Researchers in the laboratories of Prof. Stanley Cohen and Prof Tzu-Hao Cheng have discovered that Supt4h is a potential therapeutic target for reducing toxicity and restoring the functionality of deleterious proteins in Huntington's (HD) and other polyQ diseases.
Dr. Stanley Cohen and colleagues have identified small molecular compounds that may be useful in the treatment of nucleotide repeat diseases. A well-known nucleotide repeat disorder is Huntington's disease.
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 in the Fan Lab have developed a method that dramatically accelerates and optimizes metamaterial design with little computational resource and time using generative neural networks.
Activation of hedgehog signaling pathway can facilitate stem cell proliferation and holds great promise in regenerative medicine for a variety of indications.
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.
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.