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Full NameProfessor Mark Little
Department:Trinity Health Kidney centre
Organisation:Trinity College Dublin
- infectious disease and the immune system
- cell and developmental biology/regenerative medicine
- epidemiology/population health research
- Public Health
- Health Informatics
- Vascular Medicine
The Trinity Health Kidney Centre is an academic health science centre comprising the academic department of Nephrology in Trinity College Dublin and the clinical nephrology units in St James's and Tallaght Hospitals. Research is driven through several strands:
1. Renal Inflammation Group, a translational medicine programme focused on investigation of pathogenesis and discovery of biomarkers of disease in autoimmune disease. The principal research interest is in ANCA vasculitis, an autoimmune condition that causes multi-organ failure as a consequence of overwhelming necrotising inflammation affecting small blood vessels. To assist in answering these questions, we have set up infrastructure that allows collection of longitudinal clinical data in the form of a national registry and biobank (www.medicine.tcd.ie/thkc/research/RKD-Registry-Biobank.php), which is linked to a basic science laboratory.
2. Chronic Disease Informatics group (https://www.tcd.ie/medicine/thkc/research/chronic.php), a trans-disciplinary unit that combines Nephrology, computer science and statistics to address research questions related to autoimmune and kidney disease using novel data science methodologies. We are linked closely with the ADAPT SFI centre (https://www.adaptcentre.ie/).
3. Translational Immunology Research Group, a molecular biology laboratory with expertise in cell signalling, immune evasion and cellular immunology
We are seeking clinician innovators with an interest in computer science, statistics and/or cellular immunology to drive forward a novel project aimed at risk stratification in autoimmune disease.
We propose a new way of managing chronic diseases bringing together the fields of medicine, immunology and data science. We hypothesise that the immune system in the relapsing and remitting autoimmune kidney disease ANCA vasculitis is measurably different when defined using multi-modal assessments, including novel biomarkers, clinical datasets and patient-reported outcomes.
The ultimate goal of this approach is to define the signature of vasculitis relapse and use this to aid in planning and delivery of optimum immunosuppressive therapy at the level of the patient. To achieve this, we will use advanced data science methodologies and Bayesian statistical techniques to develop a data architecture that curates and combines data from four sources: Fixed patient-level factors (HLA-DP phenotype, granular clinical dataset obtained at diagnosis), External medical influences (maintenance immunosuppression, antibiotic prescriptions, Hospital Inpatient Enquiry records), patient-reported symptoms (using a smartphone app) and novel biomarker assessment (e.g. urine sCD163, calprotectin, flow cytometry, plasma transcriptomics). These will be fused using semantic web technology to create a machine-readable time series dataset.
We shall use this approach to differentiate for the first time the relapsing from the long-term remission off therapy patient.