Stanford University

About: Stanford University is a education organization based out in Stanford, California, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 125751 authors who have published 320347 publications receiving 21892059 citations. The organization is also known as: Leland Stanford Junior University & University of Stanford.

A predictive model for aggressive non-Hodgkin's lymphoma

Abstract: BACKGROUND Although many patients with intermediate-grade or high-grade (aggressive) non-Hodgkin's lymphoma are cured by combination chemotherapy, the remainder are not cured and ultimately die of their disease. The Ann Arbor classification, used to determine the stage of this disease, does not consistently distinguish between patients with different long-term prognoses. This project was undertaken to develop a model for predicting outcome in patients with aggressive non-Hodgkin's lymphoma on the basis of the patients' clinical characteristics before treatment. METHODS Adults with aggressive non-Hodgkin's lymphoma from 16 institutions and cooperative groups in the United States, Europe, and Canada who were treated between 1982 and 1987 with combination-chemotherapy regimens containing doxorubicin were evaluated for clinical features predictive of overall survival and relapse-free survival. Features that remained independently significant in step-down regression analyses of survival were incorporated into models that identified groups of patients of all ages and groups of patients no more than 60 years old with different risks of death. RESULTS In 2031 patients of all ages, our model, based on age, tumor stage, serum lactate dehydrogenase concentration, performance status, and number of extranodal disease sites, identified four risk groups with predicted five-year survival rates of 73 percent, 51 percent, 43 percent, and 26 percent. In 1274 patients 60 or younger, an age-adjusted model based on tumor stage, lactate dehydrogenase level, and performance status identified four risk groups with predicted five-year survival rates of 83 percent, 69 percent, 46 percent, and 32 percent. In both models, the increased risk of death was due to both a lower rate of complete responses and a higher rate of relapse from complete response. These two indexes, called the international index and the age-adjusted international index, were significantly more accurate than the Ann Arbor classification in predicting long-term survival. CONCLUSIONS The international index and the age-adjusted international index should be used in the design of future therapeutic trials in patients with aggressive non-Hodgkin's lymphoma and in the selection of appropriate therapeutic approaches for individual patients.

Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016

Abstract: To provide an update to “Surviving Sepsis Campaign Guidelines for Management of Sepsis and Septic Shock: 2012”. A consensus committee of 55 international experts representing 25 international organizations was convened. Nominal groups were assembled at key international meetings (for those committee members attending the conference). A formal conflict-of-interest (COI) policy was developed at the onset of the process and enforced throughout. A stand-alone meeting was held for all panel members in December 2015. Teleconferences and electronic-based discussion among subgroups and among the entire committee served as an integral part of the development. The panel consisted of five sections: hemodynamics, infection, adjunctive therapies, metabolic, and ventilation. Population, intervention, comparison, and outcomes (PICO) questions were reviewed and updated as needed, and evidence profiles were generated. Each subgroup generated a list of questions, searched for best available evidence, and then followed the principles of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system to assess the quality of evidence from high to very low, and to formulate recommendations as strong or weak, or best practice statement when applicable. The Surviving Sepsis Guideline panel provided 93 statements on early management and resuscitation of patients with sepsis or septic shock. Overall, 32 were strong recommendations, 39 were weak recommendations, and 18 were best-practice statements. No recommendation was provided for four questions. Substantial agreement exists among a large cohort of international experts regarding many strong recommendations for the best care of patients with sepsis. Although a significant number of aspects of care have relatively weak support, evidence-based recommendations regarding the acute management of sepsis and septic shock are the foundation of improved outcomes for these critically ill patients with high mortality.

Reviving the lithium metal anode for high-energy batteries

Abstract: Lithium-ion batteries have had a profound impact on our daily life, but inherent limitations make it difficult for Li-ion chemistries to meet the growing demands for portable electronics, electric vehicles and grid-scale energy storage. Therefore, chemistries beyond Li-ion are currently being investigated and need to be made viable for commercial applications. The use of metallic Li is one of the most favoured choices for next-generation Li batteries, especially Li-S and Li-air systems. After falling into oblivion for several decades because of safety concerns, metallic Li is now ready for a revival, thanks to the development of investigative tools and nanotechnology-based solutions. In this Review, we first summarize the current understanding on Li anodes, then highlight the recent key progress in materials design and advanced characterization techniques, and finally discuss the opportunities and possible directions for future development of Li anodes in applications.

Synthetic biomaterials as instructive extracellular microenvironments for morphogenesis in tissue engineering

Abstract: New generations of synthetic biomaterials are being developed at a rapid pace for use as three-dimensional extracellular microenvironments to mimic the regulatory characteristics of natural extracellular matrices (ECMs) and ECM-bound growth factors, both for therapeutic applications and basic biological studies. Recent advances include nanofibrillar networks formed by self-assembly of small building blocks, artificial ECM networks from protein polymers or peptide-conjugated synthetic polymers that present bioactive ligands and respond to cell-secreted signals to enable proteolytic remodeling. These materials have already found application in differentiating stem cells into neurons, repairing bone and inducing angiogenesis. Although modern synthetic biomaterials represent oversimplified mimics of natural ECMs lacking the essential natural temporal and spatial complexity, a growing symbiosis of materials engineering and cell biology may ultimately result in synthetic materials that contain the necessary signals to recapitulate developmental processes in tissue- and organ-specific differentiation and morphogenesis.

Adversarial Discriminative Domain Adaptation

Abstract: Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They can also improve recognition despite the presence of domain shift or dataset bias: recent adversarial approaches to unsupervised domain adaptation reduce the difference between the training and test domain distributions and thus improve generalization performance. However, while generative adversarial networks (GANs) show compelling visualizations, they are not optimal on discriminative tasks and can be limited to smaller shifts. On the other hand, discriminative approaches can handle larger domain shifts, but impose tied weights on the model and do not exploit a GAN-based loss. In this work, we first outline a novel generalized framework for adversarial adaptation, which subsumes recent state-of-the-art approaches as special cases, and use this generalized view to better relate prior approaches. We then propose a previously unexplored instance of our general framework which combines discriminative modeling, untied weight sharing, and a GAN loss, which we call Adversarial Discriminative Domain Adaptation (ADDA). We show that ADDA is more effective yet considerably simpler than competing domain-adversarial methods, and demonstrate the promise of our approach by exceeding state-of-the-art unsupervised adaptation results on standard domain adaptation tasks as well as a difficult cross-modality object classification task.