Additive manufacturing (AM), otherwise known as 3D printing, is enabling the production of medical implants that are customised, in terms of size and shape, to a person's skeleton. Compared with devices of a standard size, these personalised designs fit the patient better and as such offer improved aesthetics and reduce surgery times. While customisation has many benefits, the challenge is to ensure each bespoke device is made to the same quality. This is difficult because the implant shape is completely unique and may be very complex. Currently in an effort to ensure quality, researchers make lots of plain cube test samples using various manufacturing settings and then compare properties before deciding what combination to use for the real implant. This trial and error approach takes a lot of time and may not even produce very predictable devices because the optimisation is not performed on shapes that are representative of real implants. In this project we will make various design features common to medical implants (e.g. curved surfaces, screw holes) and collect key performance data during and post manufacture. By using cutting edge mathematics, we will create a network that allows us to accurately predict which manufacturing settings will produce the best quality for any design shape. This tool will help businesses to standardise production of customised medical devices in a quick and accurate manner that is not dependent on the user's knowledge. Thereby we will open up the advantages of AM to more companies and help existing adopters to meet the standardisation requirements of the impending new Medical Device Regulations. Overall this project aims to better understand the relationships between additive manufacturing settings and implant properties, which will help us to improve the quality of these anatomically personalised devices. Beyond this we plan to create a tool to enable the creation of implants that are not only customised to the size and shape of the patient's skeleton but also two critical functionalities: mechanical strength and cell adhesion. It is known that if an implant is too strong compared with the surrounding native bone this can cause it to fail. As such, developing a way to select manufacturing or design parameters that enable mechanical matching to the patient's skeleton will help implants to last longer and reduce the number of failures. Besides mechanical mismatch, the other biggest threat to bone implants is infection. Our preliminary work has shown that surface roughness directly impacts the ability of cells, mammalian and bacterial, to stick onto AM devices. In this project we will exploit this knowledge to enable users to select manufacturing settings that result in a defined surface roughness that either enables or prevents cell attachment. This novel capability could be used, for example to create implants with a surface that stops bacterial cells from sticking and thus minimises infection risks. There is also potential that this tool could help to improve bonding between the implant and native tissue by recommending manufacturing settings that result in surface topographies that encourage growth of bone forming osteoblast cells. In summary, this project is focused on standardising the way we use 3D printing to ensure the properties of bespoke implants are predictable. This will be achieved by using mathematics to move the AM field away from trial and error. By understanding the relationships between manufacturing settings and key properties, we will create two tools that will enable us to make functionally personalised devices. The ability to predictively and selectively tailor mechanical properties and surface roughness will drive a new generation of implants that last longer and fail less often. Thereby, this project will ultimately improve the lives of millions of people who receive bone implants and help to reduce the associated healthcare costs.

Our Centre brings together a world leading team of mathematicians, statisticians and clinicians with a range of industrial partners, patients and other stakeholders to focus on the development of new methods for managing and treating chronic health conditions using predictive mathematical models. This unique approach is underpinned by the expertise and breadth of experience of the Centre's team and innovative approaches to both the research and translational aspects. At present, many chronic disorders are diagnosed and managed based upon easily identifiable phenomena in clinically collected data. For example, features of the electrical activity of the heart of brain are used to diagnose arrhythmias and epilepsy. Sampling hormone levels in the blood is used for a range of endocrine conditions, and psychological testing is used in dementia and schizophrenia. However, it is becoming increasingly understood that these clinical observables are not static, but rather a reflection of a highly dynamic and evolving system at a single snapshot in time. The qualitative nature of these criteria, combined with observational data which is incomplete and changes over time, results in the potential for non-optimal decision-making. As our population ages, the number of people living with a chronic disorder is forecast to rise dramatically, increasing an already unsustainable financial burden of healthcare costs on society and potentially a substantial reduction in quality of life for the many affected individuals. Critical to averting this are early and accurate diagnoses, optimal use of available medications, as well as new methods of surgery. Our Centre will facilitate these through developing mathematical and statistical tools necessary to inform clinical decision making on a patient-by-patient basis. The basis of this approach is patient-specific mathematical models, the parameters of which are determined directly from clinical data obtained from the patient. As an example of this, our recent research in the field of epilepsy has revealed that seizures may emerge from the interplay between the activity in specific regions of the brain, and the network structures formed between those regions. This hypothesis has been tested in a cohort of people with epilepsy and we identified differences in their brain networks, compared to healthy volunteers. Mathematical analysis of these networks demonstrated that they had a significantly increased propensity to generate seizures, in silico, which we proposed as a novel biomarker of epilepsy. To validate this, an early phase clinical trial at King's Health Partners in London has recently commenced, the success of which could ultimately lead to a revolution in diagnosis of epilepsy by enabling diagnosis from markers that are present even in the absence of seizures; reducing time spent in clinic and increasing accuracy of diagnosis. Indeed it may even make diagnosis in the GP clinic a reality. However, epilepsy is just the tip of the iceberg! Patient-specific mathematical models have the potential to revolutionise a wide range of clinical conditions. For example, early diagnosis of dementia could enable much more effective use of existing medication and result in enhanced quality and quantity of life for millions of people. For other conditions, such as cortisolism and 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.

E-textiles are advanced textiles that include electronic functionality such as conductive tracks to sensing/actuating, communications and microprocessing. The emergence of advanced e-textiles offers opportunities for the self-management of health conditions with tangible benefits for the individual and healthcare providers. E-textiles can be used in many healthcare applications such as health monitoring (e.g. electrocardiogram (ECG) and Electroencephalography (EEG)) and treatment (e.g. pain relief, rehabilitation). The applicant and her team have developed a novel platform manufacturing method that enables the packaging of electronic components (e.g. microcontrollers, sensors) in ultra-thin die form that can be hidden within textile yarns. The team has also developed a patented dry fabric electrode technology using novel materials and fabrication methods for wearable medical devices offering the competitive advantages of comfort (no gel needed), ease of use, unobtrusive implementation, and being washable. The combination of these two technologies will enable wearable healthcare with improved user experience (e.g. comfort, unobtrusive, independent use) and improved compliance with treatment requirements. The project will enable the Fellowship applicant to lead a multi-disciplinary team to address the fundamental underlying research challenges of integration and durability to enable the e-textile technology to progress from the research laboratory towards real world applications and improve options for healthcare provision. The application of the advanced e-textile technology will be demonstrated through a wearable therapeutic clothing item for pain relief of osteoarthritis which is an age related disease affecting 8.75 million people in the UK. The collaboration with industrial partners and the engagement with project advisors (e.g. healthcare professionals and patient and public involvement representatives), end users and other key stakeholders will ensure industry/clinical relevance and establish collaborations for the follow on exploitation of the technology.

Our Centre brings together a world leading team of mathematicians, statisticians and clinicians with a range of industrial partners, patients and other stakeholders to focus on the development of new methods for managing and treating chronic health conditions using predictive mathematical models. This unique approach is underpinned by the expertise and breadth of experience of the Centre's team and innovative approaches to both the research and translational aspects. At present, many chronic disorders are diagnosed and managed based upon easily identifiable phenomena in clinically collected data. For example, features of the electrical activity of the heart of brain are used to diagnose arrhythmias and epilepsy. Sampling hormone levels in the blood is used for a range of endocrine conditions, and psychological testing is used in dementia and schizophrenia. However, it is becoming increasingly understood that these clinical observables are not static, but rather a reflection of a highly dynamic and evolving system at a single snapshot in time. The qualitative nature of these criteria, combined with observational data which is incomplete and changes over time, results in the potential for non-optimal decision-making. As our population ages, the number of people living with a chronic disorder is forecast to rise dramatically, increasing an already unsustainable financial burden of healthcare costs on society and potentially a substantial reduction in quality of life for the many affected individuals. Critical to averting this are early and accurate diagnoses, optimal use of available medications, as well as new methods of surgery. Our Centre will facilitate these through developing mathematical and statistical tools necessary to inform clinical decision making on a patient-by-patient basis. The basis of this approach is patient-specific mathematical models, the parameters of which are determined directly from clinical data obtained from the patient. As an example of this, our recent research in the field of epilepsy has revealed that seizures may emerge from the interplay between the activity in specific regions of the brain, and the network structures formed between those regions. This hypothesis has been tested in a cohort of people with epilepsy and we identified differences in their brain networks, compared to healthy volunteers. Mathematical analysis of these networks demonstrated that they had a significantly increased propensity to generate seizures, in silico, which we proposed as a novel biomarker of epilepsy. To validate this, an early phase clinical trial at King's Health Partners in London has recently commenced, the success of which could ultimately lead to a revolution in diagnosis of epilepsy by enabling diagnosis from markers that are present even in the absence of seizures; reducing time spent in clinic and increasing accuracy of diagnosis. Indeed it may even make diagnosis in the GP clinic a reality. However, epilepsy is just the tip of the iceberg! Patient-specific mathematical models have the potential to revolutionise a wide range of clinical conditions. For example, early diagnosis of dementia could enable much more effective use of existing medication and result in enhanced quality and quantity of life for millions of people. For other conditions, such as cortisolism and 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.