PhD opportunities

Muscle function is essential for healthy ageing, yet we know deficits in muscle stem cell (muSC) function occur when we get older, resulting in weakness and frailty. Increasingly, ageing research is looking to find novel and effective interventions to enhance muSC function in vivo and so promote a better regenerative response to injury and impaired muscle function. The challenge is how to identify such factors in the complex environment of the muscle, in which many cell types are important for controlling muSC function. By understanding how different cells communicate in regenerating muscle and how these affect the ability of the muSC to migrate to damaged muscle and effectively replace damaged myofibres forms the basis for this project.

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There are three unique features common to all vertebrates: the vertebral column, the brain as part of the nervous system and a complex head where sense organs became concentrated.

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One of the key questions in biology is to uncover how cells with the same genomic information become different from each other. This is not only important to understand embryo development, but also to determine what goes wrong in disease, how we can use this information to promote tissues regeneration or to reprogram cells for stem cell-based therapies.

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Hearing as one of the five human senses plays a crucial role in our quality of life and integration into society, impacting on speech and language skills. Congenital hearing loss has been estimated to occur at an incidence of 1 in 1000 births, and as such has a major impact on the life of many children.

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Periodontitis is a global disease burden, underpinned by chronic inflammation, driven by microbial dysbiosis and an exaggerated host immune response, leading to irreversible destruction of the supporting structures of the teeth. Emerging evidence have revealed that features consistent with trained immunity are present in periodontitis, including hyper-responsive circulating monocytes and altered neutrophil behaviour. In particular, monocyte-derived macrophages display a heightened inflammatory state, contributing to excessive cytokine release, oxidative damage, and osteoclast-mediated bone loss, yet the mechanisms sustaining this hyper-inflammatory phenotype remain poorly defined. Vitamin D deficiency is associated with increased severity of periodontal disease and increased tooth loss. Vitamin D is a potent immunomodulator that regulates epigenetic and metabolic pathways central to trained immunity; however, its impact on macrophage-trained immunity and its ability to reverse maladaptive immune memory remain unclear. This PhD project will test the central hypothesis that vitamin D modulates trained immunity in macrophages by reshaping their epigenetic and metabolic programming, thereby restoring a balanced inflammatory and antibacterial phenotype and enabling the therapeutic reversal of established trained immunity and periodontitis-induced alveolar bone loss.

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This PhD will build on previous pioneering studies from our group, which have shown the clinical results of minimally-invasive non-surgical therapy (MINST) and how they are mediated by molecular changes in inflammatory and repair biomarkers.

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This project addresses the urgent need for novel and effective therapeutic strategies for the management of peri-implantitis, a highly prevalent condition associated with severe biological and functional consequences if left untreated. The European Federation of Periodontology (EFP) S3 guidelines highlight the current lack of clear, reliable, and predictable treatment protocols for managing peri-implant defects. Recent randomised controlled trials indicate that the adjunctive use of bone grafts does not confer a clear advantage over open flap debridement (OFD) alone in peri-implantitis therapy. Platelet-rich fibrin (PRF), an autologous platelet concentrate, offers multiple biological advantages, including the promotion of tissue regeneration and wound healing. The PhD study will test the hypothesis that the use of PRF in the surgical management of peri-implantitis will improve outcomes at 12 months.

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The majority of emerging infectious diseases in humans over the past few decades have resulted from the interspecies transmission of RNA viruses. The PhD will use computational tools and machine learning techniques to predict how these viruses infect cells in the human upper respiratory tract and avoid the host immune response.

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The project aims to explore the intricate relationship between the human microbiome and Alzheimer's Disease (AD). Despite increasing evidence suggesting a link between the microbiome and AD, the detailed molecular interactions remain largely unexplored. This study seeks to bridge this knowledge gap by employing a multi-omics approach, integrating genomics, transcriptomics, proteomics, and metabolomics to comprehensively analyze these interactions.

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There is wide recognition that dentistry is changing and that the skills and knowledge needed by people training to be dentists is also changing. A growing body of researchers are now looking at workforce planning and shaping the workforce of the future with a focus on the mix of skills and professions needed within the dental team (see for example Wanyonyi et al. 2014). There are also plenty of papers looking at the different ways in which dental students can best be taught the skills needed (see for example: Fincher & Shuker 2001, McAndrew et al. 2015; Reissman et al. 2015) at who they should be taught with (Nadershahi et al. 2012) and at professionalism within dentistry (see for example Trathen & Gallagher 2009). But pedagogical concerns and workforce planning are only part of the picture. The focus on providing patient centred dental care, coupled with an increased emphasis on prevention and widening understanding of the unequal distribution of oral health problems and related risk behaviours, all pose challenges to traditional paternalistic and/or business oriented models of dental care and require a change in mindset alongside the changing skillset.

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One fully funded UK/International PhD studentship available in the broad area of statistical machine learning, with a desirable focus on multimodal large language models, uncertainty-aware AI, and/or 3D images. Exemplary applications include Vision Language models for fusion of medical images, videos, and tabular data for disease detection, prediction, and decision making.

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The aim of this research project is to explore structural ableism in dentistry, not just as a form of discrimination that disabled people face within the healthcare sector but as a form of discrimination that is embedded in the structures, organisations and institutions that make up the healthcare system itself.

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Endodontic-periodontal disease has been characterized by the involvement of the pulp and periodontal diseases in the same tooth. The anatomic connections between the dental pulp and the periodontium provide pathways for perio-endo communication which result in the clinical presentation of the disease. This study is designed to address the following questions: Is the outcome of combined Endodontic-periodontal disease managed by root canal treatment using Hydraulic calcium silicate sealer and guided tissue regeneration (GTR) with adjunctive use of PRF superior compared to the above treatment with GTR alone? Are there any diagnostic or prognostic biomarkers (inflammatory markers or microbes) associated with the Endodontic-periodontal disease?

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Learning in the digital age gives educational institutions opportunities to gather rich data which could be used for inferring the progress of learners in learning technologies and environments. These digital data can include records of computer operations in the form of log data of students’ activities, feedback provided by teachers, academic performance, etc. However, while large amounts of data are available, little interpretation about them is being made. This project will examine digital data from several sources (e.g. Virtual Learning Environment usage, lecture capture logs, access to digital readings and teachers’ clinical feedback from a mobile app) to explore the kinds of insights that can be drawn through doing some ‘basic’ learning analytics. Learning analytics is a means to gather and analyse online learning activities captured digitally (for more information about learning analytics, see the 2016 Horizon Report1). There is much to learn from analysing such ‘learning’ data, but for dental education it is important to understand how these data can be related to learning of knowledge and skills that are particular to the dental discipline and profession. The analysis of these digital data will be complemented with observational studies, surveys and interviews.

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