Chao‐Ke Tang

8.5k total citations · 3 hit papers
145 papers, 6.4k citations indexed

About

Chao‐Ke Tang is a scholar working on Surgery, Molecular Biology and Cancer Research. According to data from OpenAlex, Chao‐Ke Tang has authored 145 papers receiving a total of 6.4k indexed citations (citations by other indexed papers that have themselves been cited), including 83 papers in Surgery, 70 papers in Molecular Biology and 53 papers in Cancer Research. Recurrent topics in Chao‐Ke Tang's work include Cholesterol and Lipid Metabolism (72 papers), Peroxisome Proliferator-Activated Receptors (37 papers) and Cancer, Lipids, and Metabolism (31 papers). Chao‐Ke Tang is often cited by papers focused on Cholesterol and Lipid Metabolism (72 papers), Peroxisome Proliferator-Activated Receptors (37 papers) and Cancer, Lipids, and Metabolism (31 papers). Chao‐Ke Tang collaborates with scholars based in China, Canada and United States. Chao‐Ke Tang's co-authors include Xiao-Hua Yu, Xi‐Long Zheng, Dawei Zhang, Kai Yin, Yuchang Fu, Heng Li, Shilin Tang, Shangming Liu, Xiaohua Yu and Xin-Ping Ouyang and has published in prestigious journals such as Journal of Biological Chemistry, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Chao‐Ke Tang

142 papers receiving 6.3k citations

Hit Papers

Coronavirus disease 2019 (COVID-19): cu... 2013 2026 2017 2021 2020 2013 2021 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chao‐Ke Tang China 43 2.5k 2.0k 1.5k 1.4k 864 145 6.4k
Paolo Parini Sweden 42 2.7k 1.1× 2.7k 1.3× 860 0.6× 795 0.6× 1.2k 1.3× 135 7.5k
Laurent Lagrost France 47 2.5k 1.0× 2.9k 1.4× 894 0.6× 828 0.6× 897 1.0× 186 6.9k
Robert J. Konrad United States 47 2.2k 0.9× 2.4k 1.2× 503 0.3× 1.6k 1.2× 394 0.5× 151 6.9k
Giuseppe Danilo Norata Italy 57 2.7k 1.1× 3.0k 1.5× 1.4k 0.9× 3.3k 2.4× 2.0k 2.3× 235 10.5k
John Cijiang He United States 61 3.9k 1.6× 766 0.4× 512 0.3× 1.3k 1.0× 945 1.1× 243 10.3k
David P. Hajjar United States 47 3.9k 1.6× 1.9k 0.9× 1.0k 0.7× 2.6k 1.9× 1.4k 1.7× 111 8.5k
Teresa Padró Spain 43 2.9k 1.1× 1.5k 0.7× 1.3k 0.8× 1.1k 0.8× 597 0.7× 197 7.2k
Koutaro Yokote Japan 47 4.6k 1.9× 2.8k 1.4× 1.4k 0.9× 883 0.7× 1.1k 1.3× 337 12.1k
Alexander N. Orekhov Russia 39 2.5k 1.0× 967 0.5× 658 0.4× 1.7k 1.3× 1.1k 1.3× 225 6.4k
Cheng Zhang China 43 2.6k 1.0× 761 0.4× 584 0.4× 836 0.6× 741 0.9× 216 6.2k

Countries citing papers authored by Chao‐Ke Tang

Since Specialization
Citations

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

Fields of papers citing papers by Chao‐Ke Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chao‐Ke Tang. 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 Chao‐Ke Tang. The network helps show where Chao‐Ke Tang may publish in the future.

Co-authorship network of co-authors of Chao‐Ke Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Chao‐Ke Tang. A scholar is included among the top collaborators of Chao‐Ke Tang 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 Chao‐Ke Tang. Chao‐Ke Tang 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.
Wang, Yang, et al.. (2024). Ilexgenin A inhibits lipid accumulation in macrophages and reduces the progression of atherosclerosis through PTPN2/ERK1/2/ABCA1 signalling pathway. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids. 1869(7). 159533–159533. 3 indexed citations
2.
Guo, Min, et al.. (2023). History and Development of ABCA1. Current Problems in Cardiology. 49(1). 102036–102036. 5 indexed citations
3.
Li, Heng, et al.. (2023). Pax‐8: Molecular biology, pathophysiology, and potential pathogenesis. BioFactors. 50(3). 408–421. 1 indexed citations
4.
Yuan, Wen, et al.. (2020). Long Non Coding RNA SNHG16 Facilitates Proliferation, Migration, Invasion and Autophagy of Neuroblastoma Cells via Sponging miR-542-3p and Upregulating ATG5 Expression. SHILAP Revista de lepidopterología. 1 indexed citations
5.
Ou, Xiang, Jiahui Gao, Xiao-Hua Yu, et al.. (2019). Angiopoietin-1 aggravates atherosclerosis by inhibiting cholesterol efflux and promoting inflammatory response. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids. 1865(2). 158535–158535. 29 indexed citations
6.
7.
Cayabyab, Francisco S., et al.. (2016). Sortilin: A novel regulator in lipid metabolism and atherogenesis. Clinica Chimica Acta. 460. 11–17. 36 indexed citations
8.
Yao, Yan, Xin Zhang, Haipeng Chen, et al.. (2016). MicroRNA-186 promotes macrophage lipid accumulation and secretion of pro-inflammatory cytokines by targeting cystathionine γ-lyase in THP-1 macrophages. Atherosclerosis. 250. 122–132. 36 indexed citations
9.
Wu, Jianfeng, Yanyan Tang, Min Zhang, et al.. (2014). Urotensin II increases foam cell formation by repressing ABCA1 expression through the ERK/NF-κB pathway in THP-1 macrophages. Biochemical and Biophysical Research Communications. 452(4). 998–1003. 21 indexed citations
10.
Yu, Xiaohua, Kai Wu, Xi‐Long Zheng, et al.. (2014). Hydrogen sulfide as a potent cardiovascular protective agent. Clinica Chimica Acta. 437. 78–87. 61 indexed citations
11.
Lv, Yun-Cheng, Kai Yin, Yuchang Fu, et al.. (2013). Posttranscriptional Regulation of ATP-Binding Cassette Transporter A1 in Lipid Metabolism. DNA and Cell Biology. 32(7). 348–358. 31 indexed citations
12.
Yu, Xiao-Hua, Kun Qian, Na Jiang, et al.. (2013). ABCG5/ABCG8 in cholesterol excretion and atherosclerosis. Clinica Chimica Acta. 428. 82–88. 154 indexed citations
13.
Yu, Xiao-Hua, Li-Jing Liu, Hong Qian, et al.. (2013). Apelin and its receptor APJ in cardiovascular diseases. Clinica Chimica Acta. 428. 1–8. 112 indexed citations
14.
Yin, Kai, Shilin Tang, Xiaohua Yu, et al.. (2013). Apolipoprotein A-I inhibits LPS-induced atherosclerosis in ApoE−/− mice possibly via activated STAT3-mediated upregulation of tristetraprolin. Acta Pharmacologica Sinica. 34(6). 837–846. 35 indexed citations
15.
Yin, Kai, Zhongcheng Mo, Guojun Zhao, et al.. (2011). Tristetraprolin-dependent Post-transcriptional Regulation of Inflammatory Cytokine mRNA Expression by Apolipoprotein A-I. Journal of Biological Chemistry. 286(16). 13834–13845. 48 indexed citations
16.
Xiao, Ji, Xiehong Liu, Meimei Liu, et al.. (2010). Ibrolipim increases ABCA1/G1 expression by the LXRα signaling pathway in THP-1 macrophage-derived foam cells. Acta Pharmacologica Sinica. 31(10). 1343–1349. 28 indexed citations
18.
Tang, Chao‐Ke. (2007). Roles of NPC1 and NPC2 in intracellular lipid homeostasis. Zhongguo bingli shengli zazhi. 1 indexed citations
19.
Zhang, Chi, Weidong Yin, Duan‐Fang Liao, et al.. (2006). NO-1886 upregulates ATP binding cassette transporter A1 and inhibits diet-induced atherosclerosis in Chinese Bama minipigs. Journal of Lipid Research. 47(9). 2055–2063. 28 indexed citations
20.
Tang, Chao‐Ke, HE Xiu-sheng, Guanghui Yi, et al.. (2003). The action of liver X receptor #alpha# on cholesterol efflux in THP-1 macrophage-derived foam cells. PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS. 30(6). 940–944. 1 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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