Suitable carriers for encapsulation and distribution of endostar: comparison of endostar-loaded particulate carriers

Weijie Chen, Sanyuan HuDepartment of General Surgery, Qilu Hospital of Shandong University, Jinan, Shandong Province, People's Republic of ChinaBackground: Particulate carriers are necessary to control the release of endostar and prolong its circulation in vivo. The purpose of this study was to identify a suitable carrier for the capsulation and delivery of endostar.Methods: We prepared a series of poly (DL-lactide-co-glycolide) (PLGA) and poly (ethylene glycol) (PEG)-modified PLGA (PEG-PLGA) particulate carriers, and then characterized them according to their ability to prolong the circulation of endostar, their physicochemical properties, endostar-loading content, and in vitro and in vivo particulate carrier release profiles.Results: All the particulate carriers had spherical core shell structures. The PEG-PLGA material and nanosize range appeared to enable the carriers to encapsulate more endostar, release endostar faster in vitro, and accumulate more endostar in vivo. The drug loading capacity of PEG-PLGA and PLGA nanoparticles was 8.03% ± 3.41% and 3.27% ± 5.26%, respectively, and for PEG-PLGA and PLGA microspheres was 15.32% ± 5.61% and 9.21% ± 4.73%. The cumulative amount of endostar released from the carriers in phosphate-buffered saline over 21 days was 23.79%, 20.45%, 15.13%, and 10.41%, respectively. Moreover, the terminal elimination half-life of endostar in the rabbit was 26.91 ± 7.93 hours and 9.32 ± 5.53 hours in the PEG-PLGA group and the PLGA nanoparticle group. Peak endostar concentration was reached at day 7 in the group treated with subcutaneous injection of PEG-PLGA microspheres and at day 14 in the group receiving subcutaneous injection of PLGA microspheres. Endostar was detectable in vivo in both groups after injection of the particulate carriers.Conclusion: PEG-PLGA nanoparticles might be better than other nanoparticulate carriers for encapsulation and distribution of endostar.Keywords: poly(DL-lactide-co-glycolide), nanoparticle, microsphere, endostar, peptide deliver

Data Augmentation Vision Transformer for Fine-grained Image Classification

Recently, the vision transformer (ViT) has made breakthroughs in image recognition. Its self-attention mechanism (MSA) can extract discriminative labeling information of different pixel blocks to improve image classification accuracy. However, the classification marks in their deep layers tend to ignore local features between layers. In addition, the embedding layer will be fixed-size pixel blocks. Input network Inevitably introduces additional image noise. To this end, we study a data augmentation vision transformer (DAVT) based on data augmentation and proposes a data augmentation method for attention cropping, which uses attention weights as the guide to crop images and improve the ability of the network to learn critical features. Secondly, we also propose a hierarchical attention selection (HAS) method, which improves the ability of discriminative markers between levels of learning by filtering and fusing labels between levels. Experimental results show that the accuracy of this method on the two general datasets, CUB-200-2011, and Stanford Dogs, is better than the existing mainstream methods, and its accuracy is 1.4\% and 1.6\% higher than the original ViT, respectivelyComment: IEEE Signal Processing Letter

A Lightweight Reconstruction Network for Surface Defect Inspection

Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and $\mathit{L}1$ loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.Comment: Journal of Mathematical Imaging and Vision(JMIV

Research of Driving Circuit in Coaxial Induction Coilgun

Power supply is crucial equipment in coaxial induction coil launcher. Configuration of the driving circuit directly influences the efficiency of the coil launcher.This paper gives a detailed analysis of the properties of the driving circuit construction based on the capacitor source. Three topologies of the driving circuit are compared including oscillation, crowbar and half-wave circuits. It is proved that which circuit has the better efficiency depends on the detailed parameters of the experiment, especially the crowbar resistance. Crowbar resistor regulates not only efficiency of the system, but also temperature rise of the coil. Electromagnetic force (EMF) applied on the armature will be another problem which influences service condition of the driving circuits. Oscillation and crowbar circuits should be applied to both of the synchronous and asynchronous induction coil launchers, respectively. Half-wave circuit is seldom used in the experiment. Although efficiency of the half-wave circuit is very high, the speed of the armature is low. A simple independent half-wave circuit is proposed in this paper. In general, the comprehensive property of crowbar circuit is the most practical in the three typical circuits. Conclusions of the paper could provide guidelines for practice

A Plug-and-Play Image Registration Network

Deformable image registration (DIR) is an active research topic in biomedical imaging. There is a growing interest in developing DIR methods based on deep learning (DL). A traditional DL approach to DIR is based on training a convolutional neural network (CNN) to estimate the registration field between two input images. While conceptually simple, this approach comes with a limitation that it exclusively relies on a pre-trained CNN without explicitly enforcing fidelity between the registered image and the reference. We present plug-and-play image registration network (PIRATE) as a new DIR method that addresses this issue by integrating an explicit data-fidelity penalty and a CNN prior. PIRATE pre-trains a CNN denoiser on the registration field and "plugs" it into an iterative method as a regularizer. We additionally present PIRATE+ that fine-tunes the CNN prior in PIRATE using deep equilibrium models (DEQ). PIRATE+ interprets the fixed-point iteration of PIRATE as a network with effectively infinite layers and then trains the resulting network end-to-end, enabling it to learn more task-specific information and boosting its performance. Our numerical results on OASIS and CANDI datasets show that our methods achieve state-of-the-art performance on DIR

Block Coordinate Plug-and-Play Methods for Blind Inverse Problems

Plug-and-play (PnP) prior is a well-known class of methods for solving imaging inverse problems by computing fixed-points of operators combining physical measurement models and learned image denoisers. While PnP methods have been extensively used for image recovery with known measurement operators, there is little work on PnP for solving blind inverse problems. We address this gap by presenting a new block-coordinate PnP (BC-PnP) method that efficiently solves this joint estimation problem by introducing learned denoisers as priors on both the unknown image and the unknown measurement operator. We present a new convergence theory for BC-PnP compatible with blind inverse problems by considering nonconvex data-fidelity terms and expansive denoisers. Our theory analyzes the convergence of BC-PnP to a stationary point of an implicit function associated with an approximate minimum mean-squared error (MMSE) denoiser. We numerically validate our method on two blind inverse problems: automatic coil sensitivity estimation in magnetic resonance imaging (MRI) and blind image deblurring. Our results show that BC-PnP provides an efficient and principled framework for using denoisers as PnP priors for jointly estimating measurement operators and images

Electrochemical Characteristics of AZ31 Magnesium Alloys with Microarc Oxidation Coating

poster abstractWhen considering implantable biomaterials, one possible solution that has arisen in recent years is the use of magnesium alloys due to their excellent mechanical properties. Magnesium alloys have many properties comparable to bone, including strength-to-weight ratio, density, and yield strength. For those reasons, magnesium alloys have been viewed as a promising biomaterial. Unfortunately, magnesium alloys are also prone to corrosion attack. To decrease the corrosion, studies have been taken to find appropriate coatings. One possibility is microarc oxidation (MAO) coating. However, studies have yet to be conducted to determine the corrosion of magnesium alloys with MAO coating. In this study, both MAO-coated and uncoated magnesium alloys will be placed in 0.9% saline solution and simulated body fluid and a time study will be conducted. The corrosion properties will be measured through use of a computer-generated Tafel curve, as well as through optical microscopy of the corrosion over the course of time.When considering implantable biomaterials, one possible solution that has arisen in recent years is the use of magnesium alloys due to their excellent mechanical properties. Magnesium alloys have many properties comparable to bone, including strength-to-weight ratio, density, and yield strength. For those reasons, magnesium alloys have been viewed as a promising biomaterial. Unfortunately, magnesium alloys are also prone to corrosion attack. To decrease the corrosion, studies have been taken to find appropriate coatings. One possibility is microarc oxidation (MAO) coating. However, studies have yet to be conducted to determine the corrosion of magnesium alloys with MAO coating. In this study, both MAO-coated and uncoated magnesium alloys will be placed in 0.9% saline solution and simulated body fluid and a time study will be conducted. The corrosion properties will be measured through use of a computer-generated Tafel curve, as well as through optical microscopy of the corrosion over the course of time

Study on Coulomb Stress Triggering of the April 2015 M7.8 Nepal Earthquake Sequence

In April 2015, a M7.8 earthquake occurred less than one month before a M7.3 earthquake near Kodari, Nepal. The Nepal earthquake sequences also include four larger (M > 6) aftershocks. To reveal the interrelation between the main shock and the aftershocks, we check the role of coseismic coulomb stress triggering on aftershocks that follow the M7.8 main shock. Based on the focal mechanisms of the aftershocks and source models of the main shock, the coulomb failure stress changes on both of the focal mechanism nodal planes are calculated. In addition, the coulomb stress changes on the focal sources of each aftershock are also calculated. A large proportion of the M > 6 aftershocks occurred in positive coulomb stress areas triggered by the M7.8 main shock. The secondary triggering effect of the M7.3 aftershock is also found in this paper. More specifically, the M7.3 aftershock promoted failure on the rupture plane of the M6.3 aftershock. Therefore, we may conclude that the majority of larger aftershocks, which accumulated positive coulomb stress changes during the sequence, were promoted or triggered by the main shock failure. It suggests that coulomb stress triggering contributed to the evolution of the Nepal M7.8 earthquake sequence