This research develops nanoscale “smart package” delivery systems for PROTAC cancer drugs. Antibody nanogel conjugates selectively target cancer cells, enter them, and release therapeutic molecules while minimizing exposure to healthy tissue. The approach improves delivery efficiency and aims to reduce the severe side effects that often limit cancer treatment.

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.

Using a Twilight analogy, this research explains antibiotic-resistant bacteria as “vampires” protected by membranes. By crystallizing membrane proteins and analyzing them with X-ray techniques, the study reveals their structure and function. This enables precise drug design to block these proteins, potentially overcoming antibiotic resistance and targeting harmful bacteria more effectively.

This research uses spatial transcriptomics to map interactions between T cells, cancer cells, and immunosuppressive cells in tumours. Findings suggest cancer suppresses immune responses by surrounding and weakening T cells. The work aims to improve immunotherapy and enable personalised cancer treatment through detailed tumour mapping.

This research develops targeted radiopharmaceutical therapies for HER2-positive cancers. By attaching radioactive isotopes to trastuzumab, treatment delivers precise radiation to cancer cells, overcoming drug resistance. The work includes creating practical drug kits and aims to improve cancer outcomes by replacing non-specific therapies with highly accurate, targeted interventions.

This talk traces the devastation of the Black Death to highlight a modern crisis: antibiotic resistance. Misuse of antibiotics accelerates the rise of superbugs. Using AI and machine learning, the research identifies genetic resistance patterns and guides effective treatments, aiming to improve clinical decisions and prevent a return to a pre-antibiotic era.

This research targets rare genetic diseases caused by frameshift mutations using antisense oligonucleotides as “genetic band-aids.” By masking faulty DNA segments, it restores functional protein production. Demonstrated in muscular dystrophy models, this approach offers a scalable strategy to treat multiple rare diseases, addressing a major gap where most conditions lack effective therapies.

This research develops a spatial “GPS” for lung cancer screening by mapping CT scans into a shared coordinate system. By identifying high-risk regions for malignant nodules, it supports radiologists and AI in improving diagnostic accuracy, decision-making, and interpretability, transforming screening from broad search to targeted, data-driven precision.

This research investigates how melanoma switches between two gene states—one fast-growing and treatable, the other slow but highly invasive and responsible for brain metastases. By identifying genes that control this transition, the study aims to force melanoma into a more treatable form, improving therapeutic options and patient outcomes.

This research engineers immune T cells to better fight ovarian cancer. By modifying them to recognize tumor-specific proteins and resist cancer’s suppressive signals, the project strengthens the body’s natural defenses. The goal is to improve immunotherapy effectiveness, overcome tumor resistance, and increase survival rates for women facing this deadly disease.