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 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 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.