Jun Wu

1.9k total citations
141 papers, 1.3k citations indexed

About

Jun Wu is a scholar working on Neurology, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Jun Wu has authored 141 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 87 papers in Neurology, 26 papers in Radiology, Nuclear Medicine and Imaging and 25 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Jun Wu's work include Intracranial Aneurysms: Treatment and Complications (65 papers), Vascular Malformations Diagnosis and Treatment (46 papers) and Intracerebral and Subarachnoid Hemorrhage Research (28 papers). Jun Wu is often cited by papers focused on Intracranial Aneurysms: Treatment and Complications (65 papers), Vascular Malformations Diagnosis and Treatment (46 papers) and Intracerebral and Subarachnoid Hemorrhage Research (28 papers). Jun Wu collaborates with scholars based in China, United States and South Korea. Jun Wu's co-authors include Yong Cao, Shuo Wang, Fuxin Lin, Xianzeng Tong, Bing Zhao, Shuo Wang, Yuanli Zhao, Qingyuan Liu, Pengjun Jiang and Jizong Zhao and has published in prestigious journals such as SHILAP Revista de lepidopterología, Journal of neurosurgery and Journal of Hepatology.

In The Last Decade

Jun Wu

131 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
Jun Wu China 19 757 250 222 148 122 141 1.3k
Joost Bot Netherlands 17 517 0.7× 459 1.8× 117 0.5× 151 1.0× 64 0.5× 38 1.5k
John Huston United States 23 916 1.2× 333 1.3× 673 3.0× 201 1.4× 167 1.4× 46 1.6k
Binbin Sui China 17 362 0.5× 212 0.8× 395 1.8× 261 1.8× 167 1.4× 89 910
J. Jarosz United Kingdom 14 491 0.6× 261 1.0× 244 1.1× 218 1.5× 67 0.5× 22 1.2k
Steven G. Imbesi United States 19 342 0.5× 118 0.5× 315 1.4× 140 0.9× 66 0.5× 41 817
Vance T. Lehman United States 24 680 0.9× 260 1.0× 440 2.0× 143 1.0× 94 0.8× 111 1.5k
Akihiko Hino Japan 18 757 1.0× 80 0.3× 187 0.8× 195 1.3× 58 0.5× 66 1.2k
Alexandra Seewann Netherlands 16 596 0.8× 622 2.5× 221 1.0× 237 1.6× 100 0.8× 19 2.0k
Dorith Goldsher Israel 20 383 0.5× 367 1.5× 136 0.6× 152 1.0× 210 1.7× 40 1.3k
Toshiki Endo Japan 24 897 1.2× 140 0.6× 335 1.5× 300 2.0× 80 0.7× 153 2.0k

Countries citing papers authored by Jun Wu

Since Specialization
Citations

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

Fields of papers citing papers by Jun Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Wu. A scholar is included among the top collaborators of Jun Wu 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 Jun Wu. Jun Wu 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.
Xiong, Li, Pengjun Jiang, Jun Wu, et al.. (2025). Development and validation of a score for clinical deterioration in patients with cerebral venous thrombosis. Neurosurgical Review. 48(1). 56–56. 1 indexed citations
2.
3.
Yang, Yi, et al.. (2024). Integrated Deep Learning Model for the Detection, Segmentation, and Morphologic Analysis of Intracranial Aneurysms Using CT Angiography. Radiology Artificial Intelligence. 7(1). e240017–e240017. 1 indexed citations
4.
5.
Liu, Qingyuan, Kaiwen Wang, Yanan Zhang, et al.. (2023). Model based on single-nucleotide polymorphism to discriminate aspirin resistance patients. Stroke and Vascular Neurology. 9(3). 212–220. 4 indexed citations
6.
Liu, Qingyuan, Pengjun Jiang, Chengcheng Zhu, et al.. (2023). Serum interleukin-1 is a new biomarker to predict the risk of rebleeding of ruptured intracranial aneurysm after admission. Neurosurgical Review. 46(1). 123–123. 1 indexed citations
7.
Chen, Junyi, et al.. (2023). Measurement model: a generic model for size measurement of aquatic products using instance segmentation. Aquaculture International. 32(2). 2263–2277. 2 indexed citations
8.
9.
Li, Haiyan, Jun Wu, Dan Xu, et al.. (2021). Pore texture analysis in automated 3D breast ultrasound images for implanted lightweight hernia mesh identification: a preliminary study. BioMedical Engineering OnLine. 20(1). 23–23. 1 indexed citations
10.
Li, Maogui, Jun Wu, Pengjun Jiang, et al.. (2020). Corpus Callosum Diffusion Anisotropy and Hemispheric Lateralization of Language in Patients with Brain Arteriovenous Malformations. Brain Connectivity. 11(6). 447–456. 3 indexed citations
11.
Wu, Jun, Qingyuan Liu, Kaiwen Wang, et al.. (2020). Emergency surgery is an effective way to improve the outcome of severe spontaneous intracerebral hemorrhage patients on long-term oral antiplatelet therapy. Neurosurgical Review. 44(2). 1205–1216. 15 indexed citations
12.
Liu, Qingyuan, Pengjun Jiang, Jun Wu, et al.. (2019). Intracranial aneurysm rupture score may correlate to the risk of rebleeding before treatment of ruptured intracranial aneurysms. Neurological Sciences. 40(8). 1683–1693. 11 indexed citations
13.
Li, Hao, Jun Wu, Maogui Li, et al.. (2017). Relationship of A1 Segment Hypoplasia with the Radiologic and Clinical Outcomes of Surgical Clipping of Anterior Communicating Artery Aneurysms. World Neurosurgery. 106. 806–812. 10 indexed citations
14.
Wang, Lijun, Fuxin Lin, Jun Wu, et al.. (2016). Plasticity of motor function and surgical outcomes in patients with cerebral arteriovenous malformation involving primary motor area: insight from fMRI and DTI. Chinese Neurosurgical Journal. 2(1). 3 indexed citations
15.
Tong, Xianzeng, Jun Wu, Jianji Pan, et al.. (2016). Microsurgical Resection for Persistent Arteriovenous Malformations Following Gamma Knife Radiosurgery: A Case-Control Study. World Neurosurgery. 88. 277–288. 9 indexed citations
16.
Tong, Xianzeng, Jun Wu, Yong Cao, et al.. (2016). Microsurgical Outcome of Unruptured Brain Arteriovenous Malformations: A Single-Center Experience. World Neurosurgery. 99. 644–655. 8 indexed citations
17.
Zhao, Bing, Yong Cao, Xianxi Tan, et al.. (2015). Complications and outcomes after early surgical treatment for poor-grade ruptured intracranial aneurysms: A multicenter retrospective cohort. International Journal of Surgery. 23(Pt A). 57–61. 15 indexed citations
18.
Zhao, Bing, Yuanli Zhao, Xianxi Tan, et al.. (2015). Primary decompressive craniectomy for poor-grade middle cerebral artery aneurysms with associated intracerebral hemorrhage. Clinical Neurology and Neurosurgery. 133. 1–5. 14 indexed citations
19.
Zhao, Bing, Yong Cao, Yuanli Zhao, Jun Wu, & Shuo Wang. (2014). Functional MRI-guided microsurgery of intracranial arteriovenous malformations: study protocol for a randomised controlled trial. BMJ Open. 4(10). e006618–e006618. 15 indexed citations
20.
Tang, Ribo, Xueliang Yan, Jianzeng Dong, et al.. (2014). Predictors of recurrence after a repeat ablation procedure for paroxysmal atrial fibrillation: role of left atrial enlargement. EP Europace. 16(11). 1569–1574. 25 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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