Yajing Fu

2.0k total citations · 1 hit paper
58 papers, 1.3k citations indexed

About

Yajing Fu is a scholar working on Immunology, Virology and Molecular Biology. According to data from OpenAlex, Yajing Fu has authored 58 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Immunology, 33 papers in Virology and 14 papers in Molecular Biology. Recurrent topics in Yajing Fu's work include Immune Cell Function and Interaction (34 papers), HIV Research and Treatment (33 papers) and T-cell and B-cell Immunology (11 papers). Yajing Fu is often cited by papers focused on Immune Cell Function and Interaction (34 papers), HIV Research and Treatment (33 papers) and T-cell and B-cell Immunology (11 papers). Yajing Fu collaborates with scholars based in China, United States and United Kingdom. Yajing Fu's co-authors include Yuntao Wu, Yuanxiong Cheng, Yongjun Jiang, Zining Zhang, Hong Shang, Haibo Ding, Junjie Xu, Xiaoxu Han, Zhenxing Chu and Hong Shang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Immunology and The Journal of Infectious Diseases.

In The Last Decade

Yajing Fu

54 papers receiving 1.2k citations

Hit Papers

Understanding SARS-CoV-2-Mediated Inflammatory Responses:... 2020 2026 2022 2024 2020 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yajing Fu China 17 529 419 247 216 210 58 1.3k
Mathieu Surénaud France 22 298 0.6× 451 1.1× 256 1.0× 90 0.4× 417 2.0× 44 1.3k
Philip A. Mudd United States 17 491 0.9× 351 0.8× 88 0.4× 174 0.8× 253 1.2× 39 1.2k
Xiangfan Yin United States 15 215 0.4× 182 0.4× 143 0.6× 55 0.3× 318 1.5× 29 797
Enrico Brunetta Italy 20 193 0.4× 574 1.4× 237 1.0× 79 0.4× 211 1.0× 44 1.3k
Sijia He China 12 398 0.8× 131 0.3× 108 0.4× 70 0.3× 101 0.5× 37 773
Nzeera Ketter United States 17 270 0.5× 165 0.4× 351 1.4× 64 0.3× 219 1.0× 23 1.2k
Alicja Wiercińska‐Drapało Poland 17 334 0.6× 150 0.4× 232 0.9× 47 0.2× 344 1.6× 119 1.3k
Arjun Rustagi United States 15 871 1.6× 625 1.5× 150 0.6× 394 1.8× 465 2.2× 21 1.7k
Javier García‐Pérez Spain 18 372 0.7× 283 0.7× 444 1.8× 26 0.1× 269 1.3× 42 1.1k
Madison Schank United States 18 162 0.3× 236 0.6× 155 0.6× 32 0.1× 245 1.2× 30 714

Countries citing papers authored by Yajing Fu

Since Specialization
Citations

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

Fields of papers citing papers by Yajing Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yajing Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Yajing Fu. A scholar is included among the top collaborators of Yajing Fu 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 Yajing Fu. Yajing Fu 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.
Mao, Aiping, et al.. (2025). Phenotypic screening identified polydatin alleviating cartilage degeneration by modulating SIRT3-dependent mitochondrial dysfunction. Phytomedicine. 144. 156948–156948. 1 indexed citations
2.
Liu, Jiani, Jixiao Wang, Ziyao Wang, et al.. (2024). PGC-1α/LDHA signaling facilitates glycolysis initiation to regulate mechanically induced bone remodeling under inflammatory microenvironment. Bone. 185. 117132–117132. 4 indexed citations
4.
Spear, Mark, Jia Guo, Yajing Fu, et al.. (2024). Suppression of viral rebound by a Rev-dependent lentiviral particle in SIV-infected rhesus macaques. Gene Therapy. 32(1). 16–24.
5.
Liu, Mei, Jinpeng Liu, Pan Wang, et al.. (2024). Approaches for Performance Verification Toward Standardization of Peripheral Blood Regulatory T-Cell Detection by Flow Cytometry. Archives of Pathology & Laboratory Medicine. 148(11). 1234–1243. 1 indexed citations
6.
Wang, Meiting, Zining Zhang, Yajing Fu, et al.. (2023). Inhibitory receptor CD47 binding to plasma TSP1 suppresses NK-cell IFN-γ production via activating the JAK/STAT3 pathway during HIV infection. Journal of Translational Medicine. 21(1). 869–869. 5 indexed citations
7.
Cai, Hongjiao, Jue Shi, Yajing Fu, et al.. (2022). Downregulation of TCF1 in HIV Infection Impairs T-cell Proliferative Capacity by Disrupting Mitochondrial Function. Frontiers in Microbiology. 13. 880873–880873.
9.
He, Sijia, Jia Guo, Yajing Fu, et al.. (2021). Prestimulation of CD2 confers resistance to HIV-1 latent infection in blood resting CD4 T cells. iScience. 24(11). 103305–103305. 1 indexed citations
10.
Fu, Yajing, Zining Zhang, Yongjun Jiang, et al.. (2021). CD27−CD38+ B cells accumulated in early HIV infection exhibit transitional profile and promote HIV disease progression. Cell Reports. 36(2). 109344–109344. 8 indexed citations
11.
Shi, Qian, Xi Chen, Tong Wu, et al.. (2021). The accumulation of plasma acylcarnitines are associated with poor immune recovery in HIV-infected individuals. BMC Infectious Diseases. 21(1). 808–808. 14 indexed citations
12.
Wang, Yue, Yufei Zhang, Wen Zhao, et al.. (2020). Natural killer cell counts in primary HIV infection predicts disease progression and immune restoration after treatment. Virology. 550. 89–98. 3 indexed citations
13.
He, Sijia, Yajing Fu, Jia Guo, et al.. (2019). Cofilin hyperactivation in HIV infection and targeting the cofilin pathway using an anti-α 4 β 7 integrin antibody. Science Advances. 5(1). eaat7911–eaat7911. 14 indexed citations
14.
Pan, Ying, et al.. (2019). Reduced eIF3d accelerates HIV disease progression by attenuating CD8+ T cell function. Journal of Translational Medicine. 17(1). 167–167. 8 indexed citations
15.
Wu, Xian, Li Yao, Yali Chen, et al.. (2018). Increased Expression of sST2 in Early HIV Infected Patients Attenuated the IL-33 Induced T Cell Responses. Frontiers in Immunology. 9. 2850–2850. 8 indexed citations
16.
Liu, Tingting, Zhuo Wang, Jie Lei, et al.. (2018). Expression of the Inhibitory Receptor TIGIT Is Up-Regulated Specifically on NK Cells With CD226 Activating Receptor From HIV-Infected Individuals. Frontiers in Immunology. 9. 2341–2341. 49 indexed citations
17.
Chen, Xi, Qian Shi, Zining Zhang, et al.. (2018). The Early Antibody-Dependent Cell-Mediated Cytotoxicity Response Is Associated With Lower Viral Set Point in Individuals With Primary HIV Infection. Frontiers in Immunology. 9. 2322–2322. 25 indexed citations
18.
Wu, Xian, Lele Zhang, Yajing Fu, et al.. (2017). Deregulated MicroRNA-21 Expression in Monocytes from HIV-Infected Patients Contributes to Elevated IP-10 Secretion in HIV Infection. Frontiers in Immunology. 8. 1122–1122. 17 indexed citations
19.
Liu, Tingting, et al.. (2016). Studies on activation,apoptosis and proliferation of NKT like cells in untreated HIV infected patients. 32(12). 1800. 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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