Maryna Baydyuk

1.2k total citations
19 papers, 907 citations indexed

About

Maryna Baydyuk is a scholar working on Developmental Neuroscience, Cellular and Molecular Neuroscience and Neurology. According to data from OpenAlex, Maryna Baydyuk has authored 19 papers receiving a total of 907 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Developmental Neuroscience, 8 papers in Cellular and Molecular Neuroscience and 6 papers in Neurology. Recurrent topics in Maryna Baydyuk's work include Neurogenesis and neuroplasticity mechanisms (9 papers), Nerve injury and regeneration (6 papers) and Neuroinflammation and Neurodegeneration Mechanisms (6 papers). Maryna Baydyuk is often cited by papers focused on Neurogenesis and neuroplasticity mechanisms (9 papers), Nerve injury and regeneration (6 papers) and Neuroinflammation and Neurodegeneration Mechanisms (6 papers). Maryna Baydyuk collaborates with scholars based in United States, South Korea and Canada. Maryna Baydyuk's co-authors include Baoji Xu, Juan Ji An, Guey‐Ying Liao, Bai Lu, Emily G. Waterhouse, Kang Zheng, Lauren L. Orefice, Ling-Gang Wu, Kenneth J. Kellar and Yingxian Xiao and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of Neuroscience.

In The Last Decade

Maryna Baydyuk

19 papers receiving 900 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maryna Baydyuk United States 14 457 351 254 118 113 19 907
Hisatsugu Koshimizu Japan 19 621 1.4× 561 1.6× 202 0.8× 96 0.8× 171 1.5× 34 1.2k
David González‐Forero Spain 16 431 0.9× 329 0.9× 120 0.5× 105 0.9× 146 1.3× 26 783
Margarida Caldeira Portugal 9 621 1.4× 347 1.0× 222 0.9× 113 1.0× 86 0.8× 12 979
Sònia Marco Spain 16 674 1.5× 524 1.5× 164 0.6× 150 1.3× 79 0.7× 20 1.1k
Barbara Di Benedetto Germany 19 386 0.8× 479 1.4× 258 1.0× 251 2.1× 87 0.8× 37 1.1k
José Luis Nieto-González Spain 16 371 0.8× 348 1.0× 96 0.4× 89 0.8× 97 0.9× 25 784
Karine Cambon France 15 398 0.9× 367 1.0× 144 0.6× 128 1.1× 43 0.4× 20 816
Yukio Akaneya Japan 14 678 1.5× 364 1.0× 307 1.2× 132 1.1× 47 0.4× 25 1.0k
Ning Cai United States 7 802 1.8× 319 0.9× 342 1.3× 52 0.4× 55 0.5× 11 980
Mizuki Kanemoto Japan 6 648 1.4× 379 1.1× 139 0.5× 123 1.0× 63 0.6× 11 1.0k

Countries citing papers authored by Maryna Baydyuk

Since Specialization
Citations

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

Fields of papers citing papers by Maryna Baydyuk

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maryna Baydyuk

This figure shows the co-authorship network connecting the top 25 collaborators of Maryna Baydyuk. A scholar is included among the top collaborators of Maryna Baydyuk 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 Maryna Baydyuk. Maryna Baydyuk 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.
Gharibani, Payam, Matthew D. Smith, Judy Lee, et al.. (2025). The protein kinase C modulator bryostatin-1 therapeutically targets microglia to attenuate neuroinflammation and promote remyelination. Science Translational Medicine. 17(780). eadk3434–eadk3434. 4 indexed citations
2.
Gross, Phillip S., et al.. (2025). Senescent cell reduction does not improve recovery in mice under experimental autoimmune encephalomyelitis (EAE) induced demyelination. Journal of Neuroinflammation. 22(1). 101–101. 4 indexed citations
3.
Chamberlain, Kelly A., Nataliia V. Shults, Erqiu Li, et al.. (2024). Myeloid cell-associated aromatic amino acid metabolism facilitates CNS myelin regeneration. npj Regenerative Medicine. 9(1). 1–1. 6 indexed citations
4.
Baydyuk, Maryna, et al.. (2022). Impact of amino acids on microglial activation and CNS remyelination. Current Opinion in Pharmacology. 66. 102287–102287. 6 indexed citations
5.
Baydyuk, Maryna, et al.. (2020). Extrinsic Factors Driving Oligodendrocyte Lineage Cell Progression in CNS Development and Injury. Neurochemical Research. 45(3). 630–642. 26 indexed citations
6.
Baydyuk, Maryna, et al.. (2020). EphA7 isoforms differentially regulate cortical dendrite development. PLoS ONE. 15(12). e0231561–e0231561. 7 indexed citations
7.
Baydyuk, Maryna, et al.. (2019). Tracking the evolution of CNS remyelinating lesion in mice with neutral red dye. Proceedings of the National Academy of Sciences. 116(28). 14290–14299. 27 indexed citations
8.
Baydyuk, Maryna, Jianhua Xu, & Ling-Gang Wu. (2016). The calyx of Held in the auditory system: Structure, function, and development. Hearing Research. 338. 22–31. 29 indexed citations
9.
Baydyuk, Maryna, Xinsheng Wu, Liming He, & Ling‐Gang Wu. (2015). Brain-Derived Neurotrophic Factor Inhibits Calcium Channel Activation, Exocytosis, and Endocytosis at a Central Nerve Terminal. Journal of Neuroscience. 35(11). 4676–4682. 28 indexed citations
10.
Ahuja, Malini, Min Seuk Kim, G. Cristina Brailoiu, et al.. (2015). Fusion of lysosomes with secretory organelles leads to uncontrolled exocytosis in the lysosomal storage disease mucolipidosis type IV. EMBO Reports. 17(2). 266–278. 33 indexed citations
11.
Chiang, Hsueh‐Cheng, Wonchul Shin, Edaeni Hamid, et al.. (2014). Post-fusion structural changes and their roles in exocytosis and endocytosis of dense-core vesicles. Nature Communications. 5(1). 3356–3356. 69 indexed citations
12.
Baydyuk, Maryna & Baoji Xu. (2014). BDNF signaling and survival of striatal neurons. Frontiers in Cellular Neuroscience. 8. 254–254. 182 indexed citations
13.
Baydyuk, Maryna, Yuxiang Xie, Lino Tessarollo, & Baoji Xu. (2013). Midbrain-Derived Neurotrophins Support Survival of Immature Striatal Projection Neurons. Journal of Neuroscience. 33(8). 3363–3369. 24 indexed citations
14.
Waterhouse, Emily G., Juan Ji An, Lauren L. Orefice, et al.. (2012). BDNF Promotes Differentiation and Maturation of Adult-born Neurons through GABAergic Transmission. Journal of Neuroscience. 32(41). 14318–14330. 239 indexed citations
15.
Baydyuk, Maryna, Theron A. Russell, Guey‐Ying Liao, et al.. (2011). TrkB receptor controls striatal formation by regulating the number of newborn striatal neurons. Proceedings of the National Academy of Sciences. 108(4). 1669–1674. 74 indexed citations
16.
Baydyuk, Maryna, et al.. (2010). Chronic deprivation of TrkB signaling leads to selective late-onset nigrostriatal dopaminergic degeneration. Experimental Neurology. 228(1). 118–125. 61 indexed citations
17.
Xiao, Yingxian, et al.. (2009). Rat neuronal nicotinic acetylcholine receptors containing α7 subunit: pharmacological properties of ligand binding and function. Acta Pharmacologica Sinica. 30(6). 842–850. 21 indexed citations
18.
Xiao, Yingxian, et al.. (2004). Pharmacology of the agonist binding sites of rat neuronal nicotinic receptor subtypes expressed in HEK 293 cells. Bioorganic & Medicinal Chemistry Letters. 14(8). 1845–1848. 33 indexed citations
19.

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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