Baoxia Cui

1.9k total citations
84 papers, 1.3k citations indexed

About

Baoxia Cui is a scholar working on Obstetrics and Gynecology, Epidemiology and Immunology. According to data from OpenAlex, Baoxia Cui has authored 84 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Obstetrics and Gynecology, 22 papers in Epidemiology and 19 papers in Immunology. Recurrent topics in Baoxia Cui's work include Endometrial and Cervical Cancer Treatments (23 papers), Cervical Cancer and HPV Research (21 papers) and Ovarian cancer diagnosis and treatment (10 papers). Baoxia Cui is often cited by papers focused on Endometrial and Cervical Cancer Treatments (23 papers), Cervical Cancer and HPV Research (21 papers) and Ovarian cancer diagnosis and treatment (10 papers). Baoxia Cui collaborates with scholars based in China, United States and Hong Kong. Baoxia Cui's co-authors include Daoxin Ma, Jun Jiao, Youzhong Zhang, Xinlin Jiao, Teng Zhang, Xingsheng Yang, Beihua Kong, Wei Li, Chunyan Ji and Yunbo Qiao and has published in prestigious journals such as The Journal of Immunology, PLoS ONE and International Journal of Molecular Sciences.

In The Last Decade

Baoxia Cui

80 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Baoxia Cui China 20 513 396 295 253 221 84 1.3k
Pamela A. Groben United States 20 406 0.8× 108 0.3× 184 0.6× 460 1.8× 143 0.6× 51 1.3k
Michaël Noë United States 18 297 0.6× 172 0.4× 119 0.4× 408 1.6× 89 0.4× 40 1.0k
Kohei Nakamura Japan 18 264 0.5× 200 0.5× 80 0.3× 413 1.6× 239 1.1× 113 1.1k
Asima Mukhopadhyay India 17 520 1.0× 143 0.4× 83 0.3× 641 2.5× 252 1.1× 59 1.3k
Kun Mu China 23 949 1.8× 310 0.8× 293 1.0× 419 1.7× 30 0.1× 59 1.6k
Shir‐Hwa Ueng Taiwan 23 326 0.6× 408 1.0× 129 0.4× 459 1.8× 239 1.1× 78 1.8k
Jianxin Qiu China 22 1.2k 2.3× 964 2.4× 275 0.9× 182 0.7× 33 0.1× 65 2.0k
Shunda Du China 20 363 0.7× 297 0.8× 102 0.3× 463 1.8× 82 0.4× 126 1.5k
Liting Qian China 17 407 0.8× 280 0.7× 86 0.3× 227 0.9× 116 0.5× 53 926

Countries citing papers authored by Baoxia Cui

Since Specialization
Citations

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

Fields of papers citing papers by Baoxia Cui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Baoxia Cui

This figure shows the co-authorship network connecting the top 25 collaborators of Baoxia Cui. A scholar is included among the top collaborators of Baoxia Cui 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 Baoxia Cui. Baoxia Cui 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.
Hou, Fei, et al.. (2025). GATA1-mediated macrophage polarization via TrkB/cGMP–PKG signaling pathway to promote the development of preeclampsia. European journal of medical research. 30(1). 12–12. 2 indexed citations
2.
Wang, Xiuyun, Yang Jiao, Baoxia Cui, Hongbin Zhu, & Rutian Wang. (2025). Two‐Stage Optimal Operation of Integrated Energy System Considering Electricity–Heat Demand Response and Time‐of‐Use Energy Price. International Journal of Energy Research. 2025(1). 1 indexed citations
3.
Bian, Tingting, Lingwei Sun, Xinlin Jiao, et al.. (2025). SLC7A11 is a potential therapeutic target and prognostic biomarker correlated with immune cell infiltration in cervical cancer. Discover Oncology. 16(1). 125–125. 4 indexed citations
4.
Liu, Qingqing, Xiaofang Zhang, Jiamei Li, et al.. (2024). A Histopathologic Image Analysis for the Classification of Endocervical Adenocarcinoma Silva Patterns Depend on Weakly Supervised Deep Learning. American Journal Of Pathology. 194(5). 735–746. 2 indexed citations
5.
Wang, Zixiang, Jinwen Ye, Teng Zhang, et al.. (2024). Targeting tumor-associated macrophage-derived CD74 improves efficacy of neoadjuvant chemotherapy in combination with PD-1 blockade for cervical cancer. Journal for ImmunoTherapy of Cancer. 12(8). e009024–e009024. 8 indexed citations
6.
Wang, Bingyu, Jinwen Ye, Xinlin Jiao, et al.. (2024). Glycolysis Induced by METTL14 Is Essential for Macrophage Phagocytosis and Phenotype in Cervical Cancer. The Journal of Immunology. 212(4). 723–736. 14 indexed citations
7.
8.
9.
Han, Sai, Xiaoli Liu, Jiaqi Yang, et al.. (2023). New mechanisms and biomarkers of lymph node metastasis in cervical cancer: reflections from plasma proteomics. Clinical Proteomics. 20(1). 35–35. 7 indexed citations
10.
Liu, Qingqing, Nan Jiang, Chunyan Hao, et al.. (2023). Identification of lymph node metastasis in pre‐operation cervical cancer patients by weakly supervised deep learning from histopathological whole‐slide biopsy images. Cancer Medicine. 12(17). 17952–17966. 8 indexed citations
11.
Zhang, Qianqian, Liju Zong, Hui Zhang, et al.. (2021). B7‐H4 Expression in Precancerous Lesions of the Uterine Cervix. BioMed Research International. 2021(1). 5857092–5857092. 1 indexed citations
12.
Zhang, Qianqian, Liju Zong, Hui Zhang, et al.. (2021). Expression of B7-H3 Correlates with PD-L1 and Poor Prognosis in Patients with Cervical Cancer. OncoTargets and Therapy. Volume 14. 4275–4283. 10 indexed citations
13.
Qu, Wenjie, Qingqing Liu, Xinlin Jiao, et al.. (2021). Development and Validation of a Personalized Survival Prediction Model for Uterine Adenosarcoma: A Population-Based Deep Learning Study. Frontiers in Oncology. 10. 623818–623818. 9 indexed citations
14.
Fang, Fang, Dawei Li, Lu Zhao, et al.. (2019). <p>Expression of NR1H3 in endometrial carcinoma and its effect on the proliferation of Ishikawa cells in vitro</p>. OncoTargets and Therapy. Volume 12. 685–697. 5 indexed citations
15.
Song, Kun, et al.. (2018). A comparison of the prognosis between adenocarcinoma and squamous cell carcinoma in stage IB–IIA cervical cancer. International Journal of Clinical Oncology. 23(3). 522–531. 34 indexed citations
16.
Han, Sai, Ying Wang, Xuejiao Shi, et al.. (2018). Negative roles of B7-H3 and B7-H4 in the microenvironment of cervical cancer. Experimental Cell Research. 371(1). 222–230. 40 indexed citations
17.
Zhang, Yan, Fei Hou, Daoxin Ma, et al.. (2014). Tc17 Cells in Patients with Uterine Cervical Cancer. PLoS ONE. 9(2). e86812–e86812. 16 indexed citations
18.
Hou, Fei, Zhen Li, Daoxin Ma, et al.. (2012). Distribution of Th17 cells and Foxp3-expressing T cells in tumor-infiltrating lymphocytes in patients with uterine cervical cancer. Clinica Chimica Acta. 413(23-24). 1848–1854. 40 indexed citations
19.
20.
Tian, Yu, Cunzhong Yuan, Daoxin Ma, et al.. (2011). IL-21 and IL-12 Inhibit Differentiation of Treg and TH17 Cells and Enhance Cytotoxicity of Peripheral Blood Mononuclear Cells in Patients With Cervical Cancer. International Journal of Gynecological Cancer. 21(9). 1672–1678. 18 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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