The Effect of Low-Molecular-Weight Heparin on Immune Balance of Patients with Repeated Implantation Failure During the Implantation Window.

This study investigates the effect of low-molecular-weight heparin (LMWH) on cytokines TNF-α, IFN-γ, IL-2, IL-4, IL-6, and IL-10 in peripheral blood of patients with repeated implantation failure during the implantation window. From May 2019 to March 2021, we enrolled 32 patients with recurrent implantation failure (RIF group) and 30 patients with successful pregnancy after the first frozen embryo transfer (control group) in the Reproductive Medicine Centre of Wuxi Maternity and Child Health Care Hospital. During the implantation window, the following features were compared between two groups and between different time points using ELISA: the status of immune cytokines in peripheral blood; Th1 cytokines (TNF-α, IFN-γ, and IL-2) and Th2 cytokines (IL-4, IL-6, and IL-10) in peripheral blood. The levels of Th1 cytokines in the RIF group before treatment were higher in comparison with the control group. In the RIF group, the LMWH treatment can inhibit the expression of Th1 cytokines and enhance the expression of Th2 cytokines. Using LMWH during the implantation window can improve the immune imbalance of patients with repeated implantation failure, which makes it a potential treatment strategy for patients with abnormal cellular immunity.

[Study on the sensitivity of a volumetric modulated arc therapy plan verification equipment on multi-leaf collimator opening and closing errors and its gamma pass rate limit].

To investigate the γ pass rate limit of plan verification equipment for volumetric modulated arc therapy (VMAT) plan verification and its sensitivity on the opening and closing errors of multi-leaf collimator (MLC), 50 cases of nasopharyngeal carcinoma VMAT plan with clockwise and counterclockwise full arcs were randomly selected. Eight kinds of MLC opening and closing errors were introduced in 10 cases of them, and 80 plans with errors were generated. Firstly, the plan verification was conducted in the form of field-by-field measurement and true composite measurement. The γ analysis with the criteria of 3% dose difference, distance to agreement of 2 mm, 10% dose threshold, and absolute dose global normalized conditions were performed for these fields. Then gradient analysis was used to investigate the sensitivity of field-by-field measurement and true composite measurement on MLC opening and closing errors, and the receiver operating characteristic curve (ROC) was used to investigate the optimal threshold of γ pass rate for identifying errors. Tolerance limits and action limits for γ pass rates were calculated using statistical process control (SPC) method for another 40 cases. The error identification ability using the tolerance limit calculated by SPC method and the universal tolerance limit (95%) were compared with using the optimal threshold of ROC. The results show that for the true composite measurement, the clockwise arc and the counterclockwise arc, the descent gradients of the γ passing rate with per millimeter MLC opening error are 10.61%, 7.62% and 6.66%, respectively, and the descent gradients with per millimeter MLC closing error are 9.75%, 7.36% and 6.37%, respectively. The optimal thresholds obtained by the ROC method are 99.35%, 97.95% and 98.25%, respectively, and the tolerance limits obtained by the SPC method are 98.98%, 97.74% and 98.62%, respectively. The tolerance limit calculated by SPC method is close to the optimal threshold of ROC, both of which could identify all errors of ±2 mm, while the universal tolerance limit can only partially identify them, indicating that the universal tolerance limit is not sensitive on some large errors. Therefore, considering the factors such as ease of use and accuracy, it is suggested to use the true composite measurement in clinical practice, and to formulate tolerance limits and action limits suitable for the actual process of the institution based on the SPC method. In conclusion, it is expected that the results of this study can provide some references for institutions to optimize the radiotherapy plan verification process, set appropriate pass rate limit, and promote the standardization of plan verification.

[PyRERT: Python Research Environment for Radiation Therapy].

OBJECTIVE: The Python research environment for radiation therapy (PyRERT) is a set of business software for hospital physicists to conduct radiation therapy research. METHODS: Choose the open source Enthought Tool Suite（ETS） as the core external dependency library of PyRERT. PyRERT is divided into base layer, content layer and interaction layer, and each layer is composed of different functional modules. RESULTS: PyRERT V1.0 provide a good development environment for scientific research programming in DICOM RT file processing, batch processing of water tank scan data, digital phantom creation, 3D medical image volume visualization, virtual radiotherapy equipment driver, and film scan image analysis. CONCLUSIONS: PyRERT enables the results of the research group to be iteratively inherited in the form of software. It's reusable basic classes and functional modules greatly improve the efficiency of scientific research task programming.

CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning.

Gesture recognition through surface electromyography (sEMG) provides a new method for the control algorithm of bionic limbs, which is a promising technology in the field of human-computer interaction. However, subject specificity of sEMG along with the offset of the electrode makes it challenging to develop a model that can quickly adapt to new subjects. In view of this, we introduce a new deep neural network called CSAC-Net. Firstly, we extract the time-frequency feature from the raw signal, which contains rich information. Secondly, we design a convolutional neural network supplemented by an attention mechanism for further feature extraction. Additionally, we propose to utilize model-agnostic meta-learning to adapt to new subjects and this learning strategy achieves better results than the state-of-the-art methods. By the basic experiment on CapgMyo and three ablation studies, we demonstrate the advancement of CSAC-Net.

FRDD-Net: Automated Carotid Plaque Ultrasound Images Segmentation Using Feature Remapping and Dense Decoding.

Automated segmentation and evaluation of carotid plaques ultrasound images is of great significance for the diagnosis and early intervention of high-risk groups of cardiovascular and cerebrovascular diseases. However, it remains challenging to develop such solutions due to the relatively low quality of ultrasound images and heterogenous characteristics of carotid plaques. To address those problems, in this paper, we propose a novel deep convolutional neural network, FRDD-Net, with an encoder-decoder architecture to automatically segment carotid plaques. We propose the feature remapping modules (FRMs) and incorporate them into the encoding and decoding blocks to ameliorate the reliability of acquired features. We also propose a new dense decoding mechanism as part of the decoder, thus promoting the utilization efficiency of encoded features. Additionally, we construct a compound loss function to train our network to further enhance its robustness in the face of numerous cases. We train and test our network in multiple carotid plaque ultrasound datasets and our method yields the best performance compared to other state-of-the-art methods. Further ablation studies consistently show the advancement of our proposed architecture.

Initial Experience of a Tele-radiotherapy System for Training Radiation Oncologists in Rural Areas.

Telemedicine is considered to be an important approach for medical education in rural areas. Due to a significant shortage of radiation oncologists in rural areas of Sichuan Province in China, a tele-radiotherapy system has been designed and developed for training radiation oncologists in the Sichuan Cancer Hospital and Research Institute. The whole process of the radiotherapy teaching platform was designed and established in the tele-radiotherapy system. A detailed radiation therapy process could be obtained in rural areas through the tele-radiotherapy system. Through the tele-radiotherapy system, oncologists in rural hospitals are trained at any time and anywhere. And the experience of experts in the Sichuan Cancer Hospital and Research Institute is effectively and quickly conveyed to rural areas. A tele-radiotherapy system is considered to be an important means to promote the level of radiotherapy and to solve the shortage of radiation oncologists in rural areas.

Inter-Level Feature Balanced Fusion Network for Street Scene Segmentation.

Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of simple concatenation or element-wise addition will lead to the problem of unbalanced fusion and low utilization of inter-level features. To solve this problem, we propose the Inter-Level Feature Balanced Fusion Network (IFBFNet) to guide the inter-level feature fusion towards a more balanced and effective direction. Our overall network architecture is based on the encoder-decoder architecture. In the encoder, we use a relatively deep convolution network to extract rich semantic information. In the decoder, skip-connections are added to connect and fuse low-level spatial features to restore a clearer boundary expression gradually. We add an inter-level feature balanced fusion module to each skip connection. Additionally, to better capture the boundary information, we added a shallower spatial information stream to supplement more spatial information details. Experiments have proved the effectiveness of our module. Our IFBFNet achieved a competitive performance on the Cityscapes dataset with only finely annotated data used for training and has been greatly improved on the baseline network.

Whole and Part Adaptive Fusion Graph Convolutional Networks for Skeleton-Based Action Recognition.

Spatiotemporal graph convolution has made significant progress in skeleton-based action recognition in recent years. Most of the existing graph convolution methods take all the joints of the human skeleton as the overall modeling graph, ignoring the differences in the movement patterns of various parts of the human, and cannot well connect the relationship between the different parts of the human skeleton. To capture the unique features of different parts of human skeleton data and the correlation of different parts, we propose two new graph convolution methods: the whole graph convolution network (WGCN) and the part graph convolution network (PGCN). WGCN learns the whole scale skeleton spatiotemporal features according to the movement patterns and physical structure of the human skeleton. PGCN divides the human skeleton graph into several subgraphs to learn the part scale spatiotemporal features. Moreover, we propose an adaptive fusion module that combines the two features for multiple complementary adaptive fusion to obtain more effective skeleton features. By coupling these proposals, we build a whole and part adaptive fusion graph convolution neural network (WPGCN) that outperforms previous state-of-the-art methods on three large-scale datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400.

PSANet: Pyramid Splitting and Aggregation Network for 3D Object Detection in Point Cloud.

3D object detection in LiDAR point clouds has been extensively used in autonomous driving, intelligent robotics, and augmented reality. Although the one-stage 3D detector has satisfactory training and inference speed, there are still some performance problems due to insufficient utilization of bird's eye view (BEV) information. In this paper, a new backbone network is proposed to complete the cross-layer fusion of multi-scale BEV feature maps, which makes full use of various information for detection. Specifically, our proposed backbone network can be divided into a coarse branch and a fine branch. In the coarse branch, we use the pyramidal feature hierarchy (PFH) to generate multi-scale BEV feature maps, which retain the advantages of different levels and serves as the input of the fine branch. In the fine branch, our proposed pyramid splitting and aggregation (PSA) module deeply integrates different levels of multi-scale feature maps, thereby improving the expressive ability of the final features. Extensive experiments on the challenging KITTI-3D benchmark show that our method has better performance in both 3D and BEV object detection compared with some previous state-of-the-art methods. Experimental results with average precision (AP) prove the effectiveness of our network.

EPGNet: Enhanced Point Cloud Generation for 3D Object Detection.

Three-dimensional object detection from point cloud data is becoming more and more significant, especially for autonomous driving applications. However, it is difficult for lidar to obtain the complete structure of an object in a real scene due to its scanning characteristics. Although the existing methods have made great progress, most of them ignore the prior information of object structure, such as symmetry. So, in this paper, we use the symmetry of the object to complete the missing part in the point cloud and then detect it. Specifically, we propose a two-stage detection framework. In the first stage, we adopt an encoder-decoder structure to generate the symmetry points of the foreground points and make the symmetry points and the non-empty voxel centers form an enhanced point cloud. In the second stage, the enhanced point cloud is input into the baseline, which is an anchor-based region proposal network, to generate the detection results. Extensive experiments on the challenging KITTI benchmark show the effectiveness of our method, which has better performance on both 3D and BEV (bird's eye view) object detection compared with some previous state-of-the-art methods.

Design and Preliminary Experience of a Tele-Radiotherapy System for a Medical Alliance in China.

Background: The medical alliance and telemedicine are considered to be important means to solve the imbalance between regions and shortage of professionals and promote the homogenization of medical services. Sichuan Provincial People's Hospital Group (SPPHG) is a network of hospitals with different levels of expertise, and all the members with radiotherapy form a radiotherapy network (RTN). Addressing the inadequacy and imbalance of radiotherapy services of Sichuan Province, China, a tele-radiotherapy system for RTN-SPPHG is designed, which includes the business model and corresponding technical implementation of an information system. Materials and Methods: In the RTN-SPPHG, a distributed remote collaboration business model is explored and a tele-radiotherapy information system is customized for this telemedicine model. Both the business model and tailored information system were evaluated in actual use. Results: Based on the tele-radiotherapy system of RTN-SPPHG, multitype hospitals are linked together and serve as a whole. Through the internet, the experience of experts of Sichuan Provincial People's Hospital is effectively deployed to member hospitals at the grassroot level. Conclusions: A close-knit medical alliance based on a tele-radiotherapy system should be a way to rapidly improve radiotherapy services and promote the homogenization of service in a region.

GOLM1 promotes prostate cancer progression through activating PI3K-AKT-mTOR signaling.

BACKGROUND: Prostate cancer (PCa) is the most commonly diagnosed cancer in men. Various molecular mechanisms account for PCa progression and elucidation of these mechanisms is key for selection of optimal therapies and improvement of patient outcome. Golgi membrane protein 1 (GOLM1) has been identified as a novel biomarker for PCa, but its biological functions and molecular mechanisms remain poorly understood. METHOD: GOLM1 expression was determined in PCa by tissue microarrays (TMAs) and real-time RT-PCR, Western blot, and immunohistochemistry (IHC) analyses. To investigate GOLM1 functions in vitro and in vivo, we overexpressed and knocked down GOLM1 in PCa cell lines and established xenograft mice models. A series of cytological function assays were used to determine the role of GOLM1 in cell proliferation, migration, invasion, and apoptosis. PI3K-AKT-mTOR signaling pathway downstream of GOLM1 was detected by Western blot and IHC analyses. RESULT: GOLM1 expression is up-regulated in PCa of all stages and grades. GOLM1 promotes proliferation, migration and invasion, and inhibits apoptosis in PCa cell lines (DU145, PC3, and CWR22Rv1) and xenograft mice models. Moreover, PI3K-AKT-mTOR signaling is positively regulated by GOLM1, whereas PI3 K inhibitor BKM120 significantly abrogates the oncogenic functions of GOLM1. CONCLUSION: GOLM1 acts as a critical oncogene by promoting PCa cell proliferation, migration and invasion, and inhibiting apoptosis. GOLM1 plays oncogenic functions mainly through activating PI3K-AKT-mTOR signaling pathway. Therefore, agents that block PI3K-AKT-mTOR signaling pathway could be used in PCa patients with GOLM1 up-regulation.

Vaccination demonstration zone successfully controls rabies in Guangxi Province, China.

BACKGROUND: Guangxi is the province most seriously affected by rabies virus (RABV) in China. Those most affected by RABV each year are people in rural areas, where dogs are the main cause of human infection with the virus. METHODS: In this study, we established a rabies vaccination demonstration program that included eradication, core, and peripheral areas. This program was implemented for 9 years and comprised three stages: 12 counties in the first stage (2008-2010), 21 counties in the second stage (2011-2013), and then extending to all counties of Guangxi Province in the third stage (2014-2016). The program included a dog vaccination campaign, surveillance of clinically healthy dogs who may be potential RABV carriers, monitoring anti-RABV antibody titers in vaccinated dogs, and compiling and reporting statistics of human rabies cases. RESULTS: The target effectiveness was achieved in the eradication, core, and peripheral areas in all three stages. The vaccination demonstration program successfully promoted RABV vaccination of domestic dogs throughout Guangxi Province by drawing upon the experience gained at key points. Compared with a vaccination coverage rate of 39.42-46.85% in Guangxi Province overall during 2003-2007, this rate gradually increased to 48.98-52.67% in 2008-2010, 60.24-69.67% in 2011-2013, and 70.09-71.53% in 2014-2016, thereby meeting World Health Organization requirements. The total cases of human rabies in the province decreased from 602 in 2004 to 41 cases in 2017. CONCLUSIONS: The present pilot vaccination program obviously increased the rabies vaccination and seroconversion rates, and effectively reduced the spread of rabies from dogs to humans as well as the number of human rabies cases, thus successfully controlling rabies in Guangxi.

[Design and Validation of Remote Radiotherapy System].

Telemedicine technology is a means of deploying medical resources with low cost and high efficiency. A set of remote radiotherapy system based on Citrix was designed in this paper, so that the senior radiation therapists from the developed areas can provide medical services effectively for the patients in the rural areas. This paper focused on the design ideas and the detail of the technical implementation of how to design a remote radiotherapy system based on the existing equipment in the primary hospital. And the technical reliability and security of the remote radiotherapy system were verified by the scientific test method with pairwise comparison. The early practical experience shows that through the remote radiotherapy system the primary radiotherapy personnel and the radiotherapy experts from thirdgrade class-A hospital can form effective alliance in radiotherapy techniques to allow patients in rural areas to receive more professional radiation therapy.

[Factors influencing the postoperative resolution of varicocele-associated scrotal pain].

OBJECTIVE: To investigate the factors influencing the postoperative resolution of varicocele-associated scrotal pain. METHODS: Using the keywords "varicocele", "testicular pain", "scrotal pain", "painful varicocele", "ligation", and "varicocelectomy", we searched the PubMed, Embase, Cochrane Collaboration's Database, CNKI, Wanfang, and VIP Database up to October 2016 for the studies relating to surgical treatment of varicocele-associated scrotal pain. We assessed the quality of the cohort studies included using the Newcastle-Ottawa Scale and that of the randomized controlled trials included with the Cochrane Collaboration's tool. We conducted a meta-analysis using the RevMan software. RESULTS: Finally 14 studies were included in this meta-analysis, of which, 2 involved the history of disease, 8 involved the nature of pain, 2 involved the intensity of pain, 9 involved the grade of varicocele, 3 involved the side of varicocele, 9 involved surgical approaches, 3 involved surgical techniques, and 4 involved postoperative recurrence. The pain resolution rate was significantly higher after subinguinal ligation than after high or inguinal ligation (RR = 0.82, 95% CI: 0.76－0.89, P <0.01; RR = 0.92, 95% CI: 0.86－0.99, P = 0.02), and so was it after microsurgery than after laparoscopic varicocelectomy (RR = 0.77, 95% CI: 0.60－0.99, P = 0.04). CONCLUSIONS: Subinguinal varicocelectomy and microsurgery are more effective options than laparoscopic and high or trans-inguinal ligation of the spermatic vein for resolution of varicocele-associated scrotal pain, while the history of disease, the nature and intensity of pain, the grade and side of varicocele, or postoperative recurrence cannot be regarded as the influencing factors.

[A Software Platform of Treatment Planning System for Radiotherapy Snake Robot].

A software platform integrated of radiation treatment planning system and navigation system has been developed for a newly-developed snake robot. This platform is developed in a module-plugin form based on 3D Slicer medical image visualization and analysis application platform and an open-source toolkit SlicerRT. The platform realized function modules of image access and visualization, image segmentation, radiation path planning, treatment planning evaluation and optical navigation, and has provided the snake robot an extensible software experiment platform. It is beneficial for the brachytherapy research work and experiments.

[Porting Radiotherapy Software of Varian to Cloud Platform].

To develop a low-cost private cloud platform of radiotherapy software. First, a private cloud platform which was based on OpenStack and the virtual GPU hardware was builded. Then on the private cloud platform, all the Varian radiotherapy software modules were installed to the virtual machine, and the corresponding function configuration was completed. Finally the software on the cloud was able to be accessed by virtual desktop client. The function test results of the cloud workstation show that a cloud workstation is equivalent to an isolated physical workstation, and any clients on the LAN can use the cloud workstation smoothly. The cloud platform transplantation in this study is economical and practical. The project not only improves the utilization rates of radiotherapy software, but also makes it possible that the cloud computing technology can expand its applications to the field of radiation oncology.

[Application of REDCap Software in Radiotherapy Quality Control Platform REDCap].

In order to digitize the radiotherapy quality control data, convenient to carry on the dynamic quantitative analysis to the equipment condition, this paper presents a low-cost open radiotherapy quality control platform based on REDCap. The platform uses the REDCap data acquisition system as a carrier to achieve the digital storage of this data, anytime, anywhere data entry, and can be easily derived for data analysis by creating REDCap research projects and inputting radiotherapy quality control data. It is proved that REDCap can be used in the construction of radiotherapy quality control platform.

[Development of a Compared Software for Automatically Generated DVH in Eclipse TPS].

Objective: This study is to automatically calculate the dose volume histogram(DVH) for the treatment plan, then to compare it with requirements of doctor's prescriptions. Methods: The scripting language Autohotkey and programming language C# were used to develop a compared software for automatically generated DVH in Eclipse TPS. This software is named Show Dose Volume Histogram (ShowDVH), which is composed of prescription documents generation, operation functions of DVH, software visualization and DVH compared report generation. Results: Ten cases in different cancers have been separately selected, in Eclipse TPS 11.0 ShowDVH could not only automatically generate DVH reports but also accurately determine whether treatment plans meet the requirements of doctor's prescriptions, then reports gave direction for setting optimization parameters of intensity modulated radiated therapy. Conclusions: The ShowDVH is an user-friendly and powerful software, and can automatically generated compared DVH reports fast in Eclipse TPS 11.0. With the help of ShowDVH, it greatly saves plan designing time and improves working efficiency of radiation therapy physicists.

[Development of a Software for Automatically Generated Contours in Eclipse TPS].

OBJECTIVE: The automatic generation of planning targets and auxiliary contours have achieved in Eclipse TPS 11.0. METHODS: The scripting language autohotkey was used to develop a software for automatically generated contours in Eclipse TPS. This software is named Contour Auto Margin (CAM), which is composed of operational functions of contours, script generated visualization and script file operations. RESULTS Ten cases in different cancers have separately selected, in Eclipse TPS 11.0 scripts generated by the software could not only automatically generate contours but also do contour post-processing. For different cancers, there was no difference between automatically generated contours and manually created contours. CONCLUSION: The CAM is a user-friendly and powerful software, and can automatically generated contours fast in Eclipse TPS 11.0. With the help of CAM, it greatly save plan preparation time and improve working efficiency of radiation therapy physicists.