Ping Luan
Impact in
- Neurology top 10%
- Biomaterials top 10%
- Nanoparticle-Based Drug Delivery
Papers in
-
- Neurological Disease Mechanisms and Treatments 3
- Co-authors
- Beibei Gu (3 shared papers)Songhua Xiao (6 shared papers)Wang Liao (6 shared papers)Jun Liu (8 shared papers)Shengnuo Fan (4 shared papers)Wenli Fang (3 shared papers)Jun Liu (2 shared papers)Enxiang Tao (4 shared papers)
- Journals
- CNS Neuroscience & Therapeutics (11 papers)Chinese Chemical Letters (2 papers)Ageing Research Reviews (2 papers)animal (2 papers)Brain and Behavior (1 paper)
- Partner nations
- ChinaUnited StatesMacao
In The Last Decade
Ping Luan
47 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 125
- Neurology 91
- Biomaterials 114
- Physiology 162
- Animal Science and Zoology 64
- Developmental Neuroscience 22
Countries citing papers authored by Ping Luan
This map shows the geographic impact of Ping Luan'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 Ping Luan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ping Luan more than expected).
Fields of papers citing papers by Ping Luan
This network shows the impact of papers produced by Ping Luan. 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 Ping Luan. The network helps show where Ping Luan may publish in the future.
Co-authors
The 25 scholars most cited alongside Ping Luan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 49 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 155 | |
| 2 | 2012 | 103 | |
| 3 | 2021 | 95 | |
| 4 | 2015 | 58 | |
| 5 | 2017 | 56 | |
| 6 | 2015 | 55 | |
| 7 | 2017 | 38 | |
| 8 | 2017 | 37 | |
| 9 | 2023 | 34 | |
| 10 | 2015 | 32 | |
| 11 | 2022 | 31 | |
| 12 | 2023 | 26 | |
| 13 | 2021 | 26 | |
| 14 | Neurotropin reduces memory impairment and neuroinflammation via BDNF/NF-κB in a transgenic mouse model of Alzheimer's disease. | 2019 | 23 |
| 15 | 2015 | 23 | |
| 16 | 2021 | 21 | |
| 17 | 2015 | 18 | |
| 18 | 2022 | 17 | |
| 19 | Tyrosine Hydroxylase as a Target for Deltamethrin in the Nigrostriatal Dopaminergic Pathway | 2006 | 15 |
| 20 | 2012 | 15 |
About Ping Luan
Ping Luan is a scholar working on Cellular and Molecular Neuroscience, Neurology, Physiology, Animal Science and Zoology and Cognitive Neuroscience, having authored 49 papers that have together received 1.1k indexed citations. Recurring topics across this work include Alzheimer's disease research and treatments (7 papers), Functional Brain Connectivity Studies (5 papers), Nanocluster Synthesis and Applications (5 papers), Animal Nutrition and Physiology (4 papers), Nanoparticle-Based Drug Delivery (3 papers), Graphene and Nanomaterials Applications (3 papers), Genetic and phenotypic traits in livestock (3 papers) and Neurological Disease Mechanisms and Treatments (3 papers). The work is most often cited by research in Neurology (91 citations), Biomaterials (114 citations), Physiology (162 citations), Animal Science and Zoology (64 citations) and Developmental Neuroscience (22 citations). Ping Luan has collaborated with scholars based in China, United States and Macao. Frequent co-authors include Beibei Gu, Songhua Xiao, Wang Liao, Jun Liu, Shengnuo Fan, Wenli Fang, Jun Liu, Enxiang Tao, Lianhong Yang and Hualin Ma. Their work appears in journals such as CNS Neuroscience & Therapeutics, Chinese Chemical Letters, Ageing Research Reviews, animal and Brain and Behavior.
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.