeted by the particular surface sugar. We will also attach antibiotics to the surface of FCDs to test whether this represents a method to deliver drugs to specific bacteria, many of which are difficult to kill with antibiotics because the drug is unable to enter the bacterial cell. The project will establish whether glycan-FCDs can form the basis of a rapid method for detecting infecting bacteria in patient samples in the clinical microbiology laboratory, and whether these can also be used to improve the effectiveness of antibiotics against many of these organisms. In so doing we will also develop new methods for synthesising complex sugar molecules that may be applied in multiple other research areas including drug and vaccine development.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Breast cancer is the commonest cause of death in women between the ages of 35 and 55 in Europe. Worldwide, a woman will die from the disease every 13 minutes. Breast cancer is very much a survivable disease however it is vital that the tumour is caught at an early stage. This requires a national screening programme for all women (in addition to regular self-examination by women of their breasts). Unfortunately the existing screening techniques are not very ideal. X-ray for example, is only suitable for older women and is also quite uncomfortable. Even in these older (post-menopausal) women, it has quite high false-positive rates (resulting in women having unnecessary biopsies) and false-negative rates (in other words, it misses some tumours). There is no suitable routine screening technique available for younger women. The aim of this proposal is to continue research into a new imaging method based on UWB radar. This sends out a short burst of radio-waves into the breast and "listens" for reflections - these radio-waves are completely harmless and the imaging procedure is quick and comfortable. At the moment this new imaging technique is in its infancy and much work remains to be done if we are to reach the ultimate goal of a cheap, quick and comfortable breast imaging method for all women. Because the imaging method is harmless, it could be repeated as often as necessary and because it will be very cheap, it could be based in a GP surgery or even a van, rather than requiring a visit to hospital.

Our Hub brings together a team of mathematicians, statisticians and clinicians with a range of industrial partners, patients and other stakeholders to focus on the development of new quantitative methods for applications to diagnosing and managing long-term health conditions such as diabetes and psychosis and combating antimicrobial infections such as sepsis and bronchiectasis. This approach is underpinned by the world-leading expertise in diabetes, microbial communities, medical mycology and mental health concentrated at the University of Exeter. It uses the breadth of theoretical and methodological expertise of the Hub's team to give innovative approaches to both research and translational aspects. Although quantitative modelling is a well-established tool used in the fields of economics and finance, cutting-edge quantitative analysis has only recently become possible in health care. However, up to now it has been restricted to health economics in the context of healthcare services and systems management. Applications to develop future therapies, optimising treatments and improving community health and care are in its infancy. This is due to a number of challenges from both mathematical (methodological) as well as clinical and patients' perspectives. Our Hub approach will allow us to develop novel statistical and mathematical methodologies of relevance to our clinical and industrial partners, informed by relevant patient groups. Building this new generation of quantitative models requires that we advance our mathematical understanding of the effective network interaction and emergent patterns of health and disease. Clinical translation of mathematical and statistical advances necessitates that we further develop robust uncertainty quantification methodology for novel therapy, treatment or intervention prediction and evaluation. NHS long-term planning aspires to deliver healthcare that is more personalised and patient centred, more focused on prevention, and more likely to be delivered in the community, out of hospital. Our Hub will contribute to this through developing mathematical and statistical tools needed to inform clinical decision making on a patient-by-patient basis. The basis of this approach is quantitative patient-specific mathematical models, the parameters of which are determined directly from individual patient's data. As an example of this, our recent research in the field of mental health has revealed that movement signatures could be used to distinguish between healthy subjects and patients with schizophrenia. This hypothesis was tested in a cohort of people with schizophrenia and we developed a quantitative analysis pipe line allowing for classification of individuals as healthy or patients. The features used for classification involving data-driven models of individual movement properties as well as measures of coordination with a virtual partner were proposed as a novel biomarker of social phobias. To validate this in an NHS setting, we have recently carried out a feasibility study in collaboration with the early intervention for psychosis teams in Devon Partnership Mental Health Trust. The success of this study could significantly advance the early detection of psychosis by enabling diagnosis using novel markers that are easily measured and analysed and improve accuracy of diagnosis. Indeed, personalised quantitative models hold the promise for transforming prognosis, diagnosis and treatment of a wide range of clinical conditions. For example, in diabetes where a range of treatment options exist, identifying the optimal medication, and the pattern of its delivery, based upon the profile of the individual will enable us to maximise efficacy, whilst minimising unwanted side effects.