Ping Qing

1.8k total citations
70 papers, 1.2k citations indexed

About

Ping Qing is a scholar working on Surgery, Cardiology and Cardiovascular Medicine and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Ping Qing has authored 70 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Surgery, 28 papers in Cardiology and Cardiovascular Medicine and 20 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Ping Qing's work include Lipoproteins and Cardiovascular Health (29 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (17 papers) and Cardiovascular Function and Risk Factors (10 papers). Ping Qing is often cited by papers focused on Lipoproteins and Cardiovascular Health (29 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (17 papers) and Cardiovascular Function and Risk Factors (10 papers). Ping Qing collaborates with scholars based in China, United States and United Kingdom. Ping Qing's co-authors include Cheng‐Gang Zhu, Na‐Qiong Wu, Yuan‐Lin Guo, Rui‐Xia Xu, Jian‐Jun Li, Sha Li, Jing Sun, Qian Dong, Geng Liu and Xiaolin Li and has published in prestigious journals such as PLoS ONE, Scientific Reports and Arteriosclerosis Thrombosis and Vascular Biology.

In The Last Decade

Ping Qing

65 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ping Qing China 20 654 437 283 168 157 70 1.2k
Nick S. Nurmohamed Netherlands 19 534 0.8× 377 0.9× 185 0.7× 140 0.8× 204 1.3× 68 1.1k
Emil M. deGoma United States 19 667 1.0× 365 0.8× 425 1.5× 242 1.4× 99 0.6× 40 1.4k
Qian Dong China 22 663 1.0× 514 1.2× 413 1.5× 165 1.0× 95 0.6× 80 1.3k
In Suck Choi South Korea 17 473 0.7× 598 1.4× 430 1.5× 124 0.7× 107 0.7× 49 1.3k
Tiziana Sampietro Italy 18 594 0.9× 299 0.7× 356 1.3× 156 0.9× 124 0.8× 75 1.1k
Gissette Reyes‐Soffer United States 18 672 1.0× 406 0.9× 369 1.3× 203 1.2× 107 0.7× 38 1.1k
Tsuyoshi Nozue Japan 20 672 1.0× 279 0.6× 293 1.0× 160 1.0× 90 0.6× 76 1.1k
Bernd Hewing Germany 18 403 0.6× 442 1.0× 241 0.9× 151 0.9× 288 1.8× 33 1.3k
Estíbaliz Jarauta Spain 17 583 0.9× 364 0.8× 283 1.0× 204 1.2× 114 0.7× 66 1.1k
Adam Oesterle United States 11 498 0.8× 409 0.9× 100 0.4× 164 1.0× 169 1.1× 29 1.2k

Countries citing papers authored by Ping Qing

Since Specialization
Citations

This map shows the geographic impact of Ping Qing'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 Ping Qing with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ping Qing more than expected).

Fields of papers citing papers by Ping Qing

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ping Qing. 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 Ping Qing. The network helps show where Ping Qing may publish in the future.

Co-authorship network of co-authors of Ping Qing

This figure shows the co-authorship network connecting the top 25 collaborators of Ping Qing. A scholar is included among the top collaborators of Ping Qing 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 Ping Qing. Ping Qing is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Wang, Xian-Qiang, Xingtong Zhou, Haibo Chen, et al.. (2024). Long-term outcomes of a novel fully magnetically levitated ventricular assist device for the treatment of advanced heart failure in China. The Journal of Heart and Lung Transplantation. 43(11). 1806–1815. 4 indexed citations
3.
4.
Li, Houpu, et al.. (2024). Side-Scan Sonar Image Augmentation Method Based on CC-WGAN. Applied Sciences. 14(17). 8031–8031. 3 indexed citations
5.
Wang, Zuoxiang, et al.. (2024). Association of the stress hyperglycemia ratio with coronary artery disease complexity as assessed by the SYNTAX score in patients with acute coronary syndrome. Diabetology & Metabolic Syndrome. 16(1). 139–139. 3 indexed citations
6.
Gao, Ying, Na‐Qiong Wu, Chenggang Zhu, et al.. (2017). Comparision of non-fasting with fasting blood lipid testing in in-hospital patients. Zhonghua jianyan yixue zazhi. 40(6). 431–435.
7.
Yang, Shenghua, Ying Du, Yan Zhang, et al.. (2017). Serum fibrinogen and cardiovascular events in Chinese patients with type 2 diabetes and stable coronary artery disease: a prospective observational study. BMJ Open. 7(6). e015041–e015041. 23 indexed citations
8.
Li, Sha, Yuan‐Lin Guo, Xi Zhao, et al.. (2017). Novel and traditional lipid-related biomarkers and their combinations in predicting coronary severity. Scientific Reports. 7(1). 360–360. 26 indexed citations
9.
Li, Sha, Na‐Qiong Wu, Cheng‐Gang Zhu, et al.. (2017). Significance of lipoprotein(a) levels in familial hypercholesterolemia and coronary artery disease. Atherosclerosis. 260. 67–74. 64 indexed citations
10.
Yang, Shenghua, Xiaolin Li, Yan Zhang, et al.. (2017). Triglyceride to High-Density Lipoprotein Cholesterol Ratio and Cardiovascular Events in Diabetics With Coronary Artery Disease. The American Journal of the Medical Sciences. 354(2). 117–124. 47 indexed citations
11.
Liu, Huihui, Yuan‐Lin Guo, Na‐Qiong Wu, et al.. (2017). High-density lipoprotein cholesterol levels are associated with coronary severity but not with outcomes in new-onset patients with stable coronary artery disease. Atherosclerosis. 263. 104–111. 6 indexed citations
12.
Li, Sha, Xiaolin Li, Cheng‐Gang Zhu, et al.. (2016). C-reactive protein as a predictor for poor collateral circulation in patients with chronic stable coronary heart disease. Annals of Medicine. 48(1-2). 83–88. 12 indexed citations
13.
Li, Sha, Yan Zhang, Cheng‐Gang Zhu, et al.. (2016). Identification of familial hypercholesterolemia in patients with myocardial infarction: A Chinese cohort study. Journal of clinical lipidology. 10(6). 1344–1352. 30 indexed citations
14.
Zhang, Yan, Na‐Qiong Wu, Sha Li, et al.. (2016). Non-HDL-C is a Better Predictor for the Severity of Coronary Atherosclerosis Compared with LDL-C. Heart Lung and Circulation. 25(10). 975–981. 50 indexed citations
15.
Fan, Ying, Sha Li, Xiaolin Li, et al.. (2016). Plasma endothelin-1 level as a predictor for poor collaterals in patients with ≥95% coronary chronic occlusion. Thrombosis Research. 142. 21–25. 7 indexed citations
16.
Wang, Yao, Cheng‐Gang Zhu, Yuan‐Lin Guo, et al.. (2016). Distribution of ABO Blood Groups and Coronary Artery Calcium. Heart Lung and Circulation. 26(6). 593–598. 6 indexed citations
17.
Qing, Ping, et al.. (2015). Association of Big Endothelin-1 with Coronary Artery Calcification. PLoS ONE. 10(11). e0142458–e0142458. 12 indexed citations
18.
Li, Sha, Yan Zhang, Rui‐Xia Xu, et al.. (2015). Proprotein convertase subtilisin-kexin type 9 as a biomarker for the severity of coronary artery disease. Annals of Medicine. 47(5). 386–393. 76 indexed citations
19.
Li, Xiaolin, Yuan‐Lin Guo, Cheng‐Gang Zhu, et al.. (2014). Impact of admission triglyceride for early outcome in diabetic patients with stable coronary artery disease. Lipids in Health and Disease. 13(1). 73–73. 9 indexed citations
20.
Qing, Ping & Tian Li. (2002). Cholera toxin B subunit labeling in lamina II of spinal cord dorsal horn following chronic inflammation in rats. Neuroscience Letters. 327(3). 161–164. 7 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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