An AI model for transplant risk in myelofibrosis; preventing priapism in men with sickle cell anemia; hallmarks of T cell exhaustion absent in newly diagnosed MM

In this week's episode, we'll learn about using AI to assess transplant risk in myelofibrosis. In a step toward personalized medicine, researchers report on a machine learning model that identifies 25% of patients with poor outcomes. After that: preventing priapism in men with sickle cell anemia. A recent phase 2 feasibility study shows high rates of recruitment, retention, and adherence to oral therapies, coupled with a significant reduction in the risk of this difficult complication. Finally, new research indicates that hallmarks of terminal T-cell exhaustion are absent in multiple myeloma, from diagnosis through maintenance therapy. We explore these provocative and counterintuitive findings arising from profiling of blood and marrow samples.

Featured Articles:

• Use of machine learning techniques to predict poor survival after hematopoietic cell transplantation for myelofibrosis
• A controlled trial for preventing priapism in sickle cell anemia: hydroxyurea plus placebo vs hydroxyurea plus tadalafil
• Hallmarks of T-cell exhaustion and antigen experience are absent in multiple myeloma from diagnosis to maintenance therapy