Has the expansion in extended criteria deceased donors led to a different type of delayed graft function and poorer outcomes?

Objectives: There has been considerable change in the practice of deceased kidney transplantation in the past 15 years, with more extreme phenotypes implanted. The aim of this study was to determine whether increased use of expanded criteria donors (extended criteria donors and donors after circulatory death) affected clinical outcomes, including the incidence and pattern of delayed graft function. Methods and materials: A retrospective analysis of 1359 renal transplants was performed over 15 years. The first 10 years of data (group 1) were compared with the subsequent 5 years (group 2). Outcomes were analyzed at 6 months and 12 months in addition to serum creatinine and patterns of delayed graft function (posttransplant times: on hemodialysis, to peak creatinine, for creatinine to half, and for creatinine to fall within 10% of baseline). Results: There was a significant increase in the percentage of expanded criteria donor allografts used in group 2 with a significant increase in the incidence of delayed graft function. Despite this, serum creatinine and the incidence of biopsy-proven acute rejection had both improved in group 2. Group 2 expanded criteria donor kidneys had a significantly lower incidence of type 1 delayed graft function and a significantly higher incidence of types 3 and 4 delayed graft function. Time for creatinine to half in both groups was the best predictor of a serum creatinine <180 μmol/L at 1 year. Conclusion: The increased use of expanded criteria donor kidneys has led to a higher incidence of delayed graft function, but the pattern has shown that the requirement for hemodialysis has significantly reduced

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches

<p>There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most efficient electronic structure methods such as density functional theory, are too time consuming to explore more than a tiny fraction of all possible hybrid 2D materials. Here we demonstrate that a combination of DFT and machine learning techniques provide a practical method for exploring this parameter space much more efficiently than by DFT or experiment. As a proof of concept we applied this methodology to predict the interlayer distance and band gap of bilayer heterostructures. Our methods quickly and accurately predicted these important properties for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.</p

Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery

The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state-of-the-art computational resources. Additionally, they can be often extremely time consuming. We describe a time and resource-efficient machine learning approach to create a large dataset of structural properties of van der Waals layered structures. In particular, we focus on the interlayer energy and the elastic constant of layered materials composed of two different 2-dimensional (2D) structures, that are important for novel solid lubricant and super-lubricant materials. We show that machine learning models can recapitulate results of computationally expansive approaches (i.e. density functional theory) with high accuracy

High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications

© 2020 Wiley-VCH GmbH The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state-of-the-art computational resources. Additionally, they can often be extremely time consuming. A time and resource efficient machine learning approach to create a dataset of structural properties of 18 million van der Waals layered structures is described. In particular, the authors focus on the interlayer energy and the elastic constant of layered materials composed of two different 2D structures that are important for novel solid lubricant and super-lubricant materials. It is shown that machine learning models can predict results of computationally expansive approaches (i.e., density functional theory) with high accuracy

Electron transfer through alpha-peptides attached to vertically aligned carbon nanotube arrays: a mechanistic transition

This article is part of the Artificial photosynthesis web themed issueThe mechanism of electron transfer in α-aminoisobutyric (Aib) homoligomers is defined by the extent of secondary structure, rather than just chain length. Helical structures (Aib units ≥3) undergo an electron hopping mechanism, while shorter disordered sequences (Aib units <3) undergo an electron superexchange mechanism.Jingxian Yu, Ondrej Zvarec, David M. Huang, Mark A. Bissett, Denis B. Scanlon, Joe G. Shapter and Andrew D. Abel