Haichun Yang

5.0k total citations
108 papers, 3.6k citations indexed

About

Haichun Yang is a scholar working on Nephrology, Molecular Biology and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Haichun Yang has authored 108 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Nephrology, 29 papers in Molecular Biology and 16 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Haichun Yang's work include Chronic Kidney Disease and Diabetes (32 papers), Renal Diseases and Glomerulopathies (26 papers) and Renin-Angiotensin System Studies (11 papers). Haichun Yang is often cited by papers focused on Chronic Kidney Disease and Diabetes (32 papers), Renal Diseases and Glomerulopathies (26 papers) and Renin-Angiotensin System Studies (11 papers). Haichun Yang collaborates with scholars based in United States, China and Japan. Haichun Yang's co-authors include Agnes B. Fogo, Yiqin Zuo, Raymond C. Harris, Valentina Kon, Lijun Ma, Jianyong Zhong, Chuan‐Ming Hao, Ming‐Zhi Zhang, Hong Fan and Wenjuan He and has published in prestigious journals such as Journal of Clinical Investigation, Blood and PLoS ONE.

In The Last Decade

Haichun Yang

103 papers receiving 3.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Haichun Yang United States 33 1.5k 1.2k 444 405 375 108 3.6k
Zhangsuo Liu China 37 1.5k 1.0× 1.7k 1.4× 362 0.8× 539 1.3× 312 0.8× 171 4.1k
Fang Liu China 30 1.2k 0.8× 882 0.7× 630 1.4× 328 0.8× 224 0.6× 194 3.5k
Richard J. Coward United Kingdom 31 2.3k 1.5× 1.4k 1.2× 619 1.4× 436 1.1× 287 0.8× 72 4.0k
Hyunjin Noh South Korea 32 976 0.7× 1.1k 0.9× 577 1.3× 298 0.7× 388 1.0× 104 3.0k
Niansong Wang China 38 1.6k 1.1× 1.9k 1.6× 583 1.3× 269 0.7× 292 0.8× 193 4.6k
Jeffrey R. Schelling United States 36 1.1k 0.7× 1.3k 1.1× 437 1.0× 285 0.7× 206 0.5× 84 3.2k
Masahiro Nezu Japan 13 1.1k 0.8× 901 0.8× 554 1.2× 326 0.8× 154 0.4× 26 3.0k
Noémie Jourde‐Chiche France 29 1.5k 1.0× 940 0.8× 323 0.7× 508 1.3× 410 1.1× 122 3.7k
Hideki Yokoi Japan 29 956 0.7× 1.3k 1.1× 402 0.9× 330 0.8× 191 0.5× 116 3.0k
Miho Shimizu Japan 26 1.2k 0.8× 571 0.5× 281 0.6× 332 0.8× 230 0.6× 121 2.7k

Countries citing papers authored by Haichun Yang

Since Specialization
Citations

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

Fields of papers citing papers by Haichun Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Haichun Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Haichun Yang. A scholar is included among the top collaborators of Haichun 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 Haichun Yang. Haichun Yang 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.
Yao, Tianyuan, et al.. (2025). GloFinder: AI-empowered QuPath plugin for WSI-level glomerular detection, visualization, and curation. Journal of Pathology Informatics. 17. 100433–100433. 1 indexed citations
2.
3.
Cui, Can, Ruining Deng, Tianyuan Yao, et al.. (2025). Assessment of cell nuclei AI foundation models in kidney pathology. PubMed. 13409. 15–15. 2 indexed citations
4.
Wu, Zhongze, et al.. (2025). Towards fine-grained renal vasculature segmentation: full-scale hierarchical learning with FH-Seg. PubMed. 13413. 6–6. 1 indexed citations
5.
Deng, Ruining, Tianyuan Yao, Jun Long, et al.. (2023). Omni-Seg: A Scale-Aware Dynamic Network for Renal Pathological Image Segmentation. IEEE Transactions on Biomedical Engineering. 70(9). 2636–2644. 24 indexed citations
6.
Zhong, Jianyong, Annet Kirabo, Haichun Yang, et al.. (2023). Intestinal Lymphatic Dysfunction in Kidney Disease. Circulation Research. 132(9). 1226–1245. 10 indexed citations
7.
Zhong, Jianyong, Haichun Yang, Elaine L. Shelton, et al.. (2022). Dicarbonyl-modified lipoproteins contribute to proteinuric kidney injury. JCI Insight. 7(21). 7 indexed citations
8.
Deng, Ruining, Haichun Yang, Shiru Wang, et al.. (2022). Dense multi-object 3D glomerular reconstruction and quantification on 2D serial section whole slide images. PubMed. 11603. 17–17. 4 indexed citations
9.
Wu, Huijuan, Christopher P. Larsen, Cesar F. Hernandez-Arroyo, et al.. (2020). AKI and Collapsing Glomerulopathy Associated with COVID-19 and APOL 1 High-Risk Genotype. Journal of the American Society of Nephrology. 31(8). 1688–1695. 192 indexed citations
10.
Khodo, Stellor Nlandu, Lauren Scarfe, Haichun Yang, et al.. (2020). Tubular β-catenin and FoxO3 interactions protect in chronic kidney disease. JCI Insight. 5(10). 23 indexed citations
11.
Soranno, Danielle E., Hyo‐Wook Gil, Christopher Altmann, et al.. (2019). Matching Human Unilateral AKI, a Reverse Translational Approach to Investigate Kidney Recovery after Ischemia. Journal of the American Society of Nephrology. 30(6). 990–1005. 27 indexed citations
12.
Clark, Amanda J., Kathy Jabs, Tracy E. Hunley, et al.. (2019). Urinary apolipoprotein AI in children with kidney disease. Pediatric Nephrology. 34(11). 2351–2360. 11 indexed citations
13.
Skrypnyk, Nataliya, Katja M. Gist, Kayo Okamura, et al.. (2019). IL-6-mediated hepatocyte production is the primary source of plasma and urine neutrophil gelatinase–associated lipocalin during acute kidney injury. Kidney International. 97(5). 966–979. 39 indexed citations
14.
Khodo, Stellor Nlandu, Marika Manolopoulou, Gautam Bhave, et al.. (2017). Blocking TGF-β and β-Catenin Epithelial Crosstalk Exacerbates CKD. Journal of the American Society of Nephrology. 28(12). 3490–3503. 48 indexed citations
15.
Lim, Beom Jin, Jae Won Yang, Jun Zou, et al.. (2017). Tubulointerstitial fibrosis can sensitize the kidney to subsequent glomerular injury. Kidney International. 92(6). 1395–1403. 42 indexed citations
16.
Zhong, Jianyong, Haichun Yang, & Agnes B. Fogo. (2016). A perspective on chronic kidney disease progression. American Journal of Physiology-Renal Physiology. 312(3). F375–F384. 94 indexed citations
17.
Kon, Valentina, Haichun Yang, & Sergio Fazio. (2015). Residual Cardiovascular Risk in Chronic Kidney Disease: Role of High-density Lipoprotein. Archives of Medical Research. 46(5). 379–391. 40 indexed citations
18.
Zhong, Jianyong, Haichun Yang, Valentina Kon, et al.. (2014). Vitronectin-binding PAI-1 protects against the development of cardiac fibrosis through interaction with fibroblasts. Laboratory Investigation. 94(6). 633–644. 22 indexed citations
19.
He, Wenjuan, Yingying Wang, Ming‐Zhi Zhang, et al.. (2010). Sirt1 activation protects the mouse renal medulla from oxidative injury. Journal of Clinical Investigation. 120(4). 1056–1068. 275 indexed citations
20.
Yang, Haichun, Lijun Ma, Ji Ma, & Agnes B. Fogo. (2006). Peroxisome proliferator-activated receptor-gamma agonist is protective in podocyte injury-associated sclerosis. Kidney International. 69(10). 1756–1764. 90 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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