Kazuma Sakamoto

2.4k total citations · 1 hit paper
43 papers, 1.9k citations indexed

About

Kazuma Sakamoto is a scholar working on Cell Biology, Molecular Biology and Cellular and Molecular Neuroscience. According to data from OpenAlex, Kazuma Sakamoto has authored 43 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Cell Biology, 17 papers in Molecular Biology and 17 papers in Cellular and Molecular Neuroscience. Recurrent topics in Kazuma Sakamoto's work include Proteoglycans and glycosaminoglycans research (19 papers), Nerve injury and regeneration (15 papers) and Glycosylation and Glycoproteins Research (5 papers). Kazuma Sakamoto is often cited by papers focused on Proteoglycans and glycosaminoglycans research (19 papers), Nerve injury and regeneration (15 papers) and Glycosylation and Glycoproteins Research (5 papers). Kazuma Sakamoto collaborates with scholars based in Japan, United States and France. Kazuma Sakamoto's co-authors include Kenji Kadomatsu, Shiro Imagama, Naoki Ishiguro, Tomohiro Ohgomori, Ken‐ichi Hirano, Kenji Uchimura, Akio Suzumura, Kazuyoshi Kobayashi, Hideyuki Takeuchi and Akihiro Hirakawa and has published in prestigious journals such as Journal of Biological Chemistry, Journal of Clinical Investigation and Journal of Neuroscience.

In The Last Decade

Kazuma Sakamoto

43 papers receiving 1.9k citations

Hit Papers

Minocycline selectively inhibits M1 polarization of micro... 2013 2026 2017 2021 2013 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kazuma Sakamoto Japan 20 662 491 428 366 353 43 1.9k
Daniela Virgintino Italy 30 862 1.3× 746 1.5× 400 0.9× 316 0.9× 204 0.6× 94 2.3k
Dongming Sun United States 22 764 1.2× 311 0.6× 530 1.2× 638 1.7× 170 0.5× 42 2.2k
Mariella Errede Italy 27 861 1.3× 633 1.3× 273 0.6× 256 0.7× 160 0.5× 75 2.1k
Bradley T. Lang United States 18 734 1.1× 528 1.1× 908 2.1× 222 0.6× 207 0.6× 20 2.2k
Robert W. Mays United States 25 1.2k 1.8× 341 0.7× 316 0.7× 241 0.7× 738 2.1× 40 2.5k
Kouko Tatsumi Japan 22 674 1.0× 379 0.8× 348 0.8× 267 0.7× 135 0.4× 60 1.9k
Maria Adele Rueger Germany 23 1.0k 1.5× 760 1.5× 527 1.2× 118 0.3× 219 0.6× 67 2.4k
Samuel Sam Wah Tay Singapore 22 746 1.1× 456 0.9× 411 1.0× 108 0.3× 398 1.1× 66 2.1k
Michael B. Keough Canada 18 639 1.0× 507 1.0× 526 1.2× 292 0.8× 101 0.3× 30 2.2k
Giorgia Dina Italy 21 1.1k 1.6× 513 1.0× 1.2k 2.7× 401 1.1× 459 1.3× 32 2.5k

Countries citing papers authored by Kazuma Sakamoto

Since Specialization
Citations

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

Fields of papers citing papers by Kazuma Sakamoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kazuma Sakamoto

This figure shows the co-authorship network connecting the top 25 collaborators of Kazuma Sakamoto. A scholar is included among the top collaborators of Kazuma Sakamoto 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 Kazuma Sakamoto. Kazuma Sakamoto 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.
Ozaki, Tomoya, Toshiharu Sugie, Yuji Suzuki, et al.. (2024). Systemic administrations of protamine heal subacute spinal cord injury in mice.. Neuroscience Research. 212. 11–19. 1 indexed citations
2.
Vân, Trần Thị Thanh, et al.. (2023). Effect of edible coating incorporating sodium carboxymethyl cellulose/cellulose nanofibers and self-produced mandarin oil on strawberries. Food Packaging and Shelf Life. 40. 101197–101197. 13 indexed citations
3.
Ouchida, Jun, Tomoya Ozaki, Naoki Segi, et al.. (2023). Glypican-2 defines age-dependent axonal response to chondroitin sulfate. Experimental Neurology. 366. 114444–114444. 5 indexed citations
4.
Sakamoto, Kazuma, Tomoya Ozaki, Yuji Suzuki, & Kenji Kadomatsu. (2021). Type IIa RPTPs and Glycans: Roles in Axon Regeneration and Synaptogenesis. International Journal of Molecular Sciences. 22(11). 5524–5524. 13 indexed citations
5.
Sakamoto, Kazuma, Tomoya Ozaki, & Kenji Kadomatsu. (2021). Axonal Regeneration by Glycosaminoglycan. Frontiers in Cell and Developmental Biology. 9. 702179–702179. 11 indexed citations
6.
Ito, Sadayuki, Tomoya Ozaki, Masayoshi Morozumi, et al.. (2021). Enoxaparin promotes functional recovery after spinal cord injury by antagonizing PTPRσ. Experimental Neurology. 340. 113679–113679. 11 indexed citations
7.
Sakamoto, Kazuma, Tomoya Ozaki, Masayoshi Morozumi, et al.. (2019). Glycan sulfation patterns define autophagy flux at axon tip via PTPRσ-cortactin axis. Nature Chemical Biology. 15(7). 699–709. 72 indexed citations
9.
Soh, Zu, Kazuma Sakamoto, Michiyo Suzuki, Yuichi Iino, & Toshio Tsuji. (2018). A computational model of internal representations of chemical gradients in environments for chemotaxis of Caenorhabditis elegans. Scientific Reports. 8(1). 17190–17190. 9 indexed citations
11.
Sakamoto, Kazuma, Zu Soh, Michiyo Suzuki, Yuichi Kurita, & Toshio Tsuji. (2015). A neural network model of Caenorhabditis elegans and simulation of chemotaxis-related information processing in the neural network. 668–673. 5 indexed citations
12.
Matsui, Hiroki, Tomohiro Ohgomori, Katsuichi Miyamoto, et al.. (2013). Keratan sulfate expression in microglia is diminished in the spinal cord in experimental autoimmune neuritis. Cell Death and Disease. 4(12). e946–e946. 21 indexed citations
13.
Kishida, Satoshi, Ping Mu, Shin Miyakawa, et al.. (2012). Midkine Promotes Neuroblastoma through Notch2 Signaling. Cancer Research. 73(4). 1318–1327. 50 indexed citations
14.
Sakamoto, Kazuma & Kenji Kadomatsu. (2012). Midkine in the pathology of cancer, neural disease, and inflammation. Pathology International. 62(7). 445–455. 44 indexed citations
15.
Sakamoto, Kazuma & Kenji Kadomatsu. (2011). Keratan Sulfate in Neuronal Network Reconstruction. Trends in Glycoscience and Glycotechnology. 23(133). 212–220. 7 indexed citations
16.
Sakamoto, Kazuma, Guojun Bu, Sen Chen, et al.. (2011). Premature Ligand-Receptor Interaction during Biosynthesis Limits the Production of Growth Factor Midkine and Its Receptor LDL Receptor-related Protein 1. Journal of Biological Chemistry. 286(10). 8405–8413. 16 indexed citations
17.
Imagama, Shiro, Kazuma Sakamoto, Ryoji Tauchi, et al.. (2011). Keratan Sulfate Restricts Neural Plasticity after Spinal Cord Injury. Journal of Neuroscience. 31(47). 17091–17102. 75 indexed citations
18.
Kato, Noritoshi, Tomoki Kosugi, Waichi Sato, et al.. (2011). Basigin/CD147 Promotes Renal Fibrosis after Unilateral Ureteral Obstruction. American Journal Of Pathology. 178(2). 572–579. 52 indexed citations
19.
Kishida, Satoshi, et al.. (2010). DRR1 is expressed in the developing nervous system and downregulated during neuroblastoma carcinogenesis. Biochemical and Biophysical Research Communications. 394(3). 829–835. 24 indexed citations
20.
Kato, Noritoshi, Yukio Yuzawa, Tomoki Kosugi, et al.. (2009). The E-Selectin Ligand Basigin/CD147 Is Responsible for Neutrophil Recruitment in Renal Ischemia/Reperfusion. Journal of the American Society of Nephrology. 20(7). 1565–1576. 82 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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