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1. Revisiting and Benchmarking Graph Autoencoders: A Contrastive Learning Perspective

Author

Li, Jintang, Wu, Ruofan, Zhu, Yuchang, Zhang, Huizhe, Jin, Xinzhou, Zhang, Guibin, Zhu, Zulun, Zheng, Zibin, and Chen, Liang

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

Graph autoencoders (GAEs) are self-supervised learning models that can learn meaningful representations of graph-structured data by reconstructing the input graph from a low-dimensional latent space. Over the past few years, GAEs have gained significant attention in academia and industry. In particular, the recent advent of GAEs with masked autoencoding schemes marks a significant advancement in graph self-supervised learning research. While numerous GAEs have been proposed, the underlying mechanisms of GAEs are not well understood, and a comprehensive benchmark for GAEs is still lacking. In this work, we bridge the gap between GAEs and contrastive learning by establishing conceptual and methodological connections. We revisit the GAEs studied in previous works and demonstrate how contrastive learning principles can be applied to GAEs. Motivated by these insights, we introduce lrGAE (left-right GAE), a general and powerful GAE framework that leverages contrastive learning principles to learn meaningful representations. Our proposed lrGAE not only facilitates a deeper understanding of GAEs but also sets a new benchmark for GAEs across diverse graph-based learning tasks. The source code for lrGAE, including the baselines and all the code for reproducing the results, is publicly available at https://github.com/EdisonLeeeee/lrGAE., Comment: Preprint, under review

Published
2024

2. One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes

Author

Zhu, Yuchang, Li, Jintang, Bian, Yatao, Zheng, Zibin, and Chen, Liang

Subjects
Computer Science - Machine Learning
Abstract

Recent studies have highlighted fairness issues in Graph Neural Networks (GNNs), where they produce discriminatory predictions against specific protected groups categorized by sensitive attributes such as race and age. While various efforts to enhance GNN fairness have made significant progress, these approaches are often tailored to specific sensitive attributes. Consequently, they necessitate retraining the model from scratch to accommodate changes in the sensitive attribute requirement, resulting in high computational costs. To gain deeper insights into this issue, we approach the graph fairness problem from a causal modeling perspective, where we identify the confounding effect induced by the sensitive attribute as the underlying reason. Motivated by this observation, we formulate the fairness problem in graphs from an invariant learning perspective, which aims to learn invariant representations across environments. Accordingly, we propose a graph fairness framework based on invariant learning, namely FairINV, which enables the training of fair GNNs to accommodate various sensitive attributes within a single training session. Specifically, FairINV incorporates sensitive attribute partition and trains fair GNNs by eliminating spurious correlations between the label and various sensitive attributes. Experimental results on several real-world datasets demonstrate that FairINV significantly outperforms state-of-the-art fairness approaches, underscoring its effectiveness. Our code is available via: https://github.com/ZzoomD/FairINV/., Comment: Accepted by KDD 2024

Published
2024

3. Fair Graph Representation Learning via Sensitive Attribute Disentanglement

Author

Zhu, Yuchang, Li, Jintang, Zheng, Zibin, and Chen, Liang

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society
Abstract

Group fairness for Graph Neural Networks (GNNs), which emphasizes algorithmic decisions neither favoring nor harming certain groups defined by sensitive attributes (e.g., race and gender), has gained considerable attention. In particular, the objective of group fairness is to ensure that the decisions made by GNNs are independent of the sensitive attribute. To achieve this objective, most existing approaches involve eliminating sensitive attribute information in node representations or algorithmic decisions. However, such ways may also eliminate task-related information due to its inherent correlation with the sensitive attribute, leading to a sacrifice in utility. In this work, we focus on improving the fairness of GNNs while preserving task-related information and propose a fair GNN framework named FairSAD. Instead of eliminating sensitive attribute information, FairSAD enhances the fairness of GNNs via Sensitive Attribute Disentanglement (SAD), which separates the sensitive attribute-related information into an independent component to mitigate its impact. Additionally, FairSAD utilizes a channel masking mechanism to adaptively identify the sensitive attribute-related component and subsequently decorrelates it. Overall, FairSAD minimizes the impact of the sensitive attribute on GNN outcomes rather than eliminating sensitive attributes, thereby preserving task-related information associated with the sensitive attribute. Furthermore, experiments conducted on several real-world datasets demonstrate that FairSAD outperforms other state-of-the-art methods by a significant margin in terms of both fairness and utility performance. Our source code is available at https://github.com/ZzoomD/FairSAD., Comment: Accepted by WWW 2024

Published
2024

4. Hetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs

Author

Li, Jintang, Wei, Zheng, Dan, Jiawang, Zhou, Jing, Zhu, Yuchang, Wu, Ruofan, Wang, Baokun, Zhen, Zhang, Meng, Changhua, Jin, Hong, Zheng, Zibin, and Chen, Liang

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literature has revealed the limitations of common graph neural networks (GNNs) in handling homogeneous graphs with heterophily, little work has been conducted on investigating the heterophily properties in the context of heterogeneous graphs. To bridge this research gap, we identify the heterophily in heterogeneous graphs using metapaths and propose two practical metrics to quantitatively describe the levels of heterophily. Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily. To address the challenge, we present Hetero$^2$Net, a heterophily-aware HGNN that incorporates both masked metapath prediction and masked label prediction tasks to effectively and flexibly handle both homophilic and heterophilic heterogeneous graphs. We evaluate the performance of Hetero$^2$Net on five real-world heterogeneous graph benchmarks with varying levels of heterophily. The results demonstrate that Hetero$^2$Net outperforms strong baselines in the semi-supervised node classification task, providing valuable insights into effectively handling more complex heterogeneous graphs., Comment: Preprint

Published
2023

5. Oversmoothing: A Nightmare for Graph Contrastive Learning?

Author

Li, Jintang, Sun, Wangbin, Wu, Ruofan, Zhu, Yuchang, Chen, Liang, and Zheng, Zibin

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Oversmoothing is a common phenomenon observed in graph neural networks (GNNs), in which an increase in the network depth leads to a deterioration in their performance. Graph contrastive learning (GCL) is emerging as a promising way of leveraging vast unlabeled graph data. As a marriage between GNNs and contrastive learning, it remains unclear whether GCL inherits the same oversmoothing defect from GNNs. This work undertakes a fundamental analysis of GCL from the perspective of oversmoothing on the first hand. We demonstrate empirically that increasing network depth in GCL also leads to oversmoothing in their deep representations, and surprisingly, the shallow ones. We refer to this phenomenon in GCL as `long-range starvation', wherein lower layers in deep networks suffer from degradation due to the lack of sufficient guidance from supervision. Based on our findings, we present BlockGCL, a remarkably simple yet effective blockwise training framework to prevent GCL from notorious oversmoothing. Without bells and whistles, BlockGCL consistently improves robustness and stability for well-established GCL methods with increasing numbers of layers on several real-world graph benchmarks., Comment: Technical report; Code available at https://github.com/EdisonLeeeee/BlockGCL

Published
2023

6. Analysis of volatile flavor components and characteristics of dough fermented with flavor fermentation liquid

Author

ZHANG Yanmei, ZHU Yuchang, YANG Dandan, PAN Xufang, BAI Hui, and ZHOU Dazhai

Subjects
flavor fermentation liquid, fermented dough, headspace solid phase microextraction, volatile flavor compounds, quality characteristics, Food processing and manufacture, TP368-456
Abstract

Objective: This study aimed to investigate the varieties and contents of volatile components in fermented dough with flavor fermentation broth at different fermentation time. Methods: Headspace solid phase microextraction-gas chromatography (HS-SPME-GC-MS) was used to determine the volatile flavor substances in the dough of the flavor ferment at 12, 24, 36, 48 and 72 h. Also, sensory evaluation of the odor and determination of pH value, total acidity (TAA) and rheological properties of the dough were measured. Results: The strength of eight odors (frankincense, cheese, flower, fruit sweet, wine, malt, grass and sour) showed different performance, depending on the types and contents of the flavour substances produced during the different fermentation periods. The pH value of the dough gradually decreases with the time of fermentation, while TTA was the opposite. The tensile resistance, stretch ratio and tensile area of dough increased first but decreased with the extension of fermentation time, while the elongation of dough decreased first then increased. These results indicated that moderate fermentation (36 h) was beneficial to the formation and stability of the gluten network. A total of 92 volatile substances were detected in the dough with 5 fermentation times, including 22 alcohols, 27 esters, 12 aldehydes, 14 ketones, 10 alkanes, and 7 acids. A total of 27, 43, 38, 41 and 37 volatile flavor components were detected from the dough fermented for 12, 24, 36, 48 and 72 h, respectively. The contents of acids and esters increased first then decreased with the prolongation of fermentation time and reaching the highest at 48 h (47.31% and 20.96%, respectively). On the other hand, the contents of aldehydes, ketones and acids all gradually decreased. Conclusion: The volatile substances of different fermentation times were alcohols mainly, and the total flavor substances and the relative content of alcohols and esters reached the highest at 48 h, indicating that alcohols and esters might be the major contributors to the flavor of dough during fermentation.

Published
2023
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15. Scaling Up, Scaling Deep: Blockwise Graph Contrastive Learning

Author

Li, Jintang, Sun, Wangbin, Wu, Ruofan, Zhu, Yuchang, Chen, Liang, and Zheng, Zibin

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Machine Learning (cs.LG)
Abstract

Oversmoothing is a common phenomenon in graph neural networks (GNNs), in which an increase in the network depth leads to a deterioration in their performance. Graph contrastive learning (GCL) is emerging as a promising way of leveraging vast unlabeled graph data. As a marriage between GNNs and contrastive learning, it remains unclear whether GCL inherits the same oversmoothing defect from GNNs. This work undertakes a fundamental analysis of GCL from the perspective of oversmoothing on the first hand. We demonstrate empirically that increasing network depth in GCL also leads to oversmoothing in their deep representations, and surprisingly, the shallow ones. We refer to this phenomenon in GCL as long-range starvation', wherein lower layers in deep networks suffer from degradation due to the lack of sufficient guidance from supervision (e.g., loss computing). Based on our findings, we present BlockGCL, a remarkably simple yet effective blockwise training framework to prevent GCL from notorious oversmoothing. Without bells and whistles, BlockGCL consistently improves robustness and stability for well-established GCL methods with increasing numbers of layers on real-world graph benchmarks. We believe our work will provide insights for future improvements of scalable and deep GCL frameworks., Preprint; Code is available at https://github.com/EdisonLeeeee/BlockGCL

Published
2023

17. The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation

Author

Zhu, Yuchang, Li, Jintang, Chen, Liang, Zheng, Zibin, Zhu, Yuchang, Li, Jintang, Chen, Liang, and Zheng, Zibin

Abstract

Graph neural networks (GNNs) are being increasingly used in many high-stakes tasks, and as a result, there is growing attention on their fairness recently. GNNs have been shown to be unfair as they tend to make discriminatory decisions toward certain demographic groups, divided by sensitive attributes such as gender and race. While recent works have been devoted to improving their fairness performance, they often require accessible demographic information. This greatly limits their applicability in real-world scenarios due to legal restrictions. To address this problem, we present a demographic-agnostic method to learn fair GNNs via knowledge distillation, namely FairGKD. Our work is motivated by the empirical observation that training GNNs on partial data (i.e., only node attributes or topology data) can improve their fairness, albeit at the cost of utility. To make a balanced trade-off between fairness and utility performance, we employ a set of fairness experts (i.e., GNNs trained on different partial data) to construct the synthetic teacher, which distills fairer and informative knowledge to guide the learning of the GNN student. Experiments on several benchmark datasets demonstrate that FairGKD, which does not require access to demographic information, significantly improves the fairness of GNNs by a large margin while maintaining their utility., Comment: Accepted by WSDM 2024

Published
2023

18. LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning

Author

Li, Jintang, Dan, Jiawang, Wu, Ruofan, Zhou, Jing, Tian, Sheng, Liu, Yunfei, Wang, Baokun, Meng, Changhua, Wang, Weiqiang, Zhu, Yuchang, Chen, Liang, Zheng, Zibin, Li, Jintang, Dan, Jiawang, Wu, Ruofan, Zhou, Jing, Tian, Sheng, Liu, Yunfei, Wang, Baokun, Meng, Changhua, Wang, Weiqiang, Zhu, Yuchang, Chen, Liang, and Zheng, Zibin

Abstract

Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data. However, many real-world applications, such as social networks and e-commerce, involve temporal graphs where nodes and edges are dynamically evolving. Temporal graph neural networks (TGNNs) have progressively emerged as an extension of GNNs to address time-evolving graphs and have gradually become a trending research topic in both academics and industry. Advancing research and application in such an emerging field necessitates the development of new tools to compose TGNN models and unify their different schemes for dealing with temporal graphs. In this work, we introduce LasTGL, an industrial framework that integrates unified and extensible implementations of common temporal graph learning algorithms for various advanced tasks. The purpose of LasTGL is to provide the essential building blocks for solving temporal graph learning tasks, focusing on the guiding principles of user-friendliness and quick prototyping on which PyTorch is based. In particular, LasTGL provides comprehensive temporal graph datasets, TGNN models and utilities along with well-documented tutorials, making it suitable for both absolute beginners and expert deep learning practitioners alike., Comment: Preprint; Work in progress

Published
2023

20. AutoMine: An Unmanned Mine Dataset

Author

Li, Yuchen, primary, Li, Zixuan, additional, Teng, Siyu, additional, Zhang, Yu, additional, Zhou, Yuhang, additional, Zhu, Yuchang, additional, Cao, Dongpu, additional, Tian, Bin, additional, Ai, Yunfeng, additional, Xuanyuan, Zhe, additional, and Chen, Long, additional

Published
2022
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21. Prevalence and Predictors of Chronic Pain with Two-Year Follow-Up After Knee Arthroplasty

Author

Tian,Meimei, Li,Zihua, Chen,Xujuan, Wu,Qiting, Shi,Huimin, Zhu,Yuchang, Shi,Yan, Tian,Meimei, Li,Zihua, Chen,Xujuan, Wu,Qiting, Shi,Huimin, Zhu,Yuchang, and Shi,Yan

Abstract

Meimei Tian,1,2,* Zihua Li,1,* Xujuan Chen,1 Qiting Wu,1 Huimin Shi,1 Yuchang Zhu,1 Yan Shi2 1Department of Orthopedics, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200072, People’s Republic of China; 2Department of Nursing, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200072, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yan Shi, Tel +86 21 66307304, Fax +86 21 66307542, Email shi_yan2018@outlook.comBackground: Pain relief is the most important issue in the long-term outcome of arthroplasty surgery, with nearly one-third of patients still suffered persistent pain and caused dissatisfaction after the surgery.Methods: A total of 713 patients underwent primary elective primary TKA and UKA were included consecutively between July 2018 and December 2019, using binary logistic method to analyze the data.Results: The prevalence of CPSP at rest and on movement at 2-year was 12.1% and 37.7% respectively after primary knee arthroplasty and CPSP at rest factors included: age above 80 (odds ratio [OR]= 6.72, 95% confidence interval [CI] 1.58 to 28.56), BMI above 30 (2.339, 1.02 to 5.383), and moderate to severe pain variables: preoperative pain, (1.95, 1.11 to 3.41); APSP on movement, 4.9 (2.31– 10.6); and follow-up contralateral knee pain-at-rest scores (12.6, 5.5 to 28.5). Factors associated with presence of CPSP on movement included: no smoking (2.59, 1.07 to 6.26); and moderate to severe pain variables: preoperative pain, (1.57, 1.073 to 2.30); APSP at rest, (1.85, 1.13 to 3.02); APSP on movement, 6.11 (3.82 to 9.78); and follow-up contralateral knee pain-on-movement scores, 3.22 (2.08 to 5.00). Factors to occurrence of moderate to severe CPSP on movement include: presence of COPD (12.20, 2.19 to 67.98); and moderate to severe pain variables: preoperative pain (2.36, 1.32 to 4.23); APSP on movement (4.68, 1.95 to 11.25)

Published
2022

26. Prevalence and Predictors of Chronic Pain with Two-Year Follow-Up After Knee Arthroplasty

Author

Tian,Meimei, Li,Zihua, Chen,Xujuan, Wu,Qiting, Shi,Huimin, Zhu,Yuchang, and Shi,Yan

Subjects
Anesthesiology and Pain Medicine, Journal of Pain Research
Abstract

Meimei Tian,1,2,* Zihua Li,1,* Xujuan Chen,1 Qiting Wu,1 Huimin Shi,1 Yuchang Zhu,1 Yan Shi2 1Department of Orthopedics, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200072, People’s Republic of China; 2Department of Nursing, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200072, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yan Shi, Tel +86 21 66307304, Fax +86 21 66307542, Email shi_yan2018@outlook.comBackground: Pain relief is the most important issue in the long-term outcome of arthroplasty surgery, with nearly one-third of patients still suffered persistent pain and caused dissatisfaction after the surgery.Methods: A total of 713 patients underwent primary elective primary TKA and UKA were included consecutively between July 2018 and December 2019, using binary logistic method to analyze the data.Results: The prevalence of CPSP at rest and on movement at 2-year was 12.1% and 37.7% respectively after primary knee arthroplasty and CPSP at rest factors included: age above 80 (odds ratio [OR]= 6.72, 95% confidence interval [CI] 1.58 to 28.56), BMI above 30 (2.339, 1.02 to 5.383), and moderate to severe pain variables: preoperative pain, (1.95, 1.11 to 3.41); APSP on movement, 4.9 (2.31– 10.6); and follow-up contralateral knee pain-at-rest scores (12.6, 5.5 to 28.5). Factors associated with presence of CPSP on movement included: no smoking (2.59, 1.07 to 6.26); and moderate to severe pain variables: preoperative pain, (1.57, 1.073 to 2.30); APSP at rest, (1.85, 1.13 to 3.02); APSP on movement, 6.11 (3.82 to 9.78); and follow-up contralateral knee pain-on-movement scores, 3.22 (2.08 to 5.00). Factors to occurrence of moderate to severe CPSP on movement include: presence of COPD (12.20, 2.19 to 67.98); and moderate to severe pain variables: preoperative pain (2.36, 1.32 to 4.23); APSP on movement (4.68, 1.95 to 11.25); and follow-up contralateral knee pain-on-movement scores (2.71, 1.66 to 4.42).Conclusion: Prevention strategies should be targeted to different types of pain, and the comorbidity of COPD undergoing knee arthroplasty should receive early identification and attention.Keywords: chronic postsurgical pain, acute-postoperative pain, total knee arthroplasty, rest pain, movement pain

Published
2021

27. Hydroxypropylcellulose Coating to Improve Graft-to-Bone Healing for Anterior Cruciate Ligament Reconstruction

Author

Peng Zhang, Nguyen T. K. Thanh, Jun Chen, Yang Wu, Chen Chen, Ngoc Quyên Trân, Shiyi Chen, Weifeng Zhou, Peiyi Wu, Yu Dong, Yang Jianjun, Zhu Yuchang, Wang Jianguang, and Tianwu Chen

Subjects
Materials science, Biocompatibility, Anterior cruciate ligament reconstruction, Anterior cruciate ligament, medicine.medical_treatment, Biomedical Engineering, Bone healing, engineering.material, musculoskeletal system, Biomaterials, chemistry.chemical_compound, medicine.anatomical_structure, Coating, Tissue engineering, chemistry, engineering, medicine, Ligament, Polyethylene terephthalate, human activities, Biomedical engineering
Abstract

An anterior cruciate ligament (ACL) injury is one of the most common injuries in sports, and ACL reconstruction with an artificial ligament is a good treatment for quick recovery. However, current artificial ligaments made of polyethylene terephthalate (PET) are still associated with some problems due to the hydrophobic nature and low biological induction activity of PET. Many efforts have been used to improve the biocompatibility of PET in recent years, and our previous work has shown that surface modification is an effective strategy. Here, a hydroxypropylcellulose (HPC) coating was applied on the surface of a PET artificial ligament order to improve its biocompatibility. The effects of the HPC coating on PET artificial ligament graft-bone healing was investigated in vitro using bone marrow stromal cells (BMSCs), fibroblasts, and RSC-364 cells as well as in vivo in a beagle dog model of ACL reconstruction. HPC was coated successfully on the PET and significantly promoted cell growth, adhesion, and capability of osteogenic differentiation compared to the PET graft without HPC coating. In vivo, the HPC coating significantly enhanced ligament tissue regeneration. Moreover, higher expression of some bone-formation- and ligament-tissue-regeneration-contributing proteins and cell factors, such as COL1, BMP-7, COL3, OCN, RUNX2, TGF-β1, and VEGF, was observed on the HPC-coated PET artificial ligament in comparison with the pure PET artificial ligament. In conclusion, HPC coating can significantly improve the cytocompatibility and graft-to-bone healing of a PET artificial ligament for ACL reconstruction.

Published
2019

28. Multi-Camera-Based Universal Measurement Method for 6-DOF of Rigid Bodies in World Coordinate System

Author

Zuoxi Zhao, Xie Chaoshi, Zhu Yuchang, Li Yuanhong, Yangfan Luo, Zhang Zhuangzhuang, and Qiu Zhi

Subjects
0209 industrial biotechnology, Computer science, Coordinate system, 02 engineering and technology, lcsh:Chemical technology, 01 natural sciences, Biochemistry, Imaging phantom, Article, Analytical Chemistry, 010309 optics, 020901 industrial engineering & automation, multiple cameras, Zhang Zhengyou’s calibration, 0103 physical sciences, Calibration, lcsh:TP1-1185, Computer vision, Matrix analysis, Electrical and Electronic Engineering, Instrumentation, rigid body, Rotational degrees of freedom, Measurement method, business.industry, six-degrees-of-freedom measurement, Rigid body, Atomic and Molecular Physics, and Optics, Photogrammetry, world coordinate system, Artificial intelligence, business
Abstract

The measurement of six-degrees-of-freedom (6-DOF) of rigid bodies plays an important role in many industries, but it often requires the use of professional instruments and software, or has limitations on the shape of measured objects. In this paper, a 6-DOF measurement method based on multi-camera is proposed, which is accomplished using at least two ordinary cameras and is made available for most morphological rigid bodies. First, multi-camera calibration based on Zhang Zhengyou&rsquo, s calibration method is introduced. In addition to the intrinsic and extrinsic parameters of cameras, the pose relationship between the camera coordinate system and the world coordinate system can also be obtained. Secondly, the 6-DOF calculation model of proposed method is gradually analyzed by the matrix analysis method. With the help of control points arranged on the rigid body, the 6-DOF of the rigid body can be calculated by the least square method. Finally, the Phantom 3D high-speed photogrammetry system (P3HPS) with an accuracy of 0.1 mm/m was used to evaluate this method. The experiment results show that the average error of the rotational degrees of freedom (DOF) measurement is less than 1.1 deg, and the average error of the movement DOF measurement is less than 0.007 m. In conclusion, the accuracy of the proposed method meets the requirements.

Published
2020
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31. One-pot synthesis of water-soluble near-infrared fluorescence RNase A capped CuInS2 quantum dots for in vivo imaging

Author

Xi Yue, Zhao Shenli, Jun Chen, Yuefeng Hao, Yunxia Li, Zhu Yuchang, Yunshen Ge, Wang Jianguang, and Yang Jianjun

Subjects
biology, RNase P, Chemistry, General Chemical Engineering, One-pot synthesis, technology, industry, and agriculture, Analytical chemistry, 02 engineering and technology, General Chemistry, equipment and supplies, 010402 general chemistry, 021001 nanoscience & nanotechnology, Photochemistry, 01 natural sciences, Imaging agent, 0104 chemical sciences, Colloid, Quantum dot, biology.protein, Ribonuclease, Viability assay, 0210 nano-technology, Preclinical imaging
Abstract

Near-infrared (NIR) quantum dots (QDs) have been treated as a promising candidate of imaging agents for NIR fluorescence-guided surgery. Herein, one-pot synthesis of water-soluble NIR fluorescence CuInS2 QDs was developed using ribonuclease A (RNase A) as the template. Our product exhibits good colloidal stability, excellent NIR fluorescence properties, and good photostability, even under acidic conditions. Furthermore, the RNase A-CuInS2 QDs have minor suppression on cell viability even at a concentration of up to 5 mg mL−1. Finally, the following in vivo imaging results confirm that RNase A-CuInS2 QDs can be used as a novel oral imaging agent in the future.

Published
2017

35. Parathyroid hormone plus alendronate in osteoporosis: a meta-analysis of randomized controlled trials

Author

Zhang, Qinggang, Qian, Jing, and Zhu, Yuchang

Subjects
Original Article, Retraction
Abstract

Background: Parathyroid hormone (PTH) increases both bone formation (BMD) and bone resorption, whereas alendronate reduces bone resorption. It is possible that the combination therapy of PTH with alendronate will enhance their effects on BMD. Therefore, we conducted this meta-analysis to evaluate the efficacy of the combination therapy of PTH with alendronate in the treatment of patients with osteoporosis. Methods: A comprehensive literature search of PubMed, Embase, and Web of Science was conducted to identify relative studies. Eligible studies were randomized controlled trials (RCT), which assessed the efficacy of combination therapy in patients with osteoporosis. The outcomes included the mean percent increases in BMD of lumbar spine, femoral neck, total hip, and distal radius. Weighted mean difference (WMD) with 95% confidence intervals (CIs) were calculated using of random-effects or fixed-effects model, depending on the heterogeneity between the included studies. Results: Six RCTs with a total number of 833 patients were included in this meta-analysis. The pooled estimates showed that, the combination therapy of PTH with alendronate resulted in a higher mean percent change of increased BMD in distal radius (WMD = 2.45, 95% CI: 1.58, 3.31; P = 0.000), but not in lumbar spine (WMD = -0.83, 95% CI: -3.48, 1.81; P = 0.538), femoral neck (WMD = -0.99, 95% CI: -2.04, 0.07; P = 0.068), and total hip (WMD = -0.06, 95% CI: -0.93, 0.81; P = 0.892). The subgroup analysis based on the dosage and schedule of PTH, study duration, gender of patients, and anabolic agents, were conducted. And results revealed that among the patients in the combination therapy group, greater increases in the spine BMD were observed when the PTH was administered with a dosage of 20 μg (WMD = 2.33, 95% CI: 1.24, 3.43; P = 0.000), or the treatment duration lasted more than 12 months (WMD = 2.23, 95% CI: 1.00, 3.47; P = 0.000), or the combination therapy was used in osteoporosis women (WMD = 1.58, 95% CI: 0.63, 2.53; P = 0.001). However, the combination of PTH of 40 μg with alendronate produced a decrease in the BMD at spine (WMD = -4.56, 95% CI: -7.56, -1.56; P = 0.003) and femoral neck (WMD = -5.82, 95% CI: -9.91, -1.72; P = 0.005). Conclusion: Our findings indicated that the addition of alendronate to PTH in the treatment of osteoporosis, reduced the ability of PTH therapy to increase the BMD at the lumbar spine, femoral neck, and total hip.

Published
2014

38. Hydroxypropylcellulose Coating to Improve Graft-to-Bone Healing for Anterior Cruciate Ligament Reconstruction

Author

Yang, Jianjun, Dong, Yu, Wang, Jianguang, Chen, Chen, Zhu, Yuchang, Wu, Yang, Zhang, Peng, Chen, Tianwu, Zhou, Weifeng, Wu, Peiyi, Thanh, Nguyen T. K., Trân, Ngoc Quyên, Chen, Jun, and Chen, Shiyi

Abstract

An anterior cruciate ligament (ACL) injury is one of the most common injuries in sports, and ACL reconstruction with an artificial ligament is a good treatment for quick recovery. However, current artificial ligaments made of polyethylene terephthalate (PET) are still associated with some problems due to the hydrophobic nature and low biological induction activity of PET. Many efforts have been used to improve the biocompatibility of PET in recent years, and our previous work has shown that surface modification is an effective strategy. Here, a hydroxypropylcellulose (HPC) coating was applied on the surface of a PET artificial ligament order to improve its biocompatibility. The effects of the HPC coating on PET artificial ligament graft-bone healing was investigated in vitrousing bone marrow stromal cells (BMSCs), fibroblasts, and RSC-364 cells as well as in vivoin a beagle dog model of ACL reconstruction. HPC was coated successfully on the PET and significantly promoted cell growth, adhesion, and capability of osteogenic differentiation compared to the PET graft without HPC coating. In vivo, the HPC coating significantly enhanced ligament tissue regeneration. Moreover, higher expression of some bone-formation- and ligament-tissue-regeneration-contributing proteins and cell factors, such as COL1, BMP-7, COL3, OCN, RUNX2, TGF-β1, and VEGF, was observed on the HPC-coated PET artificial ligament in comparison with the pure PET artificial ligament. In conclusion, HPC coating can significantly improve the cytocompatibility and graft-to-bone healing of a PET artificial ligament for ACL reconstruction.

Published
2019
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45. [Treatment of AO/OTA type 31-A3 intertrochanteric fracture with proximal femoral nail antirotation combined with mini plate reconstruction of lateral femoral wall].

Author

Li Y, Hu C, Mao L, Zhu Y, and Cai X

Subjects
Aged, Bone Nails, Bone Plates, Humans, Retrospective Studies, Treatment Outcome, Fracture Fixation, Intramedullary, Hip Fractures
Abstract

Objective: To explore the effectiveness of proximal femoral nail antirotation (PFNA) combined with mini plate for reconstruction of lateral femoral wall in the treatment of type AO/Orthopaedic Trauma Association (AO/OTA) type 31-A3 intertrochanteric fracture., Methods: The clinical data of 70 elderly patients with AO/OTA type 31-A3 intertrochanteric fracture treated between January 2013 and January 2018 were retrospectively analyzed. They were divided into group A (PFNA alone, 35 cases) and group B (PFNA combined with mini plate reconstruction of lateral femoral wall, 35 cases). There was no significant difference in the general data of gender, age, side, cause of injury, time from injury to operation between the two groups ( P >0.05). The operation time, intraoperative blood loss, fracture healing time, postoperative complications, and the tip apex distance (TAD) at 2 months after operation were recorded and compared between the two groups. Harris hip score was used to evaluate the function at 12 months after operation., Results: Both groups were followed up 9-21 months, with an average of 16.6 months. The operation time and intraoperative blood loss in group A were significantly less than those in group B ( P <0.05); there was no significant difference in TAD between the two groups at 2 months after operation ( t =0.096, P =0.462). There were 5 complications (14.3%) occurred in group A, including 2 cases of blade perforating from the hip joint, 2 cases of screw back out, and 1 case of bone nonunion; only 1 case (2.9%) in group B had screw back out after operation; there was no significant difference in the incidence of complications between the two groups ( χ 2 =2.917, P =0.088). All the fracture healed in group B, and 1 patient in group A suffered bone nonunion and eventually main nail fracture. The healing time of fracture in group A [(15.6±2.7) weeks] was significantly longer than that in group B [(12.5±2.5) weeks], showing significant difference ( t =2.064, P =0.023). At 12 months after operation, according to Harris score, the results were excellent in 5 cases, good in 9 cases, fair in 13 cases, and poor in 8 cases in group A, the qualified rate (Harris score>70) was 77.14%; and the results were excellent in 7 cases, good in 11 cases, fair in 16 cases, and poor in 1 case in group B, the qualified rate was 97.14%; there was significant difference in the qualified rate between the two groups ( χ 2 =6.248, P =0.012)., Conclusion: Compared with PFNA alone, the treatment of AO/OTA type 31-A3 intertrochanteric fracture with PFNA combined with mini plate reconstruction of lateral femoral wall can significantly reduce postoperative complications, promote fracture healing, and improve functional recovery of patients after operation.

Published
2019
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46. Piperlongumine attenuates collagen-induced arthritis via expansion of myeloid-derived suppressor cells and inhibition of the activation of fibroblast-like synoviocytes.

Author

Sun J, Xu P, Du X, Zhang Q, and Zhu Y

Subjects
Animals, Antibodies blood, Antibodies immunology, Arthritis, Experimental drug therapy, Arthritis, Experimental pathology, Arthritis, Rheumatoid immunology, Arthritis, Rheumatoid metabolism, Arthritis, Rheumatoid pathology, Cattle, Cell Movement drug effects, Collagen Type II immunology, Cytokines blood, Disease Models, Animal, Female, Inflammation Mediators blood, Lymphocyte Count, Male, Mice, Th17 Cells immunology, Th17 Cells metabolism, Arthritis, Experimental immunology, Arthritis, Experimental metabolism, Dioxolanes pharmacology, Fibroblasts drug effects, Fibroblasts metabolism, Myeloid Cells drug effects, Myeloid Cells immunology, Synovial Membrane cytology
Abstract

Piperlonguminine (PL), a key compound from the Piper longum fruit, is known to exhibit anti‑tumor and anti‑inflammatory activities. However, little is known about its effects on collagen‑induced arthritis (CIA). Fibroblast‑like synoviocytes (FLS) have a pivotal role in the development of rheumatoid arthritis (RA). Myeloid‑derived suppressor cells (MDSCs) are able to suppress T cell responses and have important roles in the regulation of autoimmune arthritis. The current study investigated whether PL alters the progression of RA. It was determined that PL reduces the arthritis score and histopathologic lesions in a mouse model of CIA. PL also reduces the expression levels of serum anti‑collagen II antibodies (anti‑CⅡ), tumor necrosis factor‑α (TNF‑α), interleukin (IL)‑1β, IL‑23 and IL‑17 in CIA mice. In draining lymph nodes (DLNs), MDSCs were significantly expanded, however, the number of Th17 cells was markedly decreased by PL treatment. Additionally, PL reduced secretion of IL‑1β, IL‑23 and IL‑17 by TNF‑α‑stimulated human RA FLS. PL significantly inhibited the migration and invasion of TNF‑α‑stimulated human RA FLS. These results indicate that PL may be a candidate therapeutic agent for the treatment of RA, via the expansion of MDSCs and the inhibition of the Th17 response and activation of FLS.

Published
2015
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47. Parathyroid hormone plus alendronate in osteoporosis: a meta-analysis of randomized controlled trials.

Author

Zhang Q, Qian J, and Zhu Y

Abstract

Background: Parathyroid hormone (PTH) increases both bone formation (BMD) and bone resorption, whereas alendronate reduces bone resorption. It is possible that the combination therapy of PTH with alendronate will enhance their effects on BMD. Therefore, we conducted this meta-analysis to evaluate the efficacy of the combination therapy of PTH with alendronate in the treatment of patients with osteoporosis., Methods: A comprehensive literature search of PubMed, Embase, and Web of Science was conducted to identify relative studies. Eligible studies were randomized controlled trials (RCT), which assessed the efficacy of combination therapy in patients with osteoporosis. The outcomes included the mean percent increases in BMD of lumbar spine, femoral neck, total hip, and distal radius. Weighted mean difference (WMD) with 95% confidence intervals (CIs) were calculated using of random-effects or fixed-effects model, depending on the heterogeneity between the included studies., Results: Six RCTs with a total number of 833 patients were included in this meta-analysis. The pooled estimates showed that, the combination therapy of PTH with alendronate resulted in a higher mean percent change of increased BMD in distal radius (WMD = 2.45, 95% CI: 1.58, 3.31; P = 0.000), but not in lumbar spine (WMD = -0.83, 95% CI: -3.48, 1.81; P = 0.538), femoral neck (WMD = -0.99, 95% CI: -2.04, 0.07; P = 0.068), and total hip (WMD = -0.06, 95% CI: -0.93, 0.81; P = 0.892). The subgroup analysis based on the dosage and schedule of PTH, study duration, gender of patients, and anabolic agents, were conducted. And results revealed that among the patients in the combination therapy group, greater increases in the spine BMD were observed when the PTH was administered with a dosage of 20 μg (WMD = 2.33, 95% CI: 1.24, 3.43; P = 0.000), or the treatment duration lasted more than 12 months (WMD = 2.23, 95% CI: 1.00, 3.47; P = 0.000), or the combination therapy was used in osteoporosis women (WMD = 1.58, 95% CI: 0.63, 2.53; P = 0.001). However, the combination of PTH of 40 μg with alendronate produced a decrease in the BMD at spine (WMD = -4.56, 95% CI: -7.56, -1.56; P = 0.003) and femoral neck (WMD = -5.82, 95% CI: -9.91, -1.72; P = 0.005)., Conclusion: Our findings indicated that the addition of alendronate to PTH in the treatment of osteoporosis, reduced the ability of PTH therapy to increase the BMD at the lumbar spine, femoral neck, and total hip.

Published
2015

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