Electrospun PCL/gelatin/arbutin nanofiber membranes as potent reactive oxygen species scavengers to accelerate cutaneous wound healing.

The presence of excessive reactive oxygen species (ROS) at a skin wound site is an important factor affecting wound healing. ROS scavenging, which regulates the ROS microenvironment, is essential for wound healing. In this study, we used novel electrospun PCL/gelatin/arbutin (PCL/G/A) nanofibrous membranes as wound dressings, with PCL/gelatin (PCL/G) as the backbone, and plant-derived arbutin (hydroquinone-ß-d-glucopyranoside, ARB) as an effective antioxidant that scavenges ROS and inhibits bacterial infection in wounds. The loading of ARB increased the mechanical strength of the nanofibres, with a water vapour transmission rate of more than 2500 g/(m2 × 24 h), and the water contact angle decreased, indicating that hydrophilicity and air permeability were significantly improved. Drug release and degradation experiments showed that the nanofibre membrane controlled the drug release and exhibited favourable degradability. Haemolysis experiments showed that the PCL/G/A nanofibre membranes were biocompatible, and DPPH and ABTS+ radical scavenging experiments indicated that PCL/G/A could effectively scavenge ROS to reflect the antioxidant activity. In addition, haemostasis experiments showed that PCL/G/A had good haemostatic effects in vitro and in vivo. In vivo animal wound closure and histological staining experiments demonstrated that PCL/G/A increased collagen deposition and remodelled epithelial tissue regeneration while showing good in vivo biocompatibility and non-toxicity. In conclusion, we successfully prepared a multifunctional wound dressing, PCL/G/A, for skin wound healing and investigated its potential role in wound healing, which is beneficial for the clinical translational application of phytomedicines.

Asymmetric transmission of sound wave in cavitating liquids.

Two modes of the asymmetric sound transmission are observed experimentally in a one-dimensional system composed of coupled two layers of liquids. Their cavitation thresholds are different from each other. When the sound wave propagates from the high-threshold liquid to the low-threshold liquid, the two liquids can avoid the cavitation for a medium driving pressure. When it propagates from the low-threshold liquid to the high-threshold liquid, however, the low-threshold liquid can be cavitated by the same driving pressure, though the high-threshold liquid remains uncavitated. Therefore, there is a sound transmission asymmetry, or sound rectification in this double-layer liquid. Furthermore, when the system is driven by a high pressure, cavitation can take place in both high- and low-threshold liquids in the sound transmission from the high-threshold liquid to the low-threshold liquid, but only the low-threshold liquid can be cavitated in the opposite transmission. This mechanism gives an asymmetry with reversed rectifying direction. The efficiency of rectification is related to the driving sound pressure and the cavitation thresholds of the two liquids based on experimental results. Finally, the experimental observations are reproduced by the numerical simulation based on the modified two-phase fluid mechanics.

Publisher's Note: Asymmetric transmission of sound wave in cavitating liquids [Phys. Rev. E 95, 033118 (2017)].

This corrects the article DOI: 10.1103/PhysRevE.95.033118.

Bayesian approaches in medical device clinical trials: a discussion with examples in the regulatory setting.

Challenging statistical issues often arise in the design and analysis of clinical trials to assess safety and effectiveness of medical devices in the regulatory setting. The use of Bayesian methods in the design and analysis of medical device clinical trials has been increasing significantly in the past decade, not only due to the availability of prior information, but mainly due to the appealing nature of Bayesian clinical trial designs. The Center for Devices and Radiological Health at the Food and Drug Administration (FDA) has gained extensive experience with the use of Bayesian statistical methods and has identified some important issues that need further exploration. In this article, we discuss several topics relating to the use of Bayesian statistical methods in medical device trials, based on our experience and real applications. We illustrate the benefits and challenges of Bayesian approaches when incorporating prior information to evaluate the effectiveness and safety of a medical device. We further present an example of a Bayesian adaptive clinical trial and compare it to a traditional frequentist design. Finally, we discuss the use of Bayesian hierarchical models for multiregional trials and highlight the advantages of the Bayesian approach when specifying clinically relevant study hypotheses.

Bayesian wombling for spatial point processes.

In many applications involving geographically indexed data, interest focuses on identifying regions of rapid change in the spatial surface, or the related problem of the construction or testing of boundaries separating regions with markedly different observed values of the spatial variable. This process is often referred to in the literature as boundary analysis or wombling. Recent developments in hierarchical models for point-referenced (geostatistical) and areal (lattice) data have led to corresponding statistical wombling methods, but there does not appear to be any literature on the subject in the point-process case, where the locations themselves are assumed to be random and likelihood evaluation is notoriously difficult. We extend existing point-level and areal wombling tools to this case, obtaining full posterior inference for multivariate spatial random effects that, when mapped, can help suggest spatial covariates still missing from the model. In the areal case we can also construct wombled maps showing significant boundaries in the fitted intensity surface, while the point-referenced formulation permits testing the significance of a postulated boundary. In the computationally demanding point-referenced case, our algorithm combines Monte Carlo approximants to the likelihood with a predictive process step to reduce the dimension of the problem to a manageable size. We apply these techniques to an analysis of colorectal and prostate cancer data from the northern half of Minnesota, where a key substantive concern is possible similarities in their spatial patterns, and whether they are affected by each patient's distance to facilities likely to offer helpful cancer screening options.

Hierarchical Multiresolution Approaches for Dense Point-Level Breast Cancer Treatment Data.

The analysis of point-level (geostatistical) data has historically been plagued by computational difficulties, owing to the high dimension of the nondiagonal spatial covariance matrices that need to be inverted. This problem is greatly compounded in hierarchical Bayesian settings, since these inversions need to take place at every iteration of the associated Markov chain Monte Carlo (MCMC) algorithm. This paper offers an approach for modeling the spatial correlation at two separate scales. This reduces the computational problem to a collection of lower-dimensional inversions that remain feasible within the MCMC framework. The approach yields full posterior inference for the model parameters of interest, as well as the fitted spatial response surface itself. We illustrate the importance and applicability of our methods using a collection of dense point-referenced breast cancer data collected over the mostly rural northern part of the state of Minnesota. Substantively, we wish to discover whether women who live more than a 60-mile drive from the nearest radiation treatment facility tend to opt for mastectomy over breast conserving surgery (BCS, or "lumpectomy"), which is less disfiguring but requires 6 weeks of follow-up radiation therapy. Our hierarchical multiresolution approach resolves this question while still properly accounting for all sources of spatial association in the data.

ANALYSIS OF MINNESOTA COLON AND RECTUM CANCER POINT PATTERNS WITH SPATIAL AND NONSPATIAL COVARIATE INFORMATION.

Colon and rectum cancer share many risk factors, and are often tabulated together as "colorectal cancer" in published summaries. However, recent work indicating that exercise, diet, and family history may have differential impacts on the two cancers encourages analyzing them separately, so that corresponding public health interventions can be more efficiently targeted. We analyze colon and rectum cancer data from the Minnesota Cancer Surveillance System from 1998-2002 over the 16-county Twin Cities (Minneapolis-St. Paul) metro and exurban area. The data consist of two marked point patterns, meaning that any statistical model must account for randomness in the observed locations, and expected positive association between the two cancer patterns. Our model extends marked spatial point pattern analysis in the context of a log Guassian Cox process to accommodate spatially referenced covariates (local poverty rate and location within the metro area), individual-level risk factors (patient age and cancer stage), and related interactions. We obtain smoothed maps of marginal log-relative intensity surfaces for colon and rectum cancer, and uncover significant age and stage differences between the two groups. This encourages more aggressive colon cancer screening in the inner Twin Cities and their southern and western exurbs, where our model indicates higher colon cancer relative intensity.