This research investigates how HIV disrupts the developing immune system in early life. Using a baby monkey model of HIV infection, the study identifies immune signals that either promote or limit infection spread. The findings could guide new immune-targeted therapies that improve survival and long-term outcomes for children living with HIV.

This research reveals how Streptococcus evades immune attack by shedding its hair-like surface proteins, distracting immune cells while provoking excessive immune activation. The findings provide a new explanation for how recurrent strep infections can trigger autoimmune diseases and suggest treatments should target both the bacteria and the immune response.

This research seeks blood-based biomarkers that predict which people infected with Chagas disease will later develop life-threatening cardiomyopathy. By analysing immune proteins in blood samples from Bolivia, it aims to enable earlier diagnosis, targeted monitoring, and preventative treatment, offering a model for predicting and preventing many chronic diseases before irreversible damage occurs.

This research investigates how lung mucus and its mucin molecules defend against Coccidioides, the fungus that causes Valley fever. By showing that mucins slow fungal growth, the work suggests mucus shapes infection before symptoms appear, opening new possibilities for earlier diagnosis and treatments against Valley fever and other infectious diseases.

This research develops orally administered nanoparticles that target the lymphatic system to treat lupus and osteoporosis simultaneously. By delivering drugs directly to affected tissues while avoiding the bloodstream, the approach reduces toxicity, suppresses inflammatory and bone-damaging genes, and offers a more effective strategy for treating these complex chronic diseases.

This research develops targeted lipid nanoparticle delivery systems to improve tuberculosis treatment and vaccination. By replacing PEG coatings and using mannose to target infected macrophages, it aims to deliver drugs more effectively, reduce treatment duration, improve vaccine performance, and contribute to the global elimination of tuberculosis.

This research uses artificial intelligence to analyse immune-system data and predict vaccine effectiveness. By identifying early biological signals associated with strong, long-lasting immunity, the work aims to improve vaccine design, personalise vaccination strategies, and support development of universal vaccines capable of protecting against rapidly evolving infectious diseases.

This thesis examines cytokine release storm, where the immune system becomes dangerously overactive. Using rat models, mathematical modelling, science and coding, she maps how corticosteroids move through organs and control inflammation. The goal is to optimise treatment for CRS during cancer therapy, COVID or future pandemics.

This research uses a high-throughput screening platform called EpiScan to identify HIV peptides that bind strongly to MHC molecules and appear on infected cell surfaces. By discovering these immune-visible targets, the work aims to improve detection and elimination of hidden HIV reservoirs, supporting the development of future HIV therapies.

This research investigates macrophages, immune cells that regulate infection, tissue repair, and cancer responses. Through laboratory experiments and machine-learning models, it aims to predict macrophage function across different diseases and patients. The work could improve prognosis, guide treatments, evaluate drug safety, and forecast recovery following major illnesses and injuries.