Clinical Medicine projects

Consult this page for a list of research projects based in the Division of Clinical Medicine.

A skin test conducted by a medical researcher

The effect of a dysregulated hypothalamic-pituitary-adrenal axis on disease outcomes in two paediatric cohorts

How can steroid hormone profiling improve understanding and monitoring of congenital adrenal hyperplasia and obesity-associated hypogonadotropic hypogonadism in children and young people?

Learn more about this project

Supervisors

Professor Nils Krone (n.krone@sheffield.ac.uk)

Dr Joseph Tonge (joseph.tonge@sheffield.ac.uk)

Abstract and methodology

The Hypothalamic-Pituitary-Adrenal (HPA) axis is a vital endocrine regulatory system responsible for the production of cortisol and adrenal sex hormones. A dysregulated HPA axis can have long term complications such as obesity, cardiovascular disease and impaired growth. This clinical based BSc project will involve consenting and recruiting patients to two clinical trials involving children with a dysregulated HP axis, at Sheffield Children’s Hospital.

Study 1: Non-invasive salivary study in children with Congenital Adrenal Hyperplasia

This is a multi-centre study, run by Sheffield. The student will be involved in the day-to-day running of this study, gaining hands on experience of clinical trial management. Ethical approval and research sponsorship are already in place for this project.

Study 2: Understanding the involvement of steroid hormones in the development of hypogonadotropic hypogonadism in males with childhood obesity

This is a single centre cohort study conducted within South Yorkshire Complications of Excessive Weight (CEW) Service. Ethical approval and research sponsorship are already in place for this project. The student will be involved in patient recruitment, patient and public involvement activities, data collection and data analysis.

Type of project

Clinical or Surgical project - based in the clinical environment with patients/including service evaluation

Additional training or teaching

We would expect that the work produced during this BSc will be presented internationally and nationally. We would also expect the student to assist writing up the data for publication in a medical journal. Students would get the opportunity to attend Paediatric Endocrinology clinics at Sheffield Children’s Hospital, should they wish.

Ethics requirements

Original research involving human tissues/human participants and/or patient details and information - UREC or NHS REC ethics approval obtained already

Investigating novel tissue resident macrophage populations in vivo

The overarching question behind this research is to understand the different roles different types of white blood cell play in vivo and the key genes that underpin those divergent roles.

Learn more about this project

Supervisors

Iwan Evans (i.r.evans@sheffield.ac.uk)

Simon Johnston (s.a.johnston@sheffield.ac.uk)

Abstract and methodology

The white blood cells known as macrophages play an essential role in development, homeostasis, immunity and repair. The macrophage lineage is highly heterogeneous owing to the existence of tissue-resident populations alongside the ability to become activated to a range of states (termed macrophage polarisation).

Our lab used the genetically-tractable model organism Drosophila melanogaster (fruit flies) to understand regulation of macrophage function in vivo. We have recently discovered, for the first time, the existence of developmentally-regulated macrophage subpopulations in this organism. Fruit flies have been extensively utilised to understand immunity, not least in its role in the discovery of the immunoregulatory role of Toll receptors, for which Hoffmann received the Nobel Prize for Medicine.

This project aims to understand the function of these subpopulation macrophages and their relationship to vertebrate macrophages in more detail.

In order to understand the importance of these subpopulations the student will use genetic strategies to manipulate subpopulation macrophages at various stages of the fly life cycle and examine the consequences for development, survival and immunity.

Genes that are differentially expressed between subpopulations will also be investigated to understand the contributions that they make to immune responses in vivo.

Type of project

Lab/Bench Project - primarily working in a lab environment

Additional training or teaching

This project involves genetics, molecular biology, in vivo imaging, microscopy, image processing and analysis and statistical analysis. The student will take charge of their own Drosophila stocks and conduct genetic crosses to generate progeny in which subpopulation macrophages have been manipulated or candidate genes removed/overexpressed.

The resulting fly embryos, larvae and adults will be analysed in a range of assays to understand the role of these subpopulation macrophages. Live imaging will then be analysed using software including Fiji and quantitative data obtained from these images. Other non-microscopic data will also be collected (eg survival of flies following infection/injury).

Ethics requirements

Non-human tissue - no ethics approval required

Clinical research studies in functional gastrointestinal disorders

Functional Gastrointestinal Disorders (FGID) affects almost 40% of the population. They are when an individual has gut symptoms but no active organic disease (e.g. no cancer, inflammation, infection etc). The most commonly recognised FGID is irritable bowel syndrome, which affects 10% of people.

We are a world-renowned clinical academic group in FGIDs and the supervisor (Dr Imran Aziz, Consultant Gastroenterologist) has over 100 publications. Since 2020, Dr Aziz has supervised seven BMedSci students, all of whom have been involved in a) clinical, patient-facing, studies, b) achieved a first for their BSc degree c) presented at national conferences, and d) published a first author scientific paper within a year. Dr Aziz has received five Educational Award Nominations.

Learn more about this projects

Supervisors

Dr Imran Aziz (imran.aziz1@nhs.net)

Professor David Sanders (david.sanders1@nhs.net)

Abstract and methodology

This clinical project in gastroenterology will focus on functional gastrointestinal disorders (FGIDs), which are the most commonly encountered within hospital gastroenterology practise and also in GP, yet largely undertaught within medical schools. The student will become familiar with understanding these conditions and also involved in clinical studies being undertaken in this patient group.

During the intercalation process, you will have the chance to meet with the consultant supervisors within our department who are offering upcoming projects. You may be working with a database or clinical letters, you may have the opportunity to get involved with departmental recruiting (if you wish) for other studies, you will work alongside our nursing and medical team and will gain clinical skills whilst supporting an endoscopy list.

The student will gather data, learn how to analyse this and write up as presentations and papers. They will be regularly supervised and mentored by Dr Imran Aziz, a senior lecturer and consultant gastroenterologist within an international profile.

Type of project

Clinical or Surgical project - based in the clinical environment with patients/including service evaluation

Additional training or teaching

The student will learn how to analyse datasets using SPSS, present work orally and as poster presentations at conferences, write original articles, submit papers as first author, and address reviewers comments. The student will also have the opportunity to attend clinical gastroenterology sessions and weekly departmental lectures. The student will be expected to submit their work to conferences and attend these. Finally, the student will have the opportunity to collect data for other ongoing clinically-based projects culminating in co-authorship in future papers.

Ethics requirements

Original research involving human tissues/human participants and/or patient details and information - UREC or NHS REC ethics approval needed

Rare disease phenotyping and genotype-phenotype correlation for HNRNP-related disorders

In-depth phenotyping of individuals with HNRNP-related disorders.

Learn more about this project

Supervisors

Professor Meena Balasubramanian (m.balasubramanian@sheffield.ac.uk)

Stuart Wilson (stuart.wilson@sheffield.ac.uk)

Abstract and methodology

Heterogeneous nuclear ribonucleoproteins (HNRNPs) are a large family of RNA-binding proteins that play a part in mRNA biogenesis with roles in pre-mRNA splicing, polyadenylation, capping, modification, export, localisation, translation, and turnover (Wu et al., 2018). Their ability to contribute to multiple steps in the biogenesis and use of mRNAs demonstrates their versatility as a protein family. The functional flexibility the HNRNP gene family possesses can be explained in part by their ability to produce multiple alternatively spliced isoforms and their ability to form complexes with other HNRNP members (Geuens et al., 2016).

HNRNPs have been linked to various diseases, including cancer; neurodevelopmental disorders such as spinal muscular atrophy, amyotrophic lateral sclerosis, congenital myasthenic syndrome, multiple sclerosis, Alzheimer’s disease, and fronto-temporal lobe dementia (Low et al., 2021). Their key roles in regulating transcriptional and post-transcriptional gene expression and their links to numerous diseases mean that it is important to research these conditions with potential wider utility.

In this project, the student will recruit patients with HNRNP-related disorders to an ongoing natural history study for which the primary supervisor is the PI. The project will focus on collating clinical information, undertaking literature review, generating methylation episignature and publishing deep dive datasets in this group of disorders.

The methods include

Clinical phenotyping;
Data collation from clinical records
Developmental assessment and co-ordination
DNA samples for methylation episignature
Patient consent
Writing up case series of patients with rare genetic disorders

Type of project

Clinical or Surgical project - based in the clinical environment with patients/including service evaluation

Additional training or teaching

Variant interpretation; clinical phenotyping; lab work if interested.

Ethics requirements

NHS Service Evaluation number required.

University ethics checks will additionally be required.

Role of NBAS in human disease using zebrafish models

Neuroblastoma amplified sequence gene (NBAS) affects skeletal development in zebrafish and drug screening assays can be used to identify targets that can rescue NBAS activity in a zebrafish model.

Learn more about this project

Supervisors

Professor Meena Balasubramanian (m.balasubramanian@sheffield.ac.uk)

Professor Steve Renshaw (s.a.renshaw@sheffield.ac.uk)

Abstract and methodology

Whole genome sequencing studies have led to a vast amount of new candidate genes for human diseases. One such gene is NBAS (neuroblastoma amplified sequence gene) which when mutated results in acute liver failure and skeletal abnormalities such as SOPH syndrome, short stature, optic atrophy or Pelger-Huet anomaly [Maksimova et al., 2010]. Patients with NBAS mutations are subjected to a lifetime of recurrent fractures, repeated episodes of acute liver failure needing recurrent hospital admissions and immune deficiency [Balasubramanian et al., 2017]. Recently we have developed a zebrafish model which carries mutations in NBAS and displays skeletal malformations that are reminiscent of the human condition. Analysis suggests that NBAS may play a role in the secretion of collagen.

The overarching hypothesis would be 'NBAS causes a multi-system disorder affecting skeletal development, liver and immune abnormalities in zebrafish: unravelling disease mechanism'.

Methods include:

Analysing skeletal and liver abnormalities in NBAS mutant zebrafish
Comparing with manifestations in Crispr fish
Drug screening to identify potential therapeutic hits for further work-up

Type of project

Lab/Bench Project - primarily working in a lab environment

Additional training or teaching

Genotype-phenotype correlation
Zebrafish genotyping
Skeletal analysis in zebrafish
Immune response in zebrafish model
Literature review of nbas phenotypes

This BSc project builds upon our work focusing on the zebrafish model to unravel the molecular causes of this disease and to develop a platform for high-throughput drug screens to identify drugs that may one day be used in the clinic. During your training year, you will use cutting-edge techniques such as lightsheet and AIRY scan microscopy, CRISPR/Cas9 gene editing and robot based drug library screening.

Ethics requirements

Non-human tissue: no ethics approval required

Developing new molecules to treat leishmaniasis and malaria

We have developed a computer-based model that has identified compounds that inhibit a key enzyme required in leishmania parasites, the causative agent of leishmaniasis . A small number of such compounds were tested in the relevant organism and were also shown to parasites. We now wish to expand the study to see whether we can

develop more potent and specific compounds
characterise the enzyme and the inhibitor complexes
assess selectivity of the compounds using in vitro and cell culture models
use the data generated to improve the predictive capability of our computer models

Learn more about this project

Supervisors

Jon Sayers (j.r.sayers@sheffield.ac.uk)

Fadi Soukarieh (f.soukarieh@sheffield.ac.uk)

Abstract and methodology

Leishmania species (and closely related genus Trypanosoma) cause a variety of neglected tropical diseases, including visceral and cutaneous leishmaniasis, sleeping sickness and Chagas disease. These infections cause significant morbidity and mortality. Using computer-based analysis of the 3D structures Leishmania flap endonuclease protein, we have identified a small set of compounds that selectively inhibit the parasite enzyme but not the human equivalent. Furthermore, the compounds killed parasites grown in the lab.

This project is laboratory based. Prior experience is unnecessary. All training will be provided by the research group. You will learn how to grow the organism safely and carry out susceptibility tests using a small number of our novel anti-FEN compounds. This is likely to take a few weeks. After this you will produce genetically engineered E. coli to manufacture Leishmania FEN proteins and use it to characterise the interaction between inhibitor and enzyme. You will also produce crystals of FEN protein with inhibitor and carry out X-ray crystallography to determine protein-inhibitor complex structure. You will be trained in the use of molecular structure viewing, fluorescence-based enzyme inhibitor assays and structure determination. Your results will support our efforts in rational drug design for the chemotherapies targeting these important pathogens.

Type of project

Lab/Bench Project: primarily working in a lab environment

Additional training or teaching

Training in molecular docking, in silico screening and protein structure analysis will be provided in-house.

Ethics requirements

Non-human tissue: no ethics approval required

Involvement of the brain in painful diabetic peripheral neuropathy

What is the progression of structure and functional changes within the brain in painful diabetic peripheral neuropathy (DPN)?

Learn more about this project

Supervisors

Dr Gordon Sloan (gordon.sloan@nhs.net)

Dr Dinesh Selvarajah (d.selvarajah@sheffield.ac.uk)

Abstract and methodology

Overall aim: Determine the changes in brain structure in key regions associated with somatosensory function (S1 cortex and thalamus) and nociception (insular cortex and anterior cingulate cortex) from baseline in patients with painful DPN compared to patients with painless DPN or no DPN.

The student will both be involved in the assessment of participants within the study, which involve detailed clinical and neurophysiological tests. The students will be trained to perform some of these study visits independently. Moreover, the study will involve analysis of the data, including brain imaging data.

In addition to the report, it is expected that this study will lead to publications in peer reviewed journals, for which the student will be included within the authorship. Moreover, the student may be able to present the work at national and international professional conferences.

Type of project

Clinical or Surgical project - based in the clinical environment with patients/including service evaluation

Additional training or teaching

The student will be given teaching on how to analyze brain imaging data using specific analysis software packages.

Ethics requirements

Original research involving human tissues/human participants and/or patient details and information: UREC or NHS REC ethics approval obtained already

AI methods for analysis of magnetic resonance imaging in people with diabetes

Can AI-driven analysis of abdominal MRI detect early effects of diabetes on multiple organs?

Learn more about this project

Supervisors

Professor Steven Sourbron (s.sourbron@sheffield.ac.uk)

Ebony Gunwhy (e.gunwhy@sheffield.ac.uk)

Abstract and methodology

In the past few years off-the-shelf artificial intelligence (AI) methods have become widely available that can analyse complex MRI images of the abdomen in minutes: a task which only five years ago would have cost a single person weeks of intensive manual labour. This vast amount of new information promises the discovery of biomarkers that can detect subtle effects of subclinical disease.

We are currently evaluating these ideas in an ongoing study which has collected with MRI scans of 525 people with diabetes, in an EU project on diabetic kidney disease (BEAt DKD). The aim of the study was to see if novel, advanced MRI methods are able to detect the effects of diabetes in the kidney long before these become apparent in current clinical indices such as blood and urine measurements. While the study focused on the kidney, diabetes is a multi-organ disease and many of the organs affect by it can be studied using the same scans (pancreas and liver, but also spleen, blood vessels, gut, fat).

Training, verifying and retraining AI systems requires a good understanding of anatomy, medical terminology as well as the effect disease may have on the images. The students who take this project will play a central role in this process, and use the results to test whether AI-generated results allow early detection of the effects of diabetes.

Type of project

Lab/Bench Project - primarily working in a lab environment

Additional training or teaching

Visualisation, interpretation and analysis of MRI
Basic understanding of AI methods for medical image analysis
Clinical interpretation of novel MRI biomarkers in diabetes.
Communication and working within an international consortium of scientists and clinicians from different backgrounds

Ethics requirements

Non-human tissue: no ethics approval required.

Secondary data or tissue sample: UREC or NHS REC ethics approval already received for the intended research project.

First-phase ejection fraction in pulmonary hypertension

Can first-phase ejection fraction (EF1) predict clinical outcomes and treatment response in pulmonary hypertension, particularly in patients with co-morbid left heart disease?

Learn more about this project

Supervisors

Dr Roger Thompson (R.Thompson@sheffield.ac.uk)

Dr Hameed Abdul (ahameed@nhs.net)

Aims

This project aims to evaluate EF1 as a biomarker for prognosis and therapeutic efficacy in PH, focusing on subgroups with left heart comorbidities.

Objectives

Assess correlations between EF1 and clinical outcomes such as survival and disease progression.
Investigate EF1 changes in response to PH-specific treatments.
Identify differences in EF1 utility between PH patients with and without left heart disease.

Methodology

The study will use data from the ASPIRE registry, a comprehensive database containing imaging, clinical, and outcome data from over 1,500 patients. Retrospective analysis will focus on PH patients diagnosed via right heart catheterisation, with subgroup stratification for those with co-morbid left heart disease. EF1 will be calculated from stored cardiac magnetic resonance images using specialised software for image analysis, measuring the fraction of stroke volume ejected in the first phase of systole. Data extraction will include demographics, haemodynamics, functional class, and follow-up events.

Statistical analysis will employ R software for multivariable Cox regression to predict outcomes, logistic regression for treatment response, and Kaplan-Meier survival curves. Machine learning techniques, such as random forests, may be applied to identify EF1-based predictive models. Training in R programming, image processing, and statistical methods will be provided. Ethical approval is in place for registry use.

The project will generate data demonstrating EF1's prognostic value, potentially identifying thresholds for risk stratification in PH with left heart comorbidities. Specific findings may include hazard ratios for mortality based on EF1 quartiles and correlations with treatment-induced improvements (e.g. in walk test distance and quality of life). Results could contribute to a peer-reviewed publication, with the student as co-author, and be presented at conferences like the European Respiratory Society meeting.

Type of project

Clinical or surgical project: based in the clinical environment with patients/including service evaluation

Additional training or teaching

Students will receive hands-on training in cardiac image analysis, data management from clinical registries, and statistics via R (including regression modelling and survival analysis). Students will attend weekly PH multidisciplinary team meetings, clinics, and research seminars. Good clinical practice training will also be completed.

While most of the research is retrospective, there will be opportunities to contribute to prospective clinically-facing research studies throughout the project. Experience of cardiac image analysis and cardiopulmonary physiology would prove particularly useful for future careers in radiology, respiratory medicine or cardiology.

Ethics requirements

Secondary data or tissue samples: UREC or NHS REC ethics approval already received for the intended research project.

Haemodynamic and autonomic profiling from reconstructed right heart catheterisation waveforms in pulmonary arterial hypertension

Can detailed reconstruction and analysis of right heart catheterisation (RHC) waveforms provide haemodynamic and autonomic indices that predict clinical outcomes in pulmonary arterial hypertension (PAH)?

Learn more about this project

Supervisors

Dr Marcelle De Paula Ribeiro (m.depaularibeiro@sheffield.ac.uk)

Professor Alex Rothman (a.rothman@sheffield.ac.uk)

Abstract and methodology

Pulmonary arterial hypertension (PAH) is a progressive disease driven by vascular remodelling and right heart dysfunction. This project aims to reconstruct and analyse right heart catheterisation (RHC) waveforms from a large clinical database to extract detailed haemodynamic and autonomic parameters.

Using advanced signal processing and statistical modelling, students will derive pressure-based indices and heart rate variability metrics, integrating these with demographic, functional, and outcome data (survival, functional class). The study will assess how autonomic and haemodynamic patterns interact to predict clinical outcomes and disease progression. The project combines computational physiology with outcome-based research to identify novel prognostic markers in PAH.

Type of project

Clinical or Surgical project: based in the clinical environment with patients/including service evaluation.

Additional training or teaching

Students will receive training in pre-processing and reconstructing RHC waveforms, deriving haemodynamic and autonomic indices, and performing statistical analyses. They will learn to link physiological metrics with clinical outcomes through data integration and modelling.

The project provides multidisciplinary experience in cardiovascular physiology, signal processing, and clinical research, equipping students with skills to explore autonomic and haemodynamic predictors of disease severity and prognosis in PAH.

Ethics requirements

Secondary data or tissue samples: UREC or NHS REC ethics approval already received for the intended research project.

The effect of breath holding on renal oxygenation as seen with magnetic resonance imaging

MRI can detected changes in renal oxygenation during a breath hold, as well as the physiological protective response of the kidney at risk of ischemia.

Learn more about this project

Supervisors

Professor Steven Sourbron (s.sourbron@sheffield.ac.uk)

Joao Periquito (j.s.periquito@sheffield.ac.uk)

Abstract and methodology

The oxygen levels in the kidney play a critical role in the progression of chronic kidney disease, but their exact role as a driver for progression in humans is poorly understood. MRI methods to study renal oxygenation have been used extensively in diseases such as diabetes and renal artery stenosis. These methods rely on subjects to hold their breath during the scan, but as breath holding induces deoxygenation of blood there is a risk that this confounds the observations.

In a recent pilot study in healthy volunteers we have demonstrated that the effect of breath holding on renal oxygenation is measurable with MRI, and reproducible. The effect was mostly linear, but an accidental finding in a single volunteer who managed a very long breath hold (90s) showed a sudden non-linear response, suggesting the activation of a (hitherto hypothetical) physiological switch to protect the kidney from ischemic damage. We want to now perform further experiments to confirm these findings, study them in more detail, and consider possible clinical implications.

A first series of experiments will continue in healthy volunteers, this time specifically aiming to recruit larger numbers of people who can manage long breath holds to see if we can reproduce the physiological switch. The students who take on this project will have to identify and recruit candidates, for instance by contact groups of free-drivers, high altitude runners, or people who practice medication. MRI scans with long breath holds will be performed in these subjects, and students will analyse the scans to see if we can identify and characterise the physiological switch reproducibly. If so, we expect this will generate exciting new insights into renal physiology and renoprotective mechanisms, and any effect their impairment may have in disease.

Type of project

Lab/Bench Project - primarily working in a lab environment

Additional training or teaching

Renal physiology and MRI of the kidney
Running and optimizing MRI scans
Interpreting MRI data
Analysing scans to derive imaging biomarkers

Ethics requirements

Original research involving human tissues/human participants and/or patient details and information: UREC or NHS REC ethics approval obtained already.

Response assessment of neoadjuvant treatment for rectal cancer with functional MRI and photon-counting CT

Novel functional MRI and photon-counting CT methods will provide more accurate response assessment of neoadjuvant treatment in rectal cancers and allow better informed decision of surgical versus conservative management options.

Learn more about this project

Supervisors

Professor Steven Sourbron (s.sourbron@sheffield.ac.uk)

Dr Nadhirah Kahar (nnabdkahar1@sheffield.ac.uk)

Abstract and methodology

Patients that are offered surgical removal of primary rectal cancers typical undergo a course of neoadjuvant chemotherapy before surgery, with a view of downstaging the cancer before surgical removal. In some cases the routine assessments that are performed after this course of chemotherapy find that the cancer is no longer visible. In that case, the patient may be offered a more conservative watch-and-wait management option, with frequent follow-ups to check for recurrence. The decision is critical as the surgery itself often has significant impact on quality of life, but also because a conservative management may allow for recurrence if the cancer has evaded detection but has not been fully destroyed.

We believe that novel MRI methods developed in Sheffield may increase confidence in these decisions by allowing us to say with more confidence whether or not a tumour has indeed been fully obliterated. These methods include diffusion- and perfusion weighted imaging, which in other application areas have demonstrated capability of early detection of recurrence. Additionally new cutting edge photon-counting CT (pCT) scanners also have shown higher sensitivity to subtle changes that could indicate early recurrence.

We are currently running a pilot study to verify this hypothesis. In this study we are recruiting patients selected for surgery, who will undergo and extra MRI and pCT before chemotherapy, and extended MRI and pCT after chemo. The images will be analysed with a view of detecting signs of recurrence that are not visible on conventional clinical assessment. The student(s) who take this project will be integrated in the study, assist the clinical fellow in performing assessments, collecting data and analysing MRI images.

Type of project

Clinical or Surgical project - based in the clinical environment with patients/including service evaluation

Additional training or teaching

MRI and pCT acquisition
Visualisation and analysis of images
Statistical analysis and interpretation of data

Ethics requirements

Original research involving human tissues/human participants and/or patient details and information: UREC or NHS REC ethics approval obtained already.

First-phase ejection fraction in pulmonary hypertension

Can first-phase ejection fraction (EF1) predict clinical outcomes and treatment response in pulmonary hypertension, particularly in patients with co-morbid left heart disease?

Learn more about this project

Supervisors

Dr Roger Thompson (R.Thompson@sheffield.ac.uk)

Dr Hameed Abdul (ahameed@nhs.net)

Abstract

This project aims to evaluate EF1 as a biomarker for prognosis and therapeutic efficacy in PH, focusing on subgroups with left heart comorbidities.

Objectives

Assess correlations between EF1 and clinical outcomes such as survival and disease progression.

Investigate EF1 changes in response to PH-specific treatments.

Identify differences in EF1 utility between PH patients with and without left heart disease.

Methodology

Statistical analysis will employ R software for multivariable Cox regression to predict outcomes, logistic regression for treatment response, and Kaplan-Meier survival curves. Machine learning techniques, such as random forests, may be applied to identify EF1-based predictive models. Training in R programming, image processing, and statistical methods will be provided. Ethical approval is in place for registry use.

The project will generate data demonstrating EF1's prognostic value, potentially identifying thresholds for risk stratification in PH with left heart comorbidities. Specific findings may include hazard ratios for mortality based on EF1 quartiles and correlations with treatment-induced improvements (eg in walk test distance and quality of life). Results could contribute to a peer-reviewed publication, with the student as co-author, and be presented at conferences like the European Respiratory Society meeting.

Type of project

Clinical or Surgical project: based in the clinical environment with patients/including service evaluation

Additional training or teaching

While most of the research is retrospective, there will be opportunities to contribute to prospective clinically-facing research studies throughout the project.

Experience of cardiac image analysis and cardiopulmonary physiology would prove particularly useful for future careers in radiology, respiratory medicine or cardiology.

Ethics requirements

Secondary data or tissue samples: UREC or NHS REC ethics approval already received for the intended research project

Bioinformatic analysis of spatial transcriptomics of human intervertebral disc tissue to unpick cellular pathogenesis.

Spatial transcriptomics holds potential to unravel the complex pathophysiology of diseases including intervertebral disc degeneration. Here, bioinformatics will be utilised to unravel the cellular pathogenesis of disc degeneration.

Learn more about this project

Supervisors

Professor Christine Le Maitre (c.lemaitre@sheffield.ac.uk)

Abstract

Spatial transcriptomics is a novel technique which enables the study of spatial changes in mRNA level across tissues, identifying cellular mRNA changes across 17000 genes simultaneously. This technique has recently been developed within our laboratories to be applied to human intervertebral disc tissues, which has not been previously achieved. Generating in depth mRNA profiles and spatial gene expression information. This project will involve the detailed bioinformatic analysis of this acquired data set.

Type of project

Lab/Bench Project: primarily working in a lab environment

Additional training or teaching

Training in bioinformatic analysis, specialist software, data analysis and Graph Pad Prism. They will also have the opportunity to shadow laboratory techniques.

Ethics requirements

Original research involving human tissues/human participants and/or patient details and information: UREC or NHS REC ethics approval obtained already

Searching for new antibiotics

How do inhibitors of bacterial nucleases kill their target organisms? What is the target spectrum of our anti-nuclease compounds?

Learn more about this project

Supervisors

Professor Jon Sayers (j.r.sayers@sheffield.ac.uk)

Professor Fadi Soukarieh (f.soukarieh@sheffield.ac.uk)

Abstract

The threat of antimicrobial resistance has led to a search for new targets. We will approach a previously unexplored target, an enzyme that destroys DNA called a flap endonuclease. Flap endonucleases (FENs) process branched DNA fragments. If this process is blocked the bacteria die rapidly. We have examined E. coli, Neisseria and Pseudomonas bacteria but want to explore other targets.

In this project you will examine a range of both Gram positive and Gram negative bacteria to determine the spectrum of these anti-FEN compounds.
You will also explore the mechanism by which the compounds act using range of lab-based and computer aided genomics analysis. For example you will use genetic engineering to produce the target protein for laboratory-based characterisation and confirmation of direct binding to the FEN enzyme.

This project is predominantly laboratory based. No prior experience is necessary as all training will be provided by the research group and our collaborators. You will learn how to handle common bacteria safely and carry out antibiotic susceptibility tests using a small number of our novel anti-FEN compounds. This is likely to take a few weeks. After this you will produce genetically engineered E. coli to manufacture specific FEN proteins from one or two susceptible bacteria and use it to characterise the interaction between inhibitor and enzyme.

You will also attempt to produce crystals of FEN protein with inhibitor (either from newly identified organisms or ones we know already are susceptible). This protein will be used in X-ray crystallography for determination of the 3D structure of the protein-inhibitor complex. You will also be trained in the use of molecular structure viewing, fluorescence-based enzyme inhibitor assays and structure determination. The outcome of this work will support our efforts in rational drug design for the antimicrobial chemotherapies of the future.

Type of project

Lab/Bench Project - primarily working in a lab environment

Additional training or teaching

All training will be done in-house.

Ethics requirements

Non-human tissue - no ethics approval required

From wearables to AI: The future of personalised diabetes care

Can digital health data, including sleep, physical activity and continuous glucose monitoring, combined with machine learning, help us understand and predict health outcomes in people living with diabetes?

Learn more about this project

Supervisors

Dr Dinesh Selvarajah (d.selvarajah@sheffield.ac.uk)

Dr Gordon Sloan (g.sloan@sheffield.ac.uk)

Abstract and methodology

Digital health technologies, such as wearable watches, sleep monitors and continuous glucose monitors (CGM), allow us to collect detailed information about people's daily lives. These technologies can track sleep patterns, physical activity and glucose levels over time. However, we still do not fully understand how these factors interact with pain and other health outcomes in people living with diabetes.

This project will use data from two research studies, to explore how digital health information can improve our understanding of diabetes. Students will use data science and machine learning methods to investigate patterns in sleep, activity and glucose levels, and explore whether these patterns are associated blood glucose control and other health outcomes.

The project is novel because it brings together information from different digital health technologies to study health in everyday life, rather than relying only on measurements taken during clinic visits. It will help identify promising digital markers that could support future research into personalised care and early identification of people who may need additional support.

Students will gain practical experience in digital health research, data analysis and artificial intelligence, developing skills that are increasingly valuable in healthcare, biomedical research and the wider technology sector. The findings will help guide future studies and the development of more personalised, data-driven approaches to diabetes care. This is an exciting opportunity to work with real-world data from major diabetes research studies, exploring how wearable technology, sleep, physical activity and glucose monitoring can help us better understand health. You will gain hands-on experience in digital health, data science and machine learning, with training and support tailored to your experience.

You do not need to be an expert in coding or AI. Curiosity, enthusiasm and a willingness to learn are what matter most. Whether your interests are in healthcare, science, computing or technology, this project offers valuable skills for the future and the chance to contribute to research that could make a real difference to patient care.

Type of project

Clinical or Surgical project - based in the clinical environment with patients/including service evaluation

Additional training or teaching

Students will receive training and supervision in:

Digital health research: Understanding wearable devices, sleep monitoring, physical activity tracking and CGM data.
Data science: Data preparation, cleaning, visualisation and exploratory analysis.
Machine learning (ML): Introduction to predictive modelling, pattern recognition and model evaluation.
Artificial intelligence (AI): Understanding how AI can be applied to real-world healthcare problems.
Research skills: Interpreting results, understanding limitations, communicating findings and considering how digital technologies could influence future clinical practice.

Students with some experience of coding languages such as Python is desirable, but this is not essential. Enthusiasm for learning, problem-solving and applying technology to healthcare is more important. The project will be tailored to the student's existing skills and experience.

Interested? Please get in touch to discuss the project, your interests and the training and support available.

Ethics requirements

Secondary data or tissue samples - UREC or NHS REC ethics approval already received for the intended research project

Original research involving human tissues/human participants and/or patient details and information - UREC or NHS REC ethics approval obtained already

Cellular memory in the airway: how past viral infections reprogram cells against new viruses

Rhinovirus infection induces epithelial cell states that exhibit cross-protective antiviral phenotypes, conferring broad-spectrum resistance against heterologous virus infection.

Learn more about this project

Supervisors

Dr Elizabeth Wignall-Fleming (e.b.wignall-fleming@sheffield.ac.uk)

Dr Ben Lindsey (b.lindsey@sheffield.ac.uk)

Abstract and methodology

Respiratory viruses cause millions of deaths every year and put significant strain on healthcare systems. Current vaccines induce a very narrow type of immunity and can be ineffective if the vaccine strain is mismatched to the circulating strain. There is growing evidence that respiratory epithelial cells can maintain a form of immune memory that can potentially protect against a broad range of viral families. We plan on studying this through the use of sequential respiratory viral infection in a primary human air-liquid interface (ALI) culture.

You will quantify viral replication following primary and secondary viral infection and validate single cell RNAseq defined cell states using either flow cytometry or immunofluorescence. By defining the specific epithelial cell states associated with heterologous viral inhibition, this research will help identify targetable host factors with the longer-term aim of designing durable, broad-spectrum antiviral prophylaxis.

Type of project

Lab/Bench Project - primarily working in a lab environment

Additional training or teaching

The student will receive hands-on training in advanced primary cell culture using Air-Liquid Interface (ALI) models and biosafety level 2 viral handling protocols. They will be trained in key wet-lab techniques, including quantitative viral replication assays, flow cytometry, and immunofluorescence microscopy.

The supervisory team will provide tailored guidance on interpreting single-cell RNA-sequencing data and performing image analysis using ImageJ. Additionally, the student will participate in weekly departmental lab meetings and work-in-progress seminars to build critical appraisal and presentation skills.

Ethics requirements

Original research involving human tissues/human participants and/or patient details and information - UREC or NHS REC ethics approval obtained already

Structural and mechanical characterisation of infant rib bone and costal cartilage in health and Vitamin D deficiency

Question: How do the structural and mechanical properties of infant rib bone and costal cartilage change during the first year of life, and how are these properties influenced by vitamin D status?

Hypothesis: The structural and mechanical properties of infant rib bone and costal cartilage change with age, reflecting normal skeletal development. Vitamin D deficiency is hypothesised to be associated with altered bone microstructure and cartilage mechanical properties, potentially affecting the stiffness and mechanical behaviour of the developing thoracic cage.

The student allocated to this project will be eligible for the Skolnick-Cannings Bursary.

Learn more about this project

Supervisors

Dr. Atousa Moayedi (a.moayedi@sheffield.ac.uk)

Prof. Enrico Dall'Ara (e.dallara@sheffield.ac.uk)

Abstract and methodology

The rib cage plays an essential role in protecting vital organs and supporting respiration. During infancy, the ribs undergo rapid structural development. Costal cartilage connecting the ribs to the sternum provides flexibility during breathing. Their mechanical behaviour depends on their density, mineralisation, and structure, which may change with age and be influenced by vitamin D status. Vitamin D deficiency can impair bone mineralisation and cartilage maturation, affecting the mechanical properties. However, quantitative information on infant ribs remains limited. The project aims to investigate changes in the structural and mechanical properties of rib bone and costal cartilage affected by vitamin D.

Specimens from infants aged between 0 and 12 months with varying vitamin D will be studied, depending on availability. Micro-Computed Tomography (Micro-CT) imaging will be used to measure cortical and trabecular bone microstructure such as thickness, bone volume, and cross-sectional geometry. Mechanical testing will assess bone and cartilage behaviour under compressive loading. 3D maps of internal displacement and strain distributions will be assessed with Digital Volume Correlation (DVC). Where feasible, histological image analysis will examine collagen organisation and its relationship with mechanical properties. Statistical comparisons and regression modelling will explore associations between age, vitamin D status, tissue structure, and mechanical behaviour.

Type of project

Lab/Bench Project - primarily working in a lab environment

Additional training or teaching

Micro-CT imaging and 3D image processing
3D image visualisation and quantitative analysis of bone microstructure and morphology
DVC (3D image analysis technique to measure full-field internal displacements and strain distributions)
Mechanical testing of bone and cartilage using specialised equipment
Histological image analysis and interpretation (where feasible)
Statistical analysis, regression modelling, and interpretation of experimental data

Ethics requirements

Secondary data or tissue samples - UREC or NHS REC ethics approval already received for the intended research project

Original research involving human tissues/human participants and/or patient details and information - UREC or NHS REC ethics approval obtained already