Preparation and Evaluation of Siderol Amorphous Solid Dispersions: Selection of Suitable Matrix/Carrier.

The present study investigates the preparation of amorphous solid dispersions (ASD) for the ent-kaurane diterpenoid siderol (SDR). Initially, evaluation of the pure drug (isolated from Sideritis scardica) revealed that the API is a non-stable glass former, and hence the selection of a suitable ASD's matrix/carrier needs special attention. For this reason, four commonly used polymers and copolymers, namely poly(vinylpyrrolidone), copovidone, hydroxypropyl cellulose, and Soluplus® (SOL), were screened via film casting and crystal growth rate measurements. Amongst them, SOL showed the highest SDR's crystal growth rate reduction, and, since it was also miscible with the drug, it was selected for further testing. In this direction, SDR-SOL ASDs were successfully prepared via melt-quench cooling. These formulations showed full API amorphization, while good physical stability (i.e., a stable SDR amorphous dispersions) were obtained after storage for several months. Finally, evaluation of molecular interactions (with the aid of ATR-FTIR spectroscopy) showed strong H-bonds between SOL and SDR, while the use of molecular dynamics (MD) simulations unraveled the nature of these interactions. Therefore, based on the findings of the present work, SOL seems to be an appropriate matrix/carrier for the preparation of SDR ASDs, although further studies are needed in order to explore its full potentials.

Important considerations for trials for peripheral arterial disease: Lessons learned from the paclitaxel mortality signal: A report on behalf of the registry assessment for peripheral interventional Devices (RAPID) Paclitaxel Pathways Program.

The Registry Assessment of Peripheral Devices (RAPID) convened a multidisciplinary group of stakeholders including clinicians, academicians, regulators and industry representatives to conduct an in-depth review of limitations associated with the data available to assess the paclitaxel mortality signal. Available studies were evaluated to identify strengths and limitations in the study design and data quality, which were translated to lessons learned to help guide the design, execution, and analyses of future studies. We suggest numerous actionable responses, such as the development and use of harmonized data points and outcomes in a consensus lean case report form. We advocate for reduction in missing data and efficient means for accrual of larger sample sizes in Peripheral arterial disease studies or use of supplemental datasets. Efforts to share lessons learned and working collaboratively to address such issues may improve future data in this device area and ultimately benefit patients. Condensed Abstract: Data sources evaluating paclitaxel-coated devices were evaluated to identify strengths and limitations in the study design and data quality, which were translated to lessons learned to help guide the design, execution, and analyses of future studies. We suggest numerous actionable responses, which we believe may improve future data in this device area and ultimately benefit patients.

Securing sustainable funding for viral hepatitis elimination plans.

The majority of people infected with chronic hepatitis C virus (HCV) in the European Union (EU) remain undiagnosed and untreated. During recent years, immigration to EU has further increased HCV prevalence. It has been estimated that, out of the 4.2 million adults affected by HCV infection in the 31 EU/ European Economic Area (EEA) countries, as many as 580 000 are migrants. Additionally, HCV is highly prevalent and under addressed in Eastern Europe. In 2013, the introduction of highly effective treatments for HCV with direct-acting antivirals created an unprecedented opportunity to cure almost all patients, reduce HCV transmission and eliminate the disease. However, in many settings, HCV elimination poses a serious challenge for countries' health spending. On 6 June 2018, the Hepatitis B and C Public Policy Association held the 2nd EU HCV Policy summit. It was emphasized that key stakeholders should work collaboratively since only a few countries in the EU are on track to achieve HCV elimination by 2030. In particular, more effort is needed for universal screening. The micro-elimination approach in specific populations is less complex and less costly than country-wide elimination programmes and is an important first step in many settings. Preliminary data suggest that implementation of the World Health Organization (WHO) Global Health Sector Strategy on Viral Hepatitis can be cost saving. However, innovative financing mechanisms are needed to raise funds upfront for scaling up screening, treatment and harm reduction interventions that can lead to HCV elimination by 2030, the stated goal of the WHO.

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.

The Lutonix® drug-coated balloon: A novel drug delivery technology for the treatment of vascular disease.

Local drug delivery of an anti-proliferative drug from balloon catheter systems to the site of arterial injury has been attempted repeatedly over the years with limited success in drug uptake and retention. Accessibility of the drug at the site is critical to combat the body's response to the procedural trauma of angioplasty. Recently, formulations have been designed which achieve delivery of therapeutic doses of the anti-proliferative drug paclitaxel to arteries with higher efficiency and longer tissue retention. These formulations succeed through formation of a drug reservoir in the artery wall enabling release after the initial angioplasty procedure. These formulations have become the cornerstone of several drug coated balloon (DCB) technologies which have found an initial, broad therapeutic application in the treatment of stenosis of the superficial femoral artery (SFA). DCBs achieve drug delivery while leaving no implant behind and represent a new class of combination products developed at the interface of engineering, chemistry and medical science. This review article summarizes the development of the LUTONIX® drug coated balloon catheter. The introduction of DCB technology has provided clinicians and patients with new SFA treatment options while ongoing clinical evidence in additional vascular beds is generated.

Modeling Image Patches with a Generic Dictionary of Mini-Epitomes.

The goal of this paper is to question the necessity of features like SIFT in categorical visual recognition tasks. As an alternative, we develop a generative model for the raw intensity of image patches and show that it can support image classification performance on par with optimized SIFT-based techniques in a bag-of-visual-words setting. Key ingredient of the proposed model is a compact dictionary of mini-epitomes, learned in an unsupervised fashion on a large collection of images. The use of epitomes allows us to explicitly account for photometric and position variability in image appearance. We show that this flexibility considerably increases the capacity of the dictionary to accurately approximate the appearance of image patches and support recognition tasks. For image classification, we develop histogram-based image encoding methods tailored to the epitomic representation, as well as an "epitomic footprint" encoding which is easy to visualize and highlights the generative nature of our model. We discuss in detail computational aspects and develop efficient algorithms to make the model scalable to large tasks. The proposed techniques are evaluated with experiments on the challenging PASCAL VOC 2007 image classification benchmark.

Learning a Dictionary of Shape Epitomes with Applications to Image Labeling.

The first main contribution of this paper is a novel method for representing images based on a dictionary of shape epitomes. These shape epitomes represent the local edge structure of the image and include hidden variables to encode shift and rotations. They are learnt in an unsupervised manner from groundtruth edges. This dictionary is compact but is also able to capture the typical shapes of edges in natural images. In this paper, we illustrate the shape epitomes by applying them to the image labeling task. In other work, described in the supplementary material, we apply them to edge detection and image modeling. We apply shape epitomes to image labeling by using Conditional Random Field (CRF) Models. They are alternatives to the superpixel or pixel representations used in most CRFs. In our approach, the shape of an image patch is encoded by a shape epitome from the dictionary. Unlike the superpixel representation, our method avoids making early decisions which cannot be reversed. Our resulting hierarchical CRFs efficiently capture both local and global class co-occurrence properties. We demonstrate its quantitative and qualitative properties of our approach with image labeling experiments on two standard datasets: MSRC-21 and Stanford Background.

Bayesian inference on multiscale models for poisson intensity estimation: applications to photon-limited image denoising.

We present an improved statistical model for analyzing Poisson processes, with applications to photon-limited imaging. We build on previous work, adopting a multiscale representation of the Poisson process in which the ratios of the underlying Poisson intensities (rates) in adjacent scales are modeled as mixtures of conjugate parametric distributions. Our main contributions include: 1) a rigorous and robust regularized expectation-maximization (EM) algorithm for maximum-likelihood estimation of the rate-ratio density parameters directly from the noisy observed Poisson data (counts); 2) extension of the method to work under a multiscale hidden Markov tree model (HMT) which couples the mixture label assignments in consecutive scales, thus modeling interscale coefficient dependencies in the vicinity of image edges; 3) exploration of a 2-D recursive quad-tree image representation, involving Dirichlet-mixture rate-ratio densities, instead of the conventional separable binary-tree image representation involving beta-mixture rate-ratio densities; and 4) a novel multiscale image representation, which we term Poisson-Haar decomposition, that better models the image edge structure, thus yielding improved performance. Experimental results on standard images with artificially simulated Poisson noise and on real photon-limited images demonstrate the effectiveness of the proposed techniques.

Multivariate analysis applied to the study of spatial distributions found in drug-eluting stent coatings by confocal Raman microscopy.

Multivariate data analysis was applied to confocal Raman measurements on stents coated with the polymers and drug used in the CYPHER Sirolimus-eluting Coronary Stents. Partial least-squares (PLS) regression was used to establish three independent calibration curves for the coating constituents: sirolimus, poly(n-butyl methacrylate) [PBMA], and poly(ethylene-co-vinyl acetate) [PEVA]. The PLS calibrations were based on average spectra generated from each spatial location profiled. The PLS models were tested on six unknown stent samples to assess accuracy and precision. The wt % difference between PLS predictions and laboratory assay values for sirolimus was less than 1 wt % for the composite of the six unknowns, while the polymer models were estimated to be less than 0.5 wt % difference for the combined samples. The linearity and specificity of the three PLS models were also demonstrated with the three PLS models. In contrast to earlier univariate models, the PLS models achieved mass balance with better accuracy. This analysis was extended to evaluate the spatial distribution of the three constituents. Quantitative bitmap images of drug-eluting stent coatings are presented for the first time to assess the local distribution of components.

An investigation of adhesion in drug-eluting stent layers.

An atomic force microscopy (AFM) method was developed to quantify the adhesion forces between and cohesive forces within the layers of a drug-eluting stent (DES). Surface pairs representing both the individual components and the complete chemistry of each layer within the DES were prepared. As a model, the CYPHER Sirolimus-eluting Coronary Stent was studied. This DES consists of a stainless steel stent substrate, a parylene C primer layer, and a drug-eluting layer that contains poly(ethylene-co-vinyl acetate), poly(n-butyl methacrylate), and sirolimus (rapamycin). Coated AFM tips and two-dimensional substrates or coupons, which act as surrogates to the CYPHER Stent, were prepared and characterized. The force-displacement measurements were conducted to evaluate the adhesion between the middle parylene C layer and the 316L stainless steel substrate, the adhesion between the parylene C layer and the outer drug-eluting layer, and the cohesion between the three constituents of the drug-eluting layer. The average adhesion forces between the parylene C to drug layer varied from 88 to 167 nN, and the drug layer-to-drug layer interactions were between 194 and 486 nN within the model CYPHER Stent coating. All the adhesion forces measured were larger than those observed for gold-gold interactions, which yielded a pull of force of 19 nN (Zong et al., J Appl Phys 2006;100:104313-104323).

Multigrid geometric active contour models.

Geometric active contour models are very popular partial differential equation-based tools in image analysis and computer vision. We present a new multigrid algorithm for the fast evolution of level-set-based geometric active contours and compare it with other established numerical schemes. We overcome the main bottleneck associated with most numerical implementations of geometric active contours, namely the need for very small time steps to avoid instability, by employing a very stable fully 2-D implicit-explicit time integration numerical scheme. The proposed scheme is more accurate and has improved rotational invariance properties compared with alternative split schemes, particularly when big time steps are utilized. We then apply properly designed multigrid methods to efficiently solve the occurring sparse linear system. The combined algorithm allows for the rapid evolution of the contour and convergence to its final configuration after very few iterations. Image segmentation experiments demonstrate the efficiency and accuracy of the method.

Twenty-eight-day efficacy and phamacokinetics of the sirolimus-eluting stent.

BACKGROUND: In-stent restenosis is caused by neointimal hyperplasia. Sirolimus (rapamycin; Wyeth Research, Radnor, Pennsylvania, USA) inhibits vascular smooth muscle cell proliferation and we evaluated the efficacy of sirolimus in reducing neointimal formation in a rabbit iliac model and in-vivo pharmacokinetics in the porcine coronary model. DESIGN: Randomized, blinded, prospective animal study. METHODS: Bilateral rabbit iliac artery stent implantation was performed using crossflex stents (Cordis Corporation, Warren, New Jersey, USA) coated with sirolimus incorporated in a nonerodable polymer. Arteries were randomized to one of four stent groups: uncoated stents (n = 8); polymer control stents (n = 10); low-dose sirolimus-eluting stents (n = 9); and high-dose sirolimus-eluting stents (n = 10). Histomorphometry was performed at 28 days. Arterial tissue and stents were retrieved at 8, 14 and 28 days and blood samples were obtained daily during the first week. RESULTS: Treatment with low-dose sirolimus was associated with a 23% (P = NS) reduction in neointimal area and treatment with high-dose sirolimus with a 45% (P < 0.05) reduction. Sustained drug release from the stent and prolonged intramural arterial deposition were confirmed for up to 28 days. No detectable sirolimus was found in the blood after 2 days. CONCLUSION: Controlled-release local delivery of a cell-cycle inhibitor from a nonerodable polymer-coated stent reduced neointimal formation in rabbit iliac arteries in a dose-dependent manner and represents a promising strategy for preventing restenosis.