Radiation is often an effective treatment modality for cancer, but its effects are limited to the targets that are directly irradiated. Regions of tumor outside the radiation field do not experience direct radiation-induced DNA damage and cellular apoptosis.
This invention is an intraoral palate expanding plate with an expansion screw for both cleft palate and restricted airways that can be produced by 3-D printing and CT scans.
A team of Stanford computer scientists have developed software that can serve as a key enabling technology for location-aware services indoors. Location-aware services are an important emerging technology for mobile devices.
Researchers at Stanford have found that nascent polypeptide-associated complex (NAC) and the apical domain of CCT1, as well as peptide fragments and fusion proteins containing them, can be used to suppress pathological protein aggregation.
Stanford inventor Dr. Anne Liu has developed an algorithm that can assess the risk of allergic reaction to antibiotics and help clinicians make decisions about which antibiotic to prescribe in patients who have a history of antibiotic allergies at the point-of-care.
Researchers at Stanford have developed a process for modifying metal powder stock to enable printing of high reflectivity metals using moderate laser powers (200-400 W) in commercially available printing systems (200-400W).
Stanford inventors have discovered a single plant protein, FLOE1, that controls a variety of processes that are crucial to timely and robust germination of seeds.
Stanford researchers have constructed a microbial cell factory by genetically modifying the bacterium Methylomicrobium alcaliphilum 20Z to convert methanol and methane into para-hydroxybenzoic acid (p-HBA).
Optimizing battery performance currently relies on empirical testing using arbitrary parameters, under-validated physiochemical models, and limited data analysis of summary trends.
Stanford researchers designed and built a light sheet microscope that can be used for deconvolution-free, high resolution volumetric imaging of cleared tissue specimens.
Researchers at Stanford University have developed a deep learning software algorithm that allows physicians running clinical trials to predict control patient outcomes using virtual control arms.