Ming-Shi Chang

2.3k total citations
42 papers, 1.8k citations indexed

About

Ming-Shi Chang is a scholar working on Immunology, Oncology and Molecular Biology. According to data from OpenAlex, Ming-Shi Chang has authored 42 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Immunology, 19 papers in Oncology and 16 papers in Molecular Biology. Recurrent topics in Ming-Shi Chang's work include Cytokine Signaling Pathways and Interactions (14 papers), Inflammasome and immune disorders (7 papers) and Psoriasis: Treatment and Pathogenesis (5 papers). Ming-Shi Chang is often cited by papers focused on Cytokine Signaling Pathways and Interactions (14 papers), Inflammasome and immune disorders (7 papers) and Psoriasis: Treatment and Pathogenesis (5 papers). Ming-Shi Chang collaborates with scholars based in Taiwan, United States and Russia. Ming-Shi Chang's co-authors include Yu‐Hsiang Hsu, Wei‐Yu Chen, Chung‐Hsi Hsing, Jiann‐Jou Yang, W. F. Liang, Chien‐Feng Li, Bor‐Chih Cheng, Ching‐Hua Yeh, Steven F. Ziegler and Nick Carpino and has published in prestigious journals such as Nature Communications, The Journal of Experimental Medicine and SHILAP Revista de lepidopterología.

In The Last Decade

Ming-Shi Chang

42 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ming-Shi Chang Taiwan 26 893 588 456 240 211 42 1.8k
Gretta L. Stritesky United States 15 2.1k 2.4× 588 1.0× 343 0.8× 161 0.7× 232 1.1× 22 2.6k
Shadi Swaidani United States 21 1.4k 1.6× 451 0.8× 631 1.4× 176 0.7× 375 1.8× 35 2.4k
Sujata Sarkar United States 14 1.0k 1.1× 279 0.5× 253 0.6× 120 0.5× 125 0.6× 21 1.6k
Wook Lew South Korea 10 950 1.1× 356 0.6× 341 0.7× 240 1.0× 186 0.9× 31 1.6k
Justin Hartupee United States 15 780 0.9× 341 0.6× 388 0.9× 127 0.5× 167 0.8× 24 1.9k
Francesca Wanda Rossi Italy 25 779 0.9× 256 0.4× 446 1.0× 108 0.5× 320 1.5× 82 1.8k
Jaewoo Hong South Korea 18 845 0.9× 386 0.7× 404 0.9× 121 0.5× 140 0.7× 48 1.5k
Hekla Sigmundsdóttir Iceland 14 1.2k 1.4× 258 0.4× 222 0.5× 520 2.2× 231 1.1× 21 1.9k
Mitsuteru Akahoshi Japan 26 1.4k 1.5× 291 0.5× 365 0.8× 171 0.7× 391 1.9× 68 2.3k
Emily Smith United States 20 853 1.0× 370 0.6× 577 1.3× 82 0.3× 81 0.4× 36 1.7k

Countries citing papers authored by Ming-Shi Chang

Since Specialization
Citations

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

Fields of papers citing papers by Ming-Shi Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming-Shi Chang. 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 Ming-Shi Chang. The network helps show where Ming-Shi Chang may publish in the future.

Co-authorship network of co-authors of Ming-Shi Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Ming-Shi Chang. A scholar is included among the top collaborators of Ming-Shi Chang 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 Ming-Shi Chang. Ming-Shi Chang 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.
Hsu, Yu‐Hsiang, Junne‐Ming Sung, Wei‐Yu Chen, et al.. (2017). Interleukin-20 targets podocytes and is upregulated in experimental murine diabetic nephropathy. Experimental & Molecular Medicine. 49(3). e310–e310. 18 indexed citations
2.
Lin, Yih-Jyh, et al.. (2014). IL-20 and IL-20R1 antibodies protect against liver fibrosis. Hepatology. 60(3). 1003–1014. 58 indexed citations
3.
Hsu, Yu‐Hsiang & Ming-Shi Chang. (2014). The Therapeutic Potential of Anti-Interleukin-20 Monoclonal Antibody. Cell Transplantation. 23(4-5). 631–639. 19 indexed citations
4.
Huang, Tsang‐Hai, Jack L. Lewis, Yuh-Shiou Tai, et al.. (2014). A Methionine-Restricted Diet and Endurance Exercise Decrease Bone Mass and Extrinsic Strength but Increase Intrinsic Strength in Growing Male Rats. Journal of Nutrition. 144(5). 621–630. 30 indexed citations
5.
Li, Chien‐Feng, et al.. (2013). Interleukin-19 in Breast Cancer. SHILAP Revista de lepidopterología. 2013. 1–9. 28 indexed citations
6.
Sun, Ding‐Ping, et al.. (2013). Interleukin (IL)-19 promoted skin wound healing by increasing fibroblast keratinocyte growth factor expression. Cytokine. 62(3). 360–368. 35 indexed citations
7.
Hsu, Yu‐Hsiang, et al.. (2012). Anti–IL-20 Monoclonal Antibody Alleviates Inflammation in Oral Cancer and Suppresses Tumor Growth. Molecular Cancer Research. 10(11). 1430–1439. 30 indexed citations
8.
Hsing, Chung‐Hsi, Hung-Chi Cheng, Yu‐Hsiang Hsu, et al.. (2011). Upregulated IL-19 in Breast Cancer Promotes Tumor Progression and Affects Clinical Outcome. Clinical Cancer Research. 18(3). 713–725. 55 indexed citations
9.
Hsu, Yu‐Hsiang, et al.. (2011). Anti–IL-20 monoclonal antibody inhibits the differentiation of osteoclasts and protects against osteoporotic bone loss. The Journal of Experimental Medicine. 208(9). 1849–1861. 67 indexed citations
10.
Yeh, Ching‐Hua, Bor‐Chih Cheng, Chuan-Chih Hsu, et al.. (2011). Induced Interleukin-19 Contributes to Cell-Mediated Immunosuppression in Patients Undergoing Coronary Artery Bypass Grafting With Cardiopulmonary Bypass. The Annals of Thoracic Surgery. 92(4). 1252–1259. 23 indexed citations
11.
Hsu, Yu‐Hsiang, et al.. (2008). Interleukin-20 targets renal cells and is associated with chronic kidney disease. Biochemical and Biophysical Research Communications. 374(3). 448–453. 25 indexed citations
12.
Sun, Kuang‐Hui, et al.. (2008). Interleukin-20 targets renal mesangial cells and is associated with lupus nephritis. Clinical Immunology. 129(2). 277–285. 37 indexed citations
13.
Chang, Ming-Shi, et al.. (2008). Mouse interleukin-20 receptor 1a targets renal epithelial cells and is associated with renal calcium deposition. Genes and Immunity. 10(3). 237–247. 2 indexed citations
14.
Hsing, Chung‐Hsi, et al.. (2007). IL-19 IS INVOLVED IN THE PATHOGENESIS OF ENDOTOXIC SHOCK. Shock. 29(1). 7–15. 39 indexed citations
15.
Hsing, Chung‐Hsi, et al.. (2006). Induction of Interleukin-19 and Interleukin-22 After Cardiac Surgery With Cardiopulmonary Bypass. The Annals of Thoracic Surgery. 81(6). 2196–2201. 38 indexed citations
16.
Hsing, Chung‐Hsi, et al.. (2006). Tissue microarray analysis of interleukin-20 expression. Cytokine. 35(1-2). 44–52. 35 indexed citations
17.
Chen, Wei‐Yu, et al.. (2006). IL-20 Is Expressed in Atherosclerosis Plaques and Promotes Atherosclerosis in Apolipoprotein E–Deficient Mice. Arteriosclerosis Thrombosis and Vascular Biology. 26(9). 2090–2095. 76 indexed citations
18.
Chen, Wei‐Yu, et al.. (2005). IL-24 inhibits the growth of hepatoma cells in vivo. Genes and Immunity. 6(6). 493–499. 33 indexed citations
19.
Senaldi, Giorgio, Marina Stolina, Jane Guo, et al.. (2002). Regulatory Effects of Novel Neurotrophin-1/B Cell-Stimulating Factor-3 (Cardiotrophin-Like Cytokine) on B Cell Function. The Journal of Immunology. 168(11). 5690–5698. 42 indexed citations
20.
Liang, W. F., et al.. (2002). IL-19 Induces Production of IL-6 and TNF-α and Results in Cell Apoptosis Through TNF-α. The Journal of Immunology. 169(8). 4288–4297. 266 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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