Yun‐An Shen

1.8k total citations · 1 hit paper
19 papers, 1.2k citations indexed

About

Yun‐An Shen is a scholar working on Developmental Neuroscience, Neurology and Pathology and Forensic Medicine. According to data from OpenAlex, Yun‐An Shen has authored 19 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Developmental Neuroscience, 7 papers in Neurology and 5 papers in Pathology and Forensic Medicine. Recurrent topics in Yun‐An Shen's work include Neurogenesis and neuroplasticity mechanisms (9 papers), Neuroinflammation and Neurodegeneration Mechanisms (7 papers) and Multiple Sclerosis Research Studies (5 papers). Yun‐An Shen is often cited by papers focused on Neurogenesis and neuroplasticity mechanisms (9 papers), Neuroinflammation and Neurodegeneration Mechanisms (7 papers) and Multiple Sclerosis Research Studies (5 papers). Yun‐An Shen collaborates with scholars based in United States, United Kingdom and Switzerland. Yun‐An Shen's co-authors include Jonah R. Chan, Feng Mei, Lan Xiao, Stephen P.J. Fancy, Ari Green, Sonia R. Mayoral, Ainhoa Etxeberría, Stephanie Redmond, Jianqin Niu and Robin J.M. Franklin and has published in prestigious journals such as Nature Medicine, Neuron and Journal of Neuroscience.

In The Last Decade

Yun‐An Shen

17 papers receiving 1.2k citations

Hit Papers

Micropillar arrays as a high-throughput screening platfor... 2014 2026 2018 2022 2014 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Micropillar arrays as a high-throughput screening platform for therapeutics in multiple sclerosis
    2014 432 citations Feng Mei, Stephen P.J. Fancy et al. Nature Medicine profile →

Peers

Yun‐An Shen
Sonia R. Mayoral United States
Qiao‐Ling Cui Canada
Sarah Jäkel Germany
Corinne Bachelin France
Tobias D. Merson Australia
Maren Lindner Germany
Andrew A. Jarjour Canada
David van Bruggen Sweden
Ana Mendanha Falcão Portugal
Elisa M. Floriddia Germany
Sonia R. Mayoral United States
Yun‐An Shen
Citations per year, relative to Yun‐An Shen Yun‐An Shen (= 1×) peers Sonia R. Mayoral

Countries citing papers authored by Yun‐An Shen

Since Specialization
Citations

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

Fields of papers citing papers by Yun‐An Shen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yun‐An Shen. 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 Yun‐An Shen. The network helps show where Yun‐An Shen may publish in the future.

Co-authorship network of co-authors of Yun‐An Shen

This figure shows the co-authorship network connecting the top 25 collaborators of Yun‐An Shen. A scholar is included among the top collaborators of Yun‐An Shen 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 Yun‐An Shen. Yun‐An Shen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Lozinski, Brian M., Samira Ghorbani, Yifei Dong, et al.. (2025). Bruton Tyrosine Kinase in Lesions of Multiple Sclerosis and 3 of Its Models. Neurology Neuroimmunology & Neuroinflammation. 12(4). e200413–e200413.
2.
Mar, Soe, Massimiliano Valeriani, Barbara Steinborn, et al.. (2025). Ocrelizumab dose selection for treatment of pediatric relapsing–remitting multiple sclerosis: results of the OPERETTA I study. Journal of Neurology. 272(2). 137–137. 1 indexed citations
3.
Newsome, Scott D., Ewa Krzystanek, Krzysztof Selmaj, et al.. (2025). Subcutaneous Ocrelizumab in Patients With Multiple Sclerosis. Neurology. 104(9). e213574–e213574. 2 indexed citations
4.
Newsome, Scott D., Lawrence Goldstick, Derrick Robertson, et al.. (2024). Subcutaneous ocrelizumab in multiple sclerosis: Results of the Phase 1b OCARINA I study. Annals of Clinical and Translational Neurology. 11(12). 3215–3226. 2 indexed citations
5.
Lange, Simona, Martin Ebeling, Will Macnair, et al.. (2024). Fenebrutinib, a Bruton’s tyrosine kinase inhibitor, blocks distinct human microglial signaling pathways. Journal of Neuroinflammation. 21(1). 276–276. 5 indexed citations
6.
Soung, Allison, Shristi Pandey, Yun‐An Shen, et al.. (2024). Modulation of OPC Mitochondrial Function by Inhibiting USP30 Promotes Their Differentiation. Glia. 73(4). 773–787. 1 indexed citations
7.
Newsome, Scott D., Ewa Krzystanek, Krzysztof Selmaj, et al.. (2024). OCARINA II, Phase III Study: Results of Subcutaneous Ocrelizumab Administration in Patients with Multiple Sclerosis (S31.006). Neurology. 102(7_supplement_1). 5 indexed citations
8.
Wang, Yuanyuan, Yun‐An Shen, Luke Xie, et al.. (2023). TREM2‐dependent microglial function is essential for remyelination and subsequent neuroprotection. Glia. 71(5). 1247–1258. 30 indexed citations
9.
Pandey, Shristi, Kimberle Shen, Seung-Hye Lee, et al.. (2022). Disease-associated oligodendrocyte responses across neurodegenerative diseases. Cell Reports. 40(8). 111189–111189. 95 indexed citations
10.
Shen, Kimberle, Mike Reichelt, Hai Ngu, et al.. (2021). Multiple sclerosis risk gene Mertk is required for microglial activation and subsequent remyelination. Cell Reports. 34(10). 108835–108835. 80 indexed citations
11.
Rankin, Kelsey A., Feng Mei, Kicheol Kim, et al.. (2019). Selective Estrogen Receptor Modulators Enhance CNS Remyelination Independent of Estrogen Receptors. Journal of Neuroscience. 39(12). 2184–2194. 52 indexed citations
12.
Mayoral, Sonia R., Ainhoa Etxeberría, Yun‐An Shen, & Jonah R. Chan. (2018). Initiation of CNS Myelination in the Optic Nerve Is Dependent on Axon Caliber. Cell Reports. 25(3). 544–550.e3. 47 indexed citations
13.
Shen, Yun‐An, et al.. (2018). Modeling CNS Myelination Using Micropillar Arrays. Methods in molecular biology. 1791. 169–177. 1 indexed citations
14.
Cree, Bruce, Jianqin Niu, Kimberly K. Hoi, et al.. (2017). Clemastine rescues myelination defects and promotes functional recovery in hypoxic brain injury. Brain. 141(1). 85–98. 92 indexed citations
15.
Mei, Feng, Klaus Lehmann‐Horn, Yun‐An Shen, et al.. (2016). Accelerated remyelination during inflammatory demyelination prevents axonal loss and improves functional recovery. eLife. 5. 226 indexed citations
16.
Redmond, Stephanie, Feng Mei, Yael Eshed‐Eisenbach, et al.. (2016). Somatodendritic Expression of JAM2 Inhibits Oligodendrocyte Myelination. Neuron. 91(4). 824–836. 74 indexed citations
17.
Büdingen, H.‐Christian von, Feng Mei, Ariele L. Greenfield, et al.. (2015). The myelin oligodendrocyte glycoprotein directly binds nerve growth factor to modulate central axon circuitry. The Journal of Cell Biology. 210(6). 891–898. 28 indexed citations
18.
Mei, Feng, Stephen P.J. Fancy, Yun‐An Shen, et al.. (2014). Micropillar arrays as a high-throughput screening platform for therapeutics in multiple sclerosis. Nature Medicine. 20(8). 954–960. 432 indexed citations breakdown →
19.
Shen, Yun‐An, et al.. (2011). Assessing the Role of the Cadherin/Catenin Complex at the Schwann Cell–Axon Interface and in the Initiation of Myelination. Journal of Neuroscience. 31(8). 3032–3043. 57 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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