Multiple Sclerosis (MS) is a potentially disabling autoimmune disease whereby autoactivated T and B cells attack and destroy protective myelin sheaths of the central nervous system(CNS).
Stanford researchers led by Stephen Tsai are advancing a new, much simplified design approach for composite laminates – termed "double-double" – that can replace conventional laminates for lighter, tougher, and lower cost airplane structures among other uses.
Stephen Tsai and researchers at Stanford University's Structures and Composite Laboratory have designed a composite grid-stiffened skin structure, which is ultra-lightweight, stiff, strong, and easier and less expensive to manufacture.
Stanford researchers have developed a time efficient and safer algorithm for autonomous cars that combines game theory and risk awareness. This algorithm computes approximate feedback Nash equilibria where all agents are risk aware, a novel approach.
Stanford researchers in the Fan Lab have developed a photonic device optimizer that generates designs with hard geometric constraints to guarantee device fabricability.
Collagen-based hydrogels behave similarly to the native tissue microenvironment, thus are widely used as scaffolds for encapsulating cells or molecules like growth factors. Collagen solution is an injectable liquid until it crosslinks at 37 C and physiological pH.
Activation of hedgehog signaling pathway can facilitate stem cell proliferation and holds great promise in regenerative medicine for a variety of indications.
Stanford researchers have developed a novel technique to control proton beams for radiation therapy to deliver a very high, full dose across a tumor in less than one second.
Tracking in vivo cell distribution, migration, and engraftment using conventional techniques including MRI, PET/CT and conventional optical imaging is often hindered by low resolution, radioactive risks, and limited tissue penetration depth.
Stanford researchers have developed a technique to interpret contact events between a human and a device equipped with a force sensor. It can detect and classify distinct touch interactions such as tap, touch, grab, and slip.
Researchers at Stanford, the University of Massachusetts and the Chan Zuckerberg Biohub have developed methods to increase or decrease RNA interference target cleavage rates.