The Stanford Rapid Online Assessment of Reading (ROAR) is an innovative tool designed to streamline and enhance the way educators, parents, and specialists assess the reading abilities of students.
Researchers at Stanford have found that applying pressure to macroencapsulation can enhance insulin transport from encapsulated islet beta cells to surrounding tissue and assist in glucose metabolism in type 1 diabetes (T1D) patients.
Many applications in cell therapy, synthetic biology, and gene therapy require extensive cell engineering, often with multiple vectors due to limitations in packaging capacity.
Genome editing of human hematopoietic stem and progenitor cells (HSPCs) has the potential to create a new class of medication for the treatment of inherited and acquired genetic diseases of the blood and immune system.
Researchers at Stanford University have discovered a way to enhance the effectiveness of CAR-T cell therapeutics through inducing a more memory-like phenotype.
Stanford scientists have discovered that blocking an immune receptor signal can lead to increased fat uptake and weight reduction in patients suffering from obesity and associated diseases.
Stanford researchers have discovered RNA signatures that can be used to predict patient outcomes and identify optimal treatments in acute myeloid leukemia.
Researchers at Stanford have created a method to differentiate human pluripotent stem cells (hPSCs) into >90% pure hematopoietic stem cell (HSC)-like cells, which serve as progenitors to blood and immune cells.
Stanford researchers have developed an innovative approach for accurate and automated cell classification on H&E-stained images using multiplexed immunofluorescence (mIF) imaging, eliminating human annotations, and enhancing biological interpretability in histopathology.
The cost of DNA and RNA sequencing have decreased in recent years to aid effective research and clinical applications; however, the labor time and throughput of preparing DNA and RNA sequencing libraries remains a challenge.
A new deep-learning system called Atomic Rotationally Equivariant Scorer (ARES) significantly improves the prediction of RNA structures over previous artificial intelligence (AI) models.