Publications by authors named "J U Galle"
Article Synopsis
- Germline genetic testing (GT) is recommended for ovarian cancer (OC) and some endometrial cancer (EC) patients, but participation is low due to various barriers.
- A quality improvement study at a medical center tracked 116 newly diagnosed OC/EC patients, achieving high GT rates (91% for OC, 75% for EC) and rapid result availability.
- Identified barriers include missed recommendations, patient overwhelm, financial/privacy concerns, and language issues; the study suggests enhancing GT encouragement throughout treatment and offering multilingual digital consent options.
Non-communicable diseases (NCDs), including coronary heart disease, stroke, hypertension, type 2 diabetes, dementia, depression and cancers, are on the rise worldwide and are often associated with a lack of physical activity (PA). Globally, the levels of PA among individuals are below WHO recommendations. A lack of PA can increase morbidity and mortality, worsen the quality of life and increase the economic burden on individuals and society.
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- A program was launched to provide free genomic testing for patients with rare cancers worldwide, specifically targeting histiocytosis, germ cell tumors (GCT), and pediatric cancers.
- Patients were recruited through social media and advocacy groups, and 333 were enrolled, with 288 providing tumor tissue for analysis, resulting in significant clinical benefits for patients receiving genomically guided therapy.
- The study highlighted the rarity of actionable genomic alterations in ovarian GCTs and demonstrated that direct outreach can effectively build cohorts for studying rare cancers' genomic landscapes.
The implementation of stem-cell-based organoid culture more than ten years ago started a development that created new avenues for diagnostic analyses and regenerative medicine. In parallel, computational modelling groups realized the potential of this culture system to support their theoretical approaches to study tissues in silico. These groups developed computational organoid models (COMs) that enabled testing consistency between cell biological data and developing theories of tissue self-organization.
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