Xiaobing Yang

813 total citations
19 papers, 568 citations indexed

About

Xiaobing Yang is a scholar working on Nephrology, Cardiology and Cardiovascular Medicine and Molecular Biology. According to data from OpenAlex, Xiaobing Yang has authored 19 papers receiving a total of 568 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Nephrology, 6 papers in Cardiology and Cardiovascular Medicine and 3 papers in Molecular Biology. Recurrent topics in Xiaobing Yang's work include Acute Kidney Injury Research (7 papers), Chronic Kidney Disease and Diabetes (5 papers) and Heart Failure Treatment and Management (3 papers). Xiaobing Yang is often cited by papers focused on Acute Kidney Injury Research (7 papers), Chronic Kidney Disease and Diabetes (5 papers) and Heart Failure Treatment and Management (3 papers). Xiaobing Yang collaborates with scholars based in China and United States. Xiaobing Yang's co-authors include Fan Fan Hou, Jianwei Tian, Chunbo Chen, Yan Zha, Jialu Liu, Jumei Zhang, Jianbin Tan, Qingping Wu, Youhua Liu and Chun Xiao and has published in prestigious journals such as Journal of the American Society of Nephrology, Gene and American Journal of Kidney Diseases.

In The Last Decade

Xiaobing Yang

19 papers receiving 563 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Xiaobing Yang China 13 311 132 98 66 66 19 568
Raúl Fernández-Prado Spain 12 237 0.8× 161 1.2× 58 0.6× 17 0.3× 42 0.6× 19 535
Edyta Gołembiewska Poland 11 189 0.6× 92 0.7× 59 0.6× 15 0.2× 74 1.1× 55 488
Caroline Pelletier France 9 221 0.7× 231 1.8× 49 0.5× 21 0.3× 37 0.6× 15 655
Rosalba Pérez‐Villalva Mexico 18 302 1.0× 234 1.8× 91 0.9× 27 0.4× 161 2.4× 30 789
Cinzia Lombardi Italy 16 150 0.5× 119 0.9× 132 1.3× 14 0.2× 53 0.8× 34 677
Hiromichi Gotoh Japan 15 189 0.6× 81 0.6× 40 0.4× 17 0.3× 31 0.5× 45 544
Junji Takaya Japan 15 115 0.4× 111 0.8× 60 0.6× 23 0.3× 84 1.3× 47 626
Liu Gao China 12 96 0.3× 354 2.7× 104 1.1× 19 0.3× 103 1.6× 46 679
L G Downs United Kingdom 10 119 0.4× 58 0.4× 117 1.2× 43 0.7× 54 0.8× 16 463
Nobuharu Fujiwara Japan 15 110 0.4× 92 0.7× 110 1.1× 18 0.3× 61 0.9× 19 497

Countries citing papers authored by Xiaobing Yang

Since Specialization
Citations

This map shows the geographic impact of Xiaobing Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xiaobing Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaobing Yang more than expected).

Fields of papers citing papers by Xiaobing Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiaobing Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xiaobing Yang. The network helps show where Xiaobing Yang may publish in the future.

Co-authorship network of co-authors of Xiaobing Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaobing Yang. A scholar is included among the top collaborators of Xiaobing Yang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xiaobing Yang. Xiaobing Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Ma, Rui, Jun Liu, Jianwei Tian, et al.. (2024). Urinary cytokeratin 20 as a predictor for chronic kidney disease following acute kidney injury. JCI Insight. 9(13). 1 indexed citations
2.
Hou, Fan Fan, Di Xie, Jun Wang, et al.. (2023). Effectiveness of Mycophenolate Mofetil Among Patients With Progressive IgA Nephropathy. JAMA Network Open. 6(2). e2254054–e2254054. 62 indexed citations
3.
Zhao, Yuan, Deyin Zhang, Xiaoxue Zhang, et al.. (2022). Expression features of the ovine FTO gene and association between FTO polymorphism and tail fat deposition related-traits in Hu sheep. Gene. 826. 146451–146451. 10 indexed citations
4.
Chen, Chunbo, et al.. (2021). Combining renal cell arrest and damage biomarkers to predict progressive AKI in patient with sepsis. BMC Nephrology. 22(1). 415–415. 12 indexed citations
5.
Ding, Hanying, Jinxiang Li, Yang Li, et al.. (2021). MicroRNA-10 negatively regulates inflammation in diabetic kidney via targeting activation of the NLRP3 inflammasome. Molecular Therapy. 29(7). 2308–2320. 52 indexed citations
6.
Yang, Xiaobing, Hong Zhang, Xin Xu, et al.. (2019). Urinary Matrix Metalloproteinase 7 and Prediction of IgA Nephropathy Progression. American Journal of Kidney Diseases. 75(3). 384–393. 37 indexed citations
7.
Fan, Fang, et al.. (2019). Urinary Matrix Metalloproteinase-7 and Prediction of AKI Progression Post Cardiac Surgery. Disease Markers. 2019. 1–7. 14 indexed citations
8.
Cao, Wei, Meng Shi, Li‐Ling Wu, et al.. (2018). A renal-cerebral-peripheral sympathetic reflex mediates insulin resistance in chronic kidney disease. EBioMedicine. 37. 281–293. 20 indexed citations
9.
Deng, Yujun, Shenglong Chen, Heng Ye, et al.. (2017). Evaluation of clinically available renal biomarkers in critically ill adults: a prospective multicenter observational study. Critical Care. 21(1). 46–46. 41 indexed citations
10.
Yang, Xiaobing, Chunbo Chen, Siyuan Teng, et al.. (2017). Urinary Matrix Metalloproteinase-7 Predicts Severe AKI and Poor Outcomes after Cardiac Surgery. Journal of the American Society of Nephrology. 28(11). 3373–3382. 52 indexed citations
11.
Hou, Fan Fan & Xiaobing Yang. (2016). Advances in the Management of Acute Cardiorenal Syndrome in China: Biomarkers for Predicting Development and Outcomes. Kidney Diseases. 2(4). 145–150. 6 indexed citations
12.
Chen, Chunbo, Xiaobing Yang, Yan Zha, et al.. (2016). Urinary Biomarkers at the Time of AKI Diagnosis as Predictors of Progression of AKI among Patients with Acute Cardiorenal Syndrome. Clinical Journal of the American Society of Nephrology. 11(9). 1536–1544. 71 indexed citations
13.
Yang, Xiaobing, Chunbo Chen, Jianwei Tian, et al.. (2015). Urinary Angiotensinogen Level Predicts AKI in Acute Decompensated Heart Failure. Journal of the American Society of Nephrology. 26(8). 2032–2041. 57 indexed citations
14.
Yang, Xiaobing, Xin Lin, Tao Lu, et al.. (2015). Fasting Plasma Glucose Levels Predict Steroid-Induced Abnormal Glucose Metabolism in Patients with Non-Diabetic Chronic Kidney Disease: A Prospective Cohort Study. American Journal of Nephrology. 41(2). 107–115. 6 indexed citations
15.
Luo, Sheng-Kang, et al.. (2015). Periodontitis contributes to aberrant metabolism in type 2 diabetes mellitus rats by stimulating the expression of adipokines. Journal of Periodontal Research. 51(4). 453–461. 20 indexed citations
16.
Xiao, Chun, et al.. (2012). Hypoglycemic effects of Ganoderma lucidum polysaccharides in type 2 diabetic mice. Archives of Pharmacal Research. 35(10). 1793–1801. 82 indexed citations
17.
Wang, Aifeng, Yong-Ping Wang, Guobao Wang, Zhanmei Zhou, & Xiaobing Yang. (2010). Infective endocarditis associated with acute renal failure: Repeat renal biopsy and successful recovery. Experimental and Therapeutic Medicine. 1(3). 433–436. 5 indexed citations
18.
Zhang, Weiru, Fan Fan Hou, Xiaobing Yang, et al.. (2006). Level of asymmetric dimethylarginine and carotid atherosclerosis in patients with chronic kidney disease.. PubMed. 31(5). 621–8. 2 indexed citations
19.
Yang, Xiaobing, Fan Fan Hou, Qiang Wu, et al.. (2005). [Increased levels of advanced oxidation protein products are associated with atherosclerosis in chronic kidney disease].. PubMed. 44(5). 342–6. 18 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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