Limei Zhong

411 total citations
19 papers, 330 citations indexed

About

Limei Zhong is a scholar working on Molecular Biology, Immunology and Cancer Research. According to data from OpenAlex, Limei Zhong has authored 19 papers receiving a total of 330 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 7 papers in Immunology and 5 papers in Cancer Research. Recurrent topics in Limei Zhong's work include Immune cells in cancer (5 papers), Cancer-related molecular mechanisms research (5 papers) and MicroRNA in disease regulation (4 papers). Limei Zhong is often cited by papers focused on Immune cells in cancer (5 papers), Cancer-related molecular mechanisms research (5 papers) and MicroRNA in disease regulation (4 papers). Limei Zhong collaborates with scholars based in China, United States and Spain. Limei Zhong's co-authors include Jie Zhou, Quan Yang, Maohua Shi, Huiyong Yin, Dehong Yan, Tao Meng, Yufeng Liu, Changyou Wu, Donglin Cao and Fengbin Liu and has published in prestigious journals such as Molecular and Cellular Biology, Scientific Reports and Biochemical and Biophysical Research Communications.

In The Last Decade

Limei Zhong

18 papers receiving 327 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Limei Zhong China 12 185 146 74 65 28 19 330
Ai Ikejiri Japan 5 247 1.3× 115 0.8× 34 0.5× 59 0.9× 13 0.5× 6 358
Maria I. Edilova Canada 5 135 0.7× 120 0.8× 64 0.9× 55 0.8× 15 0.5× 6 310
Jordan Loeliger Switzerland 4 251 1.4× 83 0.6× 36 0.5× 51 0.8× 18 0.6× 8 328
Yaxing Hao China 8 284 1.5× 156 1.1× 102 1.4× 69 1.1× 31 1.1× 11 466
Saumya Maru United States 8 203 1.1× 84 0.6× 60 0.8× 69 1.1× 18 0.6× 12 328
Xiangming Hu China 14 109 0.6× 406 2.8× 64 0.9× 93 1.4× 19 0.7× 22 532
Julia Lopatnikova Russia 12 237 1.3× 137 0.9× 24 0.3× 108 1.7× 13 0.5× 57 419
Piyush Sharma Germany 9 184 1.0× 93 0.6× 47 0.6× 56 0.9× 35 1.3× 15 408
Mikhail Chernov United States 7 144 0.8× 192 1.3× 67 0.9× 192 3.0× 15 0.5× 8 390
Liying Zhang China 10 91 0.5× 165 1.1× 98 1.3× 50 0.8× 13 0.5× 22 295

Countries citing papers authored by Limei Zhong

Since Specialization
Citations

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

Fields of papers citing papers by Limei Zhong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Limei Zhong

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

All Works

19 of 19 papers shown
1.
Liu, Yufeng, Yushan Li, Xiaoyu Lin, et al.. (2025). Single‐cell RNA sequencing of peripheral blood mononuclear cells from bronchopulmonary dysplasia. Clinical and Translational Medicine. 15(3). e70276–e70276.
2.
Carnevali, Davide, Limei Zhong, Esther González-Almela, et al.. (2024). A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features. Nature Machine Intelligence. 6(9). 1021–1033. 10 indexed citations
3.
Wu, Xiaojun, Limei Zhong, Ning Wang, et al.. (2024). MDSCs promote pathological angiogenesis in ocular neovascular disease. Biomedicine & Pharmacotherapy. 178. 117222–117222. 3 indexed citations
4.
Wang, Jianwei, Ruiqing Zhou, Limei Zhong, et al.. (2023). High-dimensional immune profiling using mass cytometry reveals IL-17A-producing γδ T cells as biomarkers in patients with T-cell-activated idiopathic severe aplastic anemia. International Immunopharmacology. 125(Pt B). 111163–111163. 2 indexed citations
5.
6.
Jiang, Xiaotao, Yi Wen, Yanhua Yan, et al.. (2021). CXCR4 is a Novel Biomarker Correlated With Malignant Transformation and Immune Infiltrates in Gastric Precancerous Lesions. Frontiers in Molecular Biosciences. 8. 697993–697993. 1 indexed citations
7.
Liu, Zhiguo, Hongyan Liu, Qian Dong, et al.. (2021). Prognostic role of DFNA5 in head and neck squamous cell carcinoma revealed by systematic expression analysis. BMC Cancer. 21(1). 951–951. 16 indexed citations
8.
Huang, Yu‐Sheng, Limei Zhong, Kechao Nie, et al.. (2021). Identification of LINC00665-miR-let-7b-CCNA2 competing endogenous RNA network associated with prognosis of lung adenocarcinoma. Scientific Reports. 11(1). 4434–4434. 13 indexed citations
9.
Huang, Yu‐Sheng, Zhiguo Liu, Limei Zhong, et al.. (2020). Construction of an 11-microRNA-based signature and a prognostic nomogram to predict the overall survival of head and neck squamous cell carcinoma patients. BMC Genomics. 21(1). 691–691. 11 indexed citations
10.
Zhong, Limei, Zhiyong Yang, Lijuan Li, et al.. (2020). Prognostic value of S1PR1 and its correlation with immune infiltrates in breast and lung cancers. BMC Cancer. 20(1). 766–766. 13 indexed citations
11.
Zhong, Limei, Sitao Li, Yi Wen, et al.. (2020). Expansion of Polymorphonuclear Myeloid-Derived Suppressor Cells in Patients With Gout. Frontiers in Immunology. 11. 567783–567783. 7 indexed citations
12.
Zhong, Limei, Zhiyong Yang, Lijuan Li, et al.. (2020). Bromodomain 4 is a potent prognostic marker associated with immune cell infiltration in breast cancer. Basic & Clinical Pharmacology & Toxicology. 128(1). 169–182. 11 indexed citations
13.
Zhong, Limei, Zhiguo Liu, Xuan Zhou, et al.. (2019). Expansion of PMN-myeloid derived suppressor cells and their clinical relevance in patients with oral squamous cell carcinoma. Oral Oncology. 95. 157–163. 32 indexed citations
14.
Liu, Yufeng, Bin Chen, Peiwu Li, et al.. (2018). Expansion and activation of monocytic-myeloid-derived suppressor cell via STAT3/arginase-I signaling in patients with ankylosing spondylitis. Arthritis Research & Therapy. 20(1). 168–168. 21 indexed citations
15.
Zhang, Shaoyang, Limei Zhong, Bing Chen, et al.. (2015). Identification of an HIV-1 replication inhibitor which rescues host restriction factor APOBEC3G in Vif–APOBEC3G complex. Antiviral Research. 122. 20–27. 22 indexed citations
16.
Zhong, Limei, Quan Yang, Wen Xie, & Jie Zhou. (2014). Liver X receptor regulates mouse GM-CSF-derived dendritic cell differentiation in vitro. Molecular Immunology. 60(1). 32–43. 14 indexed citations
17.
Yang, Quan, Limei Zhong, Maohua Shi, et al.. (2014). Cross Talk between Histone Deacetylase 4 and STAT6 in the Transcriptional Regulation of Arginase 1 during Mouse Dendritic Cell Differentiation. Molecular and Cellular Biology. 35(1). 63–75. 39 indexed citations
18.
Cao, Donglin, Liangshan Hu, Zhihong Zhang, et al.. (2014). MicroRNA-196b promotes cell proliferation and suppress cell differentiation in vitro. Biochemical and Biophysical Research Communications. 457(1). 1–6. 14 indexed citations
19.
Yan, Dehong, Quan Yang, Maohua Shi, et al.. (2013). Polyunsaturated fatty acids promote the expansion of myeloid‐derived suppressor cells by activating the JAK/STAT3 pathway. European Journal of Immunology. 43(11). 2943–2955. 96 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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