Qingfei Kong

2.5k total citations
64 papers, 1.8k citations indexed

About

Qingfei Kong is a scholar working on Molecular Biology, Immunology and Neurology. According to data from OpenAlex, Qingfei Kong has authored 64 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Molecular Biology, 16 papers in Immunology and 12 papers in Neurology. Recurrent topics in Qingfei Kong's work include Myasthenia Gravis and Thymoma (10 papers), Neuroinflammation and Neurodegeneration Mechanisms (7 papers) and Immune Cell Function and Interaction (7 papers). Qingfei Kong is often cited by papers focused on Myasthenia Gravis and Thymoma (10 papers), Neuroinflammation and Neurodegeneration Mechanisms (7 papers) and Immune Cell Function and Interaction (7 papers). Qingfei Kong collaborates with scholars based in China, United States and Sweden. Qingfei Kong's co-authors include Guangyou Wang, Hulun Li, Lili Mu, Beixue Gao, Deyu Fang, Dandan Wang, Jianxun Song, Donna D. Zhang, Zhenghong Lin and Yumei Liu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Molecular Cell.

In The Last Decade

Qingfei Kong

64 papers receiving 1.8k citations

Peers

Qingfei Kong

IMMUN

NEURO

ONCOL

NEURO

Alpa Trivedi United States

Yang Mao‐Draayer United States

Munehisa Shimamura Japan

Liping Qian China

Yi Zhong China

Haitao Sun China

Mi Tian China

Geneviève Soucy Canada

J. Steven Alexander United States

Yueting Zhang China

Alpa Trivedi United States

Qingfei Kong

1.1k ×1.5

206 ×0.5

IMMUN

252 ×1.0

NEURO

218 ×0.9

ONCOL

176 ×0.9

NEURO

Citations per year, relative to Qingfei Kong Qingfei Kong (= 1×) peers Alpa Trivedi

Countries citing papers authored by Qingfei Kong

Since Specialization

Citations

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

Fields of papers citing papers by Qingfei Kong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qingfei Kong

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

All Works

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20 of 20 papers shown

Zhang, Wenyuan, Yang Liu, Yu Guo, et al.. (2024). Impaired cerebral microvascular endothelial cells integrity due to elevated dopamine in myasthenic model. Journal of Neuroinflammation. 21(1). 10–10. 3 indexed citations

Cheng, Lei, Jiaqi Wang, Xiaoyu Zhang, et al.. (2024). The wnt/pyruvate kinase, muscle axis plays an essential role in the differentiation of mouse neuroblastoma cells. Neurochemistry International. 181. 105901–105901. 2 indexed citations

Kong, Qingfei, Yanlin Zhang, Ruiting Zhang, et al.. (2024). Nucleic acid aptamer-based electrochemical sensor for the detection of serum P-tau231 and the instant screening test of Alzheimer’s disease. Microchimica Acta. 191(6). 328–328. 12 indexed citations

Li, Xiangmei, et al.. (2023). DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19. Computers in Biology and Medicine. 159. 106969–106969. 9 indexed citations

Zhao, Wei, Xingfan Chen, Yang Liu, et al.. (2022). Metformin inhibits the pathogenic functions of AChR-specific B and Th17 cells by targeting miR-146a. Immunology Letters. 250. 29–40. 4 indexed citations

Wang, Qian, Xiangmei Li, Yahui Wang, et al.. (2022). Development and Validation of a Three-Gene Prognostic Signature Based on Tumor Microenvironment for Gastric Cancer. Frontiers in Genetics. 12. 801240–801240. 5 indexed citations

Zhang, Sifan, Bo Sun, Qingfei Kong, et al.. (2020). Combining miR-23b exposure with mesenchymal stem cell transplantation enhances therapeutic effects on EAE. Immunology Letters. 229. 18–26. 7 indexed citations

Han, Junwei, Xudong Han, Qingfei Kong, & Liang Cheng. (2019). psSubpathway: a software package for flexible identification of phenotype-specific subpathways in cancer progression. Bioinformatics. 36(7). 2303–2305. 39 indexed citations

Li, Zhaoying, Wen Wang, Xi Wang, et al.. (2018). Functional network analysis reveals biological roles of lncRNAs and mRNAs in MOG35–55 specific CD4+T helper cells. Genomics. 110(6). 337–346. 4 indexed citations

10.

Gao, Beixue, Qingfei Kong, Yana Zhang, et al.. (2017). The Histone Acetyltransferase Gcn5 Positively Regulates T Cell Activation. The Journal of Immunology. 198(10). 3927–3938. 36 indexed citations

11.

Wang, Yajun, Chawon Yun, Beixue Gao, et al.. (2017). The Lysine Acetyltransferase GCN5 Is Required for iNKT Cell Development through EGR2 Acetylation. Cell Reports. 20(3). 600–612. 30 indexed citations

12.

Liu, Chuanliang, Jing Jia, Yun Zhang, et al.. (2017). Regulator of G protein signaling 5 (RGS5) inhibits sonic hedgehog function in mouse cortical neurons. Molecular and Cellular Neuroscience. 83. 65–73. 5 indexed citations

13.

Li, Lei, Shaohong Fang, Wei Sun, et al.. (2016). MicroRNA 182 inhibits CD4+CD25+Foxp3+ Treg differentiation in experimental autoimmune encephalomyelitis. Clinical Immunology. 173. 109–116. 19 indexed citations

14.

Zhang, Yao, Dandan Wang, Tongshuai Zhang, et al.. (2014). Accumulation of natural killer cells in ischemic brain tissues and the chemotactic effect of IP-10. Journal of Neuroinflammation. 11(1). 79–79. 90 indexed citations

15.

Li, Na, Geng Wang, Xiuhua Yao, et al.. (2014). Adenosine receptor expression in a rat model of experimental autoimmune myasthenia gravis. Cellular Immunology. 290(2). 217–225. 4 indexed citations

16.

Wang, Hongwei, Xinyue Wang, Lili Mu, et al.. (2013). The Mechanism of Effective Electroacupuncture on T Cell Response in Rats with Experimental Autoimmune Encephalomyelitis. PLoS ONE. 8(1). e51573–e51573. 24 indexed citations

17.

Lin, Zhenghong, Hee‐Young Yang, Qingfei Kong, et al.. (2012). USP22 Antagonizes p53 Transcriptional Activation by Deubiquitinating Sirt1 to Suppress Cell Apoptosis and Is Required for Mouse Embryonic Development. Molecular Cell. 46(4). 484–494. 257 indexed citations

18.

Mu, Lili, Yao Zhang, Bo Sun, et al.. (2011). Activation of the receptor for advanced glycation end products (RAGE) exacerbates experimental autoimmune myasthenia gravis symptoms. Clinical Immunology. 141(1). 36–48. 26 indexed citations

19.

Zhai, Dongxu, Qingfei Kong, Wangshu Xu, et al.. (2008). RAGE expression is up-regulated in human cerebral ischemia and pMCAO rats. Neuroscience Letters. 445(1). 117–121. 41 indexed citations

20.

Zhao, Wei, Dandan Wang, Bo Sun, et al.. (2008). TGF-β expression by allogeneic bone marrow stromal cells ameliorates diabetes in NOD mice through modulating the distribution of CD4+ T cell subsets. Cellular Immunology. 253(1-2). 23–30. 22 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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