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Your search keyword '"Ji, Jianguang"' showing total 678 results
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678 results on '"Ji, Jianguang"'

4. SSRI antidepressant citalopram reverses the Warburg effect to inhibit hepatocellular carcinoma by directly targeting GLUT1

Author

Dong, Fangyuan, He, Kang, Zhang, Shan, Song, Kaiyuan, Jiang, Luju, Hu, Li-Peng, Li, Qing, Zhang, Xue-Li, Zhang, Naiqi, Li, Bo-Tai, Zhu, Li-Li, Li, Jun, Feng, Mingxuan, Gao, Yunchen, Chen, Jie, Hu, Xiaona, Wang, Jiaofeng, Jiang, Chongyi, Wang, Cun, Zhu, Helen He, Da, Lin-Tai, Ji, Jianguang, Zhang, Zhi-Gang, Bao, Zhijun, and Jiang, Shu-Heng

Published
2024
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21. Development and validation of a machine learning‐based model to predict isolated post‐challenge hyperglycemia in middle‐aged and elder adults: Analysis from a multicentric study.

Author

Hou, Rui, Dou, Jingtao, Wu, Lijuan, Zhang, Xiaoyu, Li, Changwei, Wang, Weiqing, Gao, Zhengnan, Tang, Xulei, Yan, Li, Wan, Qin, Luo, Zuojie, Qin, Guijun, Chen, Lulu, Ji, Jianguang, He, Yan, Wang, Wei, Mu, Yiming, and Zheng, Deqiang

Subjects
ARTIFICIAL neural networks, MIDDLE-aged persons, MACHINE learning, HYPERGLYCEMIA, GLUCOSE tolerance tests
Abstract

Introduction: Due to the high cost and complexity, the oral glucose tolerance test is not adopted as the screening method for identifying diabetes patients, which leads to the misdiagnosis of patients with isolated post‐challenge hyperglycemia (IPH), that is., patients with normal fasting plasma glucose (<7.0 mmoL/L) and abnormal 2‐h postprandial blood glucose (≥11.1 mmoL/L). We aimed to develop a model to differentiate individuals with IPH from the normal population. Methods: Data from 54301 eligible participants were obtained from the Risk Evaluation of Cancers in Chinese Diabetic Individuals: a longitudinal (REACTION) study in China. Data from 37740 participants were used to develop the diagnostic system. External validation was performed among 16561 participants. Three machine learning algorithms were used to create the predictive models, which were further evaluated by various classification algorithms to establish the best predictive model. Results: Ten features were selected to develop an IPH diagnosis system (IPHDS) based on an artificial neural network. In external validation, the AUC of the IPHDS was 0.823 (95% CI 0.811–0.836), which was significantly higher than the AUC of the Taiwan model [0.799 (0.786–0.813)] and that of the Chinese Diabetes Risk Score model [0.648 (0.635–0.662)]. The IPHDS model had a sensitivity of 75.6% and a specificity of 74.6%. This model outperformed the Taiwan and CDRS models in subgroup analyses. An online site with instant predictions was deployed at https://app‐iphds‐e1fc405c8a69.herokuapp.com/. Conclusions: The proposed IPHDS could be a convenient and user‐friendly screening tool for diabetes during health examinations in a large general population. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Revealing genes associated with cervical cancer in distinct immune cells: A comprehensive Mendelian randomization analysis.

Author

Li, Ning, Yi, Huan, Sun, Wen, Sundquist, Jan, Sundquist, Kristina, Zhang, Xiaoyu, Zheng, Deqiang, and Ji, Jianguang

Subjects
CERVICAL cancer, GENOME-wide association studies, LOCUS (Genetics), HUMAN papillomavirus, FALSE discovery rate
Abstract

Human papillomavirus can be contracted by sexually active women. However, only a small proportion of these infections persist and have the potential to progress into cervical cancers, indicating a significant involvement of the immune system in cervical cancer development. Despite this, our understanding of the precise contributions of genes from different immune cell types in cervical cancers remains limited. Therefore, the primary objective of our study was to investigate the potential causal relationships between specific immune cell genes and the development of cervical cancers. By accessing expression quantitative trait loci datasets of 14 distinct immune cell types and genome wide association study of cervical cancers, we employed the summary data‐based Mendelian randomization (SMR) along with multi‐single nucleotide polymorphism (SNP)‐based SMR to identify significant genes associated with cervical cancers. Colocalization analysis was further conducted to explore the shared genetic causality. A total of 10 genes across 11 immune cell types (26 significant gene‐trait associations) were found to be associated with cervical cancers after false discovery rate correction. Notably, the ORMDL3, BRK1 and HMGN1 gene expression levels showed significant association with cervical cancer in specific immune cell types, respectively. These associations were supported by strong evidence of colocalization analyses. Our study has identified several genes in different immune cells that were associated with cervical cancer. However, further research is necessary to confirm these findings and provide more comprehensive insights into the association between these gene expressions and cervical cancer risk. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Long-Term Outcomes after Cervical Cold Knife and Loop Electrosurgical Excision Procedure Conization: Insights from a Multicenter Chinese Database and Swedish Nationwide Cohort Study

Author

Yi, Huan, primary, Liu, Yishan, additional, Chen, Xiaohong, additional, Zheng, Hui, additional, Zhang, Yulong, additional, Huang, jimiao, additional, Liu, Shihuang, additional, Chen, Yusha, additional, Chen, Fulian, additional, Hong, Qiuhua, additional, Zhang, Shaoyu, additional, Shi, Feifeng, additional, Sundquist, Jan, additional, Sundquist, Kristina, additional, Yang, Yan, additional, Chen, Jiancui, additional, Zheng, Xiangqin, additional, and Ji, Jianguang, additional

Published
2024
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43. Additional file 1 of Glucagon-like peptide-1 receptor agonists and diabetic retinopathy: nationwide cohort and Mendelian randomization studies

Author

Zheng, Deqiang, Li, Ning, Hou, Rui, Zhang, Xiaoyu, Wu, Lijuan, Sundquist, Jan, Sundquist, Kristina, and Ji, Jianguang

Abstract

Additional file 1: Table S1. Diagnosis codes to assist in identifying type 2 diabetes. Table S2. Prescription codes to assist in identifying anti-diabetic drugs. Table S3. Diagnosis codes to assist in identifying other types of diabetes excluding type 2 diabetes. Table S4. Diagnosis codes to assist in identifying pre-existing conditions that could disqualify patients for study eligibility. Table S5. Diagnosis codes to assist in identifying retinal disorders. Table S6. Detailed information of eQTL and GWAS summary data in SMR analyses. Table S7. Diagnosis codes to assist in identifying baseline hypertension and CVD. Table S8. Hazard ratios and 95% confidence intervals for the GLP-1 RAs group versus nonusers from fully-adjusted Cox models for DR for five iterations of index date random sampling. Figure S1. Matched cohort design. Figure S2. Hazard ratios and 95% confidence intervals for the GLP-1 RAs group versus nonusers from fully-adjusted Cox models for DR for five iterations of index date random sampling. Figure S3. The association of a 1-SD increase of GLP1R gene expression in pancreas tissue with the risk of DR using SMR method.

Published
2023
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