Zhouping Tang

3.0k total citations · 1 hit paper
139 papers, 1.9k citations indexed

About

Zhouping Tang is a scholar working on Neurology, Epidemiology and Molecular Biology. According to data from OpenAlex, Zhouping Tang has authored 139 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Neurology, 38 papers in Epidemiology and 29 papers in Molecular Biology. Recurrent topics in Zhouping Tang's work include Intracerebral and Subarachnoid Hemorrhage Research (63 papers), Neurosurgical Procedures and Complications (34 papers) and Acute Ischemic Stroke Management (33 papers). Zhouping Tang is often cited by papers focused on Intracerebral and Subarachnoid Hemorrhage Research (63 papers), Neurosurgical Procedures and Complications (34 papers) and Acute Ischemic Stroke Management (33 papers). Zhouping Tang collaborates with scholars based in China, United States and Canada. Zhouping Tang's co-authors include Gaigai Li, Chao Pan, Hong Deng, Ping Zhang, Yingxin Tang, Shiling Chen, Suiqiang Zhu, Hao Nie, Wenliang Guo and Guofeng Wu and has published in prestigious journals such as SHILAP Revista de lepidopterología, Neurology and Endocrinology.

In The Last Decade

Zhouping Tang

124 papers receiving 1.9k citations

Hit Papers

Neuroscience of cancer: unraveling the complex interplay ... 2025 2026 2025 5 10 15

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Zhouping Tang China 25 657 431 344 237 234 139 1.9k
Jun Yu China 24 390 0.6× 416 1.0× 300 0.9× 112 0.5× 171 0.7× 90 1.6k
Jen‐Tsung Yang Taiwan 25 646 1.0× 368 0.9× 435 1.3× 194 0.8× 151 0.6× 104 1.8k
Nathalie Kubis France 29 568 0.9× 534 1.2× 247 0.7× 352 1.5× 550 2.4× 100 2.3k
Raymond J. Grill United States 24 470 0.7× 532 1.2× 306 0.9× 387 1.6× 174 0.7× 42 1.9k
Chao Han China 28 535 0.8× 785 1.8× 287 0.8× 276 1.2× 321 1.4× 179 3.0k
Kojiro Wada Japan 22 931 1.4× 336 0.8× 454 1.3× 211 0.9× 131 0.6× 153 1.9k
Maria Laura Manca Italy 25 621 0.9× 623 1.4× 258 0.8× 295 1.2× 92 0.4× 80 2.3k
Masayuki Fujioka Japan 26 687 1.0× 392 0.9× 448 1.3× 441 1.9× 544 2.3× 75 2.5k
Lijun Hou China 25 694 1.1× 553 1.3× 420 1.2× 233 1.0× 151 0.6× 73 2.0k
Cesar Reis United States 27 784 1.2× 592 1.4× 413 1.2× 232 1.0× 407 1.7× 56 2.1k

Countries citing papers authored by Zhouping Tang

Since Specialization
Citations

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

Fields of papers citing papers by Zhouping Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zhouping Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Zhouping Tang. A scholar is included among the top collaborators of Zhouping Tang 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 Zhouping Tang. Zhouping Tang 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.
Chen, Danyang, Jian Shi, Bo Tao, et al.. (2025). A Novel Transfer Learning‐Based Hybrid EEG‐fNIRS Brain‐Computer Interface for Intracerebral Hemorrhage Rehabilitation. Advanced Science. 12(43). e05426–e05426. 1 indexed citations
3.
Chen, Kai, Diansheng Chen, Ruijie Zhang, Meng Cai, & Zhouping Tang. (2025). MonoTracker: Monocular-Based Fully Automatic Registration and Real-Time Tracking Method for Neurosurgical Robots. Chinese Journal of Mechanical Engineering. 38(1).
5.
Li, Yuanwei, et al.. (2024). Mechanism and application of immune interventions in intracerebral haemorrhage. Expert Reviews in Molecular Medicine. 26. e22–e22. 5 indexed citations
6.
Cai, Meng, et al.. (2024). Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation. IEEE Journal of Biomedical and Health Informatics. 29(4). 2871–2884. 2 indexed citations
7.
Li, Qi, Lan Deng, & Zhouping Tang. (2024). Delayed hematoma growth in a patient with thrombocytopenia. 5(4). 201–203.
8.
9.
Chen, Danyang, Shiling Chen, Jian Shi, et al.. (2023). Surgical Robotics for Intracerebral Hemorrhage Treatment: State of the Art and Future Directions. Annals of Biomedical Engineering. 51(9). 1933–1941. 12 indexed citations
10.
Li, Yunjie, Xia Liu, Jingxuan Wang, Chao‐Yang Pan, & Zhouping Tang. (2023). A Nomogram Model for Predicting Prognosis in Spontaneous Intracerebral Hemorrhage Patients. Journal of Integrative Neuroscience. 22(2). 42–42. 2 indexed citations
11.
Zhao, Xingwei, et al.. (2023). Attention-Focused Triggering Strategy for Dynamic Classification in SSVEP-Based Brain–Computer Interface. IEEE Transactions on Instrumentation and Measurement. 73. 1–13.
12.
Guo, Guangyu, Wenliang Guo, Hong Deng, et al.. (2023). Homocysteine impedes neurite outgrowth recovery after intracerebral haemorrhage by downregulating pCAMK2A. Stroke and Vascular Neurology. 8(4). 335–348. 2 indexed citations
13.
Li, Yunjie, Xia Liu, Shiling Chen, et al.. (2023). Effect of antiplatelet therapy on the incidence, prognosis, and rebleeding of intracerebral hemorrhage. CNS Neuroscience & Therapeutics. 29(6). 1484–1496. 9 indexed citations
14.
Li, Qi, Rui Li, Libo Zhao, et al.. (2020). Intraventricular Hemorrhage Growth: Definition, Prevalence and Association with Hematoma Expansion and Prognosis. Neurocritical Care. 33(3). 732–739. 40 indexed citations
15.
Nie, Hao, Yang Hu, Wenliang Guo, et al.. (2020). miR‐331‐3p Inhibits Inflammatory Response after Intracerebral Hemorrhage by Directly Targeting NLRP6. BioMed Research International. 2020(1). 6182464–6182464. 27 indexed citations
16.
Yao, Xiaolong, Shengwen Liu, Junwen Wang, et al.. (2020). The clinical characteristics and prognosis of COVID‐19 patients with cerebral stroke: A retrospective study of 113 cases from one single‐centre. European Journal of Neuroscience. 53(4). 1350–1361. 9 indexed citations
17.
Bai, Shuang, Wenliang Guo, Yangyang Feng, et al.. (2019). Efficacy and safety of anti-inflammatory agents for the treatment of major depressive disorder: a systematic review and meta-analysis of randomised controlled trials. Journal of Neurology Neurosurgery & Psychiatry. 91(1). 21–32. 151 indexed citations
18.
Li, Qi, Yiqing Shen, Xiong‐Fei Xie, et al.. (2018). Expansion-Prone Hematoma: Defining a Population at High Risk of Hematoma Growth and Poor Outcome. Neurocritical Care. 30(3). 601–608. 26 indexed citations
19.
Hu, Yang, Gaigai Li, Ye Zhang, et al.. (2018). Upregulated TSG-6 Expression in ADSCs Inhibits the BV2 Microglia-Mediated Inflammatory Response. BioMed Research International. 2018. 1–11. 18 indexed citations
20.
Tang, Zhouping, Na Liu, Zaiwang Li, et al.. (2010). In vitro evaluation of the compatibility of a novel collagen-heparan sulfate biological scaffold with olfactory ensheathing cells.. PubMed. 123(10). 1299–304. 9 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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