These dual-function nanoparticles improve selectivity of myeloid treatment via identification and reduction of tumor progression in a two-step process: initial accumulation in tumor microenvironments, followed by targeted delivery of a therapeutic payload.
Using a novel convolutional neural network architecture, PlexusNet can be used for histologic image analysis with smaller parameter and training sets than current state-of-the-art models.
Stanford researchers at the Liao and Xing Labs have developed and tested a machine learning algorithm for augmented detection of bladder cancer. Machine learning has the potential to enhance medical decision making in cancer detection and image analysis.
Stanford researches have formulated a robust database called PRECOG (Prediction of Clinical Outcomes from Genomics) that connects cancer genome expression and patient survival/outcomes in a more predictive and extensive collection than any other signature on the market.
Stanford researchers have developed a safe and effective system that enables neurofeedback training in combination with neuromodulation for the treatment of brain disorders characterized by abnormal neuronal synchrony and synaptic connectivity.
Aging is associated with the decline of mitochondrial function, particularly in related metabolic diseases such as obesity, diabetes, and heart disease.
Stanford researchers have developed deep learning methods which can more precisely localize the position and orientation of a camera in the lung anatomy in real-time.
Researchers in Prof. Michael Lin's laboratory have developed a viral-based cancer therapy platform that could be used for targeting treatment to cancer cells with aberrant signaling in EGFR or HER2 pathways.
Researchers at Stanford have developed a genetics-based approach that uses sex-specific genetic effects to predict testosterone levels in males and females.
Researchers at Stanford have developed a new water-based disinfectant with the potential to destroy a wide variety of pathogens and significantly improve healthcare settings.
Researchers at Stanford have developed prodrug derivatives of protein kinase C (PKC) modulators that have lower toxicity and are more effective than the parent compound. PKC modulators are being developed to treat a variety of diseases.
Stanford researchers have developed a damage free method for activating buried p-type or Mg-doped epitaxial layers in III-nitride devices that improves performance and can reduce device cost when used as edge termination.