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Your search keyword '"Ke, Mengying"' showing total 20 results
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20 results on '"Ke, Mengying"'

7. Inhibition of VCP modulates NF-κB signaling pathway to suppress multiple myeloma cell proliferation and osteoclast differentiation

Author

Wei, Rongfang, primary, Cao, Yuhao, additional, Wu, Hongjie, additional, Liu, Xin, additional, Jiang, Mingmei, additional, Luo, Xian, additional, Deng, Zhendong, additional, Wang, Ze, additional, Ke, Mengying, additional, Zhu, Yongqiang, additional, Chen, Siqing, additional, Gu, Chunyan, additional, and Yang, Ye, additional

Published
2023
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11. Additional file 2 of G6PD promotes cell proliferation and dexamethasone resistance in multiple myeloma via increasing anti-oxidant production and activating Wnt/β-catenin pathway

Author

Li, Rui, Ke, Mengying, Qi, Mingming, Han, Zhenru, Cao, Yuhao, Deng, Zhendong, Qian, Jinjun, Yang, Ye, and Gu, Chunyan

Abstract

Additional file 2: Fig S1. Growth curves of ARP1/H929 G6PD-OE/KD cells cultured for 7 days. Fig S2 WB analysis of mTOR and AKT in G6PD-OE/KD cells. Fig. S3 Wnt3a partially restores the suppressed expression of G6PD and β-catenin by RRX-001.

Published
2022
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14. Untargeted serum metabolomics reveals specific metabolite abnormalities in patients with Crohn's disease

Author

Liu, Huanhuan, Xu, Minmin, He, Qiongzi, Wei, Peng, Ke, Mengying, and Liu, Shijia

Subjects
General Medicine
Abstract

Crohn's disease (CD) is a subtype of inflammatory bowel disease (IBD) characterized by skip intestinal lesions that can occur in any part of the gastrointestinal tract. Currently, the diagnosis of CD is based on clinical history, physical examination and complementary diagnostic tests. It is challenging for physicians to make a definitive diagnosis. This study aimed to analyze the variation in metabolites in CD serum and identify potential predictive biomarkers of CD diagnosis. We collected serum samples from 316 subjects, including patients with CD and healthy controls (HCs). Serum metabolomics was conducted using liquid chromatography coupled to mass spectrometry. Potential biomarkers were screened and evaluated by univariate and multivariate analyses. A panel of two metabolites (deoxycholic acid and palmitic amide) was identified as a specific biomarker of CD. Receiver operating characteristic analysis (ROC) showed that the panel had a sensitivity of 80.25% with a specificity of 95.54% in discriminating CD patients from healthy controls. The biomarkers identified are increased in CD compared with healthy controls. Our approach successfully identified serum biomarkers associated with CD patients. The potential biomarkers indicated that CD metabolic disturbance might be associated with bile acid biosynthesis, fatty acids and energy metabolism.

Published
2021

15. Untargeted serum metabolomics and potential biomarkers for Sjögren's syndrome

Author

Xu, Tingting, primary, Guo, Yunke, additional, Lu, Jiawei, additional, Shan, Jinjun, additional, Lin, Lili, additional, Qian, Wenjuan, additional, Chen, Wenjun, additional, Wang, Jue, additional, Lv, Xiangyu, additional, Ke, Mengying, additional, Kong, Deshun, additional, Shen, Qiuxiang, additional, Zhu, Youjuan, additional, Liu, Ping, additional, Su, Jinfeng, additional, Lu, Wang, additional, Li, Yuhua, additional, Gao, Pan, additional, and Liu, Shijia, additional

Published
2021
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16. Lipidomics Reveals Serum Specific Lipid Alterations in Diabetic Nephropathy

Author

Xu, Tingting, primary, Xu, Xiaoyan, additional, Zhang, Lu, additional, Zhang, Ke, additional, Wei, Qiong, additional, Zhu, Lin, additional, Yu, Ying, additional, Xiao, Liangxiang, additional, Lin, Lili, additional, Qian, Wenjuan, additional, Wang, Jue, additional, Ke, Mengying, additional, An, Xiaofei, additional, and Liu, Shijia, additional

Published
2021
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19. Serum Metabolomic And Lipidomic Profiling Identifies Diagnostic Biomarkers For Seropositive And Seronegative Rheumatoid Arthritis Patients

Author

Luan, Hemi, primary, Gu, Wanjian, additional, Li, Hua, additional, Wang, Zi, additional, Lu, Lu, additional, Ke, Mengying, additional, Lu, Jiawei, additional, Chen, Wenjun, additional, Lan, Zhangzhang, additional, Xiao, Yanlin, additional, Xu, Jinyue, additional, Zhang, Yi, additional, Cai, Zongwei, additional, Liu, Shijia, additional, and Zhang, Wenyong, additional

Published
2021
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20. Serum lipidomics reveals distinct metabolic profiles for asymptomatic hyperuricemic and gout patients.

Author

Liu, Shijia, Wang, Yingzhuo, Liu, Huanhuan, Xu, Tingting, Wang, Ma-Jie, Lu, Jiawei, Guo, Yunke, Chen, Wenjun, Ke, Mengying, Zhou, Guisheng, Lu, Yan, Chen, Peidong, and Zhou, Wei

Subjects
GOUT diagnosis, HYPERURICEMIA, BIOMARKERS, TRIGLYCERIDES, PREDICTIVE tests, ACADEMIC medical centers, HIGH performance liquid chromatography, GLYCERIDES, CONFIDENCE intervals, METABOLOMICS, DIFFERENTIAL diagnosis, BLOOD collection, DISCRIMINANT analysis, COMPARATIVE studies, RISK assessment, DESCRIPTIVE statistics, MASS spectrometry, LOGISTIC regression analysis, RECEIVER operating characteristic curves, PHOSPHOLIPIDS, SENSITIVITY & specificity (Statistics), LIPIDS, GOUT, METABOLITES, LECITHIN, DISEASE risk factors
Abstract

Objectives This study aimed to characterize the systemic lipid profile of patients with asymptomatic hyperuricemia (HUA) and gout using lipidomics, and to find potential underlying pathological mechanisms therefrom. Methods Sera were collected from Affiliated Hospital of Nanjing University of Chinese Medicine as centre 1 (discovery and internal validation sets) and Suzhou Hospital of Traditional Chinese Medicine as centre 2 (external validation set), including 88 normal subjects, 157 HUA and 183 gout patients. Lipidomics was performed by ultra high performance liquid chromatography plus Q-Exactive mass spectrometry (UHPLC-Q Exactive MS). Differential metabolites were identifed by both variable importance in the projection ≥1 in orthogonal partial least-squares discriminant analysis mode and false discovery rate adjusted P ≤ 0.05. Biomarkers were found by logistic regression and receiver operating characteristic (ROC) analysis. Results In the discovery set, a total of 245 and 150 metabolites, respectively, were found for normal subjects vs HUA and normal subjects vs gout. The disturbed metabolites included diacylglycerol, triacylglycerol (TAG), phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, etc. We also found 116 differential metabolites for HUA vs gout. Among them, the biomarker panel of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 could differentiate well between HUA and gout. The area under the receiver operating characteristic ROC curve was 0.8288, the sensitivity was 82% and the specificity was 78%, at a 95% CI 0.747, 0.9106. In the internal validation set, the predictive accuracy of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 panel for differentiation of HUA and gout reached 74.38%, while it was 84.03% in external validation set. Conclusion We identified serum biomarkers panel that have the potential to predict and diagnose HUA and gout patients. [ABSTRACT FROM AUTHOR]

Published
2022
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