Search

Your search keyword '"YIJIE ZHENG"' showing total 116 results
Start Over Author "YIJIE ZHENG"
116 results on '"YIJIE ZHENG"'

1. Developing a machine learning-based prognosis and immunotherapeutic response signature in colorectal cancer: insights from ferroptosis, fatty acid dynamics, and the tumor microenvironment

Author

Junchang Zhu, Jinyuan Zhang, Yunwei Lou, Yijie Zheng, Xuzhi Zheng, Wei Cen, Lechi Ye, and Qiongying Zhang

Subjects
ferroptosis, fatty acid metabolism, TME, colorectal cancer, immunotherapy, machine learning, Immunologic diseases. Allergy, RC581-607
Abstract

InstructionColorectal cancer (CRC) poses a challenge to public health and is characterized by a high incidence rate. This study explored the relationship between ferroptosis and fatty acid metabolism in the tumor microenvironment (TME) of patients with CRC to identify how these interactions impact the prognosis and effectiveness of immunotherapy, focusing on patient outcomes and the potential for predicting treatment response.MethodsUsing datasets from multiple cohorts, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we conducted an in-depth multi-omics study to uncover the relationship between ferroptosis regulators and fatty acid metabolism in CRC. Through unsupervised clustering, we discovered unique patterns that link ferroptosis and fatty acid metabolism, and further investigated them in the context of immune cell infiltration and pathway analysis. We developed the FeFAMscore, a prognostic model created using a combination of machine learning algorithms, and assessed its predictive power for patient outcomes and responsiveness to treatment. The FeFAMscore signature expression level was confirmed using RT-PCR, and ACAA2 progression in cancer was further verified.ResultsThis study revealed significant correlations between ferroptosis regulators and fatty acid metabolism-related genes with respect to tumor progression. Three distinct patient clusters with varied prognoses and immune cell infiltration were identified. The FeFAMscore demonstrated superior prognostic accuracy over existing models, with a C-index of 0.689 in the training cohort and values ranging from 0.648 to 0.720 in four independent validation cohorts. It also responses to immunotherapy and chemotherapy, indicating a sensitive response of special therapies (e.g., anti-PD-1, anti-CTLA4, osimertinib) in high FeFAMscore patients.ConclusionFerroptosis regulators and fatty acid metabolism-related genes not only enhance immune activation, but also contribute to immune escape. Thus, the FeFAMscore, a novel prognostic tool, is promising for predicting both the prognosis and efficacy of immunotherapeutic strategies in patients with CRC.

Published
2024
Full Text
View/download PDF

2. Single-ended recovery of optical fiber transmission matrices using neural networks

Author

Yijie Zheng, Terry Wright, Zhong Wen, Qing Yang, and George S. D. Gordon

Subjects
Astrophysics, QB460-466, Physics, QC1-999
Abstract

Abstract Ultra-thin multimode optical fiber imaging promises next-generation medical endoscopes reaching high image resolution for deep tissues. However, current technology suffers from severe optical distortion, as the fiber’s calibration is sensitive to bending and temperature and thus requires in vivo re-measurement with access to a single end only. We present a neural network (NN)-based approach to reconstruct the fiber’s transmission matrix (TM) based on multi-wavelength reflection-mode measurements. We train two different NN architectures via a custom loss function insensitive to global phase-degeneracy: a fully connected NN and convolutional U-Net. We reconstruct the 64 × 64 complex-valued fiber TMs through a simulated single-ended optical fiber with ≤ 4% error and cross-validate on experimentally measured TMs, demonstrating both wide-field and confocal scanning image reconstruction with small error. Our TM recovery approach is 4500 times faster, is more robust to fiber perturbation during characterization, and operates with non-square TMs.

Published
2023
Full Text
View/download PDF

3. Correction of Inexactitude in Surficial Sediment Calcium Carbonate Content Data of the Global Ocean

Author

Xiaoqing Liu, Handan Zhang, Yijie Zheng, Hao Fang, Jinqi Xia, and Yiming Luo

Subjects
Oceanography, GC1-1581, Meteorology. Climatology, QC851-999
Abstract

Calcium carbonate (CaCO3) is a main component in marine sediment, and sedimentary CaCO3 weight percentages (wtCaCO3%) in the global ocean have been extensively measured since the early last century. The tremendous database produced has been utilized in oceanographic research to constrain oceanic carbon cycles and to dictate past changes in deep ocean circulation. Inaccurate records in terms of sediment core coordinates, elevation, and wtCaCO3% data have been introduced in the past few decades, especially during compilation practice, rendering them less effective during the reuse of the data. Therefore, we thoroughly scrutinized published wtCaCO3% data and their geographical information and corrected 570 erroneous data points. These corrections help to establish a better basin-wide distribution pattern of sedimentary CaCO3 with ocean depth, which would eventually contribute to a more valid estimate of the standing stock of erodible CaCO3 in the global ocean. An accurate carbonate dataset could also facilitate applications of wtCaCO3% in paleoceanography and predictions of the buffering capacity of CaCO3 to ocean acidification.

Published
2023
Full Text
View/download PDF

4. Nitrogen mineralization and immobilization in surface sediments of coastal reclaimed aquaculture ecosystems

Author

Xianbiao Lin, Genmei Lin, Yijie Zheng, Wenjing Li, Peng Guo, Shiyuan Fan, Tiantian Kong, Dongfan Tian, Dongyao Sun, and Zhuo Shen

Subjects
N mineralization and immobilization, available organic carbon fractions, sediment, reclaimed aquaculture ecosystem, 15N isotope dilution technique, Science, General. Including nature conservation, geographical distribution, QH1-199.5
Abstract

Sediment nitrogen (N) mineralization and immobilization are two crucial processes driven by microorganisms, which may play significant roles in the regulation of water quality in aquaculture ecosystems. However, limited information is available about the quantitative importance of sedimentary N mineralization and immobilization in coastal aquaculture systems. Here, a combination of incubation experiments with a 15N isotope dilution technique were employed, aiming to quantify N mineralization and immobilization processes in surface sediments (0–5 cm) of three types of aquaculture ecosystems (seabass, white shrimp, and green crab ponds) reclaimed within the western bank of the Pearl River Estuary. Our results showed that no significant difference in sediment N mineralization and immobilization rates, microbial abundances, and organic matter among different aquaculture types on small-scale range. Meanwhile, prolonged pond-drying significant reduced sediment N mineralization and immobilization rates, bacterial abundances, organic matter, moisture content, ferrous ion (Fe2+), Fe2+/Fe3+, and ammonium (NH4+), while not strongly altered sediment percentage of NH4+ mineralized per day (PAM), relative ammonium immobilization (RAI), fungal abundances, TOC/TN, nitrate (NO3−), and δ13Corg. N mineralization and immobilization rates were both significantly related to overlying water NO3−, as well as sediment moisture content, bulk density, organic matter, Fe2+, and microbial abundances. In addition, the total mineralized and immobilized N in aquaculture surface sediments from the Guangdong-Hong Kong-Macao Greater Bay Area were estimated to be approximately 4.55×104 and 3.68×104 t N yr-1, respectively. Higher N mineralization relative to N immobilized fluxes indicated that the sediment serves as an important source of eutrophication in reclaimed aquaculture system of coastal wetlands.

Published
2023
Full Text
View/download PDF

5. Unsupervised 3D Reconstruction with Multi-Measure and High-Resolution Loss

Author

Yijie Zheng, Jianxin Luo, Weiwei Chen, Yanyan Zhang, Haixun Sun, and Zhisong Pan

Subjects
three-dimensional reconstruction, multi-view reconstruction, deep learning, PatchMatch, unsupervised learning, feature point consistency, Chemical technology, TP1-1185
Abstract

Multi-view 3D reconstruction technology based on deep learning is developing rapidly. Unsupervised learning has become a research hotspot because it does not need ground truth labels. The current unsupervised method mainly uses 3DCNN to regularize the cost volume to regression image depth. This approach results in high memory requirements and long computing time. In this paper, we propose an end-to-end unsupervised multi-view 3D reconstruction network framework based on PatchMatch, Unsup_patchmatchnet. It dramatically reduces memory requirements and computing time. We propose a feature point consistency loss function. We incorporate various self-supervised signals such as photometric consistency loss and semantic consistency loss into the loss function. At the same time, we propose a high-resolution loss method. This improves the reconstruction of high-resolution images. The experiment proves that the memory usage of the network is reduced by 80% and the running time is reduced by more than 50% compared with the network using 3DCNN method. The overall error of reconstructed 3D point cloud is only 0.501 mm. It is superior to most current unsupervised multi-view 3D reconstruction networks. Then, we test on different data sets and verify that the network has good generalization.

Published
2022
Full Text
View/download PDF

6. G2O-Pose: Real-Time Monocular 3D Human Pose Estimation Based on General Graph Optimization

Author

Haixun Sun, Yanyan Zhang, Yijie Zheng, Jianxin Luo, and Zhisong Pan

Subjects
monocular 3D human pose, general graph optimization, multiple constraints, real-time, Chemical technology, TP1-1185
Abstract

Monocular 3D human pose estimation is used to calculate a 3D human pose from monocular images or videos. It still faces some challenges due to the lack of depth information. Traditional methods have tried to disambiguate it by building a pose dictionary or using temporal information, but these methods are too slow for real-time application. In this paper, we propose a real-time method named G2O-pose, which has a high running speed without affecting the accuracy so much. In our work, we regard the 3D human pose as a graph, and solve the problem by general graph optimization (G2O) under multiple constraints. The constraints are implemented by algorithms including 3D bone proportion recovery, human orientation classification and reverse joint correction and suppression. When the depth of the human body does not change much, our method outperforms the previous non-deep learning methods in terms of running speed, with only a slight decrease in accuracy.

Published
2022
Full Text
View/download PDF

7. Red blood cell distribution width and platelet counts are independent prognostic factors and improve the predictive ability of IPI score in diffuse large B-cell lymphoma patients

Author

Manman Li, Hailong Xia, Huimin Zheng, Yafeng Li, Jun Liu, Linhui Hu, Jingrong Li, Yangyang Ding, Lianfang Pu, Qianle Gui, Yijie Zheng, Zhimin Zhai, and Shudao Xiong

Subjects
Prognosis, Diffuse large B-cell lymphoma (DLBCL), Red blood cell distribution width (RDW), Platelet count (PLT), Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Background Elevated red blood cell distribution width (RDW) and decreased platelet count (PLT) can be clinically relevant to the prognosis in cancer patients. However, their prognostic values in patients with diffuse large B-cell lymphoma (DLBCL) need to be further explored. Methods Healthy donors (n = 130) and patients with DLBCL (n = 349) were included and evaluated retrospectively in this study. The prognostic influence of clinical and pathological factors including RDW and PLT on overall survival (OS) and progression-free survival (PFS) were studied by Kaplan-Meier curves. To evaluate the independent prognostic relevance of RDW and PLT, univariate and multivariate Cox proportional hazards regression models were applied. The adjusted IPI model was established based on the results of multivariate analysis, and verified by Harrell’s C statistical analysis. Results Kaplan-Meier curves indicated that an elevated RDW value and thrombocytopenia are poor factors for OS (P

Published
2019
Full Text
View/download PDF

8. Diagnostic value of alpha-fetoprotein combined with neutrophil-to-lymphocyte ratio for hepatocellular carcinoma

Author

Jian Hu, Nianyue Wang, Yongfeng Yang, Li Ma, Ruilin Han, Wei Zhang, Cunling Yan, Yijie Zheng, and Xiaoqin Wang

Subjects
Alpha-fetoprotein, Neutrophil-granulocyte ratio, Hepatocellular carcinoma, Diseases of the digestive system. Gastroenterology, RC799-869
Abstract

Abstract Background To investigate the diagnostic performance of alpha-fetoprotein (AFP) and neutrophil-to-lymphocyte ratio (NLR) as well as their combinations with other markers. Methods Serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), AFP and levels as well as the numbers of neutrophils and lymphocytes of all enrolled patients were collected. The NLR was calculated by dividing the number of neutrophils by the number of lymphocytes. Receiver operating characteristic (ROC) curve analysis was conducted to determine the ability of each marker and combination of markers to distinguish HCC and liver disease patients. Results In total, 545 patients were included in this study. The area under the ROC curve (AUC) values for AFP, ALT, AST, and NLR were 0.775 (0.738–0.810), 0.504 (0.461–0.547), 0.660 (0.618–0.699), and 0.738 (0.699–0.774) with optimal cut-off values of 24.6 ng/mL, 111 IU/mL, 27 IU/mL, and 2.979, respectively. Of the four biomarkers, AFP and NLR showed comparable specificity (0.881 and 0.858) and sensitivity (0.561 and 0.539). The combination of AFP and NLR showed the highest AUC (0.769) with a significantly higher sensitivity (0.767) and a lower specificity (0.773) compared to AFP or NLR alone, and it had the highest sum of sensitivity and specificity (1.54) among all combinations. In patients with AFP

Published
2018
Full Text
View/download PDF

9. Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia

Author

Jia Liu, ShuYang Dai, Gong Chen, Song Sun, JingYing Jiang, Shan Zheng, YiJie Zheng, and Rui Dong

Subjects
biliary atresia, obstructive jaundice, diagnosis, gamma-glutamyl transpeptidase, non-invasive, Pediatrics, RJ1-570
Abstract

Objectives: Biliary atresia (BA) is a devastating pediatric liver disease. Early diagnosis is important for timely intervention and better prognosis. Using clinical parameters for non-invasive and efficient BA diagnosis, we aimed to establish an artificial neural network (ANN).Methods: A total of 2,384 obstructive jaundice patients from 2012 to 2017 and their 137 clinical parameters were screened for eligibility. A standard binary classification feed-forward ANN was employed. The network was trained and validated for accuracy. Gamma-glutamyl transpeptidase (GGT) level was used as an independent predictor and a comparison to assess the network effectiveness.Results: We included 46 parameters and 1,452 patients for ANN modeling. Total bilirubin, direct bilirubin, and GGT were the most significant indicators. The network consisted of an input layer, 3 hidden layers with 12 neurons each, and an output layer. The network showed good predictive property with a high area under curve (AUC) (0.967, sensitivity 97.2% and specificity 91.0%). Five-fold cross validation showed the mean accuracy for training data of 93.2% and for validation data of 88.6%.Conclusions: The high accuracy and efficiency demonstrated by the ANN model is promising in the noninvasive diagnosis of BA and could be considered as in a low-cost and independent expert diagnosis system.

Published
2020
Full Text
View/download PDF

10. Circulating tumor cells detection in neuroblastoma patients by EpCAM-independent enrichment and immunostaining-fluorescence in situ hybridizationResearch in context

Author

Xiangqi Liu, Zhenzhen Zhang, Binbin Zhang, Yijie Zheng, Chao Zheng, Baihui Liu, Shan Zheng, Kuiran Dong, and Rui Dong

Subjects
Medicine, Medicine (General), R5-920
Abstract

Detecting circulating tumor cells (CTCs) has proven valuable for evaluating the prognosis of cancer patients and for studying the mechanisms of treatment resistance. Owing to the lack of universal and specific tumor markers for neuroblastoma (NB), in this prospective study, we adopted an EpCAM-independent method to detect CTCs in the peripheral blood of NB patients.We used an EpCAM-independent assay to delete leukocytes and to enrich the CTCs. CTCs were identified by immunostaining of CD45, DAPI and DNA fluorescence in situ hybridization (FISH) of the centromere of chromosome 8 probe (CEP8). Cells that were DAPI+/CD45-/CEP8 ≥ 3 were considered CTCs. We collected peripheral blood from 28 NB patients as well as clinical and follow-up data.The number of CTCs among the different risk groups were significantly different (p = .0208, Kruskal–Wallis test). Patients with metastasis had more CTCs than those without metastasis (p

Published
2018
Full Text
View/download PDF

11. Development and Validation of Novel Diagnostic Models for Biliary Atresia in a Large Cohort of Chinese PatientsResearch in Context

Author

Rui Dong, Jingying Jiang, Shouhua Zhang, Zhen Shen, Gong Chen, Yanlei Huang, Yijie Zheng, and Shan Zheng

Subjects
Medicine, Medicine (General), R5-920
Abstract

Background & aims: The overlapping features of biliary atresia (BA) and the other forms of neonatal cholestasis (NC) with different causes (non-BA) has posed challenges for the diagnosis of BA. This study aimed at developing new and better diagnostic models for BA. Methods: We retrospectively analyzed data from 1728 newborn infants with neonatal obstructive jaundice (NOJ). New prediction models, including decision tree (DT), random forest (RF), and multivariate logistic regression-based nomogram for BA were created and externally validated in an independent set of 508 infant patients. Results: Fiver predictors, including gender, weight, direct bilirubin (DB), alkaline phosphatase (ALP), and gamma-glutamyl transpeptidase (GGT) were significantly different between the BA and non-BA groups (P

Published
2018
Full Text
View/download PDF

12. Proposers’ Economic Status Affects Behavioral and Neural Responses to Unfairness

Author

Yijie Zheng, Xuemei Cheng, Jialin Xu, Li Zheng, Lin Li, Guang Yang, and Xiuyan Guo

Subjects
ultimatum game (UG), economic status, fMRI, perception of unfairness, decision making, Psychology, BF1-990
Abstract

Economic status played an important role in the modulation of economic decision making. The present fMRI study aimed at investigating how economic status modulated behavioral and neural responses to unfairness in a modified Ultimatum Game (UG). During scanning, participants played as responders in the UG, and they were informed of the economic status of proposers before receiving offers. At the behavioral level, higher rejection rates and lower fairness ratings were revealed when proposers were in high economic status than in low economic status. Besides, the most time-consuming decisions tended to occur at lower unfairness level when the proposers were in high (relative to low) economic status. At the neural level, stronger activation of left thalamus was revealed when fair offers were proposed by proposers in high rather than in low economic status. Greater activation of right medial prefrontal cortex was revealed during acceptance to unfair offers in high economic status condition rather than in low economic status condition. Taken together, these findings shed light on the significance of proposers’ economic status in responders’ social decision making in UG.

Published
2017
Full Text
View/download PDF

13. Immune to Situation: The Self-Serving Bias in Unambiguous Contexts

Author

Xiaoyan Wang, Li Zheng, Lin Li, Yijie Zheng, Peng Sun, Fanzhi A. Zhou, and Xiuyan Guo

Subjects
self-serving bias, self-enhancement, self-assessment, self-awareness, implicit causality, Psychology, BF1-990
Abstract

Traditionally, the self-serving bias has been investigated in ambiguous contexts in which participants work on tasks that measure novel abilities before making attributions without clear criteria for success or failure feedback. Prior studies have confirmed that the self-serving bias is pervasive in the general population, yet it varies significantly across situations involving ambiguous contexts. The present study features an unambiguous context encompassing interpersonal events that involved implicit causality (with the “self” as an actor or recipient), the inherent logic of which indicated attribution criteria. The aim of this study was to explore whether there is a self-serving bias in unambiguous contexts and to examine whether it is as sensitive to situation as it has been shown to be in ambiguous contexts. The results showed that, in an unambiguous context, participants exhibited self-serving bias in relation to attribution associated with negative interpersonal events. Additionally, the self-serving bias was greater in the actor condition relative to the recipient condition (Study 1), and this effect was not affected by the level of self-awareness, which was manipulated by the use or otherwise of a camera during the experiment (Study 2). Our findings provide evidence for the existence of the self-serving bias in unambiguous contexts. Moreover, the self-serving bias was shown to be immune to situation in unambiguous contexts, but it did depend on factors associated with the events per se, such as the actor versus recipient role that the self played in interpersonal events.

Published
2017
Full Text
View/download PDF

14. The Neural Responses to Social Cooperation in Gain and Loss Context.

Author

Peng Sun, Li Zheng, Lin Li, Xiuyan Guo, Weidong Zhang, and Yijie Zheng

Subjects
Medicine, Science
Abstract

Cooperation is pervasive and constitutes the core behavioral principle of human social life. Previous studies have revealed that mutual cooperation was reliably correlated with two reward-related brain regions, the ventral striatum and the orbitofrontal cortex. Using functional magnetic resonance imaging (fMRI), this study sought to investigate how the loss and gain contexts modulated the neural responses to mutual cooperation. Twenty-five female participants were scanned when they played a series of one-shot prisoner's dilemma games in the loss and gain contexts. Specifically, participants and partners independently chose to either cooperate with each other or not, and each was awarded or deprived of (in the gain context or the loss context, respectively) a sum of money which depended upon the interaction of their choices. Behavioral results indicated that participants cooperated in nearly half of the experiment trials and reported higher level of positive emotions for mutual cooperation in both contexts, but they cooperated more in the gain than in the loss context. At the neural level, stronger activities in the orbitofrontal cortex were observed for mutual cooperation compared with the other three outcomes in both contexts, while stronger activation in ventral striatum associated with mutual cooperation was observed in the gain context only. Together, our data indicated that, even in the one-shot interaction under loss context, participants still exhibited preference for cooperation and the rewarding experience from a mutually cooperative social interaction activated the ventral striatum and the orbitofrontal cortex, but the loss context weakened the association between the ventral striatum activation and mutual cooperation.

Published
2016
Full Text
View/download PDF

15. Flotillin-2 modulates fas signaling mediated apoptosis after hyperoxia in lung epithelial cells.

Author

Shuquan Wei, Hyung-Geun Moon, Yijie Zheng, Xiaoliang Liang, Chang Hyeok An, and Yang Jin

Subjects
Medicine, Science
Abstract

Lipid rafts are subdomains of the cell membrane with distinct protein composition and high concentrations of cholesterol and glycosphingolipids. Raft proteins are thought to mediate diverse cellular processes including signal transduction. However, its cellular mechanisms remain unclear. Caveolin-1 (cav-1, marker protein of caveolae) has been thought as a switchboard between extracellular matrix (ECM) stimuli and intracellular signals. Flotillin-2/reggie-1(Flot-2) is another ubiquitously expressed raft protein which defines non-caveolar raft microdomains (planar raft). Its cellular function is largely uncharacterized. Our novel studies demonstrated that Flot-2, in conjunction with cav-1, played important functions on controlling cell death via regulating Fas pathways. Using Beas2B epithelial cells, we found that in contrast to cav-1, Flot-2 conferred cytoprotection via preventing Fas mediated death-inducing signaling complex (DISC) formation, subsequently suppressed caspase-8 mediated extrinsic apoptosis. Moreover, Flot-2 reduced the mitochondria mediated intrinsic apoptosis by regulating the Bcl-2 family and suppressing cytochrome C release from mitochondria to cytosol. Flot-2 further modulated the common apoptosis pathway and inhibited caspase-3 activation via up-regulating the members in the inhibitor of apoptosis (IAP) family. Last, Flot-2 interacted with cav-1 and limited its expression. Taken together, we found that Flot-2 protected cells from Fas induced apoptosis and counterbalanced the pro-apoptotic effects of cav-1. Thus, Flot-2 played crucial functions in cellular homeostasis and cell survival, suggesting a differential role of individual raft proteins.

Published
2013
Full Text
View/download PDF

16. CCN1 secretion induced by cigarette smoking extracts augments IL-8 release from bronchial epithelial cells.

Author

Hyung-Geun Moon, Yijie Zheng, Chang Hyeok An, Yoon-Keun Kim, and Yang Jin

Subjects
Medicine, Science
Abstract

Inflammation involves in many cigarette smoke (CS) related diseases including the chronic obstructive pulmonary disease (COPD). Lung epithelial cell released IL-8 plays a crucial role in CS induced lung inflammation. CS and cigarette smoke extracts (CSE) both induce IL-8 secretion and subsequently, IL-8 recruits inflammatory cells into the lung parenchyma. However, the molecular and cellular mechanisms by which CSE triggers IL-8 release remain not completely understood. In this study, we identified a novel extracellular matrix (ECM) molecule, CCN1, which mediated CSE induced IL-8 secretion by lung epithelial cells. We first found that CS and CSE up-regulated CCN1 expression and secretion in lung epithelial cells in vivo and in vitro. CSE up-regulated CCN1 via induction of reactive oxygen spices (ROS) and endoplasmic reticulum (ER) stress. p38 MAPK and JNK activation were also found to mediate the signal pathways in CSE induced CCN1. CCN1 was secreted into ECM via Golgi and membrane channel receptor aquaporin4. After CSE exposure, elevated ECM CCN1 functioned via an autocrine or paracrine manner. Importantly, CCN1 activated Wnt pathway receptor LRP6, subsequently stimulated Wnt pathway component Dvl2 and triggered beta-catenin translocation from cell membrane to cytosol and nucleus. Treatment of Wnt pathway inhibitor suppressed CCN1 induced IL-8 secretion from lung epithelial cells. Taken together, CSE increased CCN1 expression and secretion in lung epithelial cells via induction of ROS and ER stress. Increased ECM CCN1 resulted in augmented IL-8 release through the activation of Wnt pathway.

Published
2013
Full Text
View/download PDF

18. High-performance shim coil design and engineering optimization for use in an extremely high field superconducting magnet

Author

Yijie Zheng, Yaohui Wang, Feng Liu, Ming Yan, and Qiuliang Wang

Subjects
Instrumentation
Abstract

The extremely high field has significant advantages in imaging quality and analyzing the fine structure of substances. However, its excellent performance requires the support of a higher-performance shim technique. In this paper, a novel structural design pattern of the shim coil for a 27 T extremely high field superconducting magnet is proposed. According to the contours of the stream function, we designed and optimized the shim coil pattern and engineering processing. The novel design was realized by using the contours as the centerline, and the wire spacing was controlled at 1 mm. The performance of the novel pattern was compared with those of alternative winding schemes. The results indicate that the novel design can improve coil performance, achieving higher fidelity and lower power dissipation.

Published
2023

19. Data from DNMT1–MicroRNA126 Epigenetic Circuit Contributes to Esophageal Squamous Cell Carcinoma Growth via ADAM9–EGFR–AKT Signaling

Author

Yiwei Chu, Di Ge, Guangwei Liu, Fengkai Xu, Shudao Xiong, Enyu Huang, Xuechao Jiang, Xiaoming Liu, Yijie Zheng, Pei Jiang, Jie Gu, and Ronghua Liu

Abstract

Purpose: MicroRNAs (miRNA) are involved in and are controlled by epigenetic regulation, and thereby form a reciprocal regulatory circuit. Using next-generation sequencing (NGS)–based miRNA profiling, this study aimed to discover esophageal squamous cell carcinoma (ESCC)–specific miRNAs and miRNA-related epigenetic modulations.Experimental Design: NGS-based miRNA profiles were generated for four pairs of ESCC tissues and adjacent normal tissues. In situ hybridization was used to assess miRNA expression and its correlation with prognosis. miRNA-related DNA methylations were identified using bisulfite genomic sequencing, and the role of DNA methyltransferase 1 (DNMT1) was investigated using RNA interference. miRNA targets were screened by mRNA sequencing, and functional validation was performed in vitro and in vivo.Results: NGS-based miRNA profiling identified 78 differentially expressed miRNAs in ESCC. Among them, microRNA126-3p (miR-126) was significantly downregulated, and its downregulation correlated with poor ESCC prognosis. Downregulation of miR-126 was due to promoter hypermethylation of its host gene, Egfl7. DNMT1 was aberrantly upregulated in ESCC and responsible for the hypermethylation of Egfl7. Intriguingly, DNMT1 was suppressed by overexpression of miR-126, indicating the existence of a regulatory feedback circuit. ADAM9 was identified as a key target of miR-126. Ectopic expression of miR-126 or silencing of ADAM9 reduced ESCC cell proliferation and migration by inhibiting epidermal growth factor receptor–AKT signaling.Conclusions: Our results indicate that miR-126 is a potential prognostic indicator for ESCC and suggest that a novel “DNMT1–miR-126 epigenetic circuit” is involved in ESCC progression. Consequently, miR-126–based epigenetic modulations may provide a basic rationale for new approaches to antitumor therapeutics. Clin Cancer Res; 21(4); 854–63. ©2014 AACR.

Published
2023

21. Supplementary Tables from DNMT1–MicroRNA126 Epigenetic Circuit Contributes to Esophageal Squamous Cell Carcinoma Growth via ADAM9–EGFR–AKT Signaling

Author

Yiwei Chu, Di Ge, Guangwei Liu, Fengkai Xu, Shudao Xiong, Enyu Huang, Xuechao Jiang, Xiaoming Liu, Yijie Zheng, Pei Jiang, Jie Gu, and Ronghua Liu

Abstract

The file included six supplementary tables as following: Table S1.Clinicopathological Characteristics; Table S2. Differently expressed miRNAs between ECs and NMs; Table S3. Univariate and Multivariate analysis of factors associated with disease-specific survival; Table S4. A short list of functionally relevant genes significantly downredulated in 126-EC cells vs. NC-EC cells; Table S5. Primer sequences (5'-3'), Table S6. The primary antibodies for western blot and IHC.

Published
2023

22. Supplementary Materials and Methods from DNMT1–MicroRNA126 Epigenetic Circuit Contributes to Esophageal Squamous Cell Carcinoma Growth via ADAM9–EGFR–AKT Signaling

Author

Yiwei Chu, Di Ge, Guangwei Liu, Fengkai Xu, Shudao Xiong, Enyu Huang, Xuechao Jiang, Xiaoming Liu, Yijie Zheng, Pei Jiang, Jie Gu, and Ronghua Liu

Abstract

This file contained the supplementary materials and methods including RNA extraction and quantitative real-time PCR, miRNA overexpression and RNA interference (RNAi), Cell proliferation and migration assays, Immunohistochemistry (IHC) and Western blotting, EGFR inhibitor (AG1478) and 5-aza-2'-deoxycytidine (5-aza-dC) treatments, ISH scoring, DNA methylation analysis, Target prediction, 3'-UTR vector preparation, and luciferase reporter assay, Tumor xenograft in nude mice, mRNA sequencing. As well as, legends of the six supplementary figures were included in this file.

Published
2023

26. Thallium removal from wastewater using sulfidized zero-valent manganese: Effects of sulfidation method and liquid nitrogen pretreatment

Author

Zhengqin Shi, Ping Zhang, Xiaohan Li, Yijie Zheng, Juanxi Huang, Yaxuan Wang, Xiatiao Luo, Gaosheng Zhang, Tangfu Xiao, Jianyou Long, and Huosheng Li

Subjects
Environmental Engineering, Health, Toxicology and Mutagenesis, Public Health, Environmental and Occupational Health, Environmental Chemistry, General Medicine, General Chemistry, Pollution
Published
2023

27. Oil/water separation using elastic bio-aerogels derived from bagasse: Role of fabrication steps

Author

Rongchuan Ye, Jianyou Long, Dan Peng, Yuqi Wang, Gaosheng Zhang, Guicong Xiao, Yijie Zheng, Tangfu Xiao, Yixing Wen, Jiawen Li, and Huosheng Li

Subjects
Environmental Engineering, Dehydration, Health, Toxicology and Mutagenesis, Solvents, Environmental Chemistry, Humans, Calcium, Cellulose, Pollution, Waste Management and Disposal, Gels, Oils
Abstract

Bio-aerogels hold great promise for selective oil separation from water due to their light weight and high sustainability. However, how the fabrication methods impact the elasticity and oil sorption performance of bio-aerogels still needs systematic comparison and in-depth investigation. In this study, the fabrication of hydrophobic bio-aerogels with good elasticity and reusability was optimized using a factorial design based on the dosages of bagasse-derived cellulose nanofiber, sodium alginate, and calcium carbonate. The role of each key fabrication step, including ice-templating, calcium crosslinking, solvent dehydration, freeze-drying, and silanization, played in the material properties was also elucidated. The optimized bio-aerogels had a low density (7.55 mg/cm

Published
2022

32. Global multi-stakeholder endorsement of the MAFLD definition

Author

Nahum Méndez-Sánchez, Elisabetta Bugianesi, Robert G Gish, Frank Lammert, Herbert Tilg, Mindie H Nguyen, Shiv K Sarin, Núria Fabrellas, Shira Zelber-Sagi, Jian-Gao Fan, Gamal Shiha, Giovanni Targher, Ming-Hua Zheng, Wah-Kheong Chan, Shlomo Vinker, Takumi Kawaguchi, Laurent Castera, Yusuf Yilmaz, Marko Korenjak, C Wendy Spearman, Mehmet Ungan, Melissa Palmer, Mortada El-Shabrawi, Hans-Juergen Gruss, Jean-François Dufour, Anil Dhawan, Heiner Wedemeyer, Jacob George, Luca Valenti, Yasser Fouad, Manuel Romero‐Gomez, Mohammed Eslam, Maria Lorena Abate, Bahaa Abbas, Ahmed Amr Abbassy, Waleed Abd El Ghany, Amira Abd Elkhalek, Emad Abd ElMajeed, Mohammad Abdalgaber, Mohamed AbdAllah, Marwa Abdallah, Nourhan Abdallah, Shereen Abdelaleem, Yasser Abdelghani, Wael Abdelghany, Safaa Mohamed Abdelhalim, Wafaa Abdelhamid, Nehal Abdelhamid, Nadia A. Abdelkader, Elsayed Abdelkreem, Aly Mohamed Abdelmohsen, Awny Ali Abdelrahman, Sherief M Abd-elsalam, Doaa Abdeltawab, Abdulbaset Abduh, Nada Abdulhakam, Maheeba Abdulla, Navid Abedpoor, Ludovico Abenavoli, Fredrik Åberg, Omala Ablack, Mostafa Abo elftouh, Yousry Esam-Eldin Abo-Amer, Ashraf Aboubkr, Alaa Aboud, Amr M. Abouelnaga, Galal A. Aboufarrag, Ashraf Aboutaleb, Leticia Abundis, Gupse Adalı, Enrique Adames, Leon Adams, Danjuma Adda, Noor Adel, Nada Adel, Muhammad Adel Sayed, Taiba Jibril Afaa, Nawal Afredj, Gulnara Aghayeva, Alessio Aghemo, Carlos A. Aguilar-Salinas, Golo Ahlenstiel, Walid Ahmady, Wafaa Ahmed, Amira Ahmed, Samah Nasser Ahmed, Heba Mostafa Ahmed, Rasha Ahmed, Elmar Aigner, Mesut Akarsu, Maisam Akroush, Umit Akyuz, Mamun Al Mahtab, Tahani Al Qadiri, Yusriya Al Rawahi, Razzaq AL rubaee, Muna Al Saffar, Shahinul Alam, Zaid Al-Ani, Agustín Albillos, Mohamed Alboraie, Said Al-Busafi, Mohamed Al-Emam, Jawaher Alharthi, Kareem Ali, Basma Abdelmoez Ali, Mohammad Ali, Raja Affendi Raja Ali, Anna Alisi, Ali Raad AL-Khafaji, Maryam Alkhatry, Rocio Aller, Yahya Almansoury, Khalid Al-Naamani, Alaa Alnakeeb, Anna Alonso, Saleh A. Alqahtani, Leina Alrabadi, Khalid Alswat, Mahir Altaher, Turki Altamimi, Jose Altamirano, Mario R. Alvares-da-Silva, Elsragy Adel M. Aly, Amgad Alzahaby, Ahmed Alzamzamy, Keisuke Amano, Maysa A. Amer, Mona A. Amin, Sayed A. Amin, Ashraf A. Amir, Javier Ampuero, Noha Anas, Pietro Andreone, Soa Fy Andriamandimby, Mahmoud Anees, Peltec Angela, Manal Antonios, Wael Arafat, Jose Moreno Araya, Juan Armendariz-Borunda, Matthew J. Armstrong, Hassan Ashktorab, Patricia Aspichueta, Fathia Assal, Mira Atef, Dina Attia, Hoda Atwa, Reham Awad, Mohyeldeen Abd Elaziz Awad, Sally Awny, Obafemi Awolowo, Yaw Asante Awuku, Ibrahim Ayada, Than Than Aye, Sherif Ayman, Hedy Ayman, Hesham Ayoub, Hosny M. Azmy, Romiro P. Babaran, Omneya Badreldin, Ahmed Badry, İbrahim Halil Bahçecioğlu, Amira Bahour, Jiajia Bai, Yasemin Balaban, Muthuswamy Balasubramanyam, Khaled Bamakhrama, Jesus M Banales, Babu Bangaru, Jianfeng Bao, Jorge Suazo Barahona, Salma Barakat, Sandra Maria Barbalho, Bikwa Barbra, Beatriz Barranco, Francisco Barrera, Ulrich Baumann, Shamardan Bazeed, Eva Bech, Aourarh Benayad, Andreas Benesic, David Bernstein, Fernando Bessone, Susie Birney, Cyrille Bisseye, Martin Blake, Bilal Bobat, Leonilde Bonfrate, Dmitry S Bordin, Francisco Bosques-Padilla, Jerome Boursier, Boushab Mohamed Boushab, David Bowen, Patricia Medina Bravo, Paul N Brennan, Bisi Bright, Ilse Broekaert, Xabier Buque, Diego Burgos-Santamaría, Julio Burman, Luca Busetto, Chris D. Byrne, Patricia Anne I. Cabral-Prodigalidad, Guillermo Cabrera-Alvarez, Wei Cai, Francesca Cainelli, Ali Riza Caliskan, Ali Canbay, Ana Cano-Contreras, Hai-Xia Cao, Zhujun Cao, Andres Carrion, Francesca Carubbi, Teresa Casanovas, Marlen Ivón Castellanos Fernández, Jin Chai, Siew Pheng Chan, Phunchai Charatcharoenwitthaya, Norberto Chavez-Tapia, Kazuaki Chayama, Jinjun Chen, Lin Chen, Zhong-Wei Chen, Huiting Chen, Sui-Dan Chen, Qiang Chen, Yaxi Chen, Gang Chen, En-Quang Chen, Fei Chen, Pei-Jer Chen, Robert Cheng, Wendy Cheng, Jack Tan Wei Chieh, Imad Chokr, Evangelos Cholongitas, Ashok Choudhury, Abhijit Chowdhury, Evaristus Sunday Chukwudike, Stefano Ciardullo, Michelle Clayton, Karine Clement, Marie Michelle Cloa, Cecilia Coccia, Cristina Collazos, Massimo Colombo, Arif Mansur Cosar, Helma Pinchemel Cotrim, Joris Couillerot, Alioune Coulibaly, Gonzalo Crespo, Javier Crespo, Maria Cruells, Ian Homer Y. Cua, Hesham K. Dabbous, George N Dalekos, Patricia D'Alia, Li Dan, Viet Hang Dao, Mostafa Darwish, Christian Datz, Milagros B Davalos-Moscol, Heba Dawoud, Blanca Olaechea de Careaga, Robert de Knegt, Victor de Ledinghen, Janaka de Silva, Nabil Debzi, Marie Decraecker, Elvira Del Pozo, Teresa C Delgado, Manuel Delgado-Blanco, Łukasz Dembiński, Adilson Depina, Moutaz Derbala, Hailemichael Desalegn, Christèle Desbois-Mouthon, Mahmoud Desoky, Anouk Dev, Agostino Di Ciaula, Moisés Diago, Ibrahima Diallo, Luis Antonio Díaz, Melisa Dirchwolf, Paola Dongiovanni, Andrriy Dorofeyev, Xiaoguang Dou, Mark W. Douglas, Michael Doulberis, Cecil K. Dovia, Adam Doyle, Ivana Dragojević, Joost PH Drenth, Xuefei Duan, Audrius Dulskas, Dan L Dumitrascu, Oliver Duncan, Vincent Dusabejambo, Rev. Shem N.A. Dwawhi, Sho Eiketsu, Doaa El Amrousy, Ahmed El Deeb, Ghada El Deriny, Hesham Salah El Din, Salwa El Kamshishy, Mohamed El Kassas, Maissa El Raziky, Osama A Elagamy, Wafaa Elakel, Dina Elalfy, Hanaa Elaraby, Heba ElAwady, Reda Elbadawy, Hanaa Hassan Eldash, Manal S. Eldefrawy, Carol Lezama Elecharri, Amel Elfaramawy, Mohammed Elfatih, Mahmoud Elfiky, Mohamed Elgamsy, Mohamed Elgendy, Mohamed A. El-Guindi, Nagi Elhussieny, Ahmed Maher Eliwa, Zeineb Elkabbany, Hesham El-Khayat, Nehal M. El-Koofy, Alaa Elmetwalli, Amr Elrabat, Fathiya El-Raey, Fatma Elrashdy, Medhat Elsahhar, Esraa M. Elsaid, Shimaa Elsayed, Hany Elsayed, Aly Elsayed, Amr M. Elsayed, Hamdy Elsayed, Magdy El-Serafy, Ahmed M. Elsharkawy, Reem Yehia Elsheemy, Eman Elsayed Elshemy, Sara Elsherbini, Naglaa Eltoukhy, Reda Elwakil, Ola Emad, Shaimaa Emad, Mohamed Embabi, Ilkay Ergenç, Tatiana Ermolova, Gamal Esmat, Doaa M. Esmat, Enrique Carrera Estupiñan, Said Ettair, Tcaciuc Eugen, Mohammed Ezz-Eldin, Lidia Patricia Valdivieso Falcón, Yu-Chen Fan, Samah Fandari, Mahmoud Farag, Taghreed Mohamed Farahat, Eman M. Fares, Michael Fares, Eduardo Fassio, Hayam Fathy, Dina Fathy, Wael Fathy, Soheir Fayed, Dan Feng, Gong Feng, Miguel Fernández-Bermejo, Cristina Targa Ferreira, Javier Díaz Ferrer, Alastair Forbes, Rabab Fouad, Hanan M. Fouad, Tove Frisch, Hideki Fujii, Shuhei Fukunaga, Shinya Fukunishi, Hacer Fulya, Masato Furuhashi, Yasmine Gaber, Augusto Jose G. Galang, Jacqueline Cordova Gallardo, Rocío Galloso, Mahmoud Gamal, Reham Gamal, Hadeel Gamal, Jian Gan, Anar Ganbold, Xin Gao, George Garas, Tony Garba, Miren García-Cortes, Carmelo García-Monzón, Javier García-Samaniego, Amalia Gastaldelli, Manuel Gatica, Elizabeth Gatley, Tamar Gegeshidze, Bin Geng, Hasmik Ghazinyan, Salma Ghoneem, Luca Giacomelli, Gianluigi Giannelli, Edoardo G. Giannini, Matthew Giefer, Pere Ginès, Marcos Girala, Pablo J Giraudi, George Boon-Bee Goh, Ahmed Ali Gomaa, Benbingdi Gong, Dina Hilda C. Gonzales, Humberto C. Gonzalez, Maria Saraí Gonzalez-Huezo, Isabel Graupera, Ivica Grgurevic, Henning Grønbæk, Xuelian Gu, Lin Guan, Ibrahima Gueye, Alice Nanelin Guingané, Ozen Oz Gul, Cuma Bulent Gul, Qing Guo, Pramendra Prasad Gupta, Ahmet Gurakar, Juan Carlos Restrepo Gutierrez, Ghada Habib, Azaa Hafez, Emilia Hagman, Eman Halawa, Osama Hamdy, Abd Elkhalek Hamed, Dina H. Hamed, Saeed Hamid, Waseem Hamoudi, Yu Han, James Haridy, Hanan Haridy, David C H Harris Harris, Michael Hart, Fuad Hasan, Almoutaz Hashim, Israa Hassan, Ayman Hassan, Essam Ali Hassan, Adel Ahmed Hassan, Magda Shehata Hassan, Fetouh Hassanin, Alshymaa Hassnine, John Willy Haukeland, Amr Ismael M. Hawal, Jinfan He, Qiong He, Yong He, Fang-Ping He, Mona Hegazy, Adham Hegazy, Osama Henegil, Nelia Hernández, Manuel Hernández-Guerra, Fatima Higuera-de-la-Tijera, Ibrahim Hindy, Keisuke Hirota, Lee Chi Ho, Alexander Hodge, Mohamed Hosny, Xin Hou, Jiao-Feng Huang, Yan Huang, Zhifeng Huang, Yuan Huang, Ang Huang, Xiao-Ping Huang, Sheng Hui-ping, Bela Hunyady, Mennatallah A. Hussein, Osama Hussein, Shahinaz Mahmoud Hussien, Luis Ibáñez-Samaniego, Jamal Ibdah, Luqman Ibrahim, Miada Ibrahim, Ibrahim Ibrahim, Maria E. Icaza-Chávez, Sahar Idelbi, Ramazan Idilman Idilman, Mayumi Ikeda, Giuseppe Indolfi, Federica Invernizzi, Iram Irshad, Hasan Mohamed Ali Isa, Natacha Jreige Iskandar, Abdulrahman Ismaiel, Mariam Ismail, Zulkifli Ismail, Faisal Ismail, Hideki Iwamoto, Kathryn Jack, Rachael Jacob, Fuad Jafarov, Wasim Jafri, Helen Jahshan, Prasun K Jalal, Ligita Jancoriene, Martin Janicko, Hiruni Jayasena, Meryem Jefferies, Vivekanand Jha, Fanpu Ji, Yaqiu Ji, Jidong Jia, Changtao Jiang, Ni Jiang, Zong-zhe Jiang, Xing Jin, Yi Jin, Xu Jing, Qian Jingyu, Maia Jinjolava, FX Himawan Haryanto Jong, Alina Jucov, Ibecheole Julius, Mona Kaddah, Yoshihiro Kamada, Abobakr kamal, Enas Mohamed Kamal, Ashraf Sayed Kamel, Jia-Horng Kao, Maja Karin, Thomas Karlas, Mohammad Kashwaa, Leolin Katsidzira, Eda Kaya, M.Azzam Kayasseh, Bernadette Keenan, Caglayan Keklikkiran, William Keml, Deia K. Khalaf, Rofida Khalefa, Sherin Khamis, Doaa Khater, Hamed khattab, Anatoly Khavkin, Olga Khlynova, Nabil Khmis, Nazarii Kobyliak, Apostolos Koffas, Kazuhiko Koike, Kenneth Y.Y. Kok, Tomas Koller, Narcisse Patrice Komas, Nataliya V. Korochanskaya, Yannoula Koulla, Shunji Koya, Colleen Kraft, Bledar Kraja, Marcin Krawczyk, Mohammad Shafi Kuchay, Anand V Kulkarni, Ashish Kumar, Manoj Kumar, Sulaiman Lakoh, Philip Lam, Ling Lan, Naomi F. Lange, Kamran Bagheri Lankarani, Nicolas Lanthier, Kateryna Lapshyna, Sameh A. Lashen, Konang Nguieguia Justine Laure, Leonid Lazebnik, Didier Lebrec, Samuel S. Lee, Way Seah Lee, Yeong Yeh Lee, Diana Julie Leeming, Nathalie Carvalho Leite, Roberto Leon, Cosmas Rinaldi Adithya Lesmana, Junfeng Li, Qiong Li, Jun Li, Yang-Yang Li, Yufang Li, Lei Li, Min Li, Yiling li, Huiqing Liang, Tang Lijuan, Seng Gee Lim, Lee-Ling Lim, Shumei Lin, Han-Chieh Lin, Rita Lin, Rania Lithy, Yaru Liu, Yuanyuan Liu, Xin Liu, Wen-Yue Liu, Shourong Liu, Ken Liu, Tian Liu, Amedeo Lonardo, Mariana Bravo López, Eva López-Benages, Patricio Lopez-Jaramillo, Huimin Lu, Lun Gen Lu, Yan Lu, John Lubel, Rashid Lui, Iulianna Lupasco, Elena Luzina, Xiao-Hui Lv, Kate Lynch, Hong-Lei Ma, Mariana Verdelho Machado, Nonso Maduka, Katerina Madzharova, Russellini Magdaong, Sanjiv Mahadeva, Amel Mahfouz, Nik Ritza Kosai Nik Mahmood, Eman Mahmoud, Mohamed Mahrous, Rakhi Maiwall, Ammar Majeed, Avik Majumdar, Loey Mak, Madiha M Maklouf, Reza Malekzadeh, Claudia Mandato, Alessandra Mangia, Jake Mann, Hala Hussien Mansour, Abdellah Mansouri, Alessandro Mantovani, Jun qian Mao, Flor Maramag, Giulio Marchesini, Claude Marcus, Rui António Rocha Tato Marinho, Maria L Martinez-Chantar, Antonieta A. Soares Martins, Rana Marwan, Karen Frances Mason, Ghadeer Masoud, Mohamed Naguib Massoud, Maria Amalia Matamoros, Rosa Martín Mateos, Asmaa Mawed, Jean Claude Mbanya, Charles Mbendi, Lone McColaugh, Duncan McLeod, Juan Francisco Rivera Medina, Ahmed Megahed, Mai Mehrez, Iqbal Memon, Shahin Merat, Randy Mercado, Ahmed Mesbah, Taoufik Meskini, Mayada Metwally, Rasha Metwaly, Lei Miao, Eileen Micah, Luca Miele, Vladimir Milivojevic, Tamara Milovanovic, Yvonne L. Mina, Milan Mishkovik, Amal Mishriki, Tim Mitchell, Alshaimaa Mohamed, Mona Mohamed, Sofain Mohamed, Shady Mohammed, Ahmed Mohammed, Viswanathan Mohan, Sara Mohie, Aalaa Mokhtar, Reham Moniem, Mabel Segura Montilla, Jose Antonio Orozco Morales, María María Sánchez Morata, Jose Maria Moreno-Planas, Silvia Morise, Sherif Mosaad, Mohamed Moselhy, Alaa Mohamed Mostafa, Ebraheem Mostafa, Nezha Mouane, Nasser Mousa, Hamdy Mahfouz Moustafa, Abeer Msherif, Kate Muller, Christopher Munoz, Ana Beatriz Muñoz-Urribarri, Omar Alfaro Murillo, Feisul Idzwan Mustapha, Emir Muzurović, Yehia Nabil, Shaymaa Nafady, Ayu Nagamatsu, Atsushi Nakajima, Dan Nakano, Yuemin Nan, Fabio Nascimbeni, Mirella S. Naseef, Nagwa Nashat, Taran Natalia, Francesco Negro, Alexander V. Nersesov, Manuela Neuman, Masolwa Ng'wanasayi, Yan Ni, Amanda Nicoll, Takashi Niizeki, Dafina Nikolova, Wang Ningning, Madunil Niriella, K.A Nogoibaeva, Rozeena Nordien, Catherine O Sullivan, James O'Beirne, Solomon Obekpa, Ponsiano Ocama, Missiani Ochwoto, Michael Promise Ogolodom, Olusegun Ojo, Nana Okrostsvaridze, Claudia P. Oliveira, Raul Contreras Omaña, Omneya M. Omar, Hanaa Omar, Mabroka Omar, Salma Omran, Reham Omran, Marian Muse Osman, Nevin Owise, Theobald Owusu-Ansah, P. Martín Padilla- Machaca, Sirish Palle, Ziyan Pan, Xiao-Yan Pan, Qiuwei Pan, Apostolis Papaefthymiou, Feliciano Chanana Paquissi, Gabriella Par, Arit Parkash, Diana Payawal, Kevork M. Peltekian, Xuebin Peng, Liang Peng, Ying Peng, Rahul Pengoria, Martina Perez, José Luis Pérez, Norma Marlene Pérez, Marcello Persico, Mário Guimarães Pessoa, Salvatore Petta, Mathew Philip, Maria Corina Plaz Torres, Naveen Polavarapu, Jaime Poniachik, Piero Portincasa, Chunwen Pu, Tuğrul Pürnak, Edhie Purwanto, Xiaolong Qi, Xingshun Qi, Zibing Qian, Zhao Qiang, Zengpei Qiao, Liang Qiao, Alberto Queiroz, Atoosa Rabiee, Manal Radwan, Alain Marcel Rahetilahy, Yasmin Ramadan, Dina Ramadan, Anis Safura Ramli, Grant A. Ramm, Ao Ran, Ivan Rankovic, Huiying RAO, Sara Raouf, Sayantan Ray, Nancy Reau, Ahmed Refaat, Thomas Reiberger, Jose M Remes-Troche, Eira Cerda Reyes, Ben Richardson, Ezequiel Ridruejo, Sergio Riestra Jimenez, Ibrahim Rizk, Stuart Roberts, Juan Pablo Roblero, Jorge Alberto Prado Robles, Don Rockey, Manuel Rodríguez, Heriberto Rodríguez Hernández, Eva Román, Fernando Gomes Romeiro, Stefano Romeo, Jose Miguel Rosales-Zabal, Georgina R. Roshdi, Natalia Rosso, Andres Ruf, Patricia Cordero Ruiz, Nelia R. Runes, Andrea Ruzzenente, Marno Ryan, Ahmed Saad, Eman BE Sabbagh, Meriam Sabbah, Shimaa Saber, Reham Sabrey, Ramy Sabry, Maysaa Abdallah Saeed, Dina Said, Ebada M Said, Mohammad Amin Sakr, Yara Salah, Rabab Maamoun Salama, Asmaa Salama, Hussein Saleh, Ahmed Saleh, Ahmed Salem, Ahmed Thabet Salem, Alkassoum Salifou, Aso Faeq Salih, Abdallah Salman, Hanen Samouda, Faisal Sanai, Juan Francisco Sánchez-Ávila, Lakshumanan Sanker, Tomoya Sano, Miquel Sanz, Tobokalova Saparbu, Rohit Sawhney, Fatma Sayed, Sayed A. Sayed, Ashraf Othman Sayed, Manar Sayed, Giada Sebastiani, Laura Secadas, Khawaja Qamaruddin Sediqi, Sameh Seif, Nady Semida, Ebubekir Şenateş, Elena Daniela Serban, Lawrence Serfaty, Wai-Kay Seto, Ikram Sghaier, Min Sha, Hamada M. Shabaan, Lobna Shalaby, Inass Shaltout, Ala I. Sharara, Vishal Sharma, Isaac Thom Shawa, Ahmed Shawkat, Nehal Shawky, Osama Shehata, Sinead Sheils, Abate Bane Shewaye, Guojun Shi, Junping Shi, Shigeo Shimose, Tomotake Shirono, Lan Shou, Ananta Shrestha, Guanghou Shui, William Sievert, Solveig Sigurdardottir, Mostafa Mohamed Sira, Riyadh Siradj, Cecilia Sison, Linda Smyth, Reham Soliman, Jose D Sollano, Roger Sombie, Mark Sonderup, Siddharth Sood, German Soriano, Catherine A M Stedman, Oksana Stefanyuk, Davor Štimac, Simone Strasser, Pavel Strnad, Katherine Stuart, Wen Su, Minghua Su, Yoshio Sumida, Shuji Sumie, Dan-Qin Sun, Jing Sun, Hiroyuki Suzuki, Gianluca Svegliati-Baroni, Mohamed Osman Swar, S. TAHARBOUCHT, Zenab Taher, Saori Takamura, Lin Tan, Soek-Siam Tan, Tawesak Tanwandee, Sara Tarek, Ghelimici Tatiana, Federica Tavaglione, Gina Y. Tecson, Hoi-Poh Tee, Rolf Teschke, Mostafa Tharwat, Vo Duy Thong, Mark Thursz, Tulari Tine, Claudio Tiribelli, Ieva Tolmane, Jing Tong, Marco Tongo, Mamdouh Torkie, Aldo Torre, Esther A Torres, Meri Trajkovska, Sombat Treeprasertsuk, Tsubasa Tsutsumi, Thomas Tu, Josep A. Tur, Dilara Turan, Svetlana Turcan, Svetlana Turkina, Engin Tutar, Christian Tzeuton, Rose Ugiagbe, Ahmet Uygun, Michele Vacca, Pietro Vajro, David Van der Poorten, Laurens A. Van Kleef, Eliza Vashakidze, Carlos Moctezuma Velazquez, Mirtha Infante Velazquez, Sandro Vento, Veronique Verhoeven, Umberto Vespasiani-Gentilucci, Shireene Ratna Vethakkan, Josep Vilaseca, Libor Vítek, Ance Volkanovska, Michael Wallace, Wang Wan, Yan Wang, Ying Wang, Xiaolin Wang, Xuemei Wang, Chengyan Wang, Chunjiong Wang, Mingjie Wang, Pelden Wangchuk, Martin Weltman, MaryFrances White, Johannes Wiegand, Mohamed-Naguib Wifi, Alan Wigg, Markus Wilhelmi, Remon William, Henning Wittenburg, Shengjie Wu, Abdu Mohammed Wubeneh, Hongping Xia, Jian Xiao, Xiao Xiao, Wang Xiaofeng, Wanyuan Xiong, Liang Xu, Jie Xu, Weiguo Xu, Jing-Hang Xu, Keshu Xu, Yumin Xu, Shi-Hao Xu, Meng Xu, Aimin Xu, Chengfu Xu, Hongmei Yan, Jingyi Yang, Rui-Xu Yang, Yating Yang, Qinhe Yang, Naibin Yang, Jia Yao, Justine Yara, Serkan Yaraş, Nimet Yılmaz, Ramy Younes, Huda younes, Sona Young, Farah Youssef, Yanyan Yu, Ming-Lung Yu, Jing Yuan, Zhang Yue, Man-Fung Yuen, Wang Yun, Nonka Yurukova, Serag Zakaria, Samy Zaky, Maia Zaldastanishvili, Rodrigo Zapata, Nazanin Zare, Enver Zerem, Nema Zeriban, Xu Zeshuai, Huijie Zhang, Xuemei Zhang, Yupei Zhang, Wen-Hua Zhang, Xuchen Zhang, Yon-ping Zhang, Yuexin Zhang, Zhan-qing Zhang, Jingmin Zhao, Rong-Rong Zhao, Hongwei Zhao, Chao Zheng, Yijie Zheng, Ruidan Zheng, Tian-Lei Zheng, Kenneth Zheng, Xi Qiao Zhou, Yongjian Zhou, Yu-Jie Zhou, Hong Zhou, Ling Zhou, Yongning Zhou, Long dong Zhu, Yong Fen Zhu, Yueyong Zhu, Pei-Wu Zhu, Ebtesam Ziada, David Ziring, Li Ziyi, Shanshan Zou, Zhengsheng Zou, Huaibin Zou, Roberto Zuart Ruiz, UCL - (SLuc) Service de gastro-entérologie, UCL - SSS/IREC/GAEN - Pôle d'Hépato-gastro-entérologie, Mendez-Sanchez, N, Bugianesi, E, Gish, R, Lammert, F, Tilg, H, Nguyen, M, Sarin, S, Fabrellas, N, Zelber-Sagi, S, Fan, J, Shiha, G, Targher, G, Zheng, M, Chan, W, Vinker, S, Kawaguchi, T, Castera, L, Yilmaz, Y, Korenjak, M, Spearman, C, Ungan, M, Palmer, M, El-Shabrawi, M, Gruss, H, Dufour, J, Dhawan, A, Wedemeyer, H, George, J, Valenti, L, Fouad, Y, Romero-Gomez, M, Eslam, M, Abate, M, Abbas, B, Abbassy, A, Abd El Ghany, W, Abd Elkhalek, A, Abd ElMajeed, E, Abdalgaber, M, Abdallah, M, Abdallah, N, Abdelaleem, S, Abdelghani, Y, Abdelghany, W, Abdelhalim, S, Abdelhamid, W, Abdelhamid, N, Abdelkader, N, Abdelkreem, E, Abdelmohsen, A, Abdelrahman, A, Abd-elsalam, S, Abdeltawab, D, Abduh, A, Abdulhakam, N, Abdulla, M, Abedpoor, N, Abenavoli, L, Aberg, F, Ablack, O, Abo elftouh, M, Abo-Amer, Y, Aboubkr, A, Aboud, A, Abouelnaga, A, Aboufarrag, G, Aboutaleb, A, Abundis, L, Adali, G, Adames, E, Adams, L, Adda, D, Adel, N, Adel Sayed, M, Afaa, T, Afredj, N, Aghayeva, G, Aghemo, A, Aguilar-Salinas, C, Ahlenstiel, G, Ahmady, W, Ahmed, W, Ahmed, A, Ahmed, S, Ahmed, H, Ahmed, R, Aigner, E, Akarsu, M, Akroush, M, Akyuz, U, Al Mahtab, M, Al Qadiri, T, Al Rawahi, Y, AL rubaee, R, Al Saffar, M, Alam, S, Al-Ani, Z, Albillos, A, Alboraie, M, Al-Busafi, S, Al-Emam, M, Alharthi, J, Ali, K, Ali, B, Ali, M, Ali, R, Alisi, A, AL-Khafaji, A, Alkhatry, M, Aller, R, Almansoury, Y, Al-Naamani, K, Alnakeeb, A, Alonso, A, Alqahtani, S, Alrabadi, L, Alswat, K, Altaher, M, Altamimi, T, Altamirano, J, Alvares-da-Silva, M, Aly, E, Alzahaby, A, Alzamzamy, A, Amano, K, Amer, M, Amin, M, Amin, S, Amir, A, Ampuero, J, Anas, N, Andreone, P, Andriamandimby, S, Anees, M, Angela, P, Antonios, M, Arafat, W, Araya, J, Armendariz-Borunda, J, Armstrong, M, Ashktorab, H, Aspichueta, P, Assal, F, Atef, M, Attia, D, Atwa, H, Awad, R, Awad, M, Awny, S, Awolowo, O, Awuku, Y, Ayada, I, Aye, T, Ayman, S, Ayman, H, Ayoub, H, Azmy, H, Babaran, R, Badreldin, O, Badry, A, Bahcecioglu, I, Bahour, A, Bai, J, Balaban, Y, Balasubramanyam, M, Bamakhrama, K, Banales, J, Bangaru, B, Bao, J, Barahona, J, Barakat, S, Barbalho, S, Barbra, B, Barranco, B, Barrera, F, Baumann, U, Bazeed, S, Bech, E, Benayad, A, Benesic, A, Bernstein, D, Bessone, F, Birney, S, Bisseye, C, Blake, M, Bobat, B, Bonfrate, L, Bordin, D, Bosques-Padilla, F, Boursier, J, Boushab, B, Bowen, D, Bravo, P, Brennan, P, Bright, B, Broekaert, I, Buque, X, Burgos-Santamaria, D, Burman, J, Busetto, L, Byrne, C, Cabral-Prodigalidad, P, Cabrera-Alvarez, G, Cai, W, Cainelli, F, Caliskan, A, Canbay, A, Cano-Contreras, A, Cao, H, Cao, Z, Carrion, A, Carubbi, F, Casanovas, T, Castellanos Fernandez, M, Chai, J, Chan, S, Charatcharoenwitthaya, P, Chavez-Tapia, N, Chayama, K, Chen, J, Chen, L, Chen, Z, Chen, H, Chen, S, Chen, Q, Chen, Y, Chen, G, Chen, E, Chen, F, Chen, P, Cheng, R, Cheng, W, Chieh, J, Chokr, I, Cholongitas, E, Choudhury, A, Chowdhury, A, Chukwudike, E, Ciardullo, S, Clayton, M, Clement, K, Cloa, M, Coccia, C, Collazos, C, Colombo, M, Cosar, A, Cotrim, H, Couillerot, J, Coulibaly, A, Crespo, G, Crespo, J, Cruells, M, Cua, I, Dabbous, H, Dalekos, G, D'Alia, P, Dan, L, Dao, V, Darwish, M, Datz, C, Davalos-Moscol, M, Dawoud, H, de Careaga, B, de Knegt, R, de Ledinghen, V, de Silva, J, Debzi, N, Decraecker, M, Del Pozo, E, Delgado, T, Delgado-Blanco, M, Dembinski, L, Depina, A, Derbala, M, Desalegn, H, Desbois-Mouthon, C, Desoky, M, Dev, A, Di Ciaula, A, Diago, M, Diallo, I, Diaz, L, Dirchwolf, M, Dongiovanni, P, Dorofeyev, A, Dou, X, Douglas, M, Doulberis, M, Dovia, C, Doyle, A, Dragojevic, I, Drenth, J, Duan, X, Dulskas, A, Dumitrascu, D, Duncan, O, Dusabejambo, V, Dwawhi, R, Eiketsu, S, El Amrousy, D, El Deeb, A, El Deriny, G, El Din, H, El Kamshishy, S, El Kassas, M, El Raziky, M, Elagamy, O, Elakel, W, Elalfy, D, Elaraby, H, Elawady, H, Elbadawy, R, Eldash, H, Eldefrawy, M, Elecharri, C, Elfaramawy, A, Elfatih, M, Elfiky, M, Elgamsy, M, Elgendy, M, El-Guindi, M, Elhussieny, N, Eliwa, A, Elkabbany, Z, El-Khayat, H, El-Koofy, N, Elmetwalli, A, Elrabat, A, El-Raey, F, Elrashdy, F, Elsahhar, M, Elsaid, E, Elsayed, S, Elsayed, H, Elsayed, A, El-Serafy, M, Elsharkawy, A, Elsheemy, R, Elshemy, E, Elsherbini, S, Eltoukhy, N, Elwakil, R, Emad, O, Emad, S, Embabi, M, Ergenc, I, Ermolova, T, Esmat, G, Esmat, D, Estupinan, E, Ettair, S, Eugen, T, Ezz-Eldin, M, Falcon, L, Fan, Y, Fandari, S, Farag, M, Farahat, T, Fares, E, Fares, M, Fassio, E, Fathy, H, Fathy, D, Fathy, W, Fayed, S, Feng, D, Feng, G, Fernandez-Bermejo, M, Ferreira, C, Ferrer, J, Forbes, A, Fouad, R, Fouad, H, Frisch, T, Fujii, H, Fukunaga, S, Fukunishi, S, Fulya, H, Furuhashi, M, Gaber, Y, Galang, A, Gallardo, J, Galloso, R, Gamal, M, Gamal, R, Gamal, H, Gan, J, Ganbold, A, Gao, X, Garas, G, Garba, T, Garcia-Cortes, M, Garcia-Monzon, C, Garcia-Samaniego, J, Gastaldelli, A, Gatica, M, Gatley, E, Gegeshidze, T, Geng, B, Ghazinyan, H, Ghoneem, S, Giacomelli, L, Giannelli, G, Giannini, E, Giefer, M, Gines, P, Girala, M, Giraudi, P, Goh, G, Gomaa, A, Gong, B, Gonzales, D, Gonzalez, H, Gonzalez-Huezo, M, Graupera, I, Grgurevic, I, Gronbaek, H, Gu, X, Guan, L, Gueye, I, Guingane, A, Gul, O, Gul, C, Guo, Q, Gupta, P, Gurakar, A, Gutierrez, J, Habib, G, Hafez, A, Hagman, E, Halawa, E, Hamdy, O, Hamed, A, Hamed, D, Hamid, S, Hamoudi, W, Han, Y, Haridy, J, Haridy, H, Harris, D, Hart, M, Hasan, F, Hashim, A, Hassan, I, Hassan, A, Hassan, E, Hassan, M, Hassanin, F, Hassnine, A, Haukeland, J, Hawal, A, He, J, He, Q, He, Y, He, F, Hegazy, M, Hegazy, A, Henegil, O, Hernandez, N, Hernandez-Guerra, M, Higuera-de-la-Tijera, F, Hindy, I, Hirota, K, Ho, L, Hodge, A, Hosny, M, Hou, X, Huang, J, Huang, Y, Huang, Z, Huang, A, Huang, X, Hui-ping, S, Hunyady, B, Hussein, M, Hussein, O, Hussien, S, Ibanez-Samaniego, L, Ibdah, J, Ibrahim, L, Ibrahim, M, Ibrahim, I, Icaza-Chavez, M, Idelbi, S, Idilman, R, Ikeda, M, Indolfi, G, Invernizzi, F, Irshad, I, Isa, H, Iskandar, N, Ismaiel, A, Ismail, M, Ismail, Z, Ismail, F, Iwamoto, H, Jack, K, Jacob, R, Jafarov, F, Jafri, W, Jahshan, H, Jalal, P, Jancoriene, L, Janicko, M, Jayasena, H, Jefferies, M, Jha, V, Ji, F, Ji, Y, Jia, J, Jiang, C, Jiang, N, Jiang, Z, Jin, X, Jin, Y, Jing, X, Jingyu, Q, Jinjolava, M, Jong, F, Jucov, A, Julius, I, Kaddah, M, Kamada, Y, Kamal, A, Kamal, E, Kamel, A, Kao, J, Karin, M, Karlas, T, Kashwaa, M, Katsidzira, L, Kaya, E, Kayasseh, M, Keenan, B, Keklikkiran, C, Keml, W, Khalaf, D, Khalefa, R, Khamis, S, Khater, D, Khattab, H, Khavkin, A, Khlynova, O, Khmis, N, Kobyliak, N, Koffas, A, Koike, K, Kok, K, Koller, T, Komas, N, Korochanskaya, N, Koulla, Y, Koya, S, Kraft, C, Kraja, B, Krawczyk, M, Kuchay, M, Kulkarni, A, Kumar, A, Kumar, M, Lakoh, S, Lam, P, Lan, L, Lange, N, Lankarani, K, Lanthier, N, Lapshyna, K, Lashen, S, Laure, K, Lazebnik, L, Lebrec, D, Lee, S, Lee, W, Lee, Y, Leeming, D, Leite, N, Leon, R, Lesmana, C, Li, J, Li, Q, Li, Y, Li, L, Li, M, Liang, H, Lijuan, T, Lim, S, Lim, L, Lin, S, Lin, H, Lin, R, Lithy, R, Liu, Y, Liu, X, Liu, W, Liu, S, Liu, K, Liu, T, Lonardo, A, Lopez, M, Lopez-Benages, E, Lopez-Jaramillo, P, Lu, H, Lu, L, Lu, Y, Lubel, J, Lui, R, Lupasco, I, Luzina, E, Lv, X, Lynch, K, Ma, H, Machado, M, Maduka, N, Madzharova, K, Magdaong, R, Mahadeva, S, Mahfouz, A, Mahmood, N, Mahmoud, E, Mahrous, M, Maiwall, R, Majeed, A, Majumdar, A, Mak, L, Maklouf, M, Malekzadeh, R, Mandato, C, Mangia, A, Mann, J, Mansour, H, Mansouri, A, Mantovani, A, Mao, J, Maramag, F, Marchesini, G, Marcus, C, Marinho, R, Martinez-Chantar, M, Martins, A, Marwan, R, Mason, K, Masoud, G, Massoud, M, Matamoros, M, Mateos, R, Mawed, A, Mbanya, J, Mbendi, C, Mccolaugh, L, Mcleod, D, Medina, J, Megahed, A, Mehrez, M, Memon, I, Merat, S, Mercado, R, Mesbah, A, Meskini, T, Metwally, M, Metwaly, R, Miao, L, Micah, E, Miele, L, Milivojevic, V, Milovanovic, T, Mina, Y, Mishkovik, M, Mishriki, A, Mitchell, T, Mohamed, A, Mohamed, M, Mohamed, S, Mohammed, S, Mohammed, A, Mohan, V, Mohie, S, Mokhtar, A, Moniem, R, Montilla, M, Morales, J, Morata, M, Moreno-Planas, J, Morise, S, Mosaad, S, Moselhy, M, Mostafa, A, Mostafa, E, Mouane, N, Mousa, N, Moustafa, H, Msherif, A, Muller, K, Munoz, C, Munoz-Urribarri, A, Murillo, O, Mustapha, F, Muzurovic, E, Nabil, Y, Nafady, S, Nagamatsu, A, Nakajima, A, Nakano, D, Nan, Y, Nascimbeni, F, Naseef, M, Nashat, N, Natalia, T, Negro, F, Nersesov, A, Neuman, M, Ng'Wanasayi, M, Ni, Y, Nicoll, A, Niizeki, T, Nikolova, D, Ningning, W, Niriella, M, Nogoibaeva, K, Nordien, R, O Sullivan, C, O'Beirne, J, Obekpa, S, Ocama, P, Ochwoto, M, Ogolodom, M, Ojo, O, Okrostsvaridze, N, Oliveira, C, Omana, R, Omar, O, Omar, H, Omar, M, Omran, S, Omran, R, Osman, M, Owise, N, Owusu-Ansah, T, Padilla- Machaca, P, Palle, S, Pan, Z, Pan, X, Pan, Q, Papaefthymiou, A, Paquissi, F, Par, G, Parkash, A, Payawal, D, Peltekian, K, Peng, X, Peng, L, Peng, Y, Pengoria, R, Perez, M, Perez, J, Perez, N, Persico, M, Pessoa, M, Petta, S, Philip, M, Plaz Torres, M, Polavarapu, N, Poniachik, J, Portincasa, P, Pu, C, Purnak, T, Purwanto, E, Qi, X, Qian, Z, Qiang, Z, Qiao, Z, Qiao, L, Queiroz, A, Rabiee, A, Radwan, M, Rahetilahy, A, Ramadan, Y, Ramadan, D, Ramli, A, Ramm, G, Ran, A, Rankovic, I, Rao, H, Raouf, S, Ray, S, Reau, N, Refaat, A, Reiberger, T, Remes-Troche, J, Reyes, E, Richardson, B, Ridruejo, E, Riestra Jimenez, S, Rizk, I, Roberts, S, Roblero, J, Robles, J, Rockey, D, Rodriguez, M, Rodriguez Hernandez, H, Roman, E, Romeiro, F, Romeo, S, Rosales-Zabal, J, Roshdi, G, Rosso, N, Ruf, A, Ruiz, P, Runes, N, Ruzzenente, A, Ryan, M, Saad, A, Sabbagh, E, Sabbah, M, Saber, S, Sabrey, R, Sabry, R, Saeed, M, Said, D, Said, E, Sakr, M, Salah, Y, Salama, R, Salama, A, Saleh, H, Saleh, A, Salem, A, Salifou, A, Salih, A, Salman, A, Samouda, H, Sanai, F, Sanchez-Avila, J, Sanker, L, Sano, T, Sanz, M, Saparbu, T, Sawhney, R, Sayed, F, Sayed, S, Sayed, A, Sayed, M, Sebastiani, G, Secadas, L, Sediqi, K, Seif, S, Semida, N, Senates, E, Serban, E, Serfaty, L, Seto, W, Sghaier, I, Sha, M, Shabaan, H, Shalaby, L, Shaltout, I, Sharara, A, Sharma, V, Shawa, I, Shawkat, A, Shawky, N, Shehata, O, Sheils, S, Shewaye, A, Shi, G, Shi, J, Shimose, S, Shirono, T, Shou, L, Shrestha, A, Shui, G, Sievert, W, Sigurdardottir, S, Sira, M, Siradj, R, Sison, C, Smyth, L, Soliman, R, Sollano, J, Sombie, R, Sonderup, M, Sood, S, Soriano, G, Stedman, C, Stefanyuk, O, Stimac, D, Strasser, S, Strnad, P, Stuart, K, Su, W, Su, M, Sumida, Y, Sumie, S, Sun, D, Sun, J, Suzuki, H, Svegliati-Baroni, G, Swar, M, Taharboucht, S, Taher, Z, Takamura, S, Tan, L, Tan, S, Tanwandee, T, Tarek, S, Tatiana, G, Tavaglione, F, Tecson, G, Tee, H, Teschke, R, Tharwat, M, Thong, V, Thursz, M, Tine, T, Tiribelli, C, Tolmane, I, Tong, J, Tongo, M, Torkie, M, Torre, A, Torres, E, Trajkovska, M, Treeprasertsuk, S, Tsutsumi, T, Tu, T, Tur, J, Turan, D, Turcan, S, Turkina, S, Tutar, E, Tzeuton, C, Ugiagbe, R, Uygun, A, Vacca, M, Vajro, P, Van der Poorten, D, Van Kleef, L, Vashakidze, E, Velazquez, C, Velazquez, M, Vento, S, Verhoeven, V, Vespasiani-Gentilucci, U, Vethakkan, S, Vilaseca, J, Vitek, L, Volkanovska, A, Wallace, M, Wan, W, Wang, Y, Wang, X, Wang, C, Wang, M, Wangchuk, P, Weltman, M, White, M, Wiegand, J, Wifi, M, Wigg, A, Wilhelmi, M, William, R, Wittenburg, H, Wu, S, Wubeneh, A, Xia, H, Xiao, J, Xiao, X, Xiaofeng, W, Xiong, W, Xu, L, Xu, J, Xu, W, Xu, K, Xu, Y, Xu, S, Xu, M, Xu, A, Xu, C, Yan, H, Yang, J, Yang, R, Yang, Y, Yang, Q, Yang, N, Yao, J, Yara, J, Yaras, S, Yilmaz, N, Younes, R, Younes, H, Young, S, Youssef, F, Yu, Y, Yu, M, Yuan, J, Yue, Z, Yuen, M, Yun, W, Yurukova, N, Zakaria, S, Zaky, S, Zaldastanishvili, M, Zapata, R, Zare, N, Zerem, E, Zeriban, N, Zeshuai, X, Zhang, H, Zhang, X, Zhang, Y, Zhang, W, Zhang, Z, Zhao, J, Zhao, R, Zhao, H, Zheng, C, Zheng, Y, Zheng, R, Zheng, T, Zheng, K, Zhou, X, Zhou, Y, Zhou, H, Zhou, L, Zhu, L, Zhu, Y, Zhu, P, Ziada, E, Ziring, D, Ziyi, L, Zou, S, Zou, Z, Zou, H, Zuart Ruiz, R, and Global Multi-Stakeholder Consensus on the Redefinition of Fatty Liver Disease

Subjects
Renal disorders Radboud Institute for Molecular Life Sciences [Radboudumc 11], Hepatology, Non-alcoholic Fatty Liver Disease, NAFLD, consensu, Gastroenterology, MAFLD, definition, Humans, MAFLD, NAFLD, Human medicine
Abstract

Contains fulltext : 252162.pdf (Publisher’s version ) (Closed access)

Published
2022

34. A Novel Online Calculator Based on Serum Biomarkers to Detect Hepatocellular Carcinoma among Patients with Hepatitis B

Author

Wan Yee Lau, Andrew Soh, Cunling Yan, Feng Shen, Wei Chen, Yijie Zheng, Huijun Li, Meng-Chao Wu, Tian Yang, Ming Shi, Timothy M. Pawlik, Nianyue Wang, Lianhua Wei, Shangli Cai, Guoqiang Wang, Zhuping Fan, Hao Xing, Miaoxia Liu, Shunjun Li, and Agim Beshiri

Subjects
Adult, Liver Cirrhosis, Male, China, Hepatitis B virus, medicine.medical_specialty, Carcinoma, Hepatocellular, Cirrhosis, Clinical Biochemistry, Pilot Projects, medicine.disease_cause, Sensitivity and Specificity, Gastroenterology, 03 medical and health sciences, 0302 clinical medicine, Asian People, Internal medicine, Biomarkers, Tumor, medicine, Carcinoma, Humans, Protein Precursors, Aged, alpha-L-Fucosidase, business.industry, Liver Neoplasms, Biochemistry (medical), Area under the curve, Middle Aged, Hepatitis B, Nomogram, medicine.disease, digestive system diseases, ROC Curve, Area Under Curve, 030220 oncology & carcinogenesis, Hepatocellular carcinoma, Cohort, Female, Prothrombin, 030211 gastroenterology & hepatology, alpha-Fetoproteins, business, Algorithms, Biomarkers
Abstract

BACKGROUND Early detection of hepatocellular carcinoma (HCC) among hepatitis B virus (HBV)-infected patients remains a challenge, especially in China. We sought to create an online calculator of serum biomarkers to detect HCC among patients with chronic hepatitis B (CHB). METHODS Participants with HBV-HCC, CHB, HBV-related liver cirrhosis (HBV-LC), benign hepatic tumors, and healthy controls (HCs) were recruited at 11 Chinese hospitals. Potential serum HCC biomarkers, protein induced by vitamin K absence or antagonist-II (PIVKA-II), α-fetoprotein (AFP), lens culinaris agglutinin A-reactive fraction of AFP (AFP-L3) and α-L-fucosidase (AFU) were evaluated in the pilot cohort. The calculator was built in the training cohort via logistic regression model and validated in the validation cohort. RESULTS In the pilot study, PIVKA-II and AFP showed better diagnostic sensitivity and specificity compared with AFP-L3 and AFU and were chosen for further study. A combination of PIVKA-II and AFP demonstrated better diagnostic accuracy in differentiating patients with HBV-HCC from patients with CHB or HBV-LC than AFP or PIVKA-II alone [area under the curve (AUC), 0.922 (95% CI, 0.908–0.935), sensitivity 88.3% and specificity 85.1% for the training cohort; 0.902 (95% CI, 0.875–0.929), 87.8%, and 81.0%, respectively, for the validation cohort]. The nomogram including AFP, PIVKA-II, age, and sex performed well in predicting HBV-HCC with good calibration and discrimination [AUC, 0.941 (95% CI, 0.929–0.952)] and was validated in the validation cohort [AUC, 0.931 (95% CI, 0.909–0.953)]. CONCLUSIONS Our results demonstrated that a web-based calculator including age, sex, AFP, and PIVKA-II accurately predicted the presence of HCC in patients with CHB. ClinicalTrials.gov Identifier NCT03047603

Published
2019

36. EM performance of segmented diverter strips used in lightning protection of wind turbine blades

Author

Yijie Zheng, Allen Hall, Phillip Sewell, and Ana Vukovic

Subjects
Wind power, Turbine blade, business.industry, law, Separation (aeronautics), Arc flash, Mechanical engineering, STRIPS, business, Lightning, Geology, law.invention
Abstract

Segmented diverter strips are increasingly becoming used for the lightning protection of wind turbine blades. This paper investigates the electromagnetic properties of a range of segmented diverter designs that differ in terms of size, shape and the separation gap between the metallic segments. Furthermore, the performance of segmented diverters positioned on two different materials namely fiberglass epoxy and carbon fiber, are also compared.

Published
2021

38. PIVKA-II Combined with Alpha-Fetoprotein for the Diagnostic Value of Hepatic Tumors in Children: A Multicenter, Prospective Observational Study

Author

Xianying Lu, Yong-Jun Fang, Shan Wang, Song Gu, Chenjie Xie, Rui Dong, Min Xu, Hongxiang Gao, Yurui Wu, Jing Wang, Ji Ma, Qiuhui Pan, Yang Li, Li Xie, Xianwei Zhang, Yijie Zheng, Shijian Liu, and Wei-Song Li

Subjects
medicine.medical_specialty, business.industry, Internal medicine, Medicine, Observational study, business, Alpha-fetoprotein, Gastroenterology, Value (mathematics)
Abstract

Background:We aimed to investigate whether the protein induced by the vitamin K antagonist-II (PIVKA-II) combined with alpha-fetoprotein (AFP) can improve the diagnostic and differential diagnostic accuracy of childhood hepatic tumors.Methods: A multicenter prospective observational study was performed at nine regional institutions around China from October 1, 2018 to September 30, 2020, and a total of 257 eligible patients were enrolled. Children with confirmed hepatic masses were placed in the test group (Group T, n=144) and divided into the hepatoblastoma group (Group THB, n=98) and the hemangioendothelioma group (Group THE, n=46), and children with extrahepatic abdominal masses were placed in the control group (Group C, n=113). Peripheral blood was collected from each patient prior to surgery or chemotherapy. Receiver operating characteristic (ROC) curves and area under the curve (AUROC) were used to evaluate the diagnostic and differential diagnostic efficiency.Results:The mean levels of PIVKA-II and AFP were both significantly higher in Group T than in Group C (P=0.001, P＜0.001) and in Group THB than in Group THE (P=0.018, P=0.013). For the diagnosis performance, PIVKA-II had a sensitivity of 86.7% and a specificity of 81.3% at a cutoff of 32.6 mAU/ml vs. 84.1% and 81.8% for AFP at a cutoff of 120 ng/ml (AUROC 0.867 vs. 0.857, respectively). The combination of PIVKA-II and AFP had higher sensitivity (88.5%), specificity (84.7%) and AUROC (0.891) than PIVKA-II or AFP alone. The differential diagnostic value of PIVKA-II and AFP in differentiating hepatoblastoma from hemangioendothelioma was further assessed. PIVKA-II had a sensitivity of 71.7% and a specificity of 88.7% at a cutoff of 47.1 mAU/ml, and there was 63.0% and 78.6% of AFP at a cutoff of 560 ng/ml (AUROC 0.876 vs. 0.743, respectively). The combined markers also showed a higher sensitivity (72.7%), a higher specificity (91.8%) and a higher AUROC (0.895) than PIVKA-II or AFP alone.Conclusions:The serum level of PIVKA-II was significantly higher in children with hepatic tumors, especially those with malignant tumors. PIVKA-II had superior sensitivity and specificity in the diagnosis of childhood hepatic tumors. The combination of PIVKA-II and AFP further increased the diagnostic performance.Trial registration: Clinical Trials, NCT03645655. Registered 20 August 2018, https://www.clinicaltrials.gov/ct2/show/NCT03645655

Published
2021

39. A novel nomogram for the differential diagnosis between advanced and early appendicitis in pediatric patients

Author

Rui Dong, Yijie Zheng, Gong Chen, Ying Wu, Jingying Jiang, Yue Tang, Yanlei Huang, Zhen Shen, and Shan Zheng

Subjects
Male, medicine.medical_specialty, Time Factors, Multivariate analysis, genetic structures, Clinical Biochemistry, Urology, Diagnosis, Differential, 03 medical and health sciences, 0302 clinical medicine, Drug Discovery, Humans, Medicine, Child, biology, Fibrin degradation product, Receiver operating characteristic, business.industry, Biochemistry (medical), C-reactive protein, 030208 emergency & critical care medicine, Nomogram, Appendicitis, medicine.disease, Nomograms, 030220 oncology & carcinogenesis, biology.protein, Biomarker (medicine), Female, Differential diagnosis, business
Abstract

Aim: To develop a nomogram for differential diagnosis between advanced and early pediatric appendicitis (PA). Patients & methods: We retrospectively studied 669 PA patients. Patient characteristics and 24 serum markers were subjected to univariate and multivariate analysis, based on which the nomogram was constructed. Results: Fibrin degradation product, CRP and Na+ differed significantly between the advanced and early PA. The value of area under the receiver operating characteristic curve (AUC) was 0.8602, which was greater than that of a single serum marker. Furthermore, the nomogram showed better discriminative ability than the biomarker alone. Notably, validation indicated high stability and reproducibility. Conclusion: The nomogram is superior to the serum marker alone, and may hold promise in clinical application.

Published
2019

40. Clinical performance of α-L-fucosidase for early detection of hepatocellular carcinoma

Author

Han Wu, Jun Han, Yijie Zheng, Hai-Bo Qiu, Chao Li, Hao Xing, Xuemei Ding, Ruilin Han, Huijun Li, Ming-Da Wang, Zhen-Li Li, Meng-Chao Wu, Han Zhang, Feng Shen, Cunling Yan, and Tian Yang

Subjects
Adult, Male, medicine.medical_specialty, Carcinoma, Hepatocellular, Adolescent, Clinical Biochemistry, Early detection, 030204 cardiovascular system & hematology, Gastroenterology, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, parasitic diseases, Drug Discovery, medicine, Humans, Stage (cooking), neoplasms, Early Detection of Cancer, Aged, Neoplasm Staging, Aged, 80 and over, alpha-L-Fucosidase, business.industry, Liver Neoplasms, Biochemistry (medical), Area under the curve, Clinical performance, α l fucosidase, Middle Aged, medicine.disease, digestive system diseases, Tumor Burden, 030220 oncology & carcinogenesis, Hepatocellular carcinoma, Biomarker (medicine), Female, alpha-Fetoproteins, business
Abstract

Aim: To evaluate the diagnostic performance of serum AFU for early stage hepatocellular carcinoma (HCC). Methods: Concentration of AFU and AFP were measured in 512 patients. The performance was compared for AFU and AFP alone or in combination. Results: The area under the curve (AUC) for AFU was 0.68, with a sensitivity of 56.1% and specificity of 69.2% at the cut-off value of 24 U/l; whereas the AUC for AFP was 0.83, with a sensitivity of 58.2% and specificity of 85.2% at cut-off value of 20 ng/ml. The AUC of AFU alone or the combination with AFP were lower than that of AFP alone. Conclusion: AFU is a suboptimal biomarker for early detection of HCC.

Published
2019

41. Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia

Author

YiJie Zheng, Jingying Jiang, Jia Liu, Rui Dong, Shan Zheng, ShuYang Dai, Song Sun, and Gong Chen

Subjects
medicine.medical_specialty, diagnosis, non-invasive, Direct bilirubin, Value (computer science), biliary atresia, 030204 cardiovascular system & hematology, Pediatrics, Cross-validation, 03 medical and health sciences, 0302 clinical medicine, Biliary atresia, 030225 pediatrics, medicine, gamma-glutamyl transpeptidase, Original Research, Training set, Artificial neural network, business.industry, lcsh:RJ1-570, lcsh:Pediatrics, medicine.disease, Binary classification, Pediatrics, Perinatology and Child Health, Obstructive jaundice, Radiology, business, obstructive jaundice
Abstract

Objectives: Biliary atresia (BA) is a devastating pediatric liver disease. Early diagnosis is important for timely intervention and better prognosis. Using clinical parameters for non-invasive and efficient BA diagnosis, we aimed to establish an artificial neural network (ANN). Methods: A total of 2,384 obstructive jaundice patients from 2012 to 2017 and their 137 clinical parameters were screened for eligibility. A standard binary classification feed-forward ANN was employed. The network was trained and validated for accuracy. Gamma-glutamyl transpeptidase (GGT) level was used as an independent predictor and a comparison to assess the network effectiveness. Results: We included 46 parameters and 1,452 patients for ANN modeling. Total bilirubin, direct bilirubin, and GGT were the most significant indicators. The network consisted of an input layer, 3 hidden layers with 12 neurons each, and an output layer. The network showed good predictive property with a high area under curve (AUC) (0.967, sensitivity 97.2% and specificity 91.0%). Five-fold cross validation showed the mean accuracy for training data of 93.2% and for validation data of 88.6%. Conclusions: The high accuracy and efficiency demonstrated by the ANN model is promising in the noninvasive diagnosis of BA and could be considered as in a low-cost and independent expert diagnosis system.

Published
2020

42. Circulating tumor cells detection in neuroblastoma patients by EpCAM-independent enrichment and immunostaining-fluorescence in situ hybridization

Author

Chao Zheng, Rui Dong, Binbin Zhang, Baihui Liu, Shan Zheng, Zhang Zhenzhen, Xiangqi Liu, Yijie Zheng, and Kuiran Dong

Subjects
Male, 0301 basic medicine, Fluorescent Antibody Technique, General Biochemistry, Genetics and Molecular Biology, Metastasis, Neuroblastoma, Vanilmandelic Acid, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Circulating tumor cell, Risk Factors, Humans, Medicine, DAPI, Neoplasm Metastasis, Child, In Situ Hybridization, Fluorescence, Proportional Hazards Models, N-Myc Proto-Oncogene Protein, medicine.diagnostic_test, business.industry, Gene Amplification, Cancer, Chromosome, General Medicine, Epithelial Cell Adhesion Molecule, Neoplastic Cells, Circulating, medicine.disease, Logistic Models, 030104 developmental biology, chemistry, Child, Preschool, Phosphopyruvate Hydratase, 030220 oncology & carcinogenesis, Multivariate Analysis, Cancer research, Female, business, Immunostaining, Chromosomes, Human, Pair 8, Fluorescence in situ hybridization
Abstract

Detecting circulating tumor cells (CTCs) has proven valuable for evaluating the prognosis of cancer patients and for studying the mechanisms of treatment resistance. Owing to the lack of universal and specific tumor markers for neuroblastoma (NB), in this prospective study, we adopted an EpCAM-independent method to detect CTCs in the peripheral blood of NB patients. We used an EpCAM-independent assay to delete leukocytes and to enrich the CTCs. CTCs were identified by immunostaining of CD45, DAPI and DNA fluorescence in situ hybridization (FISH) of the centromere of chromosome 8 probe (CEP8). Cells that were DAPI+/CD45-/CEP8 ≥ 3 were considered CTCs. We collected peripheral blood from 28 NB patients as well as clinical and follow-up data. The number of CTCs among the different risk groups were significantly different (p = .0208, Kruskal-Wallis test). Patients with metastasis had more CTCs than those without metastasis (p .0001, Mann-Whitney test). Patients with ≥3 CTCs per 4 ml of peripheral blood had an increased likelihood of having metastasis (sensitivity, 88.89%; specificity, 78.59%), and patients with ≥10 CTCs per 4 ml of peripheral blood had poorer overall survival. The EpCAM-independent assay along with immunostaining-FISH (i-FISH) described here can detect CTCs in patients with NB at a high sensitivity and may have clinical value for prognosis evaluation and diagnosing metastasis when imaging data are ambiguous.

Published
2018

43. Development and Validation of Novel Diagnostic Models for Biliary Atresia in a Large Cohort of Chinese Patients

Author

Jingying Jiang, Rui Dong, Gong Chen, Yijie Zheng, Shouhua Zhang, Yanlei Huang, Zhen Shen, and Shan Zheng

Subjects
Male, medicine.medical_specialty, genetic structures, Direct bilirubin, Logistic regression, urologic and male genital diseases, Gastroenterology, General Biochemistry, Genetics and Molecular Biology, Nomogram, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Asian People, Biliary atresia, Biliary Atresia, 030225 pediatrics, Internal medicine, medicine, Humans, Neonatal cholestasis, Cholestasis, business.industry, Decision Trees, Infant, Newborn, Infant, Bilirubin, General Medicine, gamma-Glutamyltransferase, medicine.disease, Alkaline Phosphatase, Large cohort, Random forest, Jaundice, Neonatal, Nomograms, 030211 gastroenterology & hepatology, Obstructive jaundice, Female, Gamma-glutamyl transpeptidase, business, Research Paper
Abstract

Background & aims The overlapping features of biliary atresia (BA) and the other forms of neonatal cholestasis (NC) with different causes (non-BA) has posed challenges for the diagnosis of BA. This study aimed at developing new and better diagnostic models for BA. Methods We retrospectively analyzed data from 1728 newborn infants with neonatal obstructive jaundice (NOJ). New prediction models, including decision tree (DT), random forest (RF), and multivariate logistic regression-based nomogram for BA were created and externally validated in an independent set of 508 infant patients. Results Fiver predictors, including gender, weight, direct bilirubin (DB), alkaline phosphatase (ALP), and gamma-glutamyl transpeptidase (GGT) were significantly different between the BA and non-BA groups (P, Highlights • A novel nomogram has been established for prediction of biliary atresia (BA). • Its discriminatory ability is significantly improved compared with GGT alone. • It holds promise for clinical application for better diagnosis of BA.

Published
2018

44. MicroRNA-29b/142-5p contribute to the pathogenesis of biliary atresia by regulating the IFN-γ gene

Author

Yanlei Huang, Shan Zheng, Yifan Yang, Rui Dong, Gong Chen, Yijie Zheng, Chao Zheng, Xiaoying Luo, Zhu Jin, and Zhen Shen

Subjects
0301 basic medicine, Male, Cancer Research, Immunology, Biology, Jurkat cells, DNA methyltransferase, Article, 03 medical and health sciences, Cellular and Molecular Neuroscience, Interferon-gamma, Jurkat Cells, Alu Elements, Biliary Atresia, microRNA, Humans, DNA (Cytosine-5-)-Methyltransferases, lcsh:QH573-671, Promoter Regions, Genetic, Regulation of gene expression, lcsh:Cytology, Infant, Promoter, Cell Biology, Methylation, DNA Methylation, Molecular biology, MicroRNAs, 030104 developmental biology, Long Interspersed Nucleotide Elements, Gene Expression Regulation, DNA methylation, DNMT1, Female
Abstract

Biliary atresia is one of the most common liver disease in infancy. The cause and pathogenesis remain largely unknown. This study aimed to investigate the potential regulatory effect of miR-29b/142-5p on IFN-γ gene methylation. miRNAs microarray was performed on four pairs of liver and blood specimens from biliary atresia and choledochal cysts. We found the overexpression of miR-142-5p and mRNA level of DNA methyltransferase (DNMT) 1, and miR-29b and DNMT3a/DNMT3b were significantly negatively correlated in biliary atresia livers. Meanwhile, the methylation of the LINE-1, ALU and SAT2 repetitive sequences and the IFN-γ promoter was lower, but the expression of IFN-γ was upregulated. After transfected with DNMTs siRNAs, downregulation of DNMTs exerted a significant hypomethylating effect on the repetitive sequences, which led to upregulation of IFN-γ in Jurkat cells. The direct interactions between miR-29b and DNMT3a/3b, and miR-142-5p and DNMT1 were identified using luciferase reporter assays. By transfecting mimics of miR-29b/142-5p into Jurkat cells, we found overexpression of miR-29b/142-5p markedly suppressed expression of DNMTs. Furthermore, the methylation of repetitive sequences and the IFN-γ promoter region were remarkably downregulated, and with elevated IFN-γ expression. After transfecting the miRNA inhibitors, the levels of DNMTs and the methylation of the IFN-γ gene promoter region was upregulated, while levels of IFN-γ were markedly suppressed. Our study suggested that miRNA-29b/142-5p overexpression and targeted inhibition of DNMTs expression resulted in decreased overall gene methylation and overexpression of the methylation-sensitive IFN-γ gene.

Published
2018

45. Development and validation of a novel diagnostic nomogram model based on tumor markers for assessing cancer risk of pulmonary lesions: A multicenter study in Chinese population

Author

Andrew Soh, Yijie Zheng, Jie Guan, Agim Beshiri, Xun Li, Qiang Du, Chun Huang, Liyu Wang, Zhiyan Li, Wei Zhang, Huiling Wang, Qinghao Liu, Yiyong Yao, Cunling Yan, San-Gang Wu, Qunji Zhang, Qi Wang, and Y. Hou

Subjects
Adult, Male, Oncology, China, Cancer Research, medicine.medical_specialty, Lung Neoplasms, Multivariate analysis, Decision Support Techniques, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, medicine, Humans, 030212 general & internal medicine, Lung cancer, Lung, Aged, Tumor marker, business.industry, Univariate, Middle Aged, Nomogram, medicine.disease, Tumor Burden, Nomograms, Logistic Models, 030220 oncology & carcinogenesis, Cohort, Biomarker (medicine), Female, Tomography, X-Ray Computed, business, Cancer risk, Biomarkers
Abstract

This study aimed to build a valid diagnostic nomogram for assessing the cancer risk of the pulmonary lesions identified by chest CT.A total of 691 patients with pulmonary lesions were recruited from three centers in China. The cut-off value for each tumor marker was confirmed by minimum P value method with 1000 bootstrap replications. The nomogram was based on the predictive factors identified by univariate and multivariate analysis. The predictive performance of the nomogram was measured by concordance index and calibrated with 1000 bootstrap samples to decrease the overfit bias. We also evaluated the net benefit of the nomogram via decision curve analysis. Finally, the nomogram was validated externally using a separate cohort of 305 patients enrolled from two additional institutions.The cut-off for CEA, SCC, CYFRA21-1, pro-GRP, and HE4 was 4.8 ng/mL, 1.66 ng/mL, 1.83 ng/mL, 56.55 pg/mL, and 63.24Lpmol/L, respectively. Multivariate logistic regression model (LRM) identified tumor size, CEA, SCC, CYFRA21-1, pro-GRP, and HE4 as independent risk factors for lung cancer. The nomogram based on LRM coefficients showed concordance index of 0.901 (95% CI: 0.842-0.960; P 0.001) for lung cancer in the training set and 0.713 (95% CI: 0.599-0.827; P 0.001) in the validation set. Decision curve analysis reported a net benefit of 87.6% at 80% threshold probability superior to the baseline model.Our diagnostic nomogram provides a useful tool for assessing the cancer risk of pulmonary lesions identified in CT screening test.

Published
2018

46. Serum reference interval of ARCHITECT alpha-fetoprotein in healthy Chinese Han adults: Sub-analysis of a prospective multi-center study

Author

Zhuping Fan, Wei Chen, Jian Hu, Ruilin Han, Yijie Zheng, Ying Huang, Jiaqi Song, Agim Beshiri, Xiaoqin Wang, Wei Zhang, Lianhua Wei, Jia Yang, Cunling Yan, and Andrew Soh

Subjects
Adult, Male, Serum, China, 030213 general clinical medicine, Pediatrics, medicine.medical_specialty, Percentile, Carcinoma, Hepatocellular, Clinical Biochemistry, 03 medical and health sciences, Sex Factors, 0302 clinical medicine, Reference Values, Interquartile range, Biomarkers, Tumor, Ethnicity, Humans, Medicine, Statistical analysis, Prospective Studies, Chinese han, Immunoassay, business.industry, Liver Neoplasms, Age Factors, Chinese adults, General Medicine, Middle Aged, Reference Standards, medicine.disease, digestive system diseases, Cross-Sectional Studies, 030220 oncology & carcinogenesis, Multi center study, Hepatocellular carcinoma, Female, alpha-Fetoproteins, business, Alpha-fetoprotein
Abstract

Objectives Alpha-fetoprotein (AFP) has been widely used in clinical practice for decades. However, large-scale survey of serum reference interval for ARCHITECT AFP is still absent in Chinese population. This study aimed to measure serum AFP levels in healthy Chinese Han subjects, which is a sub-analysis of an ongoing prospective, cross-sectional, multi-center study ( ClinicalTrials.gov Identifier: NCT03047603 ). Methods This analysis included a total of 530 participants (41.43 ± 12.14 years of age on average, 48.49% males), enrolled from 5 regional centers. Serum AFP level was measured by ARCHITECT immunoassay. Statistical analysis was performed using SAS 9.4 and R software. Results AFP distribution did not show significant correlation with age or sex. The overall median and interquartile range of AFP was 2.87 (2.09, 3.83) ng/mL. AFP level did not show a trend of increasing with age. The new reference interval was 2.0–7.07 ng/mL (LOQ– 97.5th percentiles). Conclusions The reference interval for ARCHITECT AFP is updated with the data of adequate number of healthy Han adults. This new reference interval is more practical and applicable in Chinese adults.

Published
2018

47. Development and validation of a novel diagnostic model for assessing lung cancer metastasis in a Chinese population based on multicenter real-world data

Author

Cunling Yan, Zhiyan Li, Wei Zhang, San-Gang Wu, Qiang Du, Gang Zeng, Hui Zhang, Huiling Wang, Chun Huang, Xun Li, Jie Guan, Yiyong Yao, Y. Hou, Yijie Zheng, and Lixin Wang

Subjects
0301 basic medicine, Oncology, medicine.medical_specialty, Treatment of lung cancer, Metastasis, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Diagnostic model, medicine, Lung cancer, Lymph node, Original Research, CT imaging pulmonary lesions, business.industry, multicenter real-world, nomogram models, Nomogram, medicine.disease, 030104 developmental biology, medicine.anatomical_structure, Cancer Management and Research, 030220 oncology & carcinogenesis, lung cancer metastasis, biomarker, Biomarker (medicine), business, Real world data
Abstract

Yiyong Yao,1,* Cunling Yan,2,* Wei Zhang,3,* San-Gang Wu,4,* Jie Guan,2,* Gang Zeng,1,* Qiang Du,1 Chun Huang,1 Hui Zhang,5 Huiling Wang,6 Yanfeng Hou,2 Zhiyan Li,2 Lixin Wang,7 Yijie Zheng,8 Xun Li9 1Department of Respiratory Medicine, Suzhou Municipal Hospital, Nanjing Medical University, Suzhou, People’s Republic of China; 2Department of Clinical Laboratory, Peking University First Hospital, Beijing, People’s Republic of China; 3Department of Biostatistics, School of Public Health, Fudan University, Shanghai, People’s Republic of China; 4Department of Radiation Oncology, Xiamen Cancer Hospital, The First Affiliated Hospital of Xiamen University, Xiamen, People’s Republic of China; 5Department of Laboratory, Suzhou Municipal Hospital, Nanjing Medical University, Suzhou, People’s Republic of China; 6Department of Respiratory Medicine, The Second Affiliated Hospital, Dalian Medical University, Dalian, People’s Republic of China; 7Department of TCM and Western Medicine, Shanghai Pulmonary Hospital Affiliated to Tongji University, Shanghai, People’s Republic of China; 8Medical Scientific Affairs, Abbott Diagnostics Division, Abbott Laboratories, Asian Pacific Group, Shanghai, People’s Republic of China; 9Department of Laboratory Medicine, The First Affiliated Hospital, School of Medicine, Xiamen University, Xiamen, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yijie ZhengMedical Scientific Affairs, Abbott Diagnostics Division, Abbott Laboratories, 388 Nanjing Xi Rd, Shanghai 200000, People’s Republic of ChinaTel +86 212 315 4961Fax +86 216 334 6331Email yijiezheng2015@163.comXun LiDepartment of Laboratory Medicine, The First Affiliated Hospital, School of Medicine, Xiamen University, 55 Zhenhai Rd, Xiamen, Fujian 361003, People’s Republic of ChinaTel + 865922139507Email xli2001@xmu.edu.cnBackground: Accurate disease staging plays an important role in lung cancer’s clinical management. However, due to the limitation of the CT scan, it is still an unmet medical need in practice. In the present study, we attempted to develop diagnostic models based on biomarkers and clinical parameters for assessing lung cancer metastasis.Methods: This study consisted of 799 patients with pulmonary lesions from three regional centers in China. It included 274 benign lesions patients, 326 primary lung cancer patients without metastasis, and 199 advanced lung cancer patients with lymph node or organ metastasis. The patients were divided into nodules group and masses group according to tumor size.Results: Four nomogram models based on patient characteristics and tumor biomarkers were developed and evaluated for patients with nodules and masses, respectively. In patients with pulmonary nodules, the AUC to identify metastatic lung cancer from unidentified nodules (including benign nodules and lung cancer, model 1) reached 0.859 (0.827–0.887, 95% CI). Model 2 was used to predict metastasis in patients with lung cancer with AUC of 0.838 (0.795–0.876, 95% CI). In patients with pulmonary masses, the AUC to identify metastatic lung cancer from unidentified masses (model 3) reached 0.773 (0.717–0.823, 95% CI). Model 4 was used to predict metastasis in patients with lung cancer and AUC reached 0.731 (0.771–0.793, 95% CI). Decision curve analysis corroborated good clinical applicability of the nomograms in predicting metastasis.Conclusion: All new models demonstrated promising discrimination, allowing for estimating the risk of lymph node or organ metastasis of lung cancer. Such integration of blood biomarker testing with CT imaging results will be an efficient and effective approach to benefit the accurate staging and treatment of lung cancer.Keywords: CT imaging pulmonary lesions, biomarker, nomogram models, lung cancer metastasis, multicenter real-world

Published
2019

48. Red blood cell distribution width and platelet counts are independent prognostic factors and improve the predictive ability of IPI score in diffuse large B-cell lymphoma patients

Author

Yangyang Ding, Linhui Hu, Yijie Zheng, Hailong Xia, Jun Liu, Zhimin Zhai, Lianfang Pu, Jingrong Li, Yafeng Li, Shudao Xiong, Qianle Gui, Manman Li, and Huimin Zheng

Subjects
Blood Platelets, Erythrocyte Indices, Male, 0301 basic medicine, Oncology, Cancer Research, medicine.medical_specialty, Erythrocytes, Multivariate analysis, lcsh:RC254-282, 03 medical and health sciences, 0302 clinical medicine, Surgical oncology, Internal medicine, hemic and lymphatic diseases, Diffuse large B-cell lymphoma (DLBCL), Biomarkers, Tumor, Genetics, medicine, Humans, Red blood cell distribution width (RDW), Retrospective Studies, Platelet Count, business.industry, Cancer, Red blood cell distribution width, Middle Aged, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Prognosis, medicine.disease, Platelet count (PLT), Lymphoma, Nomograms, 030104 developmental biology, Case-Control Studies, 030220 oncology & carcinogenesis, Cohort, Female, Rituximab, Lymphoma, Large B-Cell, Diffuse, business, Diffuse large B-cell lymphoma, Research Article, medicine.drug
Abstract

BackgroundElevated red blood cell distribution width (RDW) and decreased platelet count (PLT) can be clinically relevant to the prognosis in cancer patients. However, their prognostic values in patients with diffuse large B-cell lymphoma (DLBCL) need to be further explored.MethodsHealthy donors (n = 130) and patients with DLBCL (n = 349) were included and evaluated retrospectively in this study. The prognostic influence of clinical and pathological factors including RDW and PLT on overall survival (OS) and progression-free survival (PFS) were studied by Kaplan-Meier curves. To evaluate the independent prognostic relevance of RDW and PLT, univariate and multivariate Cox proportional hazards regression models were applied. The adjusted IPI model was established based on the results of multivariate analysis, and verified by Harrell’s C statistical analysis.ResultsKaplan-Meier curves indicated that an elevated RDW value and thrombocytopenia are poor factors for OS (P P = 0.006) and PFS (P = 0.003,P P = 0.003) and decreased PLT count (HR =1.749, 95%CI = 1.010–3.028,P = 0.046) were both independent prognostic factors. The c-index of IPI and NCCN-IPI were increased when RDW level and PLT were supplemented in our cohort.ConclusionsOur study shows that elevated RDW level and decreased PLT are independent poor prognostic factors in newly diagnosed DLBCL patients. Adding RDW and PLT to the IPI score may improve its predictive ability, and the adjusted IPI may be more powerful in predicting the survival of DLBCL patients in the rituximab era.

Published
2019

49. Diagnostic Value and Effectiveness of an Artificial Neural Network in Biliary Atresia

Author

Song Sun, Shan Zheng, ShuYang Dai, Rui Dong, Jingying Jiang, Gong Chen, Jia Liu, and YiJie Zheng

Subjects
Pediatrics, medicine.medical_specialty, Receiver operating characteristic, Artificial neural network, business.industry, Overfitting, medicine.disease, Cross-validation, Binary classification, Biliary atresia, Informed consent, Medicine, business, Dropout (neural networks)
Abstract

Purpose: Biliary Atresia (BA) is a devastating pediatric liver disease. Therefore, early diagnosis is important for timely surgical intervention and better prognosis. Using clinical parameters for non-invasive and efficient diagnosis of BA, we aimed to apply and evaluate the effectiveness of an artificial neural network (ANN). Methods: A total of 2,384 obstructive jaundice patients who were suspected of BA from 2012 to 2017 were screened for eligibility. Furthermore, 137 clinical parameters from all patients were screened for data integrity. A standard binary classification feed-forward ANN, consisting of an input layer, 3 hidden layers, and an output layer, was employed. To avoid overfitting, a dropout method was included for the hidden layer. The network was trained and validated for accuracy. As an independent predictor, gamma-glutamyl transpeptidase (GGT) level was used as a comparison to assess the effectiveness of the network by ROC curves. Results: A total of 46 qualified parameters and 1,452 patients were included for ANN modeling, including 1274 patients (87.7%) in the BA group and 178 patients (12.3%) in the non-BA group. Total bilirubin, direct bilirubin, and GGT were the most significant indicators in BA patients. The network consisted of an input layer, 3 hidden layers with 12 neurons each, and an output layer with one neuron. The network showed good predictive property with a high Area under curve (AUC) (0.967), a sensitivity of 97.2% and a specificity of 91.0%. Five-fold cross validation for the ANN model showed a mean accuracy (95% CI) for the training data was 93.2% (92.5%-93.9%), while the mean accuracy to the validation data was 88.6% (88.2%-88.9%). Conclusions: The high accuracy and efficiency demonstrated by the ANN model is promising in the non-invasive diagnosis of BA patients. As a result, the application of ANN as a low-cost and independent expert diagnosis system for medical institutions at different levels of healthcare should be considered for the future. Funding Statement: This study received financial support from Shanghai Key Disciplines (no.2017ZZ02022), National Natural Science Foundation of China (no. 81770519, no. 81771633, no. 81873545 and no. 81974059), The Science Foundation of Shanghai (no. 14411969800, no. 16411952200, no. 18411969100 and 19ZR1406600), and Children's National Medical Center (no. EK1125180104, no. EKYY20180204, EK112520180211 and no. EK112520180310). Declaration of Interests: The authors stated: "None." Ethics Approval Statement: This study was approved by the Ethics Committee of the Children’s Hospital of Fudan University. Informed consent was obtained from the legal guardians of all patients before enrollment in the study.

Published
2019

50. MOESM1 of Red blood cell distribution width and platelet counts are independent prognostic factors and improve the predictive ability of IPI score in diffuse large B-cell lymphoma patients

Author

Manman Li, Hailong Xia, Huimin Zheng, Yafeng Li, Liu, Jun, Linhui Hu, Jingrong Li, Yangyang Ding, Lianfang Pu, Qianle Gui, Yijie Zheng, Zhimin Zhai, and Shudao Xiong

Abstract

Additional file 1: Figure S1. ROC curves analysis for RDW(a) and PLT(b) in the training set (N=200) of patients with DLBCL. Figure S2. Correlation between RDW, PLT and WBC, NLR, ALB, HB levels in all patients with DLBCL. Correlation between RDW and WBC(a), NLR(b), ALB(c) and HB(d) levels in all patients with DLBCL. (2)Correlation between PLT and WBC(e), NLR(f), ALB(g) and HB(h) levels in all patients with DLBCL. Figure S3. Correlation between HB and WBC(a), NLR(b) and ALB(c) levels in all patients with DLBCL. Figure S4. Survival curves according to RDW and PLT levels in the training, overall and testing set. (1)OS(a,c) and PFS(b,d) according to the RDW and PLT levels in the training set. (2)OS(e,g) and PFS(f,h) according to the RDW and PLT levels in the overall set. (3)OS(i,k) and PFS(j,l) according to the RDW and PLT levels in the testing set. Figure S5. Kaplan–Meier curves for OS and PFS comparing low (12 g/dL for women, >13 g/dL for men) Hb levels in the training(a,b), overall(c,d), testing set(e,f), CHOP cohort(g,h) and R-CHOP cohort(i,j). Figure S6. Survival curves according to RDW and PLT levels in CHOP cohort and the whole cohort with IPI score 3-5. (1) OS(a,c) and PFS(b,d) according to RDW levels and PLT counts in CHOP cohort with IPI score 3-5. (2) OS(e,g) and PFS(f,h) according to RDW levels and PLT counts in the whole cohort with IPI score 3-5. Table S1. Clinical characteristics of healthy donors and DLBCL patients. Table S2. Baseline clinical characteristics of patients with DLBCL. Table S3. Univariate analysis of clinicopathological parameters for the prediction of OS and PFS in CHOP cohort patients(n=175). Table S4. Univariate analysis of clinicopathological parameters for the prediction of OS and PFS in RCHOP cohort patients(n=174).

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results