Zaiwang Li

926 citations
31 papers · 733 · h-index 15

Impact in

Papers in

Zaiwang Li

31 papers receiving 728 citations

Peers

Zaiwang Li
Comparison fields: 5 of 76
  • Developmental Neuroscience 73
  • Neurology 98
  • Sensory Systems 50
  • Cellular and Molecular Neuroscience 132
  • Neurology 91
Replace Valentina Vacca with:
Valentina Vacca Italy
Kiran Kumar Bali Germany
Elías Utreras Chile
Luana Gilio Italy
Ilona Klusáková Czechia
Xi‐Ying Jiao China
Michael P. Fatt Canada
Jianping Zhang China
Yunju Jin United States
Zaiwang Li relative to Valentina Vacca Italy Valentina Vacca's profile →
Citations per field
00.5×3.0×
Valentina Vacca · 1×
Citations per year

Countries citing papers authored by Zaiwang Li

Since Specialization
Citations

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

Fields of papers citing papers by Zaiwang Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Zaiwang Li, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Zaiwang Li Line = papers co-authored together Zaiwang Li links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201473
2 201062
3 201459
4 201453
5 201852
6 201845
7 201141
8 201340
9 201831
10 201331
11
Nobiletin alleviates cerebral ischemic-reperfusion injury via MAPK signaling pathway.
201929
12 202126
13 201125
14 201022
15 201019
16 200914
17 202113
18 201913
19 202213
20 201012

About Zaiwang Li

Zaiwang Li is a scholar working on Molecular Biology, Neurology, Cellular and Molecular Neuroscience, Immunology and Genetics, having authored 31 papers that have together received 733 indexed citations. Recurring topics across this work include Nerve injury and regeneration (3 papers), Spinal Cord Injury Research (3 papers), Genetics and Neurodevelopmental Disorders (2 papers), Neurogenesis and neuroplasticity mechanisms (2 papers), interferon and immune responses (2 papers), Obsessive-Compulsive Spectrum Disorders (2 papers), Neuroinflammation and Neurodegeneration Mechanisms (2 papers) and Acute Ischemic Stroke Management (2 papers). The work is most often cited by research in Developmental Neuroscience (73 citations), Neurology (98 citations), Sensory Systems (50 citations), Cellular and Molecular Neuroscience (132 citations) and Neurology (91 citations). Zaiwang Li has collaborated with scholars based in China, United States and Norway. Frequent co-authors include Jijun Li, Jianping Zhang, Guangjun Xi, Dai‐Shi Tian, Jian Zou, Wensheng Qu, Zhouping Tang, Lin Zhao, Junli Liu and Zhiyuan Yu. Their work appears in journals such as Frontiers in Neurology, Brain and Behavior, Cell Death and Differentiation, Oncogene and Brain Research.

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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