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ANCA vasculitis is a life-threatening disease where the body's defence system becomes overactive, causing inflammation of small blood vessels and damage to the kidneys, lungs and other vital organs. New treatments are needed because: 1) 20% of patients do not respond to treatment. 2) In half the patients who respond, vasculitis disease activity flares when medicines are reduced. 3) The side effects of medicines can cause more problems than the disease. Safer medicines are needed for non-life threatening ANCA vasculitis, where aggressive immune suppressing treatment is not appropriate. Hydroxychloroquine has been used for many years to treat related diseases such as lupus and rheumatoid arthritis with fewer harmful effects than conventional immune suppressing drugs. It is therefore an ideal candidate to treat ANCA vasculitis. We have preliminary data from our clinics and research laboratories showing that hydroxychloroquine has effects on cells which cause inflammation of blood vessels and tissues and is therefore expected to work in treating ANCA vasculitis. We have pilot data on 30 patients with ANCA vasculitis, showing that hydroxychloroquine is well tolerated and improves symptoms of disease activity. This now needs to be confirmed in a controlled study. The potential of hydroxychloroquine to reduce infections, cancer, blood clots and heart disease are useful properties, as these risks are higher in ANCA vasculitis patients compared to the general public. To our knowledge, no studies have been published on hydroxychloroquine in ANCA vasculitis, although the benefits of hydroxychloroquine in related diseases such as lupus and rheumatoid arthritis are clear. Our proposed study aims to demonstrate that hydroxychloroquine reduces disease activity and flares, the need for steroids and their side effects, damage to vital organs and improves quality of life. 76 patients with non-severe ANCA vasculitis who meet the entry criteria for the trial will be invited to participate. After giving their informed consent, patients will be allocated by chance (like tossing a coin) by a computer to receive 2 tablets of hydroxychloroquine in addition to their usual medications (38 patients) or two placebo ("dummy") tablets (38 patients) and their usual medications. Treatment will continue for 1 year and, in order to improve the quality of the study, neither the doctors nor the patients will know if they are taking hydroxychloroquine or placebo. Patients will be seen and have blood tests and safety assessments monthly for the first and last 3 months of the study and every 3 months in between for 1 year. We plan to measure blood levels of hydroxychloroquine to assess adherence to the study medication after the study has been completed. This information will also allow correlations with the response of the disease to treatment. If the trial shows a small beneficial effect that is not statistically significant, but provides evidence that hydroxychloroquine could work, we will proceed to a larger study with more patients (Phase III trial). However, if our study shows a large beneficial effect of hydroxychloroquine, similar to that seen in lupus patients, a larger trial would not be needed and hydroxychloroquine could become part of the standard treatment for ANCA vasculitis patients. This would represent a significant advance in the care of these patients. Given that hydroxychloroquine is a relatively inexpensive treatment, the overall cost to the health service could be reduced.

Our plan is to reduce poor pregnancy outcomes by creating a revolutionary way to assess and analyse the pregnancy environment. Specifically, we will establish specialised MRI (which is non-invasive and emits no ionising radiation) techniques focusing on women with high-risk pregnancies, which will enable personalised care and individualised treatments for mothers and babies. The placenta (or afterbirth) is vital for a successful pregnancy, and most major pregnancy complications are associated with placental problems. Ultrasound is currently the main way to monitor pregnancies, however it has limitations: the whole placenta can't be seen at once, findings vary depending on who is performing it, and it cannot tell us how the placenta is functioning, only what it looks like. Ultrasound images can also be poor due to the baby's position and factors such as the mother's weight. We will use key features of MRI to enable a more detailed assessment of placental health - information on how the placenta is working and a more detailed picture of its structure. Although we can see clear changes in placental MRI scans for a number of important pregnancy complications, such as fetal growth restriction and pre-eclampsia, the specific changes in the placenta that affect the MRI signal are yet to be determined, not least because the relationship between the placenta's structure and how well it functions is complex. Techniques for detecting subtle placental changes would enable early and accurate diagnosis of pregnancy complications. In this project, we will develop such a technique by combining MRI with computational modelling to give valuable insight into how well the placenta is working. First of all, we will build highly detailed computational models of whole placentas, incorporating fine structural details such as tissue and blood vessel dimensions. Next we will develop tools that can predict blood flow and oxygen levels in these computational placentas. We then use simulations to predict what MRI scans would look like for our computational placentas. Finally, using machine learning techniques, we will instruct an algorithm to learn from thousands of simulations on computational placentas, in order to deliver an imaging tool that, given a placental MRI scan as input, can show the structure, blood flow, and oxygen levels of the placenta. By using this MRI-based tool to investigate pregnancies with complications, we will test its ability to identify cases where the placenta is not working properly. We will do this by comparing our tool with a post-pregnancy assessment of the delivered placenta by a specialised pathologist (many placental conditions, such as abnormal structure or poor blood supply, can currently only be diagnosed in this way). For multiple pregnant participants, we will use our MRI-based tool during the pregnancy to calculate values relating to placental structure and blood flow, then undertake an after delivery assessment of the placenta. This will allow us to test if our MRI-based tool can tell if the placenta is not working during pregnancy without having to wait for an after delivery assessment by a specialist pathologist. We will first explore ways of determining if problems with the placenta are caused by issues with the supply of maternal or fetal blood. This would be extremely important in clinical practice and in the future may allow focused treatment of the mother to reduce poor outcomes. Showing differences between maternal and fetal blood supply problems is just one example of a diagnosis that cannot be during the pregnancy at present, and we use it here to demonstrate the potential of our model-driven placenta imaging tools in a directly impactful application. Further potential conditions that could be identified with the developed tools include damage to fetal villous trees (this is the detailed structure of the placenta), and levels of oxygen supply.

With each heart beat a wave of electrical activation sweeps across the heart stimulating the muscles to contract. In the healthy heart the wave is initiated from many locations across the wall and rapidly activates the whole heart leading to a synchronous, efficient and effective pumping of blood around the body. In patients suffering dyssynchronous heart failure the activation wave starts on the right hand side of the heart and slowly progresses to the left hand side of the heart. This asynchronous activation pattern causes an asynchronous, inefficient and ineffective pumping of blood. To treat these patients a pacing device is implanted with leads attached to the left and right hand side of the heart. By activating the left and right side of the heart from these two leads the patient's activation pattern can be resynchronised leading to a synchronous and effective contraction. This treatment is referred to as cardiac resynchronisation therapy or CRT. CRT is an effective treatment in most patients but 30-50% of patients fail to improve or respond to treatment. Due to the invasive nature and cost of the procedure it is undesirable to treat patients who will not respond. Identifying the patients who cannot respond is currently obfuscated by the inability to guarantee optimal treatment in all cases. Hence it is not possible to differentiate from patients that did not respond as they did not receive the optimal treatment from those that were unable to benefit from CRT under any conditions. At present guidelines suggest a "one size fits all" approach to the location of the leads on the patient's heart despite significant evidence that the location of the leads plays a critical role in determining outcome. This indicates that some patients may respond to CRT but only if they receive optimal lead placement. The aim of this project is to determine the best location to place the pacing lead on the left side of the heart in each individual patient receiving CRT, based on the physiology and pathology of the specific patient's heart. To achieve this aim we propose to use advanced high fidelity and resolution imaging techniques to characterise the shape of the patient's heart, the potential pacing locations, and the location of any dead non-conducting tissue in the heart. We will combine this anatomical information with measurements of electrical activation time to create a biophysical model of the electrical properties of the individual patient's heart. Using the model we will be able to simulate the activation patterns in the patient's heart for each potential pacing location. In a training data set we will compare the activation patterns at each pacing location with measured pump function, in response to pacing, to identify the activation pattern that best predicts the optimal pacing location. A prospective clinical study will then be performed where patient specific models will be created for each patient prior to procedure and the optimal pacing site identified. The predictive capacity of the model will then be evaluated when the device is implanted by testing if the model has correctly predicted the optimal pacing location. The project represents a significant advance for patient specific models - moving from a technique for analysing patient data to a tool for guiding patient treatment. Improving outcomes for CRT patients will reduce morbidity and hospitalisation rates, decrease the financial burden of non-responding patients on the NHS and improve our ability to identify what characteristics determine if a patient will respond to treatment.

The EPSRC-NIHR Healthcare Technology Co-operatives Partnership Network in Medical Image Analysis seeks to bring leading UK researchers in medical image analysis together to identify new opportunities for medical image analysis methodology research within the clinical areas of high morbidity/unmet need of the NIHR Cardiovascular HTC and HTC in Colorectal Therapies. The HTCs are working with patient groups and clinicians on identifying unmet clinical needs in these areas. An aim of the new Medical Image Analysis Network is to bring together imaging scientists with different skills sets and experiences to consider how technological advances in how images are acquired and images (and associated information) are analysed can be applied to solve some of these problems. A purpose of the Network is to encourage new collaborations to be set up between academic healthcare technologists, clinicians and industry partners to develop and evaluate new solutions. We anticipate that Network members will jointly work on some of the hardest medical image analysis problems there are today. The hope is that the resulting research will lead to new image-based biomarkers which allow earlier detection of disease, better stratification of disease so that the most appropriate therapy can be selected for a patient, and new image-based quantitative tools to increase the success of interventions and therapies and improve the overall well-being of patients with these conditions. How will the Network achieve this goal? The Network will aim to encourage the development of new collaborations through workshops, joint meetings with other networks/organisations, small scale feasibility studies, Clinical Readiness events and Challenge competitions which will be open to the broader imaging research community. The hub for promotion of Network activities, dissemination of network outputs, and recruitment of new members (particularly from academia and industry not already involved in the HTCs) will be the Network website which will also act as an information source for imaging researchers interested in these clinical areas.