Yasuichiro Nishimura

416 total citations
23 papers, 313 citations indexed

About

Yasuichiro Nishimura is a scholar working on Oncology, Immunology and Pathology and Forensic Medicine. According to data from OpenAlex, Yasuichiro Nishimura has authored 23 papers receiving a total of 313 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Oncology, 5 papers in Immunology and 4 papers in Pathology and Forensic Medicine. Recurrent topics in Yasuichiro Nishimura's work include Monoclonal and Polyclonal Antibodies Research (4 papers), Multiple Myeloma Research and Treatments (3 papers) and Eosinophilic Disorders and Syndromes (3 papers). Yasuichiro Nishimura is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (4 papers), Multiple Myeloma Research and Treatments (3 papers) and Eosinophilic Disorders and Syndromes (3 papers). Yasuichiro Nishimura collaborates with scholars based in Japan, China and United States. Yasuichiro Nishimura's co-authors include Taiji Yokote, Motomu Tsuji, Shoko Nakayama, Nobuya Hiraoka, Takuji Miyoshi, Toshiaki Hanafusa, Junichi Nakagawa, Tamaki Maeda, Hiroshi Mori and Takahiro Matsuo and has published in prestigious journals such as Scientific Reports, The American Journal of Surgical Pathology and Medicine.

In The Last Decade

Yasuichiro Nishimura

23 papers receiving 304 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yasuichiro Nishimura Japan 11 65 61 56 50 48 23 313
Tatsuho Sugimoto Japan 9 48 0.7× 34 0.6× 45 0.8× 25 0.5× 66 1.4× 26 293
Tomoe Shimazaki Japan 10 97 1.5× 69 1.1× 29 0.5× 95 1.9× 30 0.6× 21 449
Douglas C. Aziz United States 6 38 0.6× 126 2.1× 27 0.5× 71 1.4× 21 0.4× 13 351
Ahu Yorulmaz Türkiye 10 76 1.2× 97 1.6× 58 1.0× 103 2.1× 95 2.0× 62 446
Sheila Waugh United Kingdom 8 51 0.8× 147 2.4× 18 0.3× 44 0.9× 51 1.1× 17 358
G Burg Switzerland 11 42 0.6× 88 1.4× 32 0.6× 31 0.6× 65 1.4× 29 321
Hidenori Sasaki Japan 9 91 1.4× 62 1.0× 17 0.3× 123 2.5× 97 2.0× 18 379
J. Lou Switzerland 9 51 0.8× 140 2.3× 78 1.4× 80 1.6× 26 0.5× 15 374
Prerna Bali India 11 29 0.4× 40 0.7× 27 0.5× 86 1.7× 38 0.8× 21 280

Countries citing papers authored by Yasuichiro Nishimura

Since Specialization
Citations

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

Fields of papers citing papers by Yasuichiro Nishimura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yasuichiro Nishimura

This figure shows the co-authorship network connecting the top 25 collaborators of Yasuichiro Nishimura. A scholar is included among the top collaborators of Yasuichiro Nishimura 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 Yasuichiro Nishimura. Yasuichiro Nishimura 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.
Nakayama, Shoko, Taiji Yokote, Nobuya Hiraoka, et al.. (2017). Transforming growth factor β – and interleukin 13–producing mast cells are associated with fibrosis in bone marrow. Human Pathology. 62. 180–186. 18 indexed citations
2.
Kotani, Takuya, Tohru Takeuchi, Takaaki Ishida, et al.. (2017). Chemokine profiles of interstitial pneumonia in patients with dermatomyositis: a case control study. Scientific Reports. 7(1). 1635–1635. 25 indexed citations
3.
Nakayama, Shoko, Taiji Yokote, Toshikazu Akioka, et al.. (2017). Infiltration of effector regulatory T cells predicts poor prognosis of diffuse large B-cell lymphoma, not otherwise specified. Blood Advances. 1(8). 486–493. 35 indexed citations
4.
Nakayama, Shoko, Taiji Yokote, Nobuya Hiraoka, et al.. (2016). Role of mast cells in fibrosis of classical Hodgkin lymphoma. International Journal of Immunopathology and Pharmacology. 29(4). 603–611. 26 indexed citations
5.
Nakayama, Shoko, Taiji Yokote, Toshikazu Akioka, et al.. (2015). Dermatopathic Lymphadenopathy With Increased IgG4-Positive Plasma Cells. Medicine. 94(22). e866–e866. 2 indexed citations
6.
Nakayama, Shoko, Taiji Yokote, Motomu Tsuji, et al.. (2014). TNF-α Receptor 1 Expression Predicts Poor Prognosis of Diffuse Large B-cell Lymphoma, Not Otherwise Specified. The American Journal of Surgical Pathology. 38(8). 1138–1146. 5 indexed citations
7.
Hirokawa, Fumitoshi, Michihiro Hayashi, Yoshiharu Miyamoto, et al.. (2013). Evaluation of postoperative antibiotic prophylaxis after liver resection: a randomized controlled trial. The American Journal of Surgery. 206(1). 8–15. 38 indexed citations
8.
9.
Nakayama, Shoko, Taiji Yokote, Yuji Hirata, et al.. (2013). TNF-α Expression in Tumor Cells as a Novel Prognostic Marker for Diffuse Large B-cell Lymphoma, Not Otherwise Specified. The American Journal of Surgical Pathology. 38(2). 228–234. 18 indexed citations
10.
Nishimura, Yasuichiro, et al.. (2012). Differences in Heat Sensitivity between Japanese Honeybees and Hornets Under High Carbon Dioxide and Humidity Conditions Inside Bee Balls. ZOOLOGICAL SCIENCE. 29(1). 30–36. 22 indexed citations
11.
Yokote, Taiji, Yuji Hirata, Toshikazu Akioka, et al.. (2012). Immunohistological diagnosis of plasma cell myeloma based on cytoplasmic kappa/lambda ratio of CD138-positive plasma cells. Leukemia & lymphoma. 53(11). 2205–2209. 3 indexed citations
12.
Nakayama, Shoko, Taiji Yokote, Yuji Hirata, et al.. (2012). An approach for diagnosing plasma cell myeloma by three-color flow cytometry based on kappa/lambda ratios of CD38-gated CD138+ cells. Diagnostic Pathology. 7(1). 131–131. 8 indexed citations
13.
Nakayama, Shoko, Taiji Yokote, Yuji Hirata, et al.. (2012). Immunohistological analysis in diagnosis of plasma cell myeloma based on cytoplasmic kappa/lambda ratio of CD38-positive plasma cells. Hematology. 17(6). 317–320. 8 indexed citations
14.
Hirata, Yuji, Taiji Yokote, Shoko Nakayama, et al.. (2010). Antifungal prophylaxis with micafungin in neutropenic patients with hematological malignancies. Leukemia & lymphoma. 51(5). 853–859. 22 indexed citations
15.
Azuma, Haruhito, Takeshi Sakamoto, Satoshi Kiyama, et al.. (2008). Anticancer Effect of Combination Therapy of VP16 and Fosfesterol in Hormone-Refractory Prostate Cancer. American Journal of Clinical Oncology. 31(2). 188–194. 3 indexed citations
16.
17.
Nishimura, Yasuichiro, Masahito Watanabe, Nobuo Jo, & Masahisa Shimada. (1997). The difference of means of two indirect unpaired groups in receptor autoradiography. Journal of Biopharmaceutical Statistics. 7(3). 441–452. 1 indexed citations
18.
Watanabe, Masahito, et al.. (1996). Statistical Analysis for Receptor Autoradiography.. ACTA HISTOCHEMICA ET CYTOCHEMICA. 29(2). 135–139. 2 indexed citations
19.
Matsuo, Takahiro, et al.. (1995). Quantification of immunohistochemistry using an image analyser: correlation with hormone concentrations in pituitary adenomas. The Histochemical Journal. 27(12). 989–996. 24 indexed citations
20.
Matsuo, Takahiro, et al.. (1995). Quantification of immunohistochemistry using an image analyser: correlation with hormone concentrations in pituitary adenomas.. PubMed. 27(12). 989–96. 25 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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