Takako Tsujimoto

577 total citations
9 papers, 513 citations indexed

About

Takako Tsujimoto is a scholar working on Hematology, Molecular Biology and Immunology. According to data from OpenAlex, Takako Tsujimoto has authored 9 papers receiving a total of 513 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Hematology, 4 papers in Molecular Biology and 4 papers in Immunology. Recurrent topics in Takako Tsujimoto's work include Multiple Myeloma Research and Treatments (5 papers), T-cell and B-cell Immunology (4 papers) and Glycosylation and Glycoproteins Research (2 papers). Takako Tsujimoto is often cited by papers focused on Multiple Myeloma Research and Treatments (5 papers), T-cell and B-cell Immunology (4 papers) and Glycosylation and Glycoproteins Research (2 papers). Takako Tsujimoto collaborates with scholars based in Japan, United Kingdom and Greece. Takako Tsujimoto's co-authors include N Huang, MM Kawano, Keichiro Mihara, A Kuramoto, Maged S. Mahmoud, Igor Lisukov, Atsushi Kuramoto, Michio Kawano, Yuka Harada and O Niwa and has published in prestigious journals such as Blood, British Journal of Haematology and International Journal of Oncology.

In The Last Decade

Takako Tsujimoto

9 papers receiving 508 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Takako Tsujimoto Japan	9	248	203	160	159	50	9	513
Maged S. Mahmoud Japan	10	134 0.5×	200 1.0×	160 1.0×	130 0.8×	34 0.7×	14	368
Caroline E. Zuber France	8	417 1.7×	80 0.4×	124 0.8×	187 1.2×	24 0.5×	8	624
F W Jacobsen Norway	8	247 1.0×	208 1.0×	207 1.3×	113 0.7×	14 0.3×	9	547
Bühring Hj Germany	7	158 0.6×	281 1.4×	211 1.3×	138 0.9×	13 0.3×	12	538
Monika Jasek Poland	17	464 1.9×	108 0.5×	106 0.7×	101 0.6×	54 1.1×	45	705
H J Gruss Germany	11	232 0.9×	165 0.8×	117 0.7×	99 0.6×	37 0.7×	13	559
Claudine Graf Germany	15	224 0.9×	126 0.6×	166 1.0×	209 1.3×	46 0.9×	26	545
K Pouwels Netherlands	11	394 1.6×	258 1.3×	178 1.1×	319 2.0×	21 0.4×	13	729
Antje van Lessen Germany	10	169 0.7×	78 0.4×	101 0.6×	133 0.8×	29 0.6×	18	322
Junko Ohata Japan	11	458 1.8×	112 0.6×	140 0.9×	133 0.8×	98 2.0×	17	718

Countries citing papers authored by Takako Tsujimoto

Since Specialization

Citations

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

Fields of papers citing papers by Takako Tsujimoto

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Takako Tsujimoto

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

All Works

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9 of 9 papers shown

1.

Katayama, Yuta, Akira Sakai, Naohide Oue, et al.. (2004). Cyclin D1 overexpression is not a specific grouping marker, but may collaborate with CDC37 in myeloma cells. International Journal of Oncology. 25(3). 579–95. 10 indexed citations

2.

Kuramoto, Ken, Akira Sakai, Kazushi Shigemasa, et al.. (2002). High expression of MCL1 gene related to vascular endothelial growth factor is associated with poor outcome  in non‐Hodgkin's lymphoma. British Journal of Haematology. 116(1). 158–161. 28 indexed citations

3.

Kuriyama, Shigeki, Manabu Yamazaki, Akira Mitoro, et al.. (1999). Analysis of intrahepatic invasion of hepatocellular carcinoma using fluorescent dye-labeled cells in mice.. PubMed. 18(6A). 4181–8. 16 indexed citations

4.

Harada, Yuka, Michio Kawano, N Huang, et al.. (1996). Identification of early plasma cells in peripheral blood and their clinical significance. British Journal of Haematology. 92(1). 184–191. 93 indexed citations

5.

Tsujimoto, Takako, et al.. (1996). Plasma cells induce apoptosis of pre-B cells by interacting with bone marrow stromal cells. Blood. 87(8). 3375–3383. 36 indexed citations

6.

Kawano, Michio, Maged S. Mahmoud, N Huang, et al.. (1995). High proportions of VLA‐5⁻ immature myeloma cells correlated well with poor response to treatment in multiple myeloma. British Journal of Haematology. 91(4). 860–864. 17 indexed citations

7.

Huang, N, et al.. (1995). Expression of CD21 antigen on myeloma cells and its involvement in their adhesion to bone marrow stromal cells. Blood. 85(12). 3704–3712. 31 indexed citations

8.

Kawano, MM, Keichiro Mihara, N Huang, Takako Tsujimoto, & A Kuramoto. (1995). Differentiation of early plasma cells on bone marrow stromal cells requires interleukin-6 for escaping from apoptosis. Blood. 85(2). 487–494. 151 indexed citations

9.

Kawano, MM, Keichiro Mihara, N Huang, Takako Tsujimoto, & A Kuramoto. (1995). Differentiation of early plasma cells on bone marrow stromal cells requires interleukin-6 for escaping from apoptosis. Blood. 85(2). 487–494. 131 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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