This research investigates how Type 1 diabetes affects bone development during childhood and adolescence. Using high-resolution bone imaging and blood glucose data, the study explores whether blood sugar levels, variability, and disease duration influence bone health. Early findings suggest that diagnosis closer to puberty may be associated with lower bone density.

This research explores how to improve STI testing uptake within African and Caribbean communities in the UK. Using evidence reviews, interviews, and co-production workshops guided by the ACE framework, the project develops community-informed sexual health interventions designed to increase trust, accessibility, and acceptance of STI testing while reducing stigma and health inequalities.

This research uses wastewater-based epidemiology to monitor antibodies excreted by communities, providing early insights into population vulnerability to infectious diseases. By analyzing antibody trends in wastewater over time, the work helps public health authorities identify at-risk communities, allocate resources more effectively, strengthen vaccination strategies, and improve outbreak preparedness.

This research introduces iCares, a smart wound-monitoring bandage designed to detect infection and inflammation before visible symptoms appear. Using biosensors, fluid sampling, and machine learning, the system provides real-time wound analysis, enabling earlier intervention, personalized treatment, reduced complications, and improved healing outcomes for patients with chronic wounds.

This research develops a fast, space-efficient method for assessing fall risk in older adults using wearable sensors and AI analysis of a seated foot-tapping task. Early findings show that slower, inconsistent tapping predicts higher fall risk. The approach could improve prevention strategies, reduce healthcare costs, and help older adults maintain independence.

This study explores anemia as a potential risk factor for dementia, finding that nearly half of dementia patients also exhibit low hemoglobin levels, often undiagnosed. By highlighting links between blood health and cognitive decline, the research advocates earlier detection and a multidisciplinary approach to reduce dementia’s growing societal and healthcare burden.

This research uses wearable data and AI to detect disease earlier by analyzing continuous health signals rather than isolated clinical snapshots. By personalizing models to individual baselines, the system identifies subtle changes linked to conditions like infections, heart issues, and mental health crises, enabling earlier intervention and potentially saving lives.

This research shows that genetic risk scores alone are insufficient for predicting chronic disease. By incorporating social and environmental factors using machine learning, disease prediction improves substantially, especially for disadvantaged populations. Integrating genetic and social risk is essential for equitable, effective personalized medicine.

This research develops a protein-based detection technology capable of identifying subtle molecular changes months before disease symptoms appear. By adapting nanopore sequencing with a protein “detangler,” it enables early warning for conditions like leukemia, shifting medicine from reactive treatment to proactive disease prevention.