Gastric antral vascular ectasia in systemic sclerosis: Association with anti-RNA polymerase III and negative anti-nuclear antibodies.

OBJECTIVE: Gastric antral vascular ectasia (GAVE) is a vascular manifestation of systemic sclerosis (SSc) that can lead to iron deficiency anemia or acute gastrointestinal (GI) bleeding. We aimed to identify clinical features associated with GAVE. METHODS: We performed a cohort study of SSc patients who were seen at Stanford between 2004 and 2018 and had undergone esophagogastroduodenoscopy (EGD). We compared the clinical features of those with and without GAVE, and multivariable logistic regression was performed to identify clinical correlates with GAVE. RESULTS: A total of 225 patients with SSc who underwent EGD were included in this study and 19 (8.4%) had GAVE. Those with GAVE were more likely to have scleroderma renal crisis (SRC) (21% vs 3%; p < 0.01), positive anti-RNA polymerase III antibody (71% vs 19%; p < 0.01), nucleolar pattern of anti-nuclear antibody (ANA) (33% vs 11%; p=0.04), and negative ANA (<1:80 by immunofluorescence) (33% vs 11%; p=0.02). On multivariate analysis with multiple imputation, anti-RNA polymerase III positivity (OR 4.57; 95% CI (1.57 - 13.23), p < 0.01) and ANA negativity (OR 3.75; 95% CI (1.21 - 11.62), p=0.02) remained significantly associated with GAVE. CONCLUSION: Positive anti-RNA polymerase III antibody and ANA negativity were significantly associated with GAVE. Further studies are necessary to determine whether patients with these autoantibody profiles should undergo screening endoscopies for GAVE.

Porous Si-Cu3 Si-Cu Microsphere@C Core-Shell Composites with Enhanced Electrochemical Lithium Storage.

Low-cost Si-based anode materials with excellent electrochemical lithium storage present attractive prospects for lithium-ion batteries (LIBs). Herein, porous Si-Cu3 Si-Cu microsphere@C composites are designed and prepared by means of an etching/electroless deposition and subsequent carbon coating. The composites show a core-shell structure, with a porous Si/Cu microsphere core surrounded by the N-doped carbon shell. The Cu and Cu3 Si nanoparticles are embedded inside porous silicon microspheres, forming the porous Si/Cu microsphere core. The microstructure and lithium storage performance of porous Si-Cu3 Si-Cu microsphere@C composites can be effectively tuned by changing electroless deposition time. The Si-Cu3 Si-Cu microsphere@C composite prepared with 12 min electroless deposition delivers a reversible capacity of 627 mAh g-1 after 200 cycles at 2 A g-1 , showing an enhanced lithium storage ability. The superior lithium storage performance of the Si-Cu3 Si-Cu microsphere@C composite can be ascribed to the improved electronic conductivity, enhanced mechanical stability, and better buffering against the large volume change in the repeated lithiation/delithiation processes.

MOF-Derived Hierarchical MnO-Doped Fe3O4@C Composite Nanospheres with Enhanced Lithium Storage.

Hierarchically nanostructured binary/multiple transition-metal oxides with electrically conductive coatings are very attractive for lithium-ion batteries owing to their excellent electrochemical properties induced by their unique compositions and microstructures. Herein, hierarchical MnO-doped Fe3O4@C composite nanospheres are prepared by a simple one-step annealing in Ar atmosphere, using Mn-doped Fe-based metal-organic frameworks (Mn-doped MIL-53(Fe)) as precursor. The MnO-doped Fe3O4@C composite particles have a uniform nanosphere structure with a diameter of ∼100 nm, and each nanosphere is composed of clustered primary nanoparticles with an amorphous carbon shell, forming a unique hierarchical nanoarchitecture. The as-prepared hierarchical MnO-doped Fe3O4@C composite nanospheres exhibit markedly enhanced lithium-storage performance, with a large capacity of 1297.5 mAh g-1 after 200 cycles at 200 mA g-1. The cycling performance is clarified through analyzing the galvanostatic discharge/charge voltage profiles and electrochemical impedance spectra at different cycles. The unique microstructures and Mn element doping of the hierarchical MnO-doped Fe3O4@C composite nanospheres lead to their enhanced lithium-storage performance.

Penalized nonparametric scalar-on-function regression via principal coordinates.

A number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. In the proposed method, which we call principal coordinate ridge regression, one regresses the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, principal coordinate ridge regression, with dynamic time warping distance used to define the principal coordinates, is shown to outperform a functional generalized linear model.

Pointwise influence matrices for functional-response regression.

We extend the notion of an influence or hat matrix to regression with functional responses and scalar predictors. For responses depending linearly on a set of predictors, our definition is shown to reduce to the conventional influence matrix for linear models. The pointwise degrees of freedom, the trace of the pointwise influence matrix, are shown to have an adaptivity property that motivates a two-step bivariate smoother for modeling nonlinear dependence on a single predictor. This procedure adapts to varying complexity of the nonlinear model at different locations along the function, and thereby achieves better performance than competing tensor product smoothers in an analysis of the development of white matter microstructure in the brain.

On sample size estimation and re-estimation adjusting for variability in confirmatory trials.

Sample size estimation (SSE) is an important issue in the planning of clinical studies. While larger studies are likely to have sufficient power, it may be unethical to expose more patients than necessary to answer a scientific question. Budget considerations may also cause one to limit the study to an adequate size to answer the question at hand. Typically at the planning stage, a statistically based justification for sample size is provided. An effective sample size is usually planned under a pre-specified type I error rate, a desired power under a particular alternative and variability associated with the observations recorded. The nuisance parameter such as the variance is unknown in practice. Thus, information from a preliminary pilot study is often used to estimate the variance. However, calculating the sample size based on the estimated nuisance parameter may not be stable. Sample size re-estimation (SSR) at the interim analysis may provide an opportunity to re-evaluate the uncertainties using accrued data and continue the trial with an updated sample size. This article evaluates a proposed SSR method based on controlling the variability of nuisance parameter. A numerical study is used to assess the performance of proposed method with respect to the control of type I error. The proposed method and concepts could be extended to SSR approaches with respect to other criteria, such as maintaining effect size, achieving conditional power, and reaching a desired reproducibility probability.