Clinical Academic Training

Projects

2025 Research Projects can be found on the full job description on the East Midlands Deanery website.

Projects by topic

Cardiology

Developing a clinical acuity score for triage and outcome prediction in the Atrial Fibrillation Virtual Ward

Supervisor: Professor Andre Ng (andre.ng@leicester.ac.uk)

Atrial Fibrillation is the most common heart rhythm disturbance in clinical practice where the patient’s heart beats irregularly and could be excessively fast, causing symptoms of palpitation, and chest pain needing urgent hospital admission. We developed and established the AF virtual ward as a new clinical pathway at Glenfield Hospital, University Hospitals of Leicester NHS Trust to manage these acute AF patients. Using digital technology and communication, we look after patients remotely via a virtual platform and Bluetooth enabled devices through smartphone / tablets to communicate with the patient to conduct hospital-level ward rounds in the comfort of their own homes.

To ensure its safety and efficacy, an independent evaluation was undertaken. This showed that the AF virtual ward service was at least as safe as standard contemporary inpatient care and was well received by both staff and patients. This new clinical pathway has won the HSJ Award 2023 – Acute Sector Innovation of the Year.

We are continuing to operate and develop the AF virtual ward at the Glenfield hospital and have secured funding to undertake a project to look at the scaling of the AF virtual ward in three additional sites. A large dataset has been compiled having onboarded 679 patients to-date. Based on clinical data captured on admission to the AF virtual ward, we would like to develop a clinical acuity score to:

Inform triage decision
Correlate with clinical outcomes to utilising the score for prediction and risk stratification.

This will improve clinical utility of the novel clinical pathway. We have active collaboration and expertise in the group to apply machine learning techniques (e.g. large language model) to apply Artificial Intelligence to progress the virtual ward development and upscaling.

The successful ACF candidate will join the existing project team to gain experience depending on their area of interest across the clinical study pathway, including:

Study recruitment, follow-up and quantitative analysis
Clinical utility of virtual ward and digital ECG recording and platform
Conducting and analysing qualitative research
Integration of multimodal findings to inform personalised risk models

The project brings together expertise in cardiology, electrophysiology, engineering, and qualitative research. It offers an excellent opportunity to contribute to multidisciplinary work with high clinical impact, supporting advances in novel clinical pathways whilst being supervised to develop a bid for a personal fellowship. Joint supervision will be provided by group lead Prof Ng and Lecturer in Engineering, Dr Xin Li.

Transforming the Understanding and Sudden Death Risk Prediction Post-MI

Supervisor: Professor Andre Ng (andre.ng@leicester.ac.uk)

The Cardiac Electrophysiology research group at the University of Leicester is conducting a clinical study at Glenfield Hospital to investigate sudden death following acute myocardial infarction (MI). The project brings together expertise across the university’s cardiology, engineering, and psychology research staff. This study uses a mixed-methods design, combining quantitative cardiac biomarkers with qualitative insights into the brain heart axis. Novel ECG-derived markers will be further developed and validated, while patient and clinician perspectives will be explored to better understand how risk assessment can be translated into real-world clinical practice.

The successful ACF candidate will join the existing project team to gain experience depending on their area of interest across the clinical study pathway, including:

Trail recruitment, follow-up and quantitative analysis
Clinical exercise testing
Conducting and analysing qualitative research
Integration of multimodal findings to inform personalised risk models

The project brings together expertise in cardiology, electrophysiology, engineering, and qualitative research. It offers an excellent opportunity to contribute to multidisciplinary work with high clinical impact, supporting advances in sudden cardiac death prevention whilst being supervised to develop a bid for a personal fellowship. Joint supervision will be provided by group lead Prof Andre Ng and PhD candidate Dr Ed Lau.

Bioimpedance In Pregnancy and Labour: A fluid balance concept study

Supervisor: Professor Tommy Mousa (hm282@le.ac.uk)

This is a prospective, proof of concept study to assess the use of BIA (Bioscan touch i8) as a simple non-invasive bed-side test during pregnancy, labour and after delivery to assess maternal hydration status and body fluid balance.

Maternal cardiovascular adaptation evolves during the course of pregnancy to accommodate the growing foetus. There is a 30% increase in global arterial compliance and stroke volume increases due to the plasma expansion and increasing total blood volume. Indirect methods have been used to assess body fluids during pregnancy, labour and postpartum period.

Bio-electrical impedance analysis (BIA) measures whole body (or regional) impedance by means of an electric current transmitted at different frequencies. New techniques allow measurement of total body water with separation into extracellular and intracellular water. Current evidence suggests that BIA may provide useful information not only in different well-established patient groups (renal dialysis, malnutrition), but also in critically ill patients with burns, trauma and sepsis undergoing fluid resuscitation.

The aim of the current project is to assess the use of BIA as a simple non-invasive bed-side test during pregnancy, labour and after delivery to assess maternal hydration status and body fluid balance. We propose to use it in women delivering by caesarean section, having severe pre-eclampsia, major postpartum haemorrhage.

Understanding the pathogenesis of post-cardiac surgery bleeding

Supervisors: Professor Gavin Murphy (gjm19@leicester.ac.uk), Professor Veryan Codd (vc15@leicester.ac.uk), Dr Hakeem Yusuff (hy135@leicester.ac.uk)

The clinical problem: Post-cardiac surgery bleeding is common and is associated with increased morbidity and mortality. Tests of coagulation in blood are poor predictors of bleeding. Our preliminary research suggests that vascular endothelial dysfunction is a major contributor to bleeding.

Hypothesis: Measures of endothelial dysfunction will have predictive accuracy for post cardiac surgery bleeding.

Aims:

To develop key skills in bioinformatics analysis of trancriptomic data.
To analyse single cell transcriptomic data from fat biopsies obtained at surgery to identify endothelial phenotypes associated with bleeding.
To use bioinformatic methods to identify potential therapeutics for the prevention of bleeding in this phenotype.
To identify the potential secretome of dysfunctional endothelium associated with bleeding.
To test whether measurement of these biomarkers can improve the predictive accuracy of established diagnostic tests (TEG/ROTEM/Multiplate) in the COPTIC Bioresource.

Data Sources: Single cell transcriptomics data from the ObCARD study, clotting factor activity, coagulation test data and clinical data from the COPTIC study, UK Biobank.

Statistical Methods: Bioinformatic methods will include differential gene expression analysis, pathway analyses, cell-cell interaction, regulon analyses, and drug target analyses. Causal inference of candidate genes will be validated using UK Biobank. Candidate biomarkers will be tested in stored samples from the COPTIC Bioresource.

Research Team: The research is undertaken in partnership with the internationally leading Cardiovascular Genomics (Codd, Samani) and Functional Genomics (Webb Solomon) groups at the University of Leicester. External collaborators include the Universities of Oxford and Humanitas in Milan.

In silico modelling of RCTs of prehabilitation interventions using routinely collected healthcare data with mechanistic validation in established bioresources

Supervisors: Professor Gavin Murphy (gjm19@leicester.ac.uk), Dr Hakeem Yusuff (hy135@leicester.ac.uk), Dr Weiqi Liao (weiqi.liao@leicester.ac.uk)

Research question: Can pharmacological prehabilitation in people with cardiovascular disease prevent organ injury and death following major surgery.

Objectives:

To develop key skills in the use of routinely collected healthcare data, modern machine learning methods, and in silico clinical trial design.
To prepare a high quality submission for an external fellowship application within the lifetime of the post.
To pilot a novel research resource for trial development.

Interventions: Weight loss interventions including GLP-1 antagonists, anti-ageing interventions including SGLT2i and metformin, and interventions targeting chromatin remodelling including sodium valproate.

Data Sources: CPRD and HES data in partnership with the Leicester Real World Evidence Centre, UK Biobank for Mendellian Randomisation, Single cell transcriptomics for informatics drug targeting analyses.

Statistical Methods: The research will develop the in silico trial methods developed by our team (doi:10.1093/eurheartj/ehac670), that capitalise in the regional variation in the roll out of interventions to model potential RCTs of pre-surgery interventions in cardiac and other major surgical procedures.

Research Team: The research is undertaken in partnership with the Leicester CTU, the Leicester Real World Evidence Centre and statisticians based in the Department of Cardiovascular Sciences at the University of Leicester.

The use of pathway analysis to explore the trajectory of immune cells in inflammageing using bone marrow and myocardial tissue from cardiac surgery patients

Supervisors: Professor Gavin Murphy (gjm19@leicester.ac.uk), Professor Veryan Codd (vc15@leicester.ac.uk), Dr Hakeem Yusuff (hy135@leicester.ac.uk)

The clinical problem: Inflammageing is a key determinant of outcome following cardiac surgery but the underlying processes are poorly understood.

Hypothesis: Chromatin adaptations to biological ageing in bone marrow cells determine cellular lineages and the development of inflammageing in cardiac tissue.

Aims

To develop key skills in the use of high precision transcriptomics and genomic data and modern bioinformatic methods to study immunosenescence and biological ageing in human tissue.
To use modern informatic methods to identify underlying disease processes in bone marrow and myocardium, and to identify novel therapeutics.
To prepare a high quality submission for an external fellowship application within the lifetime of the post.

Participants: People undergoing coronary artery bypass grafting who have provided simultaneous myocardial biopsies and sternal bone marrow isolates for analysis.

Data sources: single nuclei RNA sequencing (snRNAseq) and transposase-accessible chromatin with sequencing (ATAC-Seq) data from bone marrow and myocardial tissue from previous studies.

Methods: Bioinformatic methods will include differential gene expression analysis, pathway analyses, cell-cell interaction, and regulon analyses. Pathway analysis will assess changes in cellular gene expression and chromatin remodelling over pseudotime (PhenoAge, a biological ageing score). The results will be validated using Mendellian Randomisation in UK Biobank.

Evaluating paradoxical hypertension during ultrafiltration in a large UK haemodialysis cohort

Supervisor: Professor James Burton (jb343@le.ac.uk)

Cardiovascular disease is the leading cause of death for individuals with end-stage kidney disease requiring maintenance haemodialysis. The burden of cardiovascular disease in this population is reflected by the Standardised Outcomes in Nephrology (SONG-HD) initiative identifying cardiovascular disease as a core outcome measure for trials involving individuals on haemodialysis. Although prevalent, traditional risk factors alone do not account for the significant cardiovascular morbidity and mortality in this population.

Blood pressure management of individuals requiring maintenance haemodialysis is challenging due to medication resistance, adherence, co-morbidities, interdialytic weight gain (volume overload), and ultrafiltration during haemodialysis. Symptomatic intradialytic hypotension is a frequent encounter on the dialysis unit. However, a subset of individuals experience intradialytic hypertension and paradoxical rises in their blood pressure during ultrafiltration (i.e. post-ultrafiltration blood pressures greater than pre-ultrafiltration blood pressures). This cohort of individuals are usually asymptomatic and as a result, go largely unnoticed. However, this paradoxical rise in blood pressure has been associated with nearly a 2-fold increase in cardiovascular mortality.

This ACF project provides the opportunity to evaluate the presence of paradoxical hypertension during ultrafiltration in one of the largest haemodialysis cohorts in the UK. This will enable:

the definition of paradoxical hypertension be determined;
identification of associated patient characteristics;
identification of associated cardiovascular outcomes.

Improving Cardiovascular Outcomes through Precision Adherence Testing

Supervisors: Dr Pankaj Gupta (pg118@leicester.ac.uk), Professor Prashanth Patel (pp260@leicester.ac.uk), Professor Ian Squire (is11@leicester.ac.uk)

We are a world-leading research group in the biochemical assessment of medication adherence using LC-MS/MS-based Chemical Adherence Testing (CAT), based at University Hospitals of Leicester and the National Centre for Adherence Testing (NCAT). Our pioneering work is directly embedded in NHS clinical care and underpins national and international guidelines (ESC/ESH, ACC). We have published more than 40 original articles and a have an established track record of delivering clinical research.

We invite a motivated Academic Clinical Fellow (ACF) to join our well-established translational programme, focusing on cardiovascular adherence in heart failure and renal disease populations where non-adherence drives preventable risk.

The ACF will contribute to answer the following questions

Can CAT improve outcomes in chronic cardiovascular diseases such as heart failure and renal disease?
Why are patients non adherent?

The research will entail

Feasibility studies embedding CAT in specialist clinics.
Qualitative and implementation science research exploring why patients do not take prescribed treatments.
Co-designing and evaluating behavioural and system-level interventions to improve outcomes.

Our group has trained multiple clinical academics; two recent PhD students won national prizes for innovation and impact in adherence science. Fellows will benefit from strong mentorship, access to rich data, and opportunities to shape NICE-facing, practice-changing research.

Data-Driven Phenotyping for Multi-Omic Research

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

This project offers an academic post focused on leveraging large-scale, real-world data to address critical clinical challenges. We will employ the Observational Medical Outcomes Partnership (OMOP) Common Data Model to standardise data from diverse sources, enabling multi-cohort analysis. Our data will come from a federated network including the UK's EXCEED and Our Future Health programmes, as well as international cohorts like All of Us. This approach allows for scalability and global reach.

This standardised data forms the foundation for a DeepPheWAS approach, which integrates genomics (from cohorts like Genes & Health), longitudinal lab results, and self-reported observations to define complex, clinically relevant phenotypes. By doing so, we will investigate the genetic and environmental determinants of multiple long-term conditions, using genomic data and digital analysis methods. This powerful methodology can uncover novel associations and therapeutic targets, aligning with our focus on clinical therapeutics.

This placement provides an unparalleled opportunity to develop skills in data innovation, machine learning, and big data analysis within an established academic framework. We encourage collaboration with industry partners to translate findings into real-world patient benefits and advance both clinical therapeutics and public health.

Unlocking Respiratory Disease Mechanisms: A Machine Learning Approach to Genetic Pleiotropy

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

This academic post offers a unique opportunity to apply advanced computational methods to address a major global health challenge: respiratory disease. While lung function genetics are well-established, their pleiotropic effects—the ability of a single gene to influence multiple seemingly unrelated traits—are underexplored.

This project will use a powerful combination of machine learning and artificial intelligence to investigate the pleiotropic effects of genes with known associations to lung function. We will build on our established expertise in phenome-wide association studies (PheWAS) to systematically analyze the broad clinical consequences of these genetic variants. By linking genetic associations with a wide range of clinical phenotypes, we aim to uncover previously unknown disease subtypes and understand their underlying mechanisms.

The insights from this work will directly inform drug discovery and risk prediction, offering a clear translational pathway. This is a chance to apply cutting-edge digital and big data techniques to make a significant impact on public health, with a direct link to clinical therapeutics.

Weight loss interventions for the reversal of stage B heart failure in people living with multiple long-term conditions

Supervisor: Professor Gerry McCann (gpm12@le.ac.uk)

Heart failure (HF) affects over 1million people in the UK and the prognosis is worse than for most cancers. Obesity is an independent risk factor for HF and often co-exists with other long-term conditions such as T2D; hypertension, atrial fibrillation and kidney disease; all of which increase HF risk. Stage B HF exists when there is evidence of structural or functional cardiac abnormalities but before symptoms arise and represents an opportunity to intervene to reduce the risk of HF developing.

Working closely with the Leicester Diabetes Centre and the lifestyle theme of the NIHR Leicester BRC, Prof McCann has been conducting trials of weight loss (with low calorie meal replacement plan[MRP] and pharmacological interventions/combinations) in diabetes. The ACF will undertake a systematic review and a network meta-analysis to assess the most effective interventions for ameliorating stage B HF. The ACF will be given access to trial data (including MRI and echocardiography) of patients with T2D and stage BH to assess the effects of reverse cardiac remodelling by Liraglutide (GLP-1RA); low-calorie MRP; dapagliflozin (SGLT2i); exercise training and Tirzepatide (GLP-1RA/ GIP RA).

The ACF will join a dynamic, diverse and growing research group with an outstanding track record in developing clinical academics.

Endocrinology and Diabetes

Diabetes Research Centre

LC TO DETERMINE

Optimising pharmacological management of diabetes in pregnancy through a novel self-titration pathway.

Supervisor: Dr Claire Meek (cm881@leicester.ac.uk)

Diabetes in pregnancy is associated with suboptimal perinatal outcomes which can be ameliorated with achieving glucose concentrations within metabolic target ranges. Women with type 2 diabetes or gestational diabetes often start therapy with metformin and/or insulin during pregnancy and doses must be up-titrated rapidly to achieve optimal glycaemic control. Healthcare professionals support medication optimisation but this is often slower and requires multiple patient visits.

Aim: to design and test the feasibility of a self-titration pathway for women with diabetes in pregnancy who are starting medication, supporting more rapid attainment of optimal glycaemic control.

Methods: Using existing clinical data from 3000 service users, the trainee will develop and test a new self-titration pathway, building skills in large data analysis, and the design and conduct of clinical research studies. The trainee will also learn about recruitment and retention of diverse patient groups, and use qualitative skills to assess engagement and patient satisfaction.

Impact: A novel self-titration pathway for medication initiation could improve patient satisfaction, improve pregnancy outcomes and provide cost savings to the NHS while improving quality. If successful, this study would collect data to support a larger funding application for a full-scale clinical trial, supporting a fellowship for PhD study.

Effect of maternal diet and physical activity upon glycaemia in women with type 1 diabetes in pregnancy using Hybrid closed loop technology

Supervisor: Dr Claire Meek (cm881@leicester.ac.uk)

Women with type 1 diabetes (T1D) have suboptimal pregnancy outcomes, which can be prevented by attaining strict glucose targets, assisted by novel hybrid closed loop technologies (HCLs), such as the Ypsopump – CamAPS system. However, many women still do not achieve optimal glucose targets, perhaps due to lifestyle choices. However, the role of maternal diet, weight gain, eating behaviour, physical activity and BMI has not been widely researched in women with T1D in pregnancy, leading to substantial evidence gaps.

Aim: to assess how maternal lifestyle factors such as dietary choices, gestational weight gain, eating behaviour, physical activity and BMI influence glycaemia and pregnancy outcomes in women with T1D.

Methods: using data from our multi-centre observational study (DOMINO, diabetes in pregnancy optimising maternal and offspring outcomes), the trainee will collect and analyse data about habitual diet, physical activity, eating behaviour and glucose concentrations, in order to assess how maternal lifestyle factors influence health in pregnancy.

Impact: Addressing maternal lifestyle factors could prevent perinatal complications in mothers with T1D and their babies. Supported by a friendly and enthusiastic multidisciplinary team, the student will gain experience in designing and conducting clinical research studies and data analysis, providing an excellent foundation for a future research career.

A Machine Learning Approach to Chronic Pain: From Data Imputation to Genetic Discovery

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

This academic post offers a unique opportunity for a clinician to tackle a major global health challenge: chronic pain. With current therapies often having substantial side effects and problematic addictive properties, there is an urgent need to better understand the underlying causes of this significant morbidity burden.

This project will build on our established expertise in using electronic healthcare records from large cohort studies to conduct powerful genetic research. We are now moving to advance the field in two key areas: first, we will use machine learning and AI to impute missing data, thereby increasing case numbers and statistical power for genetic discovery. Second, we will leverage your clinical training and our cutting-edge methods to develop clinically meaningful phenotypes that distinguish between impactful, resolving, and persistent pain.

By using these refined phenotypes to conduct genetic studies on large datasets, we will seek to uncover novel genetic factors that contribute to the development of chronic pain. This work will provide critical insights, ultimately offering a clear translational pathway to new therapies and improved patient care.

Data-Driven Phenotyping for Multi-Omic Research

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

This project offers an academic post focused on leveraging large-scale, real-world data to address critical clinical challenges. We will employ the Observational Medical Outcomes Partnership (OMOP) Common Data Model to standardise data from diverse sources, enabling multi-cohort analysis. Our data will come from a federated network including the UK's EXCEED and Our Future Health programmes, as well as international cohorts like All of Us. This approach allows for scalability and global reach.

This standardised data forms the foundation for a DeepPheWAS approach, which integrates genomics (from cohorts like Genes & Health), longitudinal lab results, and self-reported observations to define complex, clinically relevant phenotypes. By doing so, we will investigate the genetic and environmental determinants of multiple long-term conditions, using genomic data and digital analysis methods. This powerful methodology can uncover novel associations and therapeutic targets, aligning with our focus on clinical therapeutics.

This placement provides an unparalleled opportunity to develop skills in data innovation, machine learning, and big data analysis within an established academic framework. We encourage collaboration with industry partners to translate findings into real-world patient benefits and advance both clinical therapeutics and public health.

Decoding the HLA Region: A Big Data Approach to Disease Associations

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

This academic post offers a unique opportunity for a clinician to investigate the complex genetic associations of the Human Leukocyte Antigen (HLA) region. While the HLA region is notoriously difficult to analyze and often excluded from genetic studies, its clear association with autoimmune and rheumatological diseases has left a significant gap in our understanding of its wider clinical impact.

This project aims to fill that gap by performing a comprehensive phenome-wide association study (PheWAS) using imputed HLA data from the UK Biobank. We will systematically explore the association results to characterize the pleiotropic effects of HLA variation across a broad range of human diseases. We expect to find many associations beyond the well-known autoimmune and rheumatologic conditions, some of which may be entirely novel.

This is a chance to move beyond the clinic and use big data analysis to translate your clinical expertise into high-impact genetic discoveries. Your clinical training is essential to interpret these results and discern between meaningful pleiotropic effects and known disease co-morbidities. This work has the potential to become an authoritative characterisation of HLA variation, with clear implications for diagnostics and therapeutic development

Weight loss interventions for the reversal of stage B heart failure in people living with multiple long-term conditions

Supervisor: Professor Gerry McCann (gpm12@le.ac.uk)

The ACF will join a dynamic, diverse and growing research group with an outstanding track record in developing clinical academics.

General Practice

Designing an Equitable Primary Care Pathway for Obesity Management

Supervisor: Professor Samuel Seidu (sis11@leicester.ac.uk)

Obesity is a major public health challenge in the UK, disproportionately affecting individuals in socioeconomically deprived communities and ethnic minority groups. Despite growing clinical need, access to effective obesity care remains uneven. This project proposes the co-design of a primary care obesity management pathway that explicitly addresses health inequalities, using a dual approach: big data analysis and local service design.

First, we will conduct large-scale data analysis using national electronic health records to identify patterns in obesity prevalence, referral rates, and treatment uptake across different demographic, ethnic, and socioeconomic groups. This will inform the identification of key inequality gaps and priority populations.

In parallel, we will collaborate with local stakeholders, GPs, public health leads, community organizations, and people living with obesity to co-develop a locally tailored care pathway. This will focus on early identification, culturally sensitive interventions, referral to weight management services, and long-term support.

The final output will be a scalable, equity-informed primary care model that integrates data-driven insights with real-world feasibility, with the potential to inform national guidelines and support targeted commissioning to reduce obesity-related disparities.

Cluster-informed care for multiple long-term conditions(MLTC): evidence synthesis and co-design

Supervisor: Dr Harini Sathanapally (hw326@leicester.ac.uk)

Multiple long-term conditions (MLTC) defined as the co-existence of two or more chronic conditions in a single individual, are a growing challenge in healthcare systems worldwide, and linked to poorer outcome. Interventional research into this topic has mainly targeted a single condition within a population with MLTC, or focused on specific comorbidity pairs. Some trials have tested organisational or patient-centred models of care, but overall effects on outcomes have been limited. In parallel, epidemiological work has identified reproducible disease clusters—such as cardiometabolic and mental health–pain groupings—that are associated with higher treatment burden; however, these clusters are yet to contribute to intervention design. This project will combine a scoping review with a co-design exercise to address this gap.

The review will map and compare MLTC interventions, including condition-focused, generalised, and any cluster-focused approaches, and synthesise outcomes such as quality of life, treatment burden, and healthcare use. Building on these findings, a co-design process with patients, carers, and clinicians will explore how interventions could be tailored to common clusters of MLTC, generating a prototype model of care that reflects both evidence and lived experience. Together, this work will create a foundation for future feasibility testing of cluster-informed, patient-centred approaches to MLTC.

Enhancing Early Detection of Glomerular Disease through Integrated Care: A LUCID+ Study

Supervisors: Dr Rupert Major (rwlm2@leicester.ac.uk), Professor Jon Barratt (jb81@leicester.ac.uk)

Background: Glomerular disease is a leading cause of chronic kidney disease (CKD) and end-stage kidney failure, yet diagnosis is often delayed. The NHS Long Term Plan emphasise early detection and personalised prevention. LUCID (Leicester, Leicestershire and Rutland CKD Integrated Care Delivery) integrates kidney care across primary and secondary services. The advent of novel therapies for glomerular disease in the last five years has made early detection an imperative. Expanding LUCID to focus on earlier identification of glomerular disease has the potential to improve outcomes and reduce inequities.

Aims: To develop and evaluate integrated care pathway for earlier detection of glomerular disease supported by novel data-driven tools.

Objectives:

Map current diagnostic pathways for proteinuria and haematuria in primary care.
Develop and test a data tool within existing clinical systems to flag patients at risk of glomerular disease.
Assess the impact on case detection using retrospective and prospective data.

Methods:

Mixed-methods approach:

Electronic care data retrospective analysis to identify missed opportunities
Co-design of an electronic risk flagging tool for LUCID

Impact: This project will inform a new LUCID pathway for glomerular disease, supporting earlier diagnosis, slowing progression, and aligning with national priorities for integrated, preventative care.

Implementation of comprehensive geriatric assessment (CGA) in primary care settings: a realist evaluation

Supervisor: Dr Lucy Beishon (lb330@leicester.ac.uk)

Comprehensive Geriatric Assessment (CGA) is a holistic care model that uses a multidisciplinary approach to assess medical, psychological and social function to provide coordinated and integrated care and treatment for older people. CGA improves the number of people living at home after discharge from acute hospitals, and quality of life, reducing care-giver burden. CGA is also cost-effective in community and ambulatory settings. The James Lind Alliance research priorities for older people with multiple conditions highlight the need to trial CGA in other care settings. The British Geriatrics Society have produced a comprehensive toolkit to support primary care providers to deliver CGA into primary care services. However, there is no guidance regarding how to implement the toolkit into existing service(s) and as such, its implementation in primary care settings has not been evaluated.

A realist evaluation methodology framework will be employed to understand the conditions affecting the implementation of CGA in primary care settings. Realist evaluation is a theory-driven approach to evaluation that aims to explain why interventions work (or not), for whom, and under which circumstance. In so doing, the project will explore the contextual factors and mechanisms that affect implementation and outcomes and develop a package to support primary care organisations to implement CGA in primary care settings.

System level evaluation of hospital at home services for diverse communities

Supervisor: Dr Lucy Beishon (lb330@leicester.ac.uk)

Hospital at home (HaH) services provide hospital-level, acute clinical care services in patient’s own homes. Importantly, HaH have been shown to have superior or equivalent outcomes to inpatient hospital care, but at a reduced cost and with greater patient and carer satisfaction. However, implementation of HaH services vary significantly across the country in terms of staffing, resources and the local population context. Therefore, how these services are implemented and thus affected by local context remains unclear. In Leicester, we have an established HaH service supporting a diverse local community embedded within a broader frailty service encompassing prehospital emergency assessments, frailty emergency service (front door) and a frailty same day emergency clinic. This project will use a mixed methods approach to undertake a system level evaluation of our HaH in the context of our broader frailty services and diverse local population. This will be achieved through qualitative interviews with staff, patients, carers, and commissioners involved in delivering and receiving these services, and quantitative analysis of clinical outcome data such as hospital admissions, length of stay, mortality, and cost effectiveness. Data will be integrated to draw conclusions on how HaH services can be implemented in the context of a broader frailty driven service, with a specific focus on diverse and underserved communities.

Improving access to physical health care for older people in mental health settings: The ImPreSs-Care Study

Supervisor: Dr Lucy Beishon (lb330@leicester.ac.uk), Professor Tom Robinson (tgr2@leicester.ac.uk)

People of all ages with serious mental illness experience reduced life expectancy compared to the general population known as “the stolen years”. Older people have a number of unique physical health challenges due to higher rates of frailty, cognitive and physical impairments, multimorbidity, polypharmacy and complex social needs. Mental and physical health services are fragmented, and access to physical healthcare remains patchy across the country.

The ImPreSs-Care study is a mixed methods study using a combination of qualitative, semi-structured interviews and a large quantitative dataset from NHS England to develop service recommendations to improve access to physical healthcare for older people in mental health settings. This project is in collaboration with Age UK, University of Loughborough and Nottingham with opportunities to model different pathways of care for polder people. Within this project there is scope to investigate a number of related themes including but not limited to:

Deprescribing approaches to physical health medications in mental health settings;
Managing frailty and multimorbidity in mental health settings;
Advanced care planning and end of life care;
Use of digital technologies and improving care transitions.

Improving Cardiovascular Outcomes through Precision Adherence Testing

Supervisors: Dr Pankaj Gupta (pg118@leicester.ac.uk), Professor Prashanth Patel (pp260@leicester.ac.uk), Professor Ian Squire (is11@leicester.ac.uk)

The ACF will contribute to answer the following questions

Can CAT improve outcomes in chronic cardiovascular diseases such as heart failure and renal disease?
Why are patients non adherent?

The research will entail

Feasibility studies embedding CAT in specialist clinics.
Qualitative and implementation science research exploring why patients do not take prescribed treatments.
Co-designing and evaluating behavioural and system-level interventions to improve outcomes.

Optimising pharmacological management of diabetes in pregnancy through a novel self-titration pathway.

Supervisor: Dr Claire Meek (cm881@leicester.ac.uk)

Effect of maternal diet and physical activity upon glycaemia in women with type 1 diabetes in pregnancy using Hybrid closed loop technology

Supervisor: Dr Claire Meek (cm881@leicester.ac.uk)

A Machine Learning Approach to Chronic Pain: From Data Imputation to Genetic Discovery

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

Data-Driven Phenotyping for Multi-Omic Research

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

Unlocking the Genetics of Bronchiectasis: A Global Multi-Cohort Study for Therapeutic Innovation

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

This project offers an academic post focused on uncovering the genetic architecture of bronchiectasis, a debilitating chronic condition with significant associated comorbidities. We will leverage a unique opportunity to lead a world-class genetic study by integrating and analysing data from large, publicly accessible databases from across the globe, including the UK's EXCEED, Our Future Health, and international cohorts like All of Us, and deeply phenotyped clinical cohorts from established collaborators.

Using a cutting-edge approach, our research aims to define disease severity and understand the epidemiological and genetic associations that contribute to the pathogenesis of bronchiectasis. We will investigate the shared genetic architecture between bronchiectasis and common comorbidities like COPD, cardiovascular diseases, and diabetes mellitus to help us better understand the disease's overall genetic profile. This effort will provide vital insights into the disease mechanism, ultimately leading to the identification of novel targets for drug development.

This placement provides an unparalleled opportunity to conduct world-leading research with a clear translational pathway into clinical therapeutics and public health. It combines advanced methods in data innovation, machine learning, and big data analysis within an established academic framework.

Unlocking Respiratory Disease Mechanisms: A Machine Learning Approach to Genetic Pleiotropy

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

This project will use a powerful combination of machine learning and artificial intelligence to investigate the pleiotropic effects of genes with known associations to lung function. We will build on our established expertise in phenome-wide association studies (PheWAS) to systematically analyze the broad clinical consequences of these genetic variants. By linking genetic associations with a wide range of clinical phenotypes, we aim to uncover previously unknown disease subtypes and understand their underlying mechanisms.

The insights from this work will directly inform drug discovery and risk prediction, offering a clear translational pathway. This is a chance to apply cutting-edge digital and big data techniques to make a significant impact on public health, with a direct link to clinical therapeutics.

Uncovering the Pleiotropic Effects of Lynch Syndrome: A Digital Approach to Diagnosis and Surveillance

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

This academic post offers a unique opportunity for a clinician to investigate Lynch syndrome, a hereditary condition leading to a significantly increased risk of various cancers. While current clinical management focuses on lifelong surveillance with colonoscopy and prophylactic surgery, over 95% of people with pathogenic mutations remain undiagnosed.

This project will characterize the underexplored pleiotropic effects of Lynch-causing variants. Leveraging increased sequencing data from large cohorts like UK Biobank, All of Us, and Our Future Health, we will conduct a comprehensive phenome wide association studies (PheWAS) for these variants. We will develop novel methods to combine multiple variants to capture the full spectrum of Lynch syndrome's pleiotropy.

Our findings will inform new strategies for screening electronic health records to identify potential undiagnosed cases using advanced clustering and machine learning techniques. This work has a clear translational pathway, with the potential to transform public health surveillance for this underdiagnosed condition. We have established expertise in PheWAS and strong links with leading experts in clinical genetics to support this project.

General Surgery

Exploring differences in the learning experiences from operating theatre between neurotypical and neurodiverse, theatre naïve, undergraduate medical students

Supervisor: Dr David Bowrey (djb57@le.ac.uk)

The recorded prevalence of neurodiversity, in particular ADHD and ASD has increased over the last decade. Consequently, medical schools facilitate an increasing number of neurodiverse students. Little is known about how neurodiversity influences student learning, particularly in the operating theatre, which can be a challenging learning environment regardless of neurodiversity status.

This project involves the undertaking of interviewer-blind, semi-structured interviews from neurotypical and neurodiverse medical students on their first and subsequent surgical placements. The aim is to explore and evaluate potential differences in the learning experience, so that appropriate changes made to the educational content and delivery of this experience (if appropriate). The ACF will join this project and develop research skills in systematic reviews, qualitative interviews, thematic analysis and reporting of qualitative analysis data.

This experience from this study will allow the ACF to apply for further funding evaluating the impact and influences of neurodiversity within the wider healthcare setting, including which adaptations are required to best facilitate effective education. This would also set the groundwork for further investigation into neurodiversity with the postgraduate training arena.

Plastic Surgery

Improving survivorship by reducing adverse effects from loco-regional breast cancer treatment

Supervisors: Mr Tim Rattay (tr104@le.ac.uk), Ms Monika Kaushik (mk874@leicester.ac.uk)

Breast surgery and radiotherapy at Leicester have a track record in survivorship research and the investigation of long-term effects of treatment. Thankfully, breast cancer survival in the UK is high. Our research is designed to help breast cancer survivors achieve the best possible long-term outcomes with minimal treatment side-effects. With major funding from Innovate UK and Horizon Europe, we access to large follow-up cohorts with available clinical, treatment, genomic, toxicity and survival data, and we are currently involved in a multi-national study of developing an AI application to predict side-effects from surgery and radiotherapy to inform patients of their individual risk, with a randomized-controlled trial focussing on lymphoedema prediction after axillary surgery and regional nodal irradiation. Our research is supported by the NIHR Leicester Biomedical Research Centre cancer theme, bringing together academics at the University of Leicester and clinicians from University Hospitals of Leicester NHS Trust.

Successful applicants will be able to choose from a range of research projects going on in Leicester using a range of research methodologies. Examples include:

epidemiological studies of breast cancer survivors
genetic association studies of treatment outcomes
qualitative research in survivorship
application of AI and machine learning to prediction and risk modelling

interventional trials of supportive interventions, in particular, focusing on feasibility data for lymphovascular intervention including licensed medical devices, to prevent or treat arm lymphoedema following breast surgery and radiotherapy.

The ACF will have the opportunity to collaborate with other researchers from across the University, in particular the Schools of Psychology, Computer Science and Mathematics, and Biosciences. The supervisory team have an established track record of hosting ACFs, MD/PhD students, and research fellows, and full research training and support will be provided.

Utilising Patient Derived Explants from Colorectal Cancer Patient Surgical Resections to Investigate Precision Therapies

Supervisor: Mr Franscois Runau (fr118@leicester.ac.uk)

Project: Preclinical models that can accurately predict outcomes in the clinic are much sought after in the field of cancer drug discovery and development. Existing models such as organoids and patient-derived xenografts have many advantages, but they suffer from the drawback of not contextually preserving human tumour architecture. This is a particular problem for the preclinical testing of immunotherapies, as these agents require an intact tumour human-specific microenvironment for them to be effective. PDEs involve the ex vivo culture of fragments of freshly resected human tumours that retain the histological features of original tumours. Working closely with clinicians in the Colorectal Surgery and Oncology Departments we have established a working PDE model in Colorectal Cancer.

Place: The Leicester Cancer Research Centre are globally recognised leaders in the development of PDEs in multiple cancers, including breast and endometrial – with recent publications in Nature Scientific Reports. We have a team of research scientists and eminent professors in translational research with a proven record of supervising candidates to completing PhDs and supporting them into future academic careers.

Person: We are seeking an individual who demonstrates exceptional motivation, ambition, and diligence to take a leading role in the development and application of PDEs in colorectal cancer.

Uncovering the Pleiotropic Effects of Lynch Syndrome: A Digital Approach to Diagnosis and Surveillance

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

Geriatric Medicine

Implementation of comprehensive geriatric assessment (CGA) in primary care settings: a realist evaluation

Supervisor: Dr Lucy Beishon (lb330@leicester.ac.uk)

System level evaluation of hospital at home services for diverse communities

Supervisor: Dr Lucy Beishon (lb330@leicester.ac.uk)

Improving access to physical health care for older people in mental health settings: The ImPreSs-Care Study

Supervisor: Dr Lucy Beishon (lb330@leicester.ac.uk), Professor Tom Robinson (tgr2@leicester.ac.uk)

Deprescribing approaches to physical health medications in mental health settings;
Managing frailty and multimorbidity in mental health settings;
Advanced care planning and end of life care;
Use of digital technologies and improving care transitions.

A Machine Learning Approach to Chronic Pain: From Data Imputation to Genetic Discovery

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

Data-Driven Phenotyping for Multi-Omic Research

Supervisor; Dr Richard Packer (richard.packer@leicester.ac.uk)

Unlocking Respiratory Disease Mechanisms: A Machine Learning Approach to Genetic Pleiotropy

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

Haematology

Liquid biopsy for T-cell lymphoma

Supervisor: Dr Matt Ahearne (mja40@le.ac.uk)

Our group leverage computational tools to identify tumour specific sequences from circulating cell-free DNA. Circulating tumour DNA (ctDNA) analysis from a simple blood test enables rapid lymphoma diagnosis, molecular stratification, and real-time tracking of clonal dynamics during therapy. We have demonstrated feasibility (PMID: 32484856, 39157626) and in collaboration with Leicester Molecular Diagnostics, are advancing our assay into a validated clinical test (LINK). A new funded sub-study of HistioNode (LINK), will launch in 2026 as part of the MRC Rare Disease Platform.

Alongside standard sequencing, we are adopting cutting-edge native sequencing using Nanopore technology, which offers several advantages for rapid ctDNA detection and characterisation. The ACF will assess mutational, methylation, and copy number changes in ctDNA, correlating results with clinicopathological and radiological data. This work will use samples from the UK T-cell lymphoma biobank, with planned European expansion, and from the MRC HistioNode project.

Opportunities exist to collaborate with Leicester’s Research Centre for Artificial Intelligence, Data Analytics and Modelling, applying machine learning to enhance ctDNA detection using public cancer datasets.

The research plan can be tailored to the Fellow’s interests and skills (wet and/or dry lab). Data generated will support the ACF in securing PhD funding, with the ultimate goal of integrating ctDNA assays into precision medicine lymphoma trials and routine clinical care.

Determinants of response to immunotherapeutic strategies in aggressive lymphomas

Supervisor: Dr Harriet Walter (hw191@le.ac.uk)

Targeting of tumour antigens with T cell engagers is a recognised cancer treatment strategy in B cell malignancies. Whilst CD20xCD3 bispecific antibodies and CD19 CAR T-cell therapies have transformed clinical outcome in relapsed/refractory disease, the clinical determinants of response and optimal sequencing strategies remain unknown. Multiple studies are ongoing to assess activity in combination and in earlier treatment settings.

Additional studies are required to understand how target antigen expression affects response and how antigen loss through cell surface remodelling and/or mutations can result in resistance. Importantly, the tumour cell surface is central to interactions with the microenvironment and is thus key to diagnosis, biological understanding, and revealing new strategies for immunotherapeutic strategies.

Aligning to existing workflows and outputs from the Dyer/Walter/Ahearne laboratory, the Fellow will explore longitudinal dynamic changes in target antigen expression during treatment using multi-omic approaches. The following methodologies will be applied:

Multicolour flow cytometry
Unbiased cell surface proteomics
Imaging of the immune synapse
RNA sequencing
WES

The Fellow will also have the opportunity to leverage through acquisition and application of computational skill pre collected datasets, datasets generated through wet lab experimental studies and clinical datasets aligning with the Fellow’s specific skills, experience and interest.

Bioimpedance In Pregnancy and Labour: A fluid balance concept study

Supervisor: Professor Tommy Mousa (hm282@le.ac.uk)

Data-Driven Phenotyping for Multi-Omic Research

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

Infectious Diseases and Medical Microbiology

Understanding ethnic differences in the risk of SARS-CoV-2 breakthrough infection using analysis of SARS-CoV-2 antibody neutralisation assays

Supervisors: Professor Manish Pareek (manish.pareek@leicester.ac.uk), Dr Christopher Martin (cm712@leicester.ac.uk)

SARS-CoV-2 continues to cause breakthrough infection, despite high levels of vaccination and previous infection. This is thought to be due to waning immunity to infection, and the constant emergence of new SARS-CoV-2 variants.

Our group has been on the forefront of research into ethnic differences in SARS-CoV-2 infection. We have a longitudinal dataset of >600 healthcare workers (HCWs) across four years; where repeated immunological measurements have been obtained across a period of one year shortly before, and following the HCW’s booster vaccine.

The ACF will undertake two main projects:

Systematic review and meta-analysis, investigating ethnic differences in the risk of SARS-CoV-2 breakthrough infection and how immunology (quantitative antibodies, neutralisation data, T-cell data and mucosal immunology) may contribute to the differences in these outcomes
Analysis of the routine HCW dataset, to identify key immunological markers of SARS-CoV-2 breakthrough infection across different ethnic groups and how this may contribute to differences in breakthrough infection.

Ultimately, this data will be used to submit an application for a doctoral research fellowship, where the ACF will undertake prospective sampling studies to investigate whether immunological correlates of protection identified in their pilot work could be used to guide when HCWs receive their next booster vaccination.

Ethnic differences in the risk of cardio-metabolic comorbidities in people living with HIV

Supervisors: Professor Manish Pareek (manish.pareek@leicester.ac.uk), Dr Joshua Nazareth (jn208@leicester.ac.uk)

HIV infection, alongside certain HIV medications, accelerates the development of cardiovascular disease (CVD) compared to HIV negative individuals. Furthermore, persons living with HIV (PLWH) often fail to achieve the evidence-based treatment goals for the prevention of CVD, and the reasons for this are currently poorly understood.

The infectious diseases team in Leicester looks after >1,500 PLWH, which contains a high degree of ethnic diversity. It is well known that those from ethnic minority groups are often at higher risk of cardio-metabolic disease; therefore, identifying and understanding how ethnicity may contribute to cardio-metabolic disease in PLWH is of critical public health importance.

The ACF will undertake two main projects:

Systematic review and meta-analysis, investigating ethnic differences in HIV outcomes and how multimorbidity may contribute to these differences
Analysis of the routine HIV dataset, to identify ethnic differences in HIV outcomes and the reasons for this (including identification of key cardiometabolic comorbidities within our cohort)

Ultimately, this data will be used to submit an application for a doctoral research fellowship, where the ACF will undertake prospective studies to confirm their pilot data findings, and potentially implement a complex intervention to address these differences in outcomes.

Disentangling environmental, demographic and biological factors associated with transmission of SARS-CoV-2 in the households of healthcare workers

Supervisors: Professor Manish Pareek (manish.pareek@leicester.ac.uk), Dr Daniel Pan (dp440@leicester.ac.uk)

Transmission of respiratory viruses continues to occur in highly vaccinated populations. Current vaccines mainly help reduce the impact of severe disease, but not the risk of infection or transmission.

Our group has developed a new technology to identify those who are most infectious with SARS-CoV-2, involving the use of specialised strips embedded within duckbilled facemasks (termed facemask sampling, FMS). Analysis of viral load from FMS shows a stronger relationship to household transmission in pilot studies, compared to upper respiratory tract sampling (URTS). Transmission of respiratory viruses however, is complex and depends on a combination of host, environmental and biological factors.

The ACF will undertake two main projects:

Systematic review and meta-analysis, investigating what are the main contributors to transmission of SARS-CoV-2 within highly vaccinated and infected populations.
Analysis of a routine household transmission study dataset containing FMS and URTS viral load, identify their contribution to transmission.

Ultimately, this data will be used to submit an application for a doctoral research fellowship. Analysis of the above data will identify key factors that are under-investigated so far – eg, host immunity in exhaled breath/mucosa; ethnic differences in household size; or quality of ventilation within the households. The candidate will then design a transmission study taking these factors into account in their application.

Earlier identification of respiratory disease and pneumonia in rough sleepers across the East Midlands

Supervisor: Professor Dominick Shaw (des21@le.ac.uk)

Pneumonia remains a leading cause of morbidity and mortality among rough sleepers, who face compounded risks due to exposure, poor nutrition, substance use and limited healthcare access.

This project will introduce a screening service for respiratory infection to see whether pneumonia can be prevented using routine microbiological techniques and other clinical interventions.

It will build on the novel rough sleepers outreach service led by Prof Shaw and will give an ACF invaluable experience in respiratory medicine and health inequality research in a vulnerable and hard to reach population, with potential for significant clinical and research impact. The project will establish participation, retention and screening ability in this vulnerable cohort and support improved population health, via linked clinical interventions.

The project will also evaluate the effectiveness of preventive measures such as pneumococcal and influenza vaccinations. Findings will inform targeted public health interventions, including mobile chest Xray resources and integrated care pathways. Qualitative research with focussed feedback from the rough sleeping population will be built into the clinical interventions.

Incorporating microbiome analysis into pneumonia research could also uncover novel risk factors and therapeutic targets.

Ultimately, the research seeks to reduce pneumonia-related hospitalizations and improve respiratory health outcomes in this vulnerable population.

Characterising the heterogeneity of Tuberculosis

Supervisor: Dr Pranabashis Haldar (ph62@leicester.ac.uk)

Although a disease caused by a single family of pathogens, a hallmark of Tuberculosis (TB) is the variability with which it causes disease in humans. This is a key reason why elimination strategies have remained unsuccessful despite availability of effective antibiotic therapy.

Our programme of research investigates the phenotypic patterns and underlying biological mechanisms of heterogeneity in human infection with Mycobacterium tuberculosis. Our work encompasses studies examining different clinical patterns of disease, heterogeneity of TB risk in those with evidence of the infection and variability in the response to treatment, Using state of the imaging methods and recruitment of longitudinal cohorts, we aim to understand the biological basis of these differences.

Potential candidates will join a diverse group of researchers with scope to consider a breadth of project types, including clinically focussed, data driven (including imaging), or laboratory based studies (to be discussed). The projects will be deliverable within the time course of an ACF rotation and provide outcomes to support a doctoral fellowship application- including supporting pilot data for a larger research study, submitted abstracts to national and international conferences, and one or more publications (either completed or in process).

Improving Cardiovascular Outcomes through Precision Adherence Testing

Supervisors: Dr Pankaj Gupta (pg118@leicester.ac.uk), Professor Prashanth Patel (pp260@leicester.ac.uk), Professor Ian Squire (is11@leicester.ac.uk)

The ACF will contribute to answer the following questions

Can CAT improve outcomes in chronic cardiovascular diseases such as heart failure and renal disease?
Why are patients non adherent?

The research will entail

Feasibility studies embedding CAT in specialist clinics.
Qualitative and implementation science research exploring why patients do not take prescribed treatments.
Co-designing and evaluating behavioural and system-level interventions to improve outcomes.

Data-Driven Phenotyping for Multi-Omic Research

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

Unlocking the Genetics of Bronchiectasis: A Global Multi-Cohort Study for Therapeutic Innovation

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

Decoding the HLA Region: A Big Data Approach to Disease Associations

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

Obstetrics and Gynaecology

Endometrial cancer prevention

Supervisor: Dr Esther Moss (em321@le.ac.uk)

Endometrial cancer (EC) is now the fourth commonest cancer in women in the UK1. Rising obesity levels, an aging population and a fall in hysterectomy for benign gynaecological conditions are contributing to a steady rising in incidence. Uterine cancer outcomes are heterogenous, with Black women experiencing more than two-fold greater mortality compared to White women 2 3, whereas women of Asian heritage are reported to have up to 3.4-fold higher incidence than women from other ethnic groups 4, and to be younger at diagnosis 5. Uterine cancer awareness initiatives 6 focused on women from Black and Asian communities have identified barriers to early presentation with symptoms and therefore alternative strategies to identify women at increased risk are needed. Although at present primary prevention strategies are focused on life-style and weight management, potential non-hormonal pharmacotherapies have been identified as having activity against endometrial cancer.

This project will build on work exploring the potential role of two compounds in pre-clinical models to prevent progression of pre-malignant changes within the endometrium. The project will involve a mix of laboratory-based research, data science and bioinformatic analysis, and clinical research, including patient recruitment and research management.

Bioimpedance In Pregnancy and Labour: A fluid balance concept study

Supervisor: Professor Tommy Mousa (hm282@le.ac.uk)

Optimising pharmacological management of diabetes in pregnancy through a novel self-titration pathway.

Supervisor: Dr Claire Meek (cm881@leicester.ac.uk)

Effect of maternal diet and physical activity upon glycaemia in women with type 1 diabetes in pregnancy using Hybrid closed loop technology

Supervisor: Dr Claire Meek (cm881@leicester.ac.uk)

Ophthalmology

Identifying biomarkers for papilloedema and pseudopapilloedema

Supervisor: Mr Mervyn Thomas (mt350@le.ac.uk)

Papilloedema, defined as optic nerve head (ONH) swelling due to raised intracranial pressure, is a critical clinical sign that requires prompt recognition and management to prevent irreversible visual loss. However, distinguishing papilloedema from pseudopapilloedema, optic disc elevation due to benign or congenital anomalies, can be diagnostically challenging, often necessitating invasive investigations such as lumbar puncture or neuroimaging.

Optical Coherence Tomography (OCT) is a rapid, non-invasive imaging modality that enables high-resolution visualisation of the ONH and peripapillary retina. Recent work from our group (PMID: 40640273) has demonstrated that OCT-derived biomarkers can reliably differentiate papilloedema from specific pseudopapilloedema subtypes, using multi-level statistical modelling.

This ACF project will build on this foundation through a real-world, prospective diagnostic accuracy study. Participants referred with optic disc swelling will undergo standard-of-care imaging alongside novel advanced imaging techniques as part of a larger funded study. The study aims to validate and refine imaging biomarkers that can serve as non-invasive, early diagnostic tools to guide clinical decision-making and reduce unnecessary invasive procedures.

The successful candidate will gain experience in ophthalmic imaging, diagnostic accuracy study design, and clinical data analysis, with support from an interdisciplinary team across ophthalmology, neurology, genetics and medical physics.

Paediatrics

Asthma diagnosis and deep phenotyping in children with preschool wheeze

Supervisors: Dr Erol Gaillard (eag15@leicester.ac.uk), Professor Dominic Shaw (des21@leicester.ac.uk)

The problem:

Severe wheezing attacks are one of the commonest reasons for emergency department attendance and hospitalisation in children younger than 5 years. This is a significant health care burden in the UK and worldwide. Our current strategies of management of preschool wheezing disorders are ineffective. Better diagnosis and understanding of the pathophysiology of preschool asthma to develop more effective treatments is an important unmet need.

Unmet need: Diagnosis and biomarkersA key problem is the lack of a diagnostic test because young children are rarely able to perform spirometry. Breath volatile organic compounds can be obtained non-invasively and portable oscillometry are both promising new technology in preschool children. Oscillometry measures airway resistance and impedance and an increase in both parameters is associated with asthma. The test is quick, non-invasive and importantly does not require any effort on the part of the child.

Additionally, further phenotyping is required to establish the best way to treat preschool children. Many do not respond to corticosteroids. Objective biomarkers and the assessment of treatable traits including allergic sensitisation, and an assessment of airway infections are needed.

Research plan: Observational study recruiting children with acute preschool wheeze from children’s ED or in the outpatient clinic, obtain breath samples and oscillometry measurements pre and post bronchodilator and phenotype severe preschool wheeze on the basis of nasal swab and blood biomarkers and study of airway infection to pave the way for a clinical trial.

The ACF will also be expected to complete a systematic review.

Prospective analysis of extent of worry in parents and carers attending emergency care settings about their child.

Supervisor: Professor Damian Roland (dr98@leicester.ac.uk)

While the incidence of serious illness in children is very low; emergency care attendances continue to climb and this creates a challenge to recognise those children in need of ongoing treatment.

Serious case review often demonstrates that parents and carers have raised concerns about their child prior to being discharged from emergency care settings. This concern is reported to be either dismissed or ignored by health care professionals.

Research previously undertaken at the Leicester Royal Infirmary Children’s Emergency Department has demonstrated that 10% of those children with the lowest risk of illness on arrival are thought to be the most unwell the parent has ever seen that child. This may partly explain why health care professionals are perceived to, or do actually, dismiss parental concern.

In this work the ACF will undertake a prospective evaluation of parental concern at arrival in the Children’s Emergency Department and for seven days afterwards. The aim to understand how concerns changes over time and if a more precise lexicology can be development when parents and carers communicate with health care professionals.

Congenital diaphragmatic hernia

Supervisor: Dr David Lo (dr98@leicester.ac.uk)

Congenital diaphragmatic hernia (CDH) is a life-threatening congenital anomaly, with an incidence of approximately 1:2,500 live births. Mortality rate remains around 40–50% and in follow-up studies, multiple complications including pulmonary damage, cardiovascular disease, gastro-intestinal disease, failure to thrive, neurocognitive defects and musculoskeletal abnormalities have been described.

Despite this, there is no consensus on how these children should be monitored long-term. Previous follow up studies have also been based on small case series’ and there is limited longitudinal data beyond the first 3-5 years of life.

Objectives:

To describe the current follow-up arrangements for CDH in UK tertiary paediatric centres
To explore long term outcomes relating to CDH in children using a large primary care database linked to hospital data

Methods:

Survey study of UK tertiary centres
Epidemiological study using routinely collected health data

Rationale:

Identify whether there are differences/inequalities in the way children with CDH are managed in the UK.
Describe long-term prognosis of children with CDH from birth to early adulthood
The above information will be utilised to inform the development of consensus guidance for the follow-up of children with CDH

Data-Driven Phenotyping for Multi-Omic Research

Supervisor; Dr Richard Packer (richard.packer@leicester.ac.uk)

Unlocking the Genetics of Bronchiectasis: A Global Multi-Cohort Study for Therapeutic Innovation

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

Renal Medicine

Identifying translational biomarkers for precision medicine in IgA nephropathy

Supervisors: Dr Chee Kay Cheung (ckc15@le.ac.uk), Professor Jon Barratt (jb81@leicester.ac.uk)

IgA nephropathy (IgAN) is a leading cause of progressive chronic kidney disease and kidney failure worldwide. Recent advances have resulted in novel therapies targeting various steps in its pathogenesis, creating opportunities for a precision medicine approach.

This project will utilise biorepository samples from ongoing or completed Phase 2 and Phase 3 industry and academic sponsored studies, including SPARTAN, PRO-IGAN, NEFEXPLORE (led by Leicester), that investigate new treatments for IgAN. The aims are to evaluate effects on clinical outcomes (e.g. proteinuria, kidney function), pharmacokinetics, and to identify novel biomarkers of kidney injury and response to treatment in blood, urine, and repeat kidney biopsies, using a multi-omic approach.

The ACF will gain hands-on training in clinical trial methodology and execution, and biomarker discovery utilising several laboratory and imaging techniques. The ACF will be based in the Mayer Laboratories for IgAN, supported by a large multidisciplinary team of clinician and post-doctoral scientists. Previous NIHR ACFs from our group have been successfully awarded personal fellowships (MRC, Wellcome Trust, KRUK) to continue their studies towards a higher degree.

This is an opportunity to gain experience in translational research and experimental medicine studies, and to develop skills to become an independent clinical investigator in renal medicine.

Delineating druggable treatment targets for IgA vasculitis and IgA nephropathy using spatial transcriptomics, proteomics and pathomics.

Supervisor: Dr Haresh Selvaskandan (hs328@le.ac.uk)

IgA nephropathy (IgAN) is the most common primary glomerular disease worldwide. It affects young adults with most progressing to kidney failure. Several treatments for IgAN have recently been approved. In contrast, IgA vasculitis (IgAV), a closely related condition histologically indistinguishable from IgAN lacks safe, evidenced-based treatment options. There is a critical need to identify disease-specific mechanisms and therapeutic targets for IgAV.

This project will leverage the expertise of the Leicester IgA nephropathy group and an international network of collaborators to apply spatial transcriptomics to kidney biopsies from patients with IgAN and IgAV. Using advanced platforms (GeoMx and Xenium), we will resolve cell specific gene signatures in glomerular and interstitial compartments to identify shared and divergent disease pathways which may be targeted for treatment. Complementary analyses including pathomics and proteomics will be conducted with collaborators.

The samples, funding, and infrastructure for this project are already in place, and the methodology has been optimised and has been deployed on several occasions. The ACF will gain experience with spatial transcriptomics, spatial proteomics and bioinformatics, giving them a comprehensive set of transferable skills. The work will serve as a pilot project to support future fellowship/grant applications, with full support from the Leicester IgAN group.

Bioimpedance In Pregnancy and Labour: A fluid balance concept study

Supervisor: Professor Tommy Mousa (hm282@le.ac.uk)

Enhancing Early Detection of Glomerular Disease through Integrated Care: A LUCID+ Study

Supervisors: Dr Rupert Major (rwlm2@leicester.ac.uk), Professor Jon Barratt (jb81@leicester.ac.uk)

Aims:To develop and evaluate integrated care pathway for earlier detection of glomerular disease supported by novel data-driven tools.

Objectives:

Map current diagnostic pathways for proteinuria and haematuria in primary care.
Develop and test a data tool within existing clinical systems to flag patients at risk of glomerular disease.
Assess the impact on case detection using retrospective and prospective data.

Methods:

Mixed-methods approach:

Electronic care data retrospective analysis to identify missed opportunities
Co-design of an electronic risk flagging tool for LUCID

Impact:This project will inform a new LUCID pathway for glomerular disease, supporting earlier diagnosis, slowing progression, and aligning with national priorities for integrated, preventative care.

Understanding the clinical utility of regular symptom screening using Patient Reported Outcome Measures (PROMs) in the dialysis population

Supervisor: Professor James Burton (jb343@le.ac.uk)

Dialysis patients have an incredibly high symptom burden, reporting at least 6 and as many as 20 different symptoms. These symptoms are intrusive and affect life participation, as well as being associated with increased rates of depression, hospital admission and mortality.

The SONG-HD study (Standard Outcomes in Nephrology) clearly identified symptom management as of critical importance to patients, caregivers, clinical staff and commissioners of care, but despite this, incorporation of tools that assess the range and impact of symptoms is not routine. The reasons behind this are multifaceted. Using itch as an example; even though as many as 40% of dialysis patient report moderate to severe itch, with a significant impact on life, 17% of patients do not report this symptom to anyone and nephrologists estimate the number to be <5%.

Working with an industry funded PhD student on the incorporation of patient reported outcome measures (PROMs) into routine care in commercial dialysis centres, this project will assess:

The routine use of PROMs (including electronic PROMs) to assess symptom burden and their acceptability to patients and staff
Ways to ensure the clinical information collected is easily available to the clinical decision maker for review
Ongoing use of PROMs to assess response to treatment and evolving symptom burden
The relevance of symptom clusters and optimum treatment for multiple symptoms

Evaluating paradoxical hypertension during ultrafiltration in a large UK haemodialysis cohort

Supervisor: Professor James Burton (jb343@le.ac.uk)

This ACF project provides the opportunity to evaluate the presence of paradoxical hypertension during ultrafiltration in one of the largest haemodialysis cohorts in the UK. This will enable:

the definition of paradoxical hypertension be determined;
identification of associated patient characteristics;
identification of associated cardiovascular outcomes.

To investigate the prevalence and pathophysiology of sleep disordered breathing in people with advanced chronic kidney disease

Supervisor: Professor James Burton (jb343@le.ac.uk)

People on haemodialysis have a very high symptom burden; both sleep and disordered breathing are common and of critical importance to patients. Presence of sleep apnoea syndrome is thought to increase as chronic kidney disease (CKD) progresses, with prevalence being highest in those with end-stage kidney disease receiving dialysis. We have previously shown a prevalence of 59% with the majority of this being obstructive in nature. One of the predominant treatments for sleep apnoea in this population is continuous positive airway pressure (CPAP), however there is little evidence that this is effective or safe for people receiving dialysis, and no long-term data looking at the outcomes of this treatment.

This project will involve liaising with the local sleep service to interrogate their long-term outcomes. Furthermore, this project will also interrogate a national primary care database (CPRD) to investigate the long term outcomes of people receiving haemodialysis and CPAP treatment.

In summary this project will:

Describe the treatment pathway for those identified with sleep disordered breathing
Evaluate the management and potential for elevated risk cardiovascular risk
To investigate the effect of CPAP on long term outcomes

Potential topics for a Doctoral Fellowship include symptom burden, sleep disordered breathing, CV risk in a CKD population

Improving Cardiovascular Outcomes through Precision Adherence Testing

Supervisors: Dr Pankaj Gupta (pg118@leicester.ac.uk), Professor Prashanth Patel (pp260@leicester.ac.uk), Professor Ian Squire (is11@leicester.ac.uk)

The ACF will contribute to answer the following questions

Can CAT improve outcomes in chronic cardiovascular diseases such as heart failure and renal disease?
Why are patients non adherent?

The research will entail

Feasibility studies embedding CAT in specialist clinics.
Qualitative and implementation science research exploring why patients do not take prescribed treatments.
Co-designing and evaluating behavioural and system-level interventions to improve outcomes.

Data-Driven Phenotyping for Multi-Omic Research

Supervisor: Dr Richard Packer (richard.packer@leicester.ac.uk)

Trauma and Orthopaedics

Biomechanical Performance of Different Surgical Techniques in Midcarpal Arthrodesis – Finite Element Analysis (FEA) Study

Supervisor: Mr Harvinder Singh (hps9@leicester.ac.uk)

Midcarpal arthritis results mainly from traumatic fractures in the intercarpal region or long- term conditions such as repetitive stress and carpal instability due to cartilage damage. The two most relatively common patterns of wrist arthritis are scapholunate advanced collapse (SLAC), occurring after attenuation of the scapholunate ligament, and scaphoid non-union advanced collapse (SNAC), occurring after a scaphoid fracture.

In advanced stages of the disease with debilitating pain and symptoms, midcarpal fusion(arthrodesis) is a mainstay of treatment. 3 Considering variation in surgical techniques and fixation methods, current practice favours four-corner fusion. However, other methods such as two or three-corner fusion, with/- out triquetral excision, have gained popularity, with comparable functional outcomes. Screw migration, however, remains a concern, particularly with two-corner fusion technique.

While FEA research assessing carpal anatomy and wrist kinematics in different pathologies has been evolving, to our knowledge, there is no published data on the biomechanical performance of different midcarpal fusion techniques and corresponding wrist kinematic. FEA allows detailed analysis of contact location and stress/pressure, which is challenging in cadaveric or in vivo studies. We therefore proposed this experimental computer-based FEA study, to answer the research questions: ‘Does the method of fusion have any impact on wrist/ midcarpal biomechanical performance, undergoing similarly applied forces? How does stress change/concentrate between bone-bone, and bone-implant interface?’

CT-Based Assessment of Glenoid Retroversion in Preoperative Planning for Reverse Shoulder Arthroplasty: A Cross-Sectional Imaging Study

Supervisor: Mr Harvinder Singh (hps9@leicester.ac.uk)

Background: Accurate assessment of glenoid morphology, particularly retroversion, is critical for optimal implant positioning in reverse shoulder arthroplasty (RSA). Glenoid retroversion has been associated with increased complication rates, implant loosening, and poorer functional outcomes if not properly addressed. While preoperative CT imaging is widely used, variation exists in methods for measuring retroversion, and its influence on surgical planning remains underexplored.

Aim: This study aims to quantify glenoid retroversion using standardized CT scan analysis in patients indicated for RSA in the local shoulder arthroplasty database and to evaluate the impact of varying degrees of retroversion on proposed surgical plans, including augmentation strategies and implant selection.

Methods: A retrospective cross-sectional study will be conducted using preoperative shoulder CT scans from patients scheduled for RSA using the local shoulder arthroplasty database at a tertiary orthopaedic centre. Glenoid version will be measured using validated 2D and 3D reconstruction techniques. Correlation with proposed surgical plans (standard vs augmented baseplates, reaming angles, correction strategies) will be assessed through multidisciplinary surgical planning meetings.

Expected Outcomes: The study will identify thresholds of retroversion influencing surgical planning decisions, percentage of shoulder with significant retroversion and provide data to support standardised CT-based planning protocols. This will enhance preoperative decision-making and may reduce intraoperative uncertainty and improve long-term outcomes in RSA.

Impact: Results will inform future prospective studies and support the development of image-guided planning tools, aligning with NIHR ACF and ACL projects and NHS goals for improving surgical precision and patient-specific orthopaedic care.

Attention-Guided Multi-View Deep Learning Model for Ankle Fracture Detection and localization in Large-Scale UK Radiograph Data

Supervisor: Mr Jitendra Mangwani (jm753@leicester.ac.uk)

An innovative deep learning model is proposed to improve detection and localization of ankle fractures using large-scale UK radiograph data. This approach aims to reduce diagnostic errors and support rapid, explainable clinical decisions in high-demand settings.

Model architecture and function: The model uses a shared feature extractor backbone common to all radiographic views, followed by two branches: a classification branch for binary fracture detection and a localisation branch producing interpretable overlays highlighting fracture regions.

Technical design details: Feature maps extracted by the shared encoder are combined with view embedding vectors and processed through cross-view attention fusion for classification, while view-specific decoders with attention modules generate segmentation masks or heatmaps for clinical interpretation.

Clinical impact and potential: The project targets emergency and orthopaedic professionals, addressing a known diagnostic challenge with a scalable, clinically informed approach that may lead to intellectual property with significant global commercial potential.

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Cardiology

Developing a clinical acuity score for triage and outcome prediction in the Atrial Fibrillation Virtual Ward

Transforming the Understanding and Sudden Death Risk Prediction Post-MI

Bioimpedance In Pregnancy and Labour: A fluid balance concept study

Understanding the pathogenesis of post-cardiac surgery bleeding

In silico modelling of RCTs of prehabilitation interventions using routinely collected healthcare data with mechanistic validation in established bioresources

The use of pathway analysis to explore the trajectory of immune cells in inflammageing using bone marrow and myocardial tissue from cardiac surgery patients

Evaluating paradoxical hypertension during ultrafiltration in a large UK haemodialysis cohort

Improving Cardiovascular Outcomes through Precision Adherence Testing

Data-Driven Phenotyping for Multi-Omic Research

Unlocking Respiratory Disease Mechanisms: A Machine Learning Approach to Genetic Pleiotropy

Weight loss interventions for the reversal of stage B heart failure in people living with multiple long-term conditions

Endocrinology and Diabetes

Diabetes Research Centre

LC TO DETERMINE

Optimising pharmacological management of diabetes in pregnancy through a novel self-titration pathway.

Effect of maternal diet and physical activity upon glycaemia in women with type 1 diabetes in pregnancy using Hybrid closed loop technology

A Machine Learning Approach to Chronic Pain: From Data Imputation to Genetic Discovery

Data-Driven Phenotyping for Multi-Omic Research

Decoding the HLA Region: A Big Data Approach to Disease Associations

Weight loss interventions for the reversal of stage B heart failure in people living with multiple long-term conditions

General Practice

Designing an Equitable Primary Care Pathway for Obesity Management

Cluster-informed care for multiple long-term conditions(MLTC): evidence synthesis and co-design

Enhancing Early Detection of Glomerular Disease through Integrated Care: A LUCID+ Study

Implementation of comprehensive geriatric assessment (CGA) in primary care settings: a realist evaluation

System level evaluation of hospital at home services for diverse communities

Improving access to physical health care for older people in mental health settings: The ImPreSs-Care Study

Improving Cardiovascular Outcomes through Precision Adherence Testing