Stanford researchers at the Taylor Lab have developed software subroutines that can be used together with the open source software system Simvascular to improve the simulation of blood flow in modeling coronary arteries.
Developed at the Taylor Lab, Simvascular is an open source software package encompassing an entire cardiovascular modeling and simulation pipeline from image segmentation, three-dimensional (3D) solid modeling, and mesh generation, to
Stanford researchers have developed a simple optical device for low-power, active light tuning. The device tunes the color of light across the visible spectrum and at select wavelengths by electrical biasing an array of micron sized pixels or nanowires.
Stanford researchers have developed an efficient, virtual keyboard to facilitate independence and faster communication for those who use assistive technology.
Histone acetyltransferase 1 (HAT1) is an enzyme which acetylates lysine on histone proteins and is intricately involved with regulating gene transcription.
Researchers in the Fan group have developed a method for epitaxial growth of double heterojunction semiconductor diodes capable of suppressing parasitic non-radiative recombination effects.
Stanford researchers have developed an algorithm using deep learning architectures to predict cardiac function (ejection fraction) and trace the endocardium of the left ventricle from ultrasound echocardiogram videos.
Two related technologies, a pipeline for generating a custom PathFX algorithm and a new algorithm named Mr. Rogers, are used to identify protein pathways around drug targets.
Researchers in the Sunwoo Lab have developed a method to differentiate intra-epithelial innate lymphoid cells type 1 (ieILC1s) from conventional peripheral natural kills cells for immunotherapeutic purposes.
Stanford researchers have developed the first topical regenerative treatment for the oral cavity following chemo/radiation. Approximately 60,000 patients in the U.S. are annually diagnosed with head and neck cancer.
Researchers in the Arbabian Lab have developed a system that uses a combination of radio frequency (RF) electromagnetic and ultrasound (US) waves to detect, localize, and identify multiple battery-free tags.