This research investigates whether weight loss from the GLP-1 drug semaglutide includes loss of muscle mass. Using an obesity mouse model and direct muscle measurements, the study found significant muscle loss in females but not males. The findings highlight important sex differences and the need to evaluate body composition, not just weight loss.

This research develops a physics-based method for measuring lung elasticity from medical imaging to predict which emphysema patients will benefit from lung valve treatment. By creating detailed elasticity maps, the work aims to improve treatment selection, enhance patient outcomes, and provide new quantitative tools for assessing lung health.

This research develops an AI model that combines thyroid ultrasound imaging with genetic testing to improve diagnosis of indeterminate thyroid nodules. By integrating molecular and imaging data, the model helps distinguish benign from cancerous nodules more accurately, reducing unnecessary surgeries and improving clinical decision-making for thyroid cancer patients.

This research develops soft, tissue-like implantable sensors capable of monitoring molecular signals inside the body in real time. By combining high-performance electronics with flexible, biocompatible materials, these devices could detect inflammation, stress, or organ damage before symptoms arise, enabling earlier diagnosis and more personalized healthcare.

This research investigates the genetic mechanisms underlying polycystic ovary syndrome (PCOS), a condition affecting one in ten women and the leading cause of female infertility. By studying thousands of genetic variants across multiple cell types, the project aims to identify the biological causes of PCOS and develop targeted treatments.

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.

This research develops nanobubble-enhanced ultrasound imaging as an accessible alternative to MRI for cancer diagnosis. Tiny gas-filled nanoparticles amplify ultrasound signals and improve image quality, particularly in prostate cancer. The technology could reduce diagnostic delays, lower costs, and provide high-quality medical imaging to more patients worldwide.

This research investigates how glioblastoma brain cancer cells invade healthy brain tissue. Using patient-derived tumor organoids and traction force microscopy, the study measures how cancer cells generate and apply forces to move through the brain. Understanding these invasion mechanisms could help develop therapies that slow tumor spread and improve patient survival.

This research develops “nanozymes,” nanoparticle-based catalysts that activate cancer drugs directly at tumor sites. Instead of carrying large amounts of chemotherapy drugs, nanozymes locally trigger inactive drugs into their active form only within cancer tissue. Early mouse studies show effective tumor destruction with significantly reduced side effects compared to conventional chemotherapy.

This research uses artificial intelligence to predict the progression of Alzheimer’s disease and cancer using medical imaging data. By analyzing brain scans, tumor scans, and treatment responses, AI models can forecast disease development and treatment outcomes, enabling earlier intervention, more personalized care, and improved quality of life for aging populations.