1
|
Xu T, Gao W, Zhu L, Chen W, Niu C, Yin W, Ma L, Zhu X, Ling Y, Gao S, Liu L, Jiao N, Chen W, Zhang G, Zhu R, Wu D. NAFLDkb: A Knowledge Base and Platform for Drug Development against Nonalcoholic Fatty Liver Disease. J Chem Inf Model 2024; 64:2817-2828. [PMID: 37167092 DOI: 10.1021/acs.jcim.3c00395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease with a broad spectrum of histologic manifestations. The rapidly growing prevalence and the complex pathologic mechanisms of NAFLD pose great challenges for treatment development. Despite tremendous efforts devoted to drug development, there are no FDA-approved medicines yet. Here, we present NAFLDkb, a specialized knowledge base and platform for computer-aided drug design against NAFLD. With multiperspective information curated from diverse source materials and public databases, NAFLDkb presents the associations of drug-related entities as individual knowledge graphs. Practical drug discovery tools that facilitate the utilization and expansion of NAFLDkb have also been implemented in the web interface, including chemical structure search, drug-likeness screening, knowledge-based repositioning, and research article annotation. Moreover, case studies of a knowledge graph repositioning model and a generative neural network model are presented herein, where three repositioning drug candidates and 137 novel lead-like compounds were newly established as NAFLD pharmacotherapy options reusing data records and machine learning tools in NAFLDkb, suggesting its clinical reliability and great potential in identifying novel drug-disease associations of NAFLD and generating new insights to accelerate NAFLD drug development. NAFLDkb is freely accessible at https://www.biosino.org/nafldkb and will be updated periodically with the latest findings.
Collapse
Affiliation(s)
- Tingjun Xu
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
- Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 LingLing Road, Shanghai 200032, P. R. China
| | - Wenxing Gao
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Lixin Zhu
- Guangdong Institute of Gastroenterology; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases; Biomedical Innovation Center, Sun Yat-sen University, Guangzhou 510655, P. R. China
- Department of General Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou 510655, P. R. China
| | - Wanning Chen
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Chaoqun Niu
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China
| | - Wenjing Yin
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Liangxiao Ma
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China
| | - Xinyue Zhu
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Yunchao Ling
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China
| | - Sheng Gao
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Lei Liu
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Na Jiao
- National Clinical Research Center for Child Health, the Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, P. R. China
| | - Weiming Chen
- Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 LingLing Road, Shanghai 200032, P. R. China
| | - Guoqing Zhang
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China
| | - Ruixin Zhu
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Dingfeng Wu
- National Clinical Research Center for Child Health, the Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, P. R. China
| |
Collapse
|
2
|
Bai X, Bao Y, Bei S, Bu C, Cao R, Cao Y, Cen H, Chao J, Chen F, Chen H, Chen K, Chen M, Chen M, Chen M, Chen Q, Chen R, Chen S, Chen T, Chen X, Chen X, Cheng Y, Chu Y, Cui Q, Dong L, Du Z, Duan G, Fan S, Fan Z, Fang X, Fang Z, Feng Z, Fu S, Gao F, Gao G, Gao H, Gao W, Gao X, Gao X, Gao X, Gong J, Gong J, Gou Y, Gu S, Guo AY, Guo G, Guo X, Han C, Hao D, Hao L, He Q, He S, He S, Hu W, Huang K, Huang T, Huang X, Huang Y, Jia P, Jia Y, Jiang C, Jiang M, Jiang S, Jiang T, Jiang X, Jin E, Jin W, Kang H, Kang H, Kong D, Lan L, Lei W, Li CY, Li C, Li C, Li H, Li J, Li J, Li L, Li P, Li R, Li X, Li Y, Li Y, Li Z, Liao X, Lin S, Lin Y, Ling Y, Liu B, Liu CJ, Liu D, Liu GH, Liu L, Liu S, Liu W, Liu X, Liu X, Liu Y, Liu Y, Lu M, Lu T, Luo H, Luo H, Luo M, Luo S, Luo X, Ma L, Ma Y, Mai J, Meng J, Meng X, Meng Y, Meng Y, Miao W, Miao YR, Ni L, Nie Z, Niu G, Niu X, Niu Y, Pan R, Pan S, Peng D, Peng J, Qi J, Qi Y, Qian Q, Qin Y, Qu H, Ren J, Ren J, Sang Z, Shang K, Shen WK, Shen Y, Shi Y, Song S, Song T, Su T, Sun J, Sun Y, Sun Y, Sun Y, Tang B, Tang D, Tang Q, Tang Z, Tian D, Tian F, Tian W, Tian Z, Wang A, Wang G, Wang G, Wang J, Wang J, Wang P, Wang P, Wang W, Wang Y, Wang Y, Wang Y, Wang Y, Wang Z, Wei H, Wei Y, Wei Z, Wu D, Wu G, Wu S, Wu S, Wu W, Wu W, Wu Z, Xia Z, Xiao J, Xiao L, Xiao Y, Xie G, Xie GY, Xie J, Xie Y, Xiong J, Xiong Z, Xu D, Xu S, Xu T, Xu T, Xue Y, Xue Y, Yan C, Yang D, Yang F, Yang F, Yang H, Yang J, Yang K, Yang N, Yang QY, Yang S, Yang X, Yang X, Yang X, Yang YG, Ye W, Yu C, Yu F, Yu S, Yuan C, Yuan H, Zeng J, Zhai S, Zhang C, Zhang F, Zhang G, Zhang M, Zhang P, Zhang Q, Zhang R, Zhang S, Zhang W, Zhang W, Zhang W, Zhang X, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang YE, Zhang Y, Zhang Z, Zhang Z, Zhao D, Zhao F, Zhao G, Zhao M, Zhao W, Zhao W, Zhao X, Zhao Y, Zhao Y, Zhao Z, Zheng X, Zheng Y, Zhou C, Zhou H, Zhou X, Zhou X, Zhou Y, Zhou Y, Zhu J, Zhu L, Zhu R, Zhu T, Zong W, Zou D, Zuo Z. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2024. Nucleic Acids Res 2024; 52:D18-D32. [PMID: 38018256 PMCID: PMC10767964 DOI: 10.1093/nar/gkad1078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/12/2023] [Accepted: 10/27/2023] [Indexed: 11/30/2023] Open
Abstract
The National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support the global academic and industrial communities. With the rapid accumulation of multi-omics data at an unprecedented pace, CNCB-NGDC continuously expands and updates core database resources through big data archiving, integrative analysis and value-added curation. Importantly, NGDC collaborates closely with major international databases and initiatives to ensure seamless data exchange and interoperability. Over the past year, significant efforts have been dedicated to integrating diverse omics data, synthesizing expanding knowledge, developing new resources, and upgrading major existing resources. Particularly, several database resources are newly developed for the biodiversity of protists (P10K), bacteria (NTM-DB, MPA) as well as plant (PPGR, SoyOmics, PlantPan) and disease/trait association (CROST, HervD Atlas, HALL, MACdb, BioKA, BioKA, RePoS, PGG.SV, NAFLDkb). All the resources and services are publicly accessible at https://ngdc.cncb.ac.cn.
Collapse
|
3
|
Yang J, Cui Y, Yu D, Zhang G, Cao R, Gu Z, Dai G, Wu X, Ling Y, Yi C, Sun X, Sun B, Lin X, Zhang Y, Zhao GP, Li Y, Pan YH, Li H. A non-coding A-to-U Kozak site change related to the high transmissibility of Alpha, Delta, and Omicron VOCs. Mol Biol Evol 2023:msad142. [PMID: 37341536 DOI: 10.1093/molbev/msad142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 04/30/2023] [Accepted: 05/30/2023] [Indexed: 06/22/2023] Open
Abstract
Three prevalent SARS-CoV-2 Variants of Concern (VOCs) emerged and caused epidemic waves. It is essential to uncover advantageous mutations that cause the high transmissibility of VOCs. However, viral mutations are tightly linked so traditional population genetic methods, including machine-learning based methods, cannot reliably detect mutations conferring a fitness advantage. In this study, we developed an approach based on the sequential occurrence order of mutations and the accelerated furcation rate in the pandemic-scale phylogenomic tree. We analyzed 3,777,753 high-quality SARS-CoV-2 genomic sequences and the epidemiology metadata using the Coronavirus GenBrowser. We found that two non-coding mutations at the same position (g.a28271-/u) may be crucial to the high transmissibility of Alpha, Delta, and Omicron VOCs although the non-coding mutations alone cannot increase viral transmissibility. Both mutations cause an A-to-U change at the core position -3 of the Kozak sequence of the N gene and significantly reduce the protein expression ratio of ORF9b to N. Using a convergent evolutionary analysis, we found that g.a28271-/u, S:p.P681H/R, and N:p.R203K/M occur independently on three VOC lineages, suggesting that coordinated changes of S, N, and ORF9b proteins are crucial to high viral transmissibility. Our results provide new insights into high viral transmissibility co-modulated by advantageous non-coding and non-synonymous changes.
Collapse
Affiliation(s)
- Jianing Yang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yingmin Cui
- Key Laboratory of Brain Functional Genomics of Ministry of Education, School of Life Science, East China Normal University, Shanghai 200062, China
| | - Dalang Yu
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Guoqing Zhang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ruifang Cao
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhili Gu
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Guangyi Dai
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoxian Wu
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Yunchao Ling
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chunyan Yi
- Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaoyu Sun
- Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Bing Sun
- Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xin Lin
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yu Zhang
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
| | - Guo-Ping Zhao
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200032, China
- School of Life and Health Sciences, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Yixue Li
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- Guangzhou Laboratory, Guangzhou 510005, China
| | - Yi-Hsuan Pan
- Key Laboratory of Brain Functional Genomics of Ministry of Education, School of Life Science, East China Normal University, Shanghai 200062, China
| | - Haipeng Li
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| |
Collapse
|
4
|
Ren H, Ling Y, Cao R, Wang Z, Li Y, Huang T. Early warning of emerging infectious diseases based on multimodal data. Biosaf Health 2023; 5:S2590-0536(23)00074-5. [PMID: 37362865 PMCID: PMC10245235 DOI: 10.1016/j.bsheal.2023.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 05/18/2023] [Accepted: 05/31/2023] [Indexed: 06/28/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research.
Collapse
Affiliation(s)
- Haotian Ren
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yunchao Ling
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ruifang Cao
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhen Wang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yixue Li
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
- School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024 China
- Guangzhou Laboratory, Guangzhou 510005, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200433, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| |
Collapse
|
5
|
Cao R, Ling Y, Meng J, Jiang A, Luo R, He Q, Li A, Chen Y, Zhang Z, Liu F, Li Y, Zhang G. SMDB: a Spatial Multimodal Data Browser. Nucleic Acids Res 2023:7175352. [PMID: 37216588 DOI: 10.1093/nar/gkad413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/01/2023] [Accepted: 05/05/2023] [Indexed: 05/24/2023] Open
Abstract
Understanding the relationship between fine-scale spatial organization and biological function necessitates a tool that effectively combines spatial positions, morphological information, and spatial transcriptomics (ST) data. We introduce the Spatial Multimodal Data Browser (SMDB, https://www.biosino.org/smdb), a robust visualization web service for interactively exploring ST data. By integrating multimodal data, such as hematoxylin and eosin (H&E) images, gene expression-based molecular clusters, and more, SMDB facilitates the analysis of tissue composition through the dissociation of two-dimensional (2D) sections and the identification of gene expression-profiled boundaries. In a digital three-dimensional (3D) space, SMDB allows researchers to reconstruct morphology visualizations based on manually filtered spots or expand anatomical structures using high-resolution molecular subtypes. To enhance user experience, it offers customizable workspaces for interactive exploration of ST spots in tissues, providing features like smooth zooming, panning, 360-degree rotation in 3D and adjustable spot scaling. SMDB is particularly valuable in neuroscience and spatial histology studies, as it incorporates Allen's mouse brain anatomy atlas for reference in morphological research. This powerful tool provides a comprehensive and efficient solution for examining the intricate relationships between spatial morphology, and biological function in various tissues.
Collapse
Affiliation(s)
- Ruifang Cao
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Yunchao Ling
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Jiayue Meng
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Ao Jiang
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Ruijin Luo
- Shanghai Southgene Technology Co., Ltd., Shanghai 201203, China
| | - Qinwen He
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou 215123, China
| | - Yujie Chen
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Zoutao Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Feng Liu
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Yixue Li
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
- Guangzhou Laboratory, Guangzhou 510005, China
| | - Guoqing Zhang
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| |
Collapse
|
6
|
Bao Y, He L, Miao B, Zhong Z, Lu G, Bai Y, Liang Q, Ling Y, Ji P, Su B, Zhao GP, Wu H, Zhang W, Wang Y, Chen Y, Xu J. BBIBP-CorV vaccination accelerates anti-viral antibody responses in heterologous Omicron infection: A retrospective observation study in Shanghai. Vaccine 2023; 41:3258-3265. [PMID: 37085449 DOI: 10.1016/j.vaccine.2023.03.070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/29/2023] [Accepted: 03/31/2023] [Indexed: 04/23/2023]
Abstract
OBJECTIVES To investigate how BBIBP-CorV vaccination affecting antibody responses upon heterologous Omicron infection. METHODS 440 Omicron-infected patients were recruited in this study. Antibodies targeting SARS-CoV-2 spike protein receptor binding domain (RBD) and nucleoprotein of both wild-type (WT) and Omicron were detected by ELISA. The clinical relevance was further analyzed. RESULTS BBIBP-CorV vaccinated patients exhibited higher anti-RBD IgG levels targeting both WT and Omicron than non-vaccinated patients at different stages. By using a 3-day moving average analysis, we found that BBIBP-CorV vaccinated patients exhibited the increases in both anti-WT and Omicron RBD IgG from the onset and reached the plateau at Day 8 whereas those in non-vaccinated patients remained low during the disease. Significant increase in anti-WT RBD IgA was observed only in vaccinated patients. anti-Omicron RBD IgA levels remained low in both vaccinated and non-vaccinated patients. Clinically, severe COVID-19 only occurred in non-vaccinated group. anti-RBD IgG and IgA targeting both WT and Omicron were negatively correlated with virus load, hospitalization days and virus elimination in vaccinated patients. CONCLUSIONS BBIBP-CorV vaccination effectively reduces the severity of Omicron infected patients. The existence of humoral memory responses established through BBIBP-CorV vaccination facilitates to induce rapid recall antibody responses when encountering SARS-CoV-2 variant infection.
Collapse
Affiliation(s)
- Yujie Bao
- Department of Infectious Diseases, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Liheng He
- Shanghai Institute of Immunology, Department of Microbiology and Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Benjie Miao
- Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Zhengrong Zhong
- Department of Clinical Diagnosis, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Guanzhu Lu
- Department of Infectious Diseases, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Yupan Bai
- Department of Infectious Diseases, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Qiming Liang
- Shanghai Institute of Immunology, Department of Microbiology and Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yunchao Ling
- Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Ping Ji
- Shanghai Institute of Immunology, Department of Microbiology and Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Bing Su
- Shanghai Institute of Immunology, Department of Microbiology and Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Guo-Ping Zhao
- Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200032, China
| | - Hao Wu
- Department of Otorhinolaryngology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Wenhong Zhang
- Department of Infectious Diseases, National Medical Center for Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, Huashan Hospital, Fudan University, Shanghai China
| | - Ying Wang
- Shanghai Institute of Immunology, Department of Microbiology and Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases (20dz2261100), Shanghai 200025, China; Key Laboratory of Parasite and Vector Biology, Ministry of Health, School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Institute of Virology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| | - Yingying Chen
- Shanghai Institute of Immunology, Department of Microbiology and Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Key Laboratory of Parasite and Vector Biology, Ministry of Health, School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Jie Xu
- Department of Infectious Diseases, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.
| |
Collapse
|
7
|
Tian BS, Ling Y, Lyu JW, Ye L, Gu B. [A retrospective analysis of clinical characteristics and prognostic factors for 152 cases of Staphylococcus aureus bloodstream infection]. Zhonghua Yu Fang Yi Xue Za Zhi 2023; 57:241-246. [PMID: 36797583 DOI: 10.3760/cma.j.cn112150-20220221-00161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
To understand the clinical characteristics of Staphylococcus aureus bloodstream infection and the main risk factors affecting clinical prognosis, providing a reference for clinical prevention and control of Staphylococcus aureus bloodstream infection. In this study, the clinical data of 152 patients with Staphylococcus aureus bloodstream infection admitted to Guangdong Provincial People's Hospital from January 2019 to December 2021 were retrospectively analyzed by reviewing the electronic medical record system, including underlying diseases, clinical characteristics, risk factors, and bacterial resistance. Statistical methods such as Chi-Squared Test and t Test were used to analyze the related risk factors that may affect the clinical characteristics and prognosis of patients with Staphylococcus aureus and methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infection, then the variables with P<0.05 in univariate analysis were included in the multivariate logistic regression model to analyze the independent risk factors of poor prognosis. The results showed among 152 patients with Staphylococcus aureus bloodstream infection, 50 patients (32.89%) were infected with MRSA. In comparison, 102 patients (67.11%) were infected with methicillin-sensitive Staphylococcus aureus (MSSA). Except for rifampicin, the resistance rate of MRSA to commonly used antibiotics was all higher than that of MSSA, and the difference was statistically significant (Chi-square values were 8.272, 11.972, 4.998, 4.776, respectively;all P-values are less than 0.05). Strains resistant to vancomycin, linezolid, and quinupristin/dalfopristin were not found. In the MRSA group, indwelling catheter and drainage tube, carbapenems, and β-lactamase inhibitor treatment were significantly higher than the MSSA group. The difference was statistically significant (P<0.05). The incidence of poor prognosis of bloodstream infection in the MRSA group was higher than that in the MSSA group (34.00% vs 13.73%), and the difference was statistically significant (χ2=8.495, P<0.05). No independent risk factors associated with poor prognosis were found in the included patients with MRSA bloodstream infection.Multivariate Logistic regression model analysis showed that solid malignant tumors (OR=13.576, 95%CI: 3.352-54.977, P<0.05), mechanical ventilation (OR=7.468, 95%CI: 1.398-39.884, P<0.05) were the most important independent risk factors for poor prognosis in patients with Staphylococcus aureus bloodstream infection. In summary, the poor prognosis rate of MRSA bloodstream infection is higher than that of MSSA. The clinical evaluation of related risk factors should be strengthened, targeted prevention and control interventions should be taken to improve the prognosis of patients with Staphylococcus aureus bloodstream infection, and the use of antibiotics should be rational and standardized, to control bacterial infection and drug resistance effectively.
Collapse
Affiliation(s)
- B S Tian
- Medical Technology School of Xuzhou Medical University, Xuzhou Key Laboratory of Laboratory Diagnostics, Xuzhou 221004, China Division of Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences,Guangzhou 510080, China
| | - Y Ling
- Division of Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences,Guangzhou 510080, China
| | - J W Lyu
- Medical Technology School of Xuzhou Medical University, Xuzhou Key Laboratory of Laboratory Diagnostics, Xuzhou 221004, China Division of Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences,Guangzhou 510080, China
| | - L Ye
- Division of Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences,Guangzhou 510080, China
| | - B Gu
- Medical Technology School of Xuzhou Medical University, Xuzhou Key Laboratory of Laboratory Diagnostics, Xuzhou 221004, China Division of Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences,Guangzhou 510080, China
| |
Collapse
|
8
|
Wu QG, Zeng LY, Li F, Zhu ZQ, Yin L, Meng XM, Zhang L, Zhang P, Jiang XH, Ling Y, Zhang LJ. Nirmatrelvir increases blood tacrolimus concentration in COVID-19 patients as determined by UHPLC-MS/MS method. Eur Rev Med Pharmacol Sci 2023; 27:818-825. [PMID: 36734723 DOI: 10.26355/eurrev_202301_31083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Transplant recipients have a higher risk of SARS-CoV-2 infection owing to the use of immunosuppressive drugs like tacrolimus (FK506). FK506 and nirmatrelvir (NMV) (an anti-SARS-CoV-2 drug) are metabolized by cytochrome P450 3A4 and may have potential drug-drug interactions. It is important to determine the effect of NMV on FK506 concentrations. PATIENTS AND METHODS Following protein precipitation from blood, FK506 and its internal standard (FK506-13C,2d4) were detected by ultra-high performance liquid chromatography/tandem mass spectrometry (UHPLC-MS/MS). Total 22 blood samples (valley concentrations) from two coronavirus disease 2019 (COVID-19) patients were collected and analyzed for FK506 concentrations. RESULTS Blood levels of FK506 (0.5-100 ng/mL) showed good linearity. The UHPLC-MS/MS method was validated with intra- and inter-batch accuracies of 104.55-107.85%, and 99.52-108.01%, respectively, and precisions of < 15%. Mean blood FK506 concentration was 12.01 ng/mL (range, 3.15-33.1 ng/mL). Five-day co-administration with NMV increased the FK506 concentrations from 3.15 ng/mL to 33.1 ng/mL, returning to 3.36 ng/mL after a 9-day-washout. CONCLUSIONS We developed a simple quantification method for therapeutic drug monitoring of FK506 in patients with COVID-19 using UHPLC-MS/MS with protein precipitation. We found that NMV increased FK506 blood concentration 10-fold. Therefore, it is necessary to re-consider co-administration of FK506 with NMV.
Collapse
Affiliation(s)
- Q-G Wu
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
9
|
Wang Y, Ling Y, Gong J, Zhao X, Zhou H, Xie B, Lou H, Zhuang X, Jin L, Fan S, Zhang G, Xu S. PGG.SV: a whole-genome-sequencing-based structural variant resource and data analysis platform. Nucleic Acids Res 2022; 51:D1109-D1116. [PMID: 36243989 PMCID: PMC9825616 DOI: 10.1093/nar/gkac905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/21/2022] [Accepted: 10/04/2022] [Indexed: 01/30/2023] Open
Abstract
Structural variations (SVs) play important roles in human evolution and diseases, but there is a lack of data resources concerning representative samples, especially for East Asians. Taking advantage of both next-generation sequencing and third-generation sequencing data at the whole-genome level, we developed the database PGG.SV to provide a practical platform for both regionally and globally representative structural variants. In its current version, PGG.SV archives 584 277 SVs obtained from whole-genome sequencing data of 6048 samples, including 1030 long-read sequencing genomes representing 177 global populations. PGG.SV provides (i) high-quality SVs with fine-scale and precise genomic locations in both GRCh37 and GRCh38, covering underrepresented SVs in existing sequencing and microarray data; (ii) hierarchical estimation of SV prevalence in geographical populations; (iii) informative annotations of SV-related genes, potential functions and clinical effects; (iv) an analysis platform to facilitate SV-based case-control association studies and (v) various visualization tools for understanding the SV structures in the human genome. Taken together, PGG.SV provides a user-friendly online interface, easy-to-use analysis tools and a detailed presentation of results. PGG.SV is freely accessible via https://www.biosino.org/pggsv.
Collapse
Affiliation(s)
| | | | | | - Xiaohan Zhao
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China,Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai 201203, China
| | - Hanwen Zhou
- Key Laboratory of Computational Biology, National Genomics Data Center & Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Bo Xie
- Key Laboratory of Computational Biology, National Genomics Data Center & Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Haiyi Lou
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Xinhao Zhuang
- Key Laboratory of Computational Biology, National Genomics Data Center & Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li Jin
- State Key Laboratory of Genetic Engineering, Center for Evolutionary Biology, Collaborative Innovation Center of Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200438, China,Human Phenome Institute, Zhangjiang Fudan International Innovation Center, and Ministry of Education Key Laboratory of Contemporary Anthropology, Fudan University, Shanghai 201203, China
| | | | - Shaohua Fan
- Correspondence may also be addressed to Shaohua Fan.
| | - Guoqing Zhang
- Correspondence may also be addressed to Guoqing Zhang.
| | - Shuhua Xu
- To whom correspondence should be addressed. Tel: +86 21 31246617; Fax: +86 21 31246617;
| |
Collapse
|
10
|
Yuan P, Guo C, Li L, Ling Y, Guo L, Ying J. EP02.01-011 Immune-related Histologic Phenotype in Pretreatment Tumor Biopsy Predicts Efficacy of Neoadjuvant Anti-PD-1 Treatment in Squamous Lung Cancer. J Thorac Oncol 2022. [DOI: 10.1016/j.jtho.2022.07.338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
11
|
Xu F, Wang Y, Ling Y, Zhou C, Wang H, Teschendorff AE, Zhao Y, Zhao H, He Y, Zhang G, Yang Z. dbDEMC 3.0: Functional Exploration of Differentially Expressed miRNAs in Cancers of Human and Model Organisms. Genomics Proteomics Bioinformatics 2022; 20:446-454. [PMID: 35643191 PMCID: PMC9801039 DOI: 10.1016/j.gpb.2022.04.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 02/11/2022] [Accepted: 05/08/2022] [Indexed: 01/26/2023]
Abstract
MicroRNAs (miRNAs) are important regulators in gene expression. The dysregulation of miRNA expression is widely reported in the transformation from physiological to pathological states of cells. A large number of differentially expressed miRNAs (DEMs) have been identified in various human cancers by using high-throughput technologies, such as microarray and miRNA-seq. Through mining of published studies with high-throughput experiment information, the database of DEMs in human cancers (dbDEMC) was constructed with the aim of providing a systematic resource for the storage and query of the DEMs. Here we report an update of the dbDEMC to version 3.0, which contains two-fold more data entries than the second version and now includes also data from mice and rats. The dbDEMC 3.0 contains 3268 unique DEMs in 40 different cancer types. The current datasets for differential expression analysis have expanded to 9 generalized categories. Moreover, the current release integrates functional annotations of DEMs obtained by using experimentally validated targets. The annotations can be of great benefit to the intensive analysis of the roles of DEMs in cancer. In summary, dbDEMC 3.0 provides a valuable resource for characterizing molecular functions and regulatory mechanisms of DEMs in human cancers. The dbDEMC 3.0 is freely accessible at https://www.biosino.org/dbDEMC.
Collapse
Affiliation(s)
- Feng Xu
- Center for Medical Research and Innovation of Pudong Hospital, Fudan University Pudong Medical Center, Shanghai 201399, China,Institutes of Biomedical Science, Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Fudan University, Shanghai 200032, China
| | - Yifan Wang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yunchao Ling
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chenfen Zhou
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Haizhou Wang
- Center for Medical Research and Innovation of Pudong Hospital, Fudan University Pudong Medical Center, Shanghai 201399, China,Institutes of Biomedical Science, Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Fudan University, Shanghai 200032, China
| | - Andrew E. Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Haitao Zhao
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Yungang He
- Institutes of Biomedical Science, Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Fudan University, Shanghai 200032, China,Shanghai Fifth People’s Hospital, Fudan University, Shanghai 200240, China,Corresponding authors.
| | - Guoqing Zhang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China,Corresponding authors.
| | - Zhen Yang
- Center for Medical Research and Innovation of Pudong Hospital, Fudan University Pudong Medical Center, Shanghai 201399, China,Institutes of Biomedical Science, Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Fudan University, Shanghai 200032, China,Corresponding authors.
| |
Collapse
|
12
|
Yuan W, Lyu Y, Shi DL, Liao YX, Li F, Shen YZ, Ling Y. [Analysis of liver function injury associated with 2019-nCoV Omicron mutant strains]. Zhonghua Gan Zang Bing Za Zhi 2022; 30:513-519. [PMID: 35764543 DOI: 10.3760/cma.j.cn501113-20220324-00136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Objective: To investigate the clinical features and influencing factors of liver function injury in patients with 2019-nCoV/SARS-CoV-2 Omicron mutant strains. Methods: 1 183 confirmed imported cases of SARS-CoV-2 who were admitted at Shanghai Public Health Clinical Center (affiliated to Fudan University) from July 1, 2021 to January 15, 2022 were collected. Clinical data, viral genotyping and laboratory test results were collected to retrospectively analyze the basic condition and clinical characteristics of liver function injury. Statistical analysis was performed using t-test or Wilcoxon rank-sum test, χ2 test or Fisher's exact test, Pearson correlation test and logistic regression analysis. Results: 125 (10.6%) cases had raised baseline ALT level and 60 (5.1%) cases had abnormal baseline AST level. Among them, 33 cases (2.8%) had received hepatoprotective drugs. Liver function injury was generally mild in SARS-CoV-2 infection and minimal in Omicron mutant strains. Leukocyte count was increased in patients with raised alanine aminotransferase (ALT) [(6.96±1.78)×109/L vs. (6.41±1.96)×109/L, P=0.005 2], CT scan showed the proportion of liver hypodensity was significantly increased (2.4% vs. 0.3%, P=0.018 0). High-sensitivity C-reactive protein [(7.83±22.36) mg/L vs. (2.68±6.21) mg/L, P=0.007 8] and D-dimer [(0.34±0.39) μg/ml vs. (0.31±0.75) μg/ml, P=0.047 5] levels were higher in patients with raised AST than normal group. 26 cases had normal liver function at hospital admission; however, abnormal liver function was occurred during the course of the disease. Another 8 patients had abnormal liver function at hospital admission, and reduced liver function further during the course of treatment. Recovery time and length of hospital stay was significantly affected in patients with worsened liver function. Baseline body mass index value [odds ratio (OR)]=1.80, P=0.047), non-Omicron strains (OR=12.63, P=0.046), D-dimer (OR=2.36, P=0.047) and interleukin-6 levels (OR=1.03, P=0.009), and those who used glucocorticoids and/or ulinastatin after hospital admission (OR=6.89, P=0.034) had a higher risk of worsening liver function. Conclusions: Liver dysfunction could be observed among COVID-19 patients. Patients infected with omicron variant generally showed mild liver injury. Dynamic monitoring of liver function is necessary, especially among those with baseline elevated IL-6, D-Dimer level and use of antiinflammation medication during treatment.
Collapse
Affiliation(s)
- W Yuan
- Department of Liver Intensive Care Unit,Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Y Lyu
- Department of Integrative Medicine, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - D L Shi
- Department of Infectious Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Y X Liao
- Scientific Department, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - F Li
- Department of Respiratory, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Y Z Shen
- Department of Infection and Immunity, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Y Ling
- Department of Infectious Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| |
Collapse
|
13
|
Ling Y, Cao R, Qian J, Li J, Zhou H, Yuan L, Wang Z, Ma L, Zheng G, Zhao G, Wang Z, Zhang G, Li Y. An interactive viral genome evolution network analysis system enabling rapid large-scale molecular tracing of SARS-CoV-2. Sci Bull (Beijing) 2022; 67:665-669. [PMID: 35036033 PMCID: PMC8743795 DOI: 10.1016/j.scib.2022.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Yunchao Ling
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Ruifang Cao
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jiaqiang Qian
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jiefu Li
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Haokui Zhou
- Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Liyun Yuan
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhen Wang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Liangxiao Ma
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Guangyong Zheng
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Guoping Zhao
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
| | - Zefeng Wang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200433, China.,CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Guoqing Zhang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yixue Li
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.,Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200433, China.,School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.,Guangzhou Laboratory, Guangzhou 510005, China
| |
Collapse
|
14
|
Xue Y, Bao Y, Zhang Z, Zhao W, Xiao J, He S, Zhang G, Li Y, Zhao G, Chen R, Zeng J, Zhang Y, Shang Y, Mai J, Shi S, Lu M, Bu C, Zhang Z, Du Z, Xiao J, Wang Y, Kang H, Xu T, Hao L, Bao Y, Jia P, Jiang S, Qian Q, Zhu T, Shang Y, Zong W, Jin T, Zhang Y, Zou D, Bao Y, Xiao J, Zhang Z, Jiang S, Du Q, Feng C, Ma L, Zhang S, Wang A, Dong L, Wang Y, Zou D, Zhang Z, Liu W, Yan X, Ling Y, Zhao G, Zhou Z, Zhang G, Kang W, Jin T, Zhang T, Ma S, Yan H, Liu Z, Ji Z, Cai Y, Wang S, Song M, Ren J, Zhou Q, Qu J, Zhang W, Bao Y, Liu G, Chen X, Chen T, Zhang S, Sun Y, Yu C, Tang B, Zhu J, Dong L, Zhai S, Sun Y, Chen Q, Yang X, Zhang X, Sang Z, Wang Y, Zhao Y, Chen H, Lan L, Wang Y, Zhao W, Ma Y, Jia Y, Zheng X, Chen M, Zhang Y, Zou D, Zhu T, Xu T, Chen M, Niu G, Zong W, Pan R, Jing W, Sang J, Liu C, Xiong Y, Sun Y, Zhai S, Chen H, Zhao W, Xiao J, Bao Y, Hao L, Zhang M, Wang G, Zou D, Yi L, Zhao W, Zong W, Wu S, Xiong Z, Li R, Zong W, Kang H, Xiong Z, Ma Y, Jin T, Gong Z, Yi L, Zhang M, Wu S, Wang G, Li R, Liu L, Li Z, Liu C, Zou D, Li Q, Feng C, Jing W, Luo S, Ma L, Wang J, Shi Y, Zhou H, Zhang P, Song T, Li Y, He S, Xiong Z, Yang F, Li M, Zhao W, Wang G, Li Z, Ma Y, Zou D, Zong W, Kang H, Jia Y, Zheng X, Li R, Tian D, Liu X, Li C, Teng X, Song S, Liu L, Zhang Y, Niu G, Li Q, Li Z, Zhu T, Feng C, Liu X, Zhang Y, Xu T, Chen R, Teng X, Zhang R, Zou D, Ma L, Xu F, Wang Y, Ling Y, Zhou C, Wang H, Teschendorff AE, He Y, Zhang G, Yang Z, Song S, Ma L, Zou D, Tian D, Li C, Zhu J, Li L, Li N, Gong Z, Chen M, Wang A, Ma Y, Teng X, Cui Y, Duan G, Zhang M, Jin T, Wu G, Huang T, Jin E, Zhao W, Kang H, Wang Z, Du Z, Zhang Y, Li R, Zeng J, Hao L, Jiang S, Chen H, Li M, Xiao J, Zhang Z, Zhao W, Xue Y, Bao Y, Ning W, Xue Y, Tang B, Liu Y, Sun Y, Duan G, Cui Y, Zhou Q, Dong L, Jin E, Liu X, Zhang L, Mao B, Zhang S, Zhang Y, Wang G, Zhao W, Wang Z, Zhu Q, Li X, Zhu J, Tian D, Kang H, Li C, Zhang S, Song S, Li M, Zhao W, Liu Y, Wang Z, Luo H, Zhu J, Wu X, Tian D, Li C, Zhao W, Jing H, Zhu J, Tang B, Zou D, Liu L, Pan Y, Liu C, Chen M, Liu X, Zhang Y, Li Z, Feng C, Du Q, Chen R, Zhu T, Ma L, Zou D, Jiang S, Zhang Z, Gong Z, Zhu J, Li C, Jiang S, Ma L, Tang B, Zou D, Chen M, Sun Y, Shi L, Song S, Zhang Z, Li M, Xiao J, Xue Y, Bao Y, Du Z, Zhao W, Li Z, Du Q, Jiang S, Ma L, Zhang Z, Xiong Z, Li M, Zou D, Zong W, Li R, Chen M, Du Z, Zhao W, Bao Y, Ma Y, Zhang X, Lan L, Xue Y, Bao Y, Jiang S, Feng C, Zhao W, Xiao J, Bao Y, Zhang Z, Zuo Z, Ren J, Zhang X, Xiao Y, Li X, Zhang X, Xiao Y, Li X, Liu D, Zhang C, Xue Y, Zhao Z, Jiang T, Wu W, Zhao F, Meng X, Chen M, Peng D, Xue Y, Luo H, Gao F, Ning W, Xue Y, Lin S, Xue Y, Liu C, Guo A, Yuan H, Su T, Zhang YE, Zhou Y, Chen M, Guo G, Fu S, Tan X, Xue Y, Zhang W, Xue Y, Luo M, Guo A, Xie Y, Ren J, Zhou Y, Chen M, Guo G, Wang C, Xue Y, Liao X, Gao X, Wang J, Xie G, Guo A, Yuan C, Chen M, Tian F, Yang D, Gao G, Tang D, Xue Y, Wu W, Chen M, Gou Y, Han C, Xue Y, Cui Q, Li X, Li CY, Luo X, Ren J, Zhang X, Xiao Y, Li X. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022. Nucleic Acids Res 2022; 50:D27-D38. [PMID: 34718731 PMCID: PMC8728233 DOI: 10.1093/nar/gkab951] [Citation(s) in RCA: 285] [Impact Index Per Article: 142.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 09/29/2021] [Accepted: 10/08/2021] [Indexed: 12/21/2022] Open
Abstract
The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a family of database resources to support global research in both academia and industry. With the explosively accumulated multi-omics data at ever-faster rates, CNCB-NGDC is constantly scaling up and updating its core database resources through big data archive, curation, integration and analysis. In the past year, efforts have been made to synthesize the growing data and knowledge, particularly in single-cell omics and precision medicine research, and a series of resources have been newly developed, updated and enhanced. Moreover, CNCB-NGDC has continued to daily update SARS-CoV-2 genome sequences, variants, haplotypes and literature. Particularly, OpenLB, an open library of bioscience, has been established by providing easy and open access to a substantial number of abstract texts from PubMed, bioRxiv and medRxiv. In addition, Database Commons is significantly updated by cataloguing a full list of global databases, and BLAST tools are newly deployed to provide online sequence search services. All these resources along with their services are publicly accessible at https://ngdc.cncb.ac.cn.
Collapse
|
15
|
Ling YT, Li JM, Ling Y, Wang SG, Wang JT, Zhang XY, Dong LH. Wernekinck Commissure Syndrome with Holmes Tremor: A Report of Two Cases and Review of Literature. Neurol India 2022; 70:281-284. [PMID: 35263896 DOI: 10.4103/0028-3886.338697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Wernekinck commissure syndrome is a rare midbrain infarction, it consists of several symptoms including bilateral cerebellar ataxia, ophthalmoplegia, and palatal tremor. Holmes tremor is a rare clinical syndrome characterized by a combination of resting, postural, and action tremors. We describe two cases of Wernekinck commissure syndrome with Holmes tremor. To the best of our knowledge, it has been rarely reported in the literature to date. Both of the cases were presented with acute onset of bilateral cerebellar ataxia, dysarthria, and Holmes tremor. In the treatment, one patient was given "clonazepam and benheisol," the other was received acupuncture therapy, both of them showed a marked improvement in ataxia and tremor.
Collapse
Affiliation(s)
- Y T Ling
- Department of Neurology, Rizhao People's Hospital, Rizhao, Shandong, China
| | - J M Li
- Department of Neurology, Rizhao People's Hospital, Rizhao, Shandong, China
| | - Y Ling
- Department of Nutrition, Rizhao People's Hospital, Rizhao, Shandong, China
| | - S G Wang
- Department of Neurology, Rizhao People's Hospital, Rizhao, Shandong, China
| | - J T Wang
- Department of Neurology, Rizhao People's Hospital, Rizhao, Shandong, China
| | - X Y Zhang
- Department of Emergency, Rizhao People's Hospital, Rizhao, Shandong, China
| | - L H Dong
- Department of Neurology, Rizhao People's Hospital, Rizhao, Shandong, China
| |
Collapse
|
16
|
Zhang L, Cao R, Mao T, Wang Y, Lv D, Yang L, Tang Y, Zhou M, Ling Y, Zhang G, Qiu T, Cao Z. SAS: A Platform of Spike Antigenicity for SARS-CoV-2. Front Cell Dev Biol 2021; 9:713188. [PMID: 34616728 PMCID: PMC8488377 DOI: 10.3389/fcell.2021.713188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 08/17/2021] [Indexed: 11/13/2022] Open
Abstract
Since the outbreak of SARS-CoV-2, antigenicity concerns continue to linger with emerging mutants. As recent variants have shown decreased reactivity to previously determined monoclonal antibodies (mAbs) or sera, monitoring the antigenicity change of circulating mutants is urgently needed for vaccine effectiveness. Currently, antigenic comparison is mainly carried out by immuno-binding assays. Yet, an online predicting system is highly desirable to complement the targeted experimental tests from the perspective of time and cost. Here, we provided a platform of SAS (Spike protein Antigenicity for SARS-CoV-2), enabling predicting the resistant effect of emerging variants and the dynamic coverage of SARS-CoV-2 antibodies among circulating strains. When being compared to experimental results, SAS prediction obtained the consistency of 100% on 8 mAb-binding tests with detailed epitope covering mutational sites, and 80.3% on 223 anti-serum tests. Moreover, on the latest South Africa escaping strain (B.1.351), SAS predicted a significant resistance to reference strain at multiple mutated epitopes, agreeing well with the vaccine evaluation results. SAS enables auto-updating from GISAID, and the current version collects 867K GISAID strains, 15.4K unique spike (S) variants, and 28 validated and predicted epitope regions that include 339 antigenic sites. Together with the targeted immune-binding experiments, SAS may be helpful to reduce the experimental searching space, indicate the emergence and expansion of antigenic variants, and suggest the dynamic coverage of representative mAbs/vaccines among the latest circulating strains. SAS can be accessed at https://www.biosino.org/sas.
Collapse
Affiliation(s)
- Lu Zhang
- Department of Gastroenterology, Shanghai 10th People’s Hospital and School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Ruifang Cao
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Shanghai, China
| | - Tiantian Mao
- Department of Gastroenterology, Shanghai 10th People’s Hospital and School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Yuan Wang
- Department of Gastroenterology, Shanghai 10th People’s Hospital and School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Daqing Lv
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Shanghai, China
| | - Liangfu Yang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Shanghai, China
| | - Yuanyuan Tang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Shanghai, China
| | - Mengdi Zhou
- Department of Gastroenterology, Shanghai 10th People’s Hospital and School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Yunchao Ling
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Shanghai, China
| | - Guoqing Zhang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Shanghai, China
| | - Tianyi Qiu
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Zhiwei Cao
- Department of Gastroenterology, Shanghai 10th People’s Hospital and School of Life Sciences and Technology, Tongji University, Shanghai, China
| |
Collapse
|
17
|
Yue LH, Ling Y, Chen J. [Spontaneous meningoencephalocele of temporal bone: report of 3 cases]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2021; 56:755-758. [PMID: 34344104 DOI: 10.3760/cma.j.cn115330-20201106-00855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Affiliation(s)
- L H Yue
- Department of Otorhinolaryngology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - Y Ling
- Department of Otorhinolaryngology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| | - J Chen
- Department of Otorhinolaryngology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009, China
| |
Collapse
|
18
|
Zhou C, Xu Q, He S, Ye W, Cao R, Wang P, Ling Y, Yan X, Wang Q, Zhang G. GTDB: an integrated resource for glycosyltransferase sequences and annotations. Database (Oxford) 2021; 2020:5857526. [PMID: 32542364 PMCID: PMC7296393 DOI: 10.1093/database/baaa047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 04/03/2020] [Accepted: 05/21/2020] [Indexed: 11/15/2022]
Abstract
Glycosyltransferases (GTs), a large class of carbohydrate-active enzymes, adds glycosyl moieties to various substrates to generate multiple bioactive compounds, including natural products with pharmaceutical or agrochemical values. Here, we first collected comprehensive information on GTs, including amino acid sequences, coding region sequences, available tertiary structures, protein classification families, catalytic reactions and metabolic pathways. Then, we developed sequence search and molecular docking processes for GTs, resulting in a GTs database (GTDB). In the present study, 520 179 GTs from approximately 21 647 species that involved in 394 kinds of different reactions were deposited in GTDB. GTDB has the following useful features: (i) text search is provided for retrieving the complete details of a query by combining multiple identifiers and data sources; (ii) a convenient browser allows users to browse data by different classifications and download data in batches; (iii) BLAST is offered for searching against pre-defined sequences, which can facilitate the annotation of the biological functions of query GTs; and lastly, (iv) GTdock using AutoDock Vina performs docking simulations of several GTs with the same single acceptor and displays the results based on 3Dmol.js allowing easy view of models.
Collapse
Affiliation(s)
- Chenfen Zhou
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
| | - Qingwei Xu
- College of Computer, Hubei University of Education, 129 Second Gaoxin Road, Wuhan Hi-Tech Zone, Wu Han 430205, China
| | - Sheng He
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China.,School of Life Science and Technology, ShanghaiTech University, 393 Middle Huaxia Road, Pudong, Shanghai 201210, China
| | - Wei Ye
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
| | - Ruifang Cao
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
| | - Pengyu Wang
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
| | - Yunchao Ling
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
| | - Xing Yan
- CAS-Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, 300 Fenglin Road, Xuhui, Shanghai 200032, China
| | - Qingzhong Wang
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
| | - Guoqing Zhang
- National Genomics Data Center, Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui, Shanghai 200031, China
| |
Collapse
|
19
|
Xu L, Chen Q, Zou T, Cheng K, Ling Y, Xu Y, Pang Y, Liu G, Zhu W, Ge J. 11-year follow-up outcomes of catheter ablation of para-hisian accessory pathways. Europace 2021. [DOI: 10.1093/europace/euab116.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Funding Acknowledgements
Type of funding sources: None.
Background
Ablation of para-hisian accessory pathways (APs) remains challenging due to anatomic characteristics and few studies have focused on the causes for recurrence of radiofrequency ablation of para-hisian APs.
Objective
This retrospective single center study was aimed to explore the risk factors for recurrence of para-hisian APs.
Methods
113 patients who had a para-hisian AP with an acute success were enrolled in the study. In the 11-year follow-up, 15 cases had a recurrent para-hisian AP. Therefore 98 patients were classified into success group while 15 patients were classified into recurrence group. Demographic and ablation characteristics were analyzed.
Results
Gender difference was similar in two groups. The median age was 36.2 years old and was younger in recurrence group. Maximum ablation power was significantly higher in success group (29 ± 7.5 vs 22.9 ± 7.8, p < 0.01). Ablation time of final target sites was found to be markedly higher in success group (123.4 ± 53.1 vs 86.7 ± 58.3, p < 0.05). Ablation time less than 60 seconds was detected in 12 (12.2%) cases in success group and 7 (46.7%) cases in recurrence group (p < 0.01). Occurrence of junctional rhythm was significantly higher in recurrence group (25.5% vs 53.3%, p < 0.05). No severe conduction block, no pacemaker implantation and no stroke were reported. Junctional rhythm during ablation (OR = 3.833, 95%CI 1.083-13.572, p = 0.037) and ablation time <60s (OR = 5.487, 95%CI 1.411-21.340, p = 0.014) were independent risk factors for the recurrence of para-hisian AP.
Conclusions
Considering the long-term safety of ablation of para-hisian AP, proper extension of ablation time and increase of ablation power could be applied during operation.
Collapse
Affiliation(s)
- L Xu
- Zhongshan Hospital, Fudan University, Cardiology Department, Shanghai, China
| | - Q Chen
- Zhongshan Hospital, Fudan University, Cardiology Department, Shanghai, China
| | - T Zou
- Zhongshan Hospital, Fudan University, Cardiology Department, Shanghai, China
| | - K Cheng
- Zhongshan Hospital, Fudan University, Cardiology Department, Shanghai, China
| | - Y Ling
- Zhongshan Hospital, Fudan University, Cardiology Department, Shanghai, China
| | - Y Xu
- Zhongshan Hospital, Fudan University, Cardiology Department, Shanghai, China
| | - Y Pang
- Zhongshan Hospital, Fudan University, Cardiology Department, Shanghai, China
| | - G Liu
- Zhongshan Hospital, Fudan University, Cardiology Department, Shanghai, China
| | - W Zhu
- Zhongshan Hospital, Fudan University, Cardiology Department, Shanghai, China
| | - J Ge
- Zhongshan Hospital, Fudan University, Cardiology Department, Shanghai, China
| |
Collapse
|
20
|
Wei JC, Yuan P, Ling Y, Li L, Guo CY, Guo L, Xue LY, Ying JM. [Histopathological features of squamous cell carcinoma of lung neoadjuvant immunotherapy focusing on responses]. Zhonghua Bing Li Xue Za Zhi 2021; 50:453-457. [PMID: 33915650 DOI: 10.3760/cma.j.cn112151-20200829-00671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Objective: To analyze the pathologic features of responses to neoadjuvant immunotherapy of squamous cell carcinoma (SCC) of the lung. Methods: The study included 31 patients with resected lung SCC post neoadjuvant immunotherapy. All patients were recruited from the neoadjuvant anti-PD-1 (Sintilimab) phase Ⅰb clinical trial (ChiCTR-OIC-17013726). The histopathological morphology and different degrees of pathologic response to immunotherapy were evaluated basing on irPRC standard. Results: According to the percentage of residual viable tumor (% RVT), pathologic responses of complete pathologic response (cPR), major pathologic response (MPR) and non-MPR were noted in 19% (n=6), 29% (n=9), and 52% (n=16) of patients respectively. In addition, extensive immune activation phenomena (immune cell infiltration, including infiltration of lymphocytes, plasma cells, foamy macrophages, lymphocyte aggregation and tertiary lymphoid structures formation) and tissue repair features (giant cells, granuloma formation, proliferative fibrosis and neovascularization) were observed in tumor regression bed. Conclusions: Neoadjuvant immunotherapy has favorable effect on lung SCC. Pathologic assessment of resected lung cancer specimens after neoadjuvant immunotherapy shows unique histopathological features consistent with its mechanism.
Collapse
Affiliation(s)
- J C Wei
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - P Yuan
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Y Ling
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Li
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - C Y Guo
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Guo
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - L Y Xue
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - J M Ying
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| |
Collapse
|
21
|
Huang W, Ling Y, Zhang S, Xia Q, Cao R, Fan X, Fang Z, Wang Z, Zhang G. TransCirc: an interactive database for translatable circular RNAs based on multi-omics evidence. Nucleic Acids Res 2021; 49:D236-D242. [PMID: 33074314 PMCID: PMC7778967 DOI: 10.1093/nar/gkaa823] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 09/09/2020] [Accepted: 09/18/2020] [Indexed: 12/17/2022] Open
Abstract
TransCirc (https://www.biosino.org/transcirc/) is a specialized database that provide comprehensive evidences supporting the translation potential of circular RNAs (circRNAs). This database was generated by integrating various direct and indirect evidences to predict coding potential of each human circRNA and the putative translation products. Seven types of evidences for circRNA translation were included: (i) ribosome/polysome binding evidences supporting the occupancy of ribosomes onto circRNAs; (ii) experimentally mapped translation initiation sites on circRNAs; (iii) internal ribosome entry site on circRNAs; (iv) published N-6-methyladenosine modification data in circRNA that promote translation initiation; (v) lengths of the circRNA specific open reading frames; (vi) sequence composition scores from a machine learning prediction of all potential open reading frames; (vii) mass spectrometry data that directly support the circRNA encoded peptides across back-splice junctions. TransCirc provides a user-friendly searching/browsing interface and independent lines of evidences to predicte how likely a circRNA can be translated. In addition, several flexible tools have been developed to aid retrieval and analysis of the data. TransCirc can serve as an important resource for investigating the translation capacity of circRNAs and the potential circRNA-encoded peptides, and can be expanded to include new evidences or additional species in the future.
Collapse
Affiliation(s)
- Wendi Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yunchao Ling
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
| | - Sirui Zhang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qiguang Xia
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ruifang Cao
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiaojuan Fan
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhaoyuan Fang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
- CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zefeng Wang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing, China
- CAS Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
| | - Guoqing Zhang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
22
|
Xue Y, Bao Y, Zhang Z, Zhao W, Xiao J, He S, Zhang G, Li Y, Zhao G, Chen R, Song S, Ma L, Zou D, Tian D, Li C, Zhu J, Gong Z, Chen M, Wang A, Ma Y, Li M, Teng X, Cui Y, Duan G, Zhang M, Jin T, Shi C, Du Z, Zhang Y, Liu C, Li R, Zeng J, Hao L, Jiang S, Chen H, Han D, Xiao J, Zhang Z, Zhao W, Xue Y, Bao Y, Zhang T, Kang W, Yang F, Qu J, Zhang W, Bao Y, Liu GH, Liu L, Zhang Y, Niu G, Zhu T, Feng C, Liu X, Zhang Y, Li Z, Chen R, Li Q, Teng X, Ma L, Hua Z, Tian D, Jiang C, Chen Z, He F, Zhao Y, Jin Y, Zhang Z, Huang L, Song S, Yuan Y, Zhou C, Xu Q, He S, Ye W, Cao R, Wang P, Ling Y, Yan X, Wang Q, Zhang G, Li Z, Liu L, Jiang S, Li Q, Feng C, Du Q, Ma L, Zong W, Kang H, Zhang M, Xiong Z, Li R, Huan W, Ling Y, Zhang S, Xia Q, Cao R, Fan X, Wang Z, Zhang G, Chen X, Chen T, Zhang S, Tang B, Zhu J, Dong L, Zhang Z, Wang Z, Kang H, Wang Y, Ma Y, Wu S, Kang H, Chen M, Li C, Tian D, Tang B, Liu X, Teng X, Song S, Tian D, Liu X, Li C, Teng X, Song S, Zhang Y, Zou D, Zhu T, Chen M, Niu G, Liu C, Xiong Y, Hao L, Niu G, Zou D, Zhu T, Shao X, Hao L, Li Y, Zhou H, Chen X, Zheng Y, Kang Q, Hao D, Zhang L, Luo H, Hao Y, Chen R, Zhang P, He S, Zou D, Zhang M, Xiong Z, Nie Z, Yu S, Li R, Li M, Li R, Bao Y, Xiong Z, Li M, Yang F, Ma Y, Sang J, Li Z, Li R, Tang B, Zhang X, Dong L, Zhou Q, Cui Y, Zhai S, Zhang Y, Wang G, Zhao W, Wang Z, Zhu Q, Li X, Zhu J, Tian D, Kang H, Li C, Zhang S, Song S, Li M, Zhao W, Yan J, Sang J, Zou D, Li C, Wang Z, Zhang Y, Zhu T, Song S, Wang X, Hao L, Liu Y, Wang Z, Luo H, Zhu J, Wu X, Tian D, Li C, Zhao W, Jing HC, Chen M, Zou D, Hao L, Zhao L, Wang J, Li Y, Song T, Zheng Y, Chen R, Zhao Y, He S, Zou D, Mehmood F, Ali S, Ali A, Saleem S, Hussain I, Abbasi AA, Ma L, Zou D, Zou D, Jiang S, Zhang Z, Jiang S, Zhao W, Xiao J, Bao Y, Zhang Z, Zuo Z, Ren J, Zhang X, Xiao Y, Li X, Zhang X, Xiao Y, Li X, Tu Y, Xue Y, Wu W, Ji P, Zhao F, Meng X, Chen M, Peng D, Xue Y, Luo H, Gao F, Zhang X, Xiao Y, Li X, Ning W, Xue Y, Lin S, Xue Y, Liu T, Guo AY, Yuan H, Zhang YE, Tan X, Xue Y, Zhang W, Xue Y, Xie Y, Ren J, Wang C, Xue Y, Liu CJ, Guo AY, Yang DC, Tian F, Gao G, Tang D, Xue Y, Yao L, Xue Y, Cui Q, An NA, Li CY, Luo X, Ren J, Zhang X, Xiao Y, Li X. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2021. Nucleic Acids Res 2021; 49:D18-D28. [PMID: 33175170 PMCID: PMC7779035 DOI: 10.1093/nar/gkaa1022] [Citation(s) in RCA: 135] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/13/2020] [Accepted: 10/16/2020] [Indexed: 12/20/2022] Open
Abstract
The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a suite of database resources to support worldwide research activities in both academia and industry. With the explosive growth of multi-omics data, CNCB-NGDC is continually expanding, updating and enriching its core database resources through big data deposition, integration and translation. In the past year, considerable efforts have been devoted to 2019nCoVR, a newly established resource providing a global landscape of SARS-CoV-2 genomic sequences, variants, and haplotypes, as well as Aging Atlas, BrainBase, GTDB (Glycosyltransferases Database), LncExpDB, and TransCirc (Translation potential for circular RNAs). Meanwhile, a series of resources have been updated and improved, including BioProject, BioSample, GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas) as well as several biodiversity and plant resources. Particularly, BIG Search, a scalable, one-stop, cross-database search engine, has been significantly updated by providing easy access to a large number of internal and external biological resources from CNCB-NGDC, our partners, EBI and NCBI. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.
Collapse
|
23
|
Lammoza N, Ratnakanthan P, Moran T, Gould R, Langenberg F, O'Sullivan P, O'Donnell K, Berman I, Ling Y, Upton A, Joshi S. CTCA Acquired at Elevated Heart Rates Using Triggered End Systolic Scanning. Heart Lung Circ 2021. [DOI: 10.1016/j.hlc.2021.06.205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
|
24
|
Zhang Y, Fang Y, Li N, Ling Y, Zhou Z. Lauren Classification Is A Predictor For Pathological Response Of Preoperative Chemoradiotherapy Compared With Preoperative Chemotherapy In Patients With Locally Advanced Gastric Cancer. Int J Radiat Oncol Biol Phys 2020. [DOI: 10.1016/j.ijrobp.2020.07.1819] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
25
|
Bi Y, Zhang J, Zeng D, Chen L, Ye W, Yang Q, Ling Y. 1204P Expression of cholinesterase is associated with prognosis and response to chemotherapy in advanced gastric cancer. Ann Oncol 2020. [DOI: 10.1016/j.annonc.2020.08.098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
|
26
|
Cao W, Ling Y, Yang L, Wu F, Cheng X, Dong Q. Assessment of Ischemic Volumes by Using Relative Filling Time Delay on CTP Source Image in Patients with Acute Stroke with Anterior Circulation Large Vessel Occlusions. AJNR Am J Neuroradiol 2020; 41:1611-1617. [PMID: 32819905 DOI: 10.3174/ajnr.a6718] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 06/10/2020] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Thrombectomy up to 24 hours after stroke onset in patients with specific ischemic brain volumes remains a challenge, because many stroke centers do not apply specialized software to calculate ischemic volumes at advanced imaging. We aimed to establish the association between relative filling time delay on CTP source imaging and ischemic volume parameters and the infarct penumbra to core volume mismatch in patients with acute ischemic stroke. MATERIALS AND METHODS Consecutive patients with acute ischemic stroke and with M1 segment MCA with or without terminal ICA occlusions on baseline CTA and CTP within 24 hours of stroke symptom onset were included. Ischemic volumes were analyzed with software based on CTP maps. Relative filling time delay was classified into 4 grades-grade 0: relative filling time delay = 0 seconds; grade 1: relative filling time delay >0 to ≤4 seconds; grade 2: relative filling time delay >4 to ≤8 seconds; and grade 3: relative filling time delay > 8 seconds. Differences in ischemic volume parameters among relative filling time delay grades were tested. RESULTS We recruited 138 patients (median age, 69 years; 62.3% male). Different median volumes of the infarct core (grade 0, 7.3 mL; grade 1, 23.3 mL; grade 2, 45.7 mL; grade 3, 135 mL [P < .001]) and the penumbra (grade 0, 47.6 mL; grade 1, 90 mL; grade 2, 110 mL; grade 3, 92 mL [P = .043]) were observed among relative filling time delay grades. Target mismatch (defined by the criteria of the DEFUSE 3 trial) was identified in 71.7% of the patients (99/138). A relative filling time delay grade ≤ 1 independently predicted target mismatch, with a sensitivity of 0.79 (95% CI, 0.7-0.87) and a specificity of 0.66 (95% CI, 0.49-0.8). CONCLUSIONS Relative filling time delay grade based on CTP source imaging is a simple and effective parameter for evaluating ischemic volumes and target mismatch in patients with acute ischemic stroke. Further studies that compare relative filling time delay grade with clinical functional outcomes are necessary.
Collapse
Affiliation(s)
- W Cao
- From the Department of Neurology and Institute of Neurology (W.C., Y.L., L.Y., F.W., X.C., Q.D.), Huashan Hospital, Fudan University, Shanghai, China
| | - Y Ling
- From the Department of Neurology and Institute of Neurology (W.C., Y.L., L.Y., F.W., X.C., Q.D.), Huashan Hospital, Fudan University, Shanghai, China
| | - L Yang
- From the Department of Neurology and Institute of Neurology (W.C., Y.L., L.Y., F.W., X.C., Q.D.), Huashan Hospital, Fudan University, Shanghai, China
| | - F Wu
- From the Department of Neurology and Institute of Neurology (W.C., Y.L., L.Y., F.W., X.C., Q.D.), Huashan Hospital, Fudan University, Shanghai, China
| | - X Cheng
- From the Department of Neurology and Institute of Neurology (W.C., Y.L., L.Y., F.W., X.C., Q.D.), Huashan Hospital, Fudan University, Shanghai, China
| | - Q Dong
- From the Department of Neurology and Institute of Neurology (W.C., Y.L., L.Y., F.W., X.C., Q.D.), Huashan Hospital, Fudan University, Shanghai, China .,State Key Laboratory of Medical Neurobiology (Q.D.), Fudan University, Shanghai, China
| |
Collapse
|
27
|
Pan LJ, Wang X, Ling Y, Gong H. MiR-24 alleviates cardiomyocyte apoptosis after myocardial infarction via targeting BIM. Eur Rev Med Pharmacol Sci 2020; 24:7549. [PMID: 32744654 DOI: 10.26355/eurrev_202007_22191] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Since this article has been suspected of research misconduct and the corresponding authors did not respond to our request to prove originality of data and figures, "MiR-24 alleviates cardiomyocyte apoptosis after myocardial infarction via targeting BIM, by L.-J. Pan, X. Wang, Y. Ling, H. Gong, published in Eur Rev Med Pharmacol Sci 2017; 21 (13): 3088-3097-PMID: 28742197" has been withdrawn. The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/13100.
Collapse
Affiliation(s)
- L-J Pan
- Division of Cardiology, Department of Medicine, Jinshan Hospital, Fudan University, Shanghai, China
| | | | | | | |
Collapse
|
28
|
Qian ZP, Mei X, Zhang YY, Zou Y, Zhang ZG, Zhu H, Guo HY, Liu Y, Ling Y, Zhang XY, Wang JF, Lu HZ. [Analysis of baseline liver biochemical parameters in 324 cases with novel coronavirus pneumonia in Shanghai area]. Zhonghua Gan Zang Bing Za Zhi 2020; 28:229-233. [PMID: 32270660 DOI: 10.3760/cma.j.cn501113-20200229-00076] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Objective: To summarize the clinical characteristics and liver biochemical parameters of 324 cases admitted with novel coronavirus pneumonia in Shanghai area. Methods: Clinical data and baseline liver biochemical parameters of 324 cases with novel coronavirus pneumonia admitted to the Shanghai Public Health Clinical Center from January 20, 2020 to February 24, 2020 were retrospectively analyzed. Patients were divided into two groups based on the status of illness: mild type (mild and typical) and severe type (severe and critical).The differences in clinical data and baseline liver biochemical parameters of the two groups were described and compared. The t-test and Wilcoxon rank-sum test were used for measurement data. The enumeration data were expressed by frequency and rate, and chi-square test was used. Results: Of the 324 cases with novel coronavirus pneumonia, 26 were severe cases (8%), with median onset of 5 days, 20 cases were HBsAg positive (6.2%), and 70 cases (21.6%) with fatty liver, diagnosed with X-ray computed tomography. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), γ-glutamyl transferase (GGT), total bilirubin (TBil), albumin(ALB) and international normalized ratio (INR) of 324 cases at baseline were 27.86 ± 20.02 U/L, 29.33 ± 21.02 U/L, 59.93 ± 18.96 U / L, 39.00 ± 54.44 U/L, 9.46 ± 4.58 μmol / L, 40.64 ± 4.13 g / L and 1.02 ± 0.10. Of which, ALT was > than the upper limit of normal (> ULN), accounting for 15.7% (51/324). ALT and AST > ULN, accounting for 10.5% (34/324). ALP > ULN, accounting for 1.2% (4/324). ALP and GGT > ULN, accounting for 0.9% (3/324). INR > ULN was lowest, accounting for 0.6% (2/324). There were no statistically significant differences (P > 0.05) in ALT [(21.5 vs. 26) U / L, P = 0.093], ALP [(57 vs.59) U/L, P = 0.674], and GGT [(24 vs.28) U/L, P = 0.101] between the severe group and the mild group. There were statistically significant differences in AST (23 U/L vs. 34 U/L, P < 0.01), TBil (10.75 vs. 8.05 μmol / L, P < 0.01), ALB (35.79 ± 4.75 vs. 41.07 ± 3.80 g/L, P < 0.01), and INR (1.00 vs. 1.04, P < 0.01). Conclusion: The baseline liver biochemical parameters of 324 cases with novel coronavirus pneumonia in Shanghai area was comparatively lower and the liverinjury degree was mild, and the bile duct cell damage was rare.
Collapse
Affiliation(s)
- Z P Qian
- Department of Severe Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - X Mei
- Department of Infection and Immunology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Y Y Zhang
- Department of Severe Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Y Zou
- Department of Severe Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Z G Zhang
- Department of Severe Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - H Zhu
- Department of Severe Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - H Y Guo
- Department of Severe Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Y Liu
- Department of Severe Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - Y Ling
- Department of Infectious Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - X Y Zhang
- Department of Education, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - J F Wang
- Department of Severe Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| | - H Z Lu
- Department of Severe Hepatology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China; Department of Infection and Immunology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China
| |
Collapse
|
29
|
Zhang Z, Zhao W, Xiao J, Bao Y, He S, Zhang G, Li Y, Zhao G, Chen R, Gao Y, Zhang C, Yuan L, Zhang G, Xu S, Zhang C, Gao Y, Ning Z, Lu Y, Xu S, Zeng J, Yuan N, Zhu J, Pan M, Zhang H, Wang Q, Shi S, Jiang M, Lu M, Qian Q, Gao Q, Shang Y, Wang J, Du Z, Xiao J, Tian D, Wang P, Tang B, Li C, Teng X, Liu X, Zou D, Song S, Xiong Z, Li M, Yang F, Ma Y, Sang J, Li Z, Li R, Wang Z, Zhu Q, Zhu J, Li X, Zhang S, Tian D, Kang H, Li C, Dong L, Ying C, Duan G, Song S, Li M, Zhao W, Zhi X, Ling Y, Cao R, Jiang Z, Zhou H, Lv D, Liu W, Klenk HP, Zhao G, Zhang G, Zhang Y, Zhang Z, Zhang H, Xiao J, Chen T, Zhang S, Chen X, Zhu J, Wang Z, Kang H, Dong L, Wang Y, Ma Y, Wu S, Li Z, Gong Z, Chen M, Li C, Tian D, Teng X, Wang P, Tang B, Liu X, Zou D, Song S, Fang S, Zhang L, Guo J, Niu Y, Wu Y, Li H, Zhao L, Li X, Teng X, Sun X, Sun L, Chen R, Zhao Y, Wang J, Zhang P, Li Y, Zheng Y, Chen R, He S, Teng X, Chen X, Xue H, Teng Y, Zhang P, Kang Q, Hao Y, Zhao Y, Chen R, He S, Cao J, Liu L, Li Z, Li Q, Zou D, Du Q, Abbasi AA, Shireen H, Pervaiz N, Batool F, Raza RZ, Ma L, Niu G, Zhang Y, Zou D, Zhu T, Sang J, Li M, Hao L, Zou D, Wang G, Li M, Li R, Li M, Li R, Bao Y, Yan J, Sang J, Zou D, Li C, Wang Z, Zhang Y, Zhu T, Song S, Wang X, Hao L, Li Z, Zhang Y, Zou D, Zhao Y, Wang H, Zhang Y, Xia X, Guo H, Zhang Z, Zou D, Ma L, Dong L, Tang B, Zhu J, Zhou Q, Wang Z, Kang H, Chen X, Lan L, Bao Y, Zhao W, Zou D, Zhu J, Tang B, Bao Y, Lan L, Zhang X, Ma Y, Xue Y, Sun Y, Zhai S, Yu L, Sun M, Chen H, Zhang Z, Zhao W, Xiao J, Bao Y, Hao L, Hu H, Guo AY, Lin S, Xue Y, Wang C, Xue Y, Ning W, Xue Y, Zhang X, Xiao Y, Li X, Tu Y, Xue Y, Wu W, Ji P, Zhao F, Luo H, Gao F, Guo Y, Xue Y, Yuan H, Zhang YE, Zhang Q, Guo AY, Zhou J, Xue Y, Huang Z, Cui Q, Miao YR, Guo AY, Ruan C, Xue Y, Yuan C, Chen M, Jin JP, Tian F, Gao G, Shi Y, Xue Y, Yao L, Xue Y, Cui Q, Li X, Li CY, Tang Q, Guo AY, Peng D, Xue Y. Database Resources of the National Genomics Data Center in 2020. Nucleic Acids Res 2020; 48:D24-D33. [PMID: 31702008 PMCID: PMC7145560 DOI: 10.1093/nar/gkz913] [Citation(s) in RCA: 115] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 09/30/2019] [Accepted: 10/02/2019] [Indexed: 11/23/2022] Open
Abstract
The National Genomics Data Center (NGDC) provides a suite of database resources to support worldwide research activities in both academia and industry. With the rapid advancements in higher-throughput and lower-cost sequencing technologies and accordingly the huge volume of multi-omics data generated at exponential scales and rates, NGDC is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. In the past year, efforts for update have been mainly devoted to BioProject, BioSample, GSA, GWH, GVM, NONCODE, LncBook, EWAS Atlas and IC4R. Newly released resources include three human genome databases (PGG.SNV, PGG.Han and CGVD), eLMSG, EWAS Data Hub, GWAS Atlas, iSheep and PADS Arsenal. In addition, four web services, namely, eGPS Cloud, BIG Search, BIG Submission and BIG SSO, have been significantly improved and enhanced. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.
Collapse
|
30
|
Alferness PL, Wiebe LA, Anderson L, Bennett O, Bosch M, Clark D, Claussen F, Colin T, Cook C, Davis H, Ely V, Graham D, Grazzini R, Hickes H, Holland P, Hom W, Ingram R, Ling Y, Markley B, Peoples G, Pitz G, Robert G, Robinson C, Sen L, Sensue A, South N, Steginsky C, Summer S, Trower T, Wieczorek P, Zheng S. Determination of Glyphosate and Aminomethylphosphonic Acid in Crops by Capillary Gas Chromatography with Mass-Selective Detection: Collaborative Study. J AOAC Int 2019. [DOI: 10.1093/jaoac/84.3.823] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Abstract
A collaborative study was conducted to validate a method for the determination of glyphosate and aminomethylphosphonic acid (AMPA) in crops. The analytes are extracted from crops with water, and the crude extracts are then subjected to a cation exchange cleanup. The analytes are derivatized by the direct addition of the aqueous extract into a mixture of heptafluorobutanol and trifluoroacetic anhydride. The derivatized analytes are quantitated by capillary gas chromatography with mass-selective detection (MSD). The collaborative study involved 13 laboratories located in 5 countries 12 laboratories returned valid data sets. The crops tested were field corn grain, soya forage, and walnut nutmeat at concentrations of 0.050, 0.40, and 2.0 mg/kg. The study used a split-level pair replication scheme with blindly coded laboratory samples. Twelve materials were analyzed, including 1 control and 3 split-level pairs for each matrix, 1 pair at each nominal concentration. For glyphosate, the mean recovery was 91%, the average intralaboratory variance, the repeatability relative standard deviation (RSDr), was 11%, and the interlaboratory variance, the reproducibility relative standard deviation (RSDR), was 16%. For AMPA, the mean recovery was 87%, the RSDr was 16%, and the RSDR was 25% at mg/kg levels.
Collapse
Affiliation(s)
- Philip L Alferness
- Zeneca Ag Products 1 , Western Research Center, 1200 S 47th St, Richmond, CA
| | - Lawrence A Wiebe
- Zeneca Ag Products 1 , Western Research Center, 1200 S 47th St, Richmond, CA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
31
|
Li N, Ying J, Tao X, Zhang F, Zhao Z, Ling Y, Gao Y, Zhao J, Xue Q, Mao Y, Lei W, Wu N, Wang S, Duan J, Gao Y, Wang Z, Sun N, Wang J, Gao S, He J, Zhou H, Wang S. JCSE01.10 Efficacy and Safety of Neoadjuvant PD-1 Blockade with Sintilimab in Resectable Squamous Non-Small Cell Lung Cancer (sqNSCLC). J Thorac Oncol 2019. [DOI: 10.1016/j.jtho.2019.08.268] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
32
|
Li N, Ying J, Tao X, Zhang F, Zhao Z, Ling Y, Gao Y, Zhao J, Xue Q, Mao Y, Lei W, Wu N, Wang S, Duan J, Gao Y, Wang Z, Sun N, Wang J, Gao S, He J, Zhou H, Wang S. P1.18-06 Efficacy and Safety of Neoadjuvant PD-1 Blockade with Sintilimab in Resectable Non-Small Cell Lung Cancer. J Thorac Oncol 2019. [DOI: 10.1016/j.jtho.2019.08.1322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
33
|
Chen Z, Yan X, Li K, Ling Y, Kang H. Stromal fibroblast-derived MFAP5 promotes the invasion and migration of breast cancer cells via Notch1/slug signaling. Clin Transl Oncol 2019; 22:522-531. [PMID: 31190277 DOI: 10.1007/s12094-019-02156-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 06/05/2019] [Indexed: 12/11/2022]
Abstract
BACKGROUND The tumor microenvironment (TME) regulates tumor progression, and cancer-associated fibroblasts (CAFs) are the primary stromal components of the TME, with the potential to drive tumor metastasis via the secretion of paracrine factors, but the specific mechanisms driving this process have not been defined. METHODS Proteins secreted from CAFs and normal fibroblasts (NFs) were analyzed via proteomic analysis (fold change > 2, p < 0.05) to identify tumor-promoting proteins secreted by CAFs. RESULTS Proteomic analysis revealed that microfibrillar-associated protein 5 (MFAP5) is preferentially expressed and secreted by CAFs relative to NFs, which was confirmed by Western blotting and RT-qPCR. Transwell and wound healing assays confirmed that MFAP5 is secreted by CAFs, and drives the invasion and migration of MCF7 breast cancer cells. We further found that in MCF7 cells MFAP5 promoted epithelial-mesenchymal transition, activating Notch1 signaling and consequently upregulating NICD1 and slug. When Notch1 was knocked down in MCF7 cells, the ability of MFAP5 to promote invasion and migration decreased. CONCLUSION CAFs promote cancer cells invasion and migration via MFAP5 secretion and activation of the Notch1/slug signaling. These data highlight this pathway as a therapeutic target to disrupt tumor progression through the interference of CAF-tumor crosstalk.
Collapse
Affiliation(s)
- Z Chen
- Department of General Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - X Yan
- School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - K Li
- Department of General Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Y Ling
- Department of General Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - H Kang
- Department of General Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
| |
Collapse
|
34
|
Tan W, Liang G, Xie X, Tan L, Sanders AJ, Liu Z, Ling Y, Zhong W, Jiang WG, Gong C. Abstract P6-09-07: Expression of miR-106b in circulating tumor cells is associated with EMT and prognosis in metastatic breast cancer patients. Cancer Res 2019. [DOI: 10.1158/1538-7445.sabcs18-p6-09-07] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
# Co-first author: W.T., G.L., X.X.
* Co-Correspondence: C.G. and W.G.J.
Abstract
Background: Circulating tumor cells (CTCs) display changes in epithelial-mesenchymal transition (EMT) markers and miRNAs regulate EMT in breast cancer cells. The association between EMT characteristics and miRNA expression in CTCs of metastatic breast cancer (MBC) patients and their clinical implications remain unknown.
Methods: CTC-specific miRNAs were screened based on comparison of the miRNA profile between CTC and primary tumor. RT-PCR was used to quantity the expression levels of EMT makers and miRNA candidates. We enrolled 219 MBC patients with CTCs ≥ 5/7.5mL blood from 2 cohorts and CTCs were detected and enriched by CellSearch. Overall survival (OS) and radiological response were analyzed. CTCs were divided into epithelial- (E-CTCs) and mesenchymal-like CTC (M-CTCs) phenotypes based on a cut-off value derived from suspended breast cancer cells recovered from PBMCs.
Results: MiR-106b displayed upregulation in CTCs, with a higher level in M-CTCs than E-CTCs. Patients with E-CTCs showed better OS than those with M-CTCs (HR 1.77, 95% CI 1.14-2.78, P =0.012). CTCs from chemo-resistant MBC patients exhibited higher miR-106b. CTC-specific miR-106b was negatively associated with therapy response and OS (HR 1.73, 95% CI 1.06-2.84, P = 0.029).
Conclusions: CTC-specific miR-106b was associated with EMT phenotypes of CTCs and may predict prognosis in MBC patients.
Citation Format: Tan W, Liang G, Xie X, Tan L, Sanders AJ, Liu Z, Ling Y, Zhong W, Jiang WG, Gong C. Expression of miR-106b in circulating tumor cells is associated with EMT and prognosis in metastatic breast cancer patients [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P6-09-07.
Collapse
Affiliation(s)
- W Tan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China; The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong Province, China; Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine; Sun Yat-Sen University, Guangzhou, Guangdong Province, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - G Liang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China; The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong Province, China; Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine; Sun Yat-Sen University, Guangzhou, Guangdong Province, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - X Xie
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China; The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong Province, China; Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine; Sun Yat-Sen University, Guangzhou, Guangdong Province, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - L Tan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China; The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong Province, China; Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine; Sun Yat-Sen University, Guangzhou, Guangdong Province, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - AJ Sanders
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China; The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong Province, China; Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine; Sun Yat-Sen University, Guangzhou, Guangdong Province, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Z Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China; The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong Province, China; Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine; Sun Yat-Sen University, Guangzhou, Guangdong Province, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Y Ling
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China; The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong Province, China; Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine; Sun Yat-Sen University, Guangzhou, Guangdong Province, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - W Zhong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China; The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong Province, China; Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine; Sun Yat-Sen University, Guangzhou, Guangdong Province, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - WG Jiang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China; The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong Province, China; Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine; Sun Yat-Sen University, Guangzhou, Guangdong Province, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - C Gong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong Province, China; The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong Province, China; Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine; Sun Yat-Sen University, Guangzhou, Guangdong Province, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Cardiff, United Kingdom
| |
Collapse
|
35
|
Zhong W, Tan L, You N, Wang Y, Liang G, Liu Z, Ling Y, Tian Z, Gong C. Abstract P2-08-56: Effects of young age on prognosis in patients with node-negative tumors 2 cm or smaller breast cancer. Cancer Res 2019. [DOI: 10.1158/1538-7445.sabcs18-p2-08-56] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Background It is still controversial to consider age as a prognostic factor into the treatment strategy of patients with T1N0M0 breast cancer.
Aim The main purpose of this study was to evaluate the effect of age on recurrence risk in patients diagnosed with T1N0M0 breast cancer as well as compare the prognosis of young aged patients(YA,≤40 years old) to non-young aged patients(non-YA,>40 years old) by using a propensity score matching(PSM) analysis.
Methods 365 patients with T1N0M0 breast cancer diagnosed between 2003 and 2016 who received surgery in Sun Yat-sen Memorial Hospital Breast Cancer Center were included.The recurrence free survival (RFS) and risk factors for recurrence were identified by using Kaplan-Meier method and Cox proportional hazards models. PSM was then used to reduce the confounding effect of known risk factors on prognosis and then to compare 5-year RFS rates in patients between two age groups.
Results After a median follow up of 79 months, 54 patients developed recurrences and 5-year RFS was 87.6%. YA patients had lower RFS estimates (80.6%), compared to patients diagnosed in a later age (89.1% if older than 40-years old; P = 0.049). YA patients tended to have Her-2 positive, TNBC tumors, higher rate of Ki-67 expression and nuclear grade tumor. At multivariate analysis, Her-2 positive (HR 2.115; 95% CI 1.103-4.055, p=0.024) and TNBC (HR 2.963; 95% CI 1.485-5.914, p=0.002) resulted independent prognostic factors of patient with T1N0M0 breast cancer. In the subgroup analysis, we found significant poor RFS for YA patients with Her-2 positive breast cancer compared to the older counterparts(p=0.006) and YA patients were associated with significantly higher rates of the locoregional recurrence rather than metastasis(p=0.004), especially in first 5 years after diagnosis. After PSM, the baseline level and treatment status including tumor size, grade, HR status, Her-2 status, Ki67 expression breast surgery type and systemic adjuvant treatment(AST) of patients in the two age groups tended to be equal. As result, we found significant difference in the 5-year RFS between two age groups(p=0.008).
Conclusion Based on equal treatment condition, young age at presentation conferred a worse prognosis in patients with T1N0M0 breast cancer is independent on other pathological features.
Citation Format: Zhong W, Tan L, You N, Wang Y, Liang G, Liu Z, Ling Y, Tian Z, Gong C. Effects of young age on prognosis in patients with node-negative tumors 2 cm or smaller breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P2-08-56.
Collapse
Affiliation(s)
- W Zhong
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong, Guangzhou, China; Sun Yat-Sen University, Guangdong, Guangzhou, China
| | - L Tan
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong, Guangzhou, China; Sun Yat-Sen University, Guangdong, Guangzhou, China
| | - N You
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong, Guangzhou, China; Sun Yat-Sen University, Guangdong, Guangzhou, China
| | - Y Wang
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong, Guangzhou, China; Sun Yat-Sen University, Guangdong, Guangzhou, China
| | - G Liang
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong, Guangzhou, China; Sun Yat-Sen University, Guangdong, Guangzhou, China
| | - Z Liu
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong, Guangzhou, China; Sun Yat-Sen University, Guangdong, Guangzhou, China
| | - Y Ling
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong, Guangzhou, China; Sun Yat-Sen University, Guangdong, Guangzhou, China
| | - Z Tian
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong, Guangzhou, China; Sun Yat-Sen University, Guangdong, Guangzhou, China
| | - C Gong
- Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangdong, Guangzhou, China; Sun Yat-Sen University, Guangdong, Guangzhou, China
| |
Collapse
|
36
|
Tan L, Chen K, Jiang WG, You N, Wang Y, Sanders A, Liang G, Liu Z, Ling Y, Zhong W, Tian Z, Gong C. Abstract P2-07-12: A prognostic prediction nomogram (PDIDC) for breast Paget's disease with infiltrating ductal carcinoma patients: A SEER cohort analysis. Cancer Res 2019. [DOI: 10.1158/1538-7445.sabcs18-p2-07-12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Purpose
The aim of the study was to develop a specific nomogram for prediction of prognosis for breast Paget's disease with infiltrating ductal carcinoma (PD-IDC) patients.
Patients and Methods
Patients data were obtained by the Surveillance, Epidemiology, and End Results (SEER) program (N=2502). Study outcome was Breast Cancer Specific Survival (BCSS). Cox proportional hazards model was applied to identify risk factors and develop predictive model. For internal validation, discrimination was calculated with the concordance index (C-index) using the bootstrap method and calibration assessed.
Results
NPI classification, skin symptom, tumor site and age showed significant association with BCSS(table.1)and were used to build the PDIDC nomogram and to calculate risk score. PDIDC nomogram's C-index (0.791, 95%CI 0.783-0.818) showed better discrimination power than NPI classification (0.691, 95%CI, 0.650-0.735, P= 0.000) and AJCC staging (0.718, 95%CI, 0.695-0.741, P=0.000). Patients were divided into high-risk (1882/2502, 75.21%) and low-risk (620/2502, 24.78%) subgroups with the optimal cut-off of risk scores (4.28). The total BCSS of low-risk subgroup was 77.8% (95%CI 74.4%-81.4%) vs. 31.1% (95%CI 19.4-49.8) of high-risk group (P=0.000). Bootstrap internal validation demonstrated an average C-index of 0.739 (95% CI, 0.692-0.746). The nomogram calibration was validated to be accurate in predicting 5-year and 10-year survival.
Variable finally selected for risk predicted model.PredictorHazard RatioP Value95% CINPI classification Good1 Moderate2.170.0001.51-3.14Poor7.260.0004.96-10.63Skin symptom Without1 With1.760.0001.34-2.32Tumor site Centrally located1 Non-centrally located1.250.0421.07-1.56Age*1.010.0001.01-1.03* Continuous variable.
Conclusion
Utilizing NPI classification, skin symptom, tumor site and age, we developed the PDIDC nomogram to predict the 5-year and 10-year BCSS of breast PD-IDC patients.
Citation Format: Tan L, Chen K, Jiang WG, You N, Wang Y, Sanders A, Liang G, Liu Z, Ling Y, Zhong W, Tian Z, Gong C. A prognostic prediction nomogram (PDIDC) for breast Paget's disease with infiltrating ductal carcinoma patients: A SEER cohort analysis [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P2-07-12.
Collapse
Affiliation(s)
- L Tan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China; School of Mathematics and Computational Science & Southern China Research Center of Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom, Cardiff, Wales, United Kingdom
| | - K Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China; School of Mathematics and Computational Science & Southern China Research Center of Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom, Cardiff, Wales, United Kingdom
| | - WG Jiang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China; School of Mathematics and Computational Science & Southern China Research Center of Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom, Cardiff, Wales, United Kingdom
| | - N You
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China; School of Mathematics and Computational Science & Southern China Research Center of Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom, Cardiff, Wales, United Kingdom
| | - Y Wang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China; School of Mathematics and Computational Science & Southern China Research Center of Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom, Cardiff, Wales, United Kingdom
| | - A Sanders
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China; School of Mathematics and Computational Science & Southern China Research Center of Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom, Cardiff, Wales, United Kingdom
| | - G Liang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China; School of Mathematics and Computational Science & Southern China Research Center of Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom, Cardiff, Wales, United Kingdom
| | - Z Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China; School of Mathematics and Computational Science & Southern China Research Center of Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom, Cardiff, Wales, United Kingdom
| | - Y Ling
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China; School of Mathematics and Computational Science & Southern China Research Center of Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom, Cardiff, Wales, United Kingdom
| | - W Zhong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China; School of Mathematics and Computational Science & Southern China Research Center of Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom, Cardiff, Wales, United Kingdom
| | - Z Tian
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China; School of Mathematics and Computational Science & Southern China Research Center of Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom, Cardiff, Wales, United Kingdom
| | - C Gong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation and Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China; School of Mathematics and Computational Science & Southern China Research Center of Statistical Science, Sun Yat-sen University, Guangzhou, Guangdong, China; Cardiff China Medical Research Collaborative, Cardiff University School of Medicine, Cardiff University, Heath Park, Cardiff, United Kingdom, Cardiff, Wales, United Kingdom
| |
Collapse
|
37
|
Du D, Liu M, Xing Y, Chen X, Zhang Y, Zhu M, Lu X, Zhang Q, Ling Y, Sang X, Li Y, Zhang C, He G. Semi-dominant mutation in the cysteine-rich receptor-like kinase gene, ALS1, conducts constitutive defence response in rice. Plant Biol (Stuttg) 2019; 21:25-34. [PMID: 30101415 DOI: 10.1111/plb.12896] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 08/04/2018] [Indexed: 06/08/2023]
Abstract
Plants have evolved a sophisticated two-branch defence system to prevent the growth and spread of pathogen infection. The novel Cys-rich repeat (CRR) containing receptor-like kinases, known as CRKs, were reported to mediate defence resistance in plants. For rice, there are only two reports of CRKs. A semi-dominant lesion mimic mutant als1 (apoptosis leaf and sheath 1) in rice was identified to demonstrate spontaneous lesions on the leaf blade and sheath. A map-based cloning strategy was used for fine mapping and cloning of ALS1, which was confirmed to be a typical CRK in rice. Functional studies of ALS1 were conducted, including phylogenetic analysis, expression analysis, subcellular location and blast resistance identification. Most pathogenesis-related (PR) genes and other defence-related genes were activated and up-regulated to a high degree. ALS1 was expressed mainly in the leaf blade and sheath, in which further study revealed that ALS1 was present in the vascular bundles. ALS1 was located in the cell membrane of rice protoplasts, and its mutation did not change its subcellular location. Jasmonic acid (JA) and salicylic acid (SA) accumulation were observed in als1, and enhanced blast resistance was also observed. The mutation of ALS1 caused a constitutively activated defence response in als1. The results of our study imply that ALS1 participates in a defence response resembling the common SA-, JA- and NH1-mediated defence responses in rice.
Collapse
Affiliation(s)
- D Du
- Rice Research Institute, Key Laboratory of Application and Safety Control of Genetically Modified Crops, Academy of Agricultural Sciences, Southwest University, Chongqing, 400715, China
| | - M Liu
- Rice Research Institute, Key Laboratory of Application and Safety Control of Genetically Modified Crops, Academy of Agricultural Sciences, Southwest University, Chongqing, 400715, China
| | - Y Xing
- Rice Research Institute, Key Laboratory of Application and Safety Control of Genetically Modified Crops, Academy of Agricultural Sciences, Southwest University, Chongqing, 400715, China
| | - X Chen
- Rice Research Institute, Key Laboratory of Application and Safety Control of Genetically Modified Crops, Academy of Agricultural Sciences, Southwest University, Chongqing, 400715, China
| | - Y Zhang
- Rice Research Institute, Key Laboratory of Application and Safety Control of Genetically Modified Crops, Academy of Agricultural Sciences, Southwest University, Chongqing, 400715, China
| | - M Zhu
- Rice Research Institute, Key Laboratory of Application and Safety Control of Genetically Modified Crops, Academy of Agricultural Sciences, Southwest University, Chongqing, 400715, China
| | - X Lu
- Rice Research Institute, Key Laboratory of Application and Safety Control of Genetically Modified Crops, Academy of Agricultural Sciences, Southwest University, Chongqing, 400715, China
| | - Q Zhang
- Rice Research Institute, Key Laboratory of Application and Safety Control of Genetically Modified Crops, Academy of Agricultural Sciences, Southwest University, Chongqing, 400715, China
| | - Y Ling
- Rice Research Institute, Key Laboratory of Application and Safety Control of Genetically Modified Crops, Academy of Agricultural Sciences, Southwest University, Chongqing, 400715, China
| | - X Sang
- Rice Research Institute, Key Laboratory of Application and Safety Control of Genetically Modified Crops, Academy of Agricultural Sciences, Southwest University, Chongqing, 400715, China
| | - Y Li
- Rice Research Institute, Key Laboratory of Application and Safety Control of Genetically Modified Crops, Academy of Agricultural Sciences, Southwest University, Chongqing, 400715, China
| | - C Zhang
- Rice Research Institute, Key Laboratory of Application and Safety Control of Genetically Modified Crops, Academy of Agricultural Sciences, Southwest University, Chongqing, 400715, China
| | - G He
- Rice Research Institute, Key Laboratory of Application and Safety Control of Genetically Modified Crops, Academy of Agricultural Sciences, Southwest University, Chongqing, 400715, China
| |
Collapse
|
38
|
Galusca B, Verney J, Meugnier E, Ling Y, Edouard P, Feasson L, Ravelojaona M, Vidal H, Estour B, Germain N. Reduced fibre size, capillary supply and mitochondrial activity in constitutional thinness' skeletal muscle. Acta Physiol (Oxf) 2018; 224:e13097. [PMID: 29754437 DOI: 10.1111/apha.13097] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 05/07/2018] [Accepted: 05/08/2018] [Indexed: 11/30/2022]
Abstract
AIM Constitutional thinness (CT) is a rare condition of natural low body weight, with no psychological issues, no marker of undernutrition and a resistance to weight gain. This study evaluated the skeletal muscle phenotype of CT women by comparison with a normal BMI control group. METHODS Ten CT women (BMI < 17.5 kg/m2 ) and 10 female controls (BMI: 18.5-25 kg/m2 ) underwent metabolic and hormonal assessment along with muscle biopsies to analyse the skeletal muscular fibres pattern, capillarity, enzymes activities and transcriptomics. RESULTS Constitutional thinness displayed similar energy balance metabolic and hormonal profile to controls. Constitutional thinness presented with lower mean area of all the skeletal muscular fibres (-24%, P = .01) and percentage of slow-twitch type I fibres (-25%, P = .02, respectively). Significant downregulation of the mRNA expression of several mitochondrial-related genes and triglycerides metabolism was found along with low cytochrome c oxidase (COX) activity and capillary network in type I fibres. Pre- and post-mitochondrial respiratory chain enzymes levels were found similar to controls. Transcriptomics also revealed downregulation of cytoskeletal-related genes. CONCLUSION Diminished type I fibres, decreased mitochondrial and metabolic activity suggested by these results are discordant with normal resting metabolic rate of CT subjects. Downregulated genes related to cytoskeletal proteins and myocyte differentiation could account for CT's resistance to weight gain.
Collapse
Affiliation(s)
- B. Galusca
- Division of Endocrinology, Diabetes, Metabolism and Eating Disorders; CHU Saint-Etienne; Saint-Etienne France
- Eating Disorders, Addictions & Extreme Bodyweight Research Group (TAPE) EA 7423; Jean Monnet University; Saint-Etienne France
| | - J. Verney
- Interuniversity Laboratory of Motricity & Biology (LIBM) EA 7424; Jean Monnet University; Saint-Etienne France
- Laboratory of Metabolic Adaptations to Exercise in Physiological and Pathological conditions (AME2P) EA 3533; Blaise Pascal University; Clermont-Ferrand France
| | - E. Meugnier
- CarMeN Laboratory, INSERM U1060, INRA U1397; INSA-Lyon, Faculté de Médecine Lyon-Sud; Université Lyon 1; Lyon University; Oullins France
| | - Y. Ling
- Eating Disorders, Addictions & Extreme Bodyweight Research Group (TAPE) EA 7423; Jean Monnet University; Saint-Etienne France
| | - P. Edouard
- Interuniversity Laboratory of Motricity & Biology (LIBM) EA 7424; Jean Monnet University; Saint-Etienne France
| | - L. Feasson
- Interuniversity Laboratory of Motricity & Biology (LIBM) EA 7424; Jean Monnet University; Saint-Etienne France
| | - M. Ravelojaona
- Interuniversity Laboratory of Motricity & Biology (LIBM) EA 7424; Jean Monnet University; Saint-Etienne France
| | - H. Vidal
- CarMeN Laboratory, INSERM U1060, INRA U1397; INSA-Lyon, Faculté de Médecine Lyon-Sud; Université Lyon 1; Lyon University; Oullins France
| | - B. Estour
- Division of Endocrinology, Diabetes, Metabolism and Eating Disorders; CHU Saint-Etienne; Saint-Etienne France
- Eating Disorders, Addictions & Extreme Bodyweight Research Group (TAPE) EA 7423; Jean Monnet University; Saint-Etienne France
| | - N. Germain
- Division of Endocrinology, Diabetes, Metabolism and Eating Disorders; CHU Saint-Etienne; Saint-Etienne France
- Eating Disorders, Addictions & Extreme Bodyweight Research Group (TAPE) EA 7423; Jean Monnet University; Saint-Etienne France
| |
Collapse
|
39
|
Ling Y, Xia J, Koji K, Zhang X, Li Z. First Report of Damping-Off Caused by Pythium arrhenomanes on Rice in China. Plant Dis 2018; 102:PDIS01180113PDN. [PMID: 30226417 DOI: 10.1094/pdis-01-18-0113-pdn] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Affiliation(s)
- Y Ling
- College of Life Sciences, Inner Mongolia University for Nationalities, Tongliao, Inner Mongolia 028000, China
| | - J Xia
- College of Plant Protection, Shandong Agricultural University, Taian, Shandong 271018, China
| | - K Koji
- River Basin Research Center, Gifu University, Gifu 501-1193, Japan
| | - X Zhang
- College of Plant Protection, Shandong Agricultural University, Taian, Shandong 271018, China
| | - Z Li
- College of Plant Protection, Shandong Agricultural University, Taian, Shandong 271018, China
| |
Collapse
|
40
|
Yang C, Ye J, Liu Y, Ding J, Liu H, Gao X, Li X, Zhang Y, Zhou J, Zhang X, Huang W, Fang F, Ling Y. Methylation pattern variation between goats and rats during the onset of puberty. Reprod Domest Anim 2018; 53:793-800. [PMID: 29577480 DOI: 10.1111/rda.13172] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 02/09/2018] [Indexed: 01/04/2023]
Abstract
Puberty is initiated by increased pulsatile gonadotropin-releasing hormone (GnRH) release from the hypothalamus. Epigenetic repression is thought to play a crucial role in the initiation of puberty, although the existence of analogous changes in methylation patterns across species is unclear. We analysed mRNA expression of DNA methyltransferases (DNMTs) and methyl-binding proteins (MBPs) in goats and rats by quantitative real-time PCR (qRT-PCR). DNA methylation profiles of hypothalamic were determined at the pre-pubertal and pubertal stages by bisulphite sequencing. In this study, expression of DNMTs and MBPs mRNA showed different patterns in goats and rats. Global methylation variation was low in goats and rats, and the profile remained stable during puberty. Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway analysis revealed the involvement of 62 pathways in puberty in goats and rats including reproduction, type I diabetes mellitus and GnRH signalling pathways and found that Edn3, PTPRN2 and GRID1 showed different methylation patterns during puberty in goats and rats and similar variation patterns for Edn3 and PTPRN2 were showed. These indicated that Edn3 and PTPRN2 would play a role in the timing of puberty. This study provides evidence of the epigenetic control of puberty.
Collapse
Affiliation(s)
- C Yang
- Anhui Provincial Laboratory of Animal Genetic Resources Protection and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China.,Anhui Provincial Laboratory for Local Livestock and Poultry, Genetic Resource Conservation and Bio-Breeding, Hefei, Anhui, China.,Department of Animal Veterinary Science, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - J Ye
- Anhui Provincial Laboratory of Animal Genetic Resources Protection and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China.,Anhui Provincial Laboratory for Local Livestock and Poultry, Genetic Resource Conservation and Bio-Breeding, Hefei, Anhui, China.,Department of Animal Veterinary Science, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Y Liu
- Anhui Provincial Laboratory of Animal Genetic Resources Protection and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China.,Anhui Provincial Laboratory for Local Livestock and Poultry, Genetic Resource Conservation and Bio-Breeding, Hefei, Anhui, China.,Department of Animal Veterinary Science, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - J Ding
- Anhui Provincial Laboratory of Animal Genetic Resources Protection and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China.,Anhui Provincial Laboratory for Local Livestock and Poultry, Genetic Resource Conservation and Bio-Breeding, Hefei, Anhui, China.,Department of Animal Veterinary Science, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - H Liu
- Anhui Provincial Laboratory of Animal Genetic Resources Protection and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China.,Anhui Provincial Laboratory for Local Livestock and Poultry, Genetic Resource Conservation and Bio-Breeding, Hefei, Anhui, China.,Department of Animal Veterinary Science, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - X Gao
- Anhui Provincial Laboratory of Animal Genetic Resources Protection and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China.,Anhui Provincial Laboratory for Local Livestock and Poultry, Genetic Resource Conservation and Bio-Breeding, Hefei, Anhui, China.,Department of Animal Veterinary Science, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - X Li
- Anhui Provincial Laboratory of Animal Genetic Resources Protection and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China.,Anhui Provincial Laboratory for Local Livestock and Poultry, Genetic Resource Conservation and Bio-Breeding, Hefei, Anhui, China.,Department of Animal Veterinary Science, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Y Zhang
- Anhui Provincial Laboratory of Animal Genetic Resources Protection and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China.,Anhui Provincial Laboratory for Local Livestock and Poultry, Genetic Resource Conservation and Bio-Breeding, Hefei, Anhui, China.,Department of Animal Veterinary Science, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - J Zhou
- Anhui Provincial Laboratory of Animal Genetic Resources Protection and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China.,Department of Animal Veterinary Science, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - X Zhang
- Anhui Provincial Laboratory of Animal Genetic Resources Protection and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China.,Anhui Provincial Laboratory for Local Livestock and Poultry, Genetic Resource Conservation and Bio-Breeding, Hefei, Anhui, China.,Department of Animal Veterinary Science, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - W Huang
- Anhui Provincial Laboratory of Animal Genetic Resources Protection and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China.,Anhui Provincial Laboratory for Local Livestock and Poultry, Genetic Resource Conservation and Bio-Breeding, Hefei, Anhui, China.,Department of Animal Veterinary Science, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - F Fang
- Anhui Provincial Laboratory of Animal Genetic Resources Protection and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China.,Anhui Provincial Laboratory for Local Livestock and Poultry, Genetic Resource Conservation and Bio-Breeding, Hefei, Anhui, China.,Department of Animal Veterinary Science, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| | - Y Ling
- Anhui Provincial Laboratory of Animal Genetic Resources Protection and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China.,Anhui Provincial Laboratory for Local Livestock and Poultry, Genetic Resource Conservation and Bio-Breeding, Hefei, Anhui, China.,Department of Animal Veterinary Science, College of Animal Science and Technology, Anhui Agricultural University, Hefei, Anhui, China
| |
Collapse
|
41
|
Yidong Z, Changjun W, Yanyan Z, Yuhua G, Yanfang G, Li P, Ling Y, Xin Y, Xuefeng X, Qiang S. Circulating tumor DNA detection in primary breast cancer patients by targeted sequencing: Consistency with tumor DNA and factors influencing detection. Ann Oncol 2017. [DOI: 10.1093/annonc/mdx655.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
|
42
|
Yokoyama K, Sato R, Makioka H, Iizuka Y, Hase M, Ling Y, Torii S, Saida T. Safety and effectiveness of natalizumab: The 2-year interim results of the post-marketing surveillance in Japan. J Neurol Sci 2017. [DOI: 10.1016/j.jns.2017.08.3022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
43
|
Mori M, Ohashi T, Onizuka Y, Hiramatsu K, Hase M, Yun J, Ling Y, Torii S. Efficacy and safety of delayed-release dimethyl fumarate in treatment-naïve Japanese patients with relapsing-remitting multiple sclerosis: A post-hoc subgroup analysis of the apex study. J Neurol Sci 2017. [DOI: 10.1016/j.jns.2017.08.2241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
44
|
Kondo T, Kawachi I, Onizuka Y, Hiramatsu K, Hase M, Yun J, Ling Y, Torii S. Efficacy of delayed-release dimethyl fumarate in Japanese patients with relapsing multiple sclerosis in the placebo-controlled phase 3 apex study. J Neurol Sci 2017. [DOI: 10.1016/j.jns.2017.08.3453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
45
|
Ochi H, Niino M, Onizuka Y, Hiramatsu K, Hase M, Yun J, Ling Y, Torii S. Safety of delayed-release dimetyl fumarate in Japanese patients with relapsing multiple sclerosis: Subgroup analysis of the apex Part 1 study. J Neurol Sci 2017. [DOI: 10.1016/j.jns.2017.08.2233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
46
|
Yan G, Zhang R, Zuo W, Wang R, Ling Y, Kobayashi Y. The safety and efficacy of Chinese formula Salviae Miltiorrhizae and Ligustrazine hydrochloride solution injection in the acute ischemic stroke patients. Am J Transl Res 2017. [DOI: 10.1055/s-0037-1608466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- G Yan
- Guizhou Baite Pharmaceutical Corporation, Guizhou, China
| | - R Zhang
- Guizhou Baite Pharmaceutical Corporation, Guizhou, China
| | - W Zuo
- Guizhou Baite Pharmaceutical Corporation, Guizhou, China
| | - R Wang
- Guizhou Baite Pharmaceutical Corporation, Guizhou, China
- Zhejiang CONBA Pharmaceutical & Drug Research Development Corporation, Hangzhou, China
| | - Y Ling
- Medical Corporation Soujikai, Osaka, Japan
| | | |
Collapse
|
47
|
Pan LJ, Wang X, Ling Y, Gong H. MiR-24 alleviates cardiomyocyte apoptosis after myocardial infarction via targeting BIM. Eur Rev Med Pharmacol Sci 2017; 21:3088-3097. [PMID: 28742197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
OBJECTIVE Ischemia hypoxia induces cardiomyocyte (CM) apoptosis in the process of acute myocardial infarction (AMI). It was showed that pro-apoptosis factor BIM participates in regulating tumor cell apoptosis under ischemia or hypoxia condition, while its role in CM apoptosis after AMI is still unclear. It was revealed that miR-24 expression was significantly reduced in myocardial tissue after AMI. Bioinformatics analysis exhibits that miR-24 is targeted to the 3'-UTR of BIM. This study aims to investigate the role of miR-24 in mediating BIM expression and CM apoptosis. PATIENTS AND METHODS Dual-luciferase assay was used to confirm the targeted regulation between miR-24 and BIM. Cells were cultured under ischemia hypoxia for 12 h after transfection for 48 h. Cell apoptosis was tested by using flow cytometry. The caspase activity was detected by using spectrophotometry. Wistar rats were divided into four groups, including Sham, AMI, AMI + agomir-control, and AMI + agomir-24 groups. Cardiac function was evaluated by using echocardiography. CM apoptosis was determined by using TUNEL. Infarction area was measured by using evans blue staining. MiR-24 targeted suppressed BIM expression. RESULTS MiR-24 mimic and/or si-BIM transfection significantly declined the BIM expression, inhibited caspase-9 and caspase-3 activities, and reduced cell apoptosis in H9C2 cells. MiR-24 expression was decreased, while BIM levels were up-regulated in myocardium after AMI. Agomir-24 injection down-regulated the BIM expression in myocardium, reduced CM apoptosis, narrowed infarction area, and improved cardiac function in rats. CONCLUSIONS MiR-24 was reduced, whereas BIM was enhanced in the CM after AMI. MiR-24 up-regulation plays a critical role in decreasing BIM expression, reducing CM apoptosis, and improving cardiac function after AMI.
Collapse
Affiliation(s)
- L-J Pan
- Division of Cardiology, Department of Medicine, Jinshan Hospital, Fudan University, Shanghai, China.
| | | | | | | |
Collapse
|
48
|
Zhang H, Zhao N, Lu Y, Chen M, Guo X, Ling Y. Two-step shoulder delivery method reduces the incidence of shoulder dystocia. CLIN EXP OBSTET GYN 2017. [DOI: 10.12891/ceog3188.2017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
|
49
|
Zeng Y, Ling Y, Huebner ES, He Y, Lei X. The psychometric properties of the 5-item gratitude questionnaire in Chinese adolescents. J Psychiatr Ment Health Nurs 2017; 24:203-210. [PMID: 28140495 DOI: 10.1111/jpm.12372] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/26/2017] [Indexed: 12/01/2022]
Abstract
UNLABELLED WHAT IS KNOWN ON THE SUBJECT?: The GQ-6 is one of the most widely used self-report questionnaires to evaluate the level of gratitude among adults. The GQ-5 appears suitable for adolescents. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: We developed a Chinese version of the GQ-5 and examined evidence for its reliability and validity. Results demonstrated adequate reliability and validity, indicating that it is appropriate for the assessment of gratitude in Chinese adolescents. In addition, Chinese early adolescent females reported higher gratitude than adolescent males. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Screening adolescents who have lower levels of gratitude through the GQ-5 could help identify students who may benefit from empirically validated interventions to promote higher levels of gratitude in an effort to promote positive psychosocial and academic outcomes. ABSTRACT Background This study was conducted to evaluate the psychometric properties of the Chinese version of the 5-item Gratitude Questionnaire (GQ-5). Method The sample consisted of 2093 middle school students (46.8% males) in mainland China. Confirmatory factor analysis and multigroup confirmatory factor analysis were performed to examine the factor structure and the measurement equivalence across gender. The convergent validity, Cronbach's α and mean interitem correlations of the GQ-5 were also evaluated. Results The results provided evidence of internal consistency reliability through a Cronbach's α of 0.812 and a mean interitem correlation of 0.463 for the total sample. The results also supported a one-dimensional factor structure. In addition, convergent validity was assessed by statistically significant positive correlations between the GQ-5 and the two subscales of the Children's Hope Scale (CHS) and the Brief Multidimensional Students' Life Satisfaction Scale (BMSLSS) total score. Finally, multigroup confirmatory factor analysis also demonstrated measurement equivalence across gender. Subsequent analyses of latent mean revealed gender differences in early adolescent male and female students. Conclusions The Chinese version of the GQ-5 appears to be a reliable and valid measure of gratitude among Chinese early adolescents. Early adolescent female students reported higher gratitude than early adolescent male students.
Collapse
Affiliation(s)
- Y Zeng
- College of Education, Hunan Agriculture University, Changsha, China
| | - Y Ling
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - E S Huebner
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Y He
- College of Education, Hunan Agriculture University, Changsha, China
| | - X Lei
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
| |
Collapse
|
50
|
Hajar A, Ionescu L, Ling Y, West L, Urschel S. Age-Related Differences in the Regulatory Capacity of CD5+CD1d+ B-Cells in the Context of Heart Graft Acceptance. J Heart Lung Transplant 2017. [DOI: 10.1016/j.healun.2017.01.264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
|