Mitsunori Fujimura

697 total citations
16 papers, 542 citations indexed

About

Mitsunori Fujimura is a scholar working on Cardiology and Cardiovascular Medicine, Molecular Biology and Epidemiology. According to data from OpenAlex, Mitsunori Fujimura has authored 16 papers receiving a total of 542 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Cardiology and Cardiovascular Medicine, 3 papers in Molecular Biology and 3 papers in Epidemiology. Recurrent topics in Mitsunori Fujimura's work include Atherosclerosis and Cardiovascular Diseases (2 papers), Streptococcal Infections and Treatments (2 papers) and Heart Failure Treatment and Management (2 papers). Mitsunori Fujimura is often cited by papers focused on Atherosclerosis and Cardiovascular Diseases (2 papers), Streptococcal Infections and Treatments (2 papers) and Heart Failure Treatment and Management (2 papers). Mitsunori Fujimura collaborates with scholars based in Japan and Spain. Mitsunori Fujimura's co-authors include Ken‐ichi Aihara, Hiroyuki Azuma, Toshio Matsumoto, Masashi Akaike, Yasumasa Ikeda, Midori Kato, Shunji Hashizume, Shigeaki Kato, Tomonori Yoshida and Hiroshi Yamaguchi and has published in prestigious journals such as Journal of Biological Chemistry, Circulation and Atherosclerosis.

In The Last Decade

Mitsunori Fujimura

15 papers receiving 529 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mitsunori Fujimura Japan 8 173 142 107 90 83 16 542
Shunji Hashizume Japan 5 170 1.0× 106 0.7× 85 0.8× 88 1.0× 82 1.0× 9 461
Rolf Michael Klein Germany 11 247 1.4× 183 1.3× 81 0.8× 82 0.9× 78 0.9× 23 621
Kurt Sowers United States 6 129 0.7× 67 0.5× 141 1.3× 122 1.4× 89 1.1× 6 681
Pedro Leão Neves Portugal 14 69 0.4× 88 0.6× 83 0.8× 162 1.8× 77 0.9× 55 604
Danuta Fedak Poland 15 44 0.3× 101 0.7× 140 1.3× 56 0.6× 84 1.0× 43 615
Emilio Orfei United States 13 187 1.1× 122 0.9× 126 1.2× 54 0.6× 57 0.7× 18 684
Lawrence R. Shoemaker United States 15 62 0.4× 60 0.4× 153 1.4× 68 0.8× 22 0.3× 35 520
Mansi Dalal United States 9 89 0.5× 40 0.3× 94 0.9× 56 0.6× 109 1.3× 16 647
Ding Ding China 15 85 0.5× 120 0.8× 193 1.8× 54 0.6× 105 1.3× 27 716
Kentaro Omori Japan 13 100 0.6× 41 0.3× 126 1.2× 119 1.3× 42 0.5× 23 678

Countries citing papers authored by Mitsunori Fujimura

Since Specialization
Citations

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

Fields of papers citing papers by Mitsunori Fujimura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mitsunori Fujimura

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

All Works

16 of 16 papers shown
1.
Yagi, Shusuke, Mitsunori Fujimura, Masashi Akaike, et al.. (2014). Increase in serum triglyceride was associated with coronary plaque vulnerability in a patient with rheumatoid arthritis. Journal of Cardiology Cases. 10(2). 54–57. 1 indexed citations
2.
Kato, Shuji, Itsuro Endo, Mitsunori Fujimura, et al.. (2013). Serum carboxy-terminal telopeptide of type I collagen (ICTP) as a surrogate marker for vulnerable plaques in atherosclerotic patients: A pilot study. Atherosclerosis. 229(1). 182–185. 9 indexed citations
3.
Fujimura, Mitsunori, Masashi Akaike, Takashi Iwase, et al.. (2009). Decrease in plasma brain natriuretic peptide level in the early phase after the start of carvedilol therapy is a novel predictor of long-term outcome in patients with chronic heart failure. Acta Cardiologica. 64(5). 589–595. 4 indexed citations
4.
Fujimura, Mitsunori, Sumiko Yoshida, Yasumasa Ikeda, et al.. (2008). Infective Endocarditis Caused by Lactobacillus. Internal Medicine. 47(12). 1162–1162. 1 indexed citations
5.
Yagi, Shusuke, Mitsunori Fujimura, Takayuki Ise, et al.. (2008). Infective Endocarditis Caused by Lactobacillus. Internal Medicine. 47(12). 1113–1116. 14 indexed citations
6.
Yagi, Shusuke, Masashi Akaike, Shuji Ozaki, et al.. (2007). Improvement of Cardiac Diastolic Function and Prognosis After Autologous Peripheral Blood Stem Cell Transplantation in AL Cardiac Amyloidosis. Internal Medicine. 46(20). 1705–1710. 6 indexed citations
7.
Ikeda, Yasumasa, Shusuke Yagi, Hiroshi Yamaguchi, et al.. (2006). Intra-Vascular Ultrasound Findings of Diffuse Coronary Atherosclerotic Change in Systemic Lupus Erythematosus With Secondary Antiphospholipid Syndrome. Circulation Journal. 70(8). 1082–1085. 3 indexed citations
8.
Kato, Midori, Hiroyuki Azuma, Masashi Akaike, et al.. (2005). Aspirin inhibits thrombin action on endothelial cells via up-regulation of aminopeptidase N/CD13 expression. Atherosclerosis. 183(1). 49–55. 3 indexed citations
9.
Ikeda, Yasumasa, Ken‐ichi Aihara, Takashi Sato, et al.. (2005). Androgen Receptor Gene Knockout Male Mice Exhibit Impaired Cardiac Growth and Exacerbation of Angiotensin II-induced Cardiac Fibrosis. Journal of Biological Chemistry. 280(33). 29661–29666. 123 indexed citations
10.
Yoshida, Tomonori, Hiroyuki Azuma, Ken‐ichi Aihara, et al.. (2004). Vascular smooth muscle cell proliferation is dependent upon upregulation of mitochondrial transcription factor A (mtTFA) expression in injured rat carotid artery. Atherosclerosis. 178(1). 39–47. 17 indexed citations
11.
Aihara, Ken‐ichi, Hiroyuki Azuma, Masashi Akaike, et al.. (2004). Disruption of Nuclear Vitamin D Receptor Gene Causes Enhanced Thrombogenicity in Mice. Journal of Biological Chemistry. 279(34). 35798–35802. 210 indexed citations
12.
Aihara, Ken‐ichi, Hiroyuki Azuma, Masashi Akaike, et al.. (2004). Heparin Cofactor II Is a Novel Protective Factor Against Carotid Atherosclerosis in Elderly Individuals. Circulation. 109(22). 2761–2765. 61 indexed citations
13.
Fujimura, Mitsunori, Masashi Akaike, Midori Kato, et al.. (2003). Aggressive Antiplatelet Therapy Before Coronary Stent Implantation in Acute Coronary Syndrome with Essential Thrombocythemia. Angiology. 54(4). 485–490. 5 indexed citations
14.
Shiozaki, Arihiro, Keiko Kataoka, Mitsunori Fujimura, et al.. (2002). Survivin Inhibits Apoptosis in Cytotrophoblasts. Placenta. 24(1). 65–76. 37 indexed citations
15.
Fujimura, Mitsunori, Yoshio Yasumura, Yoshio Ishida, et al.. (2000). Improvement in left ventricular function in response to carvedilol is accompanied by attenuation of neurohumoral activation in patients with dilated cardiomyopathy. Journal of Cardiac Failure. 6(1). 3–10. 48 indexed citations
16.
Wada, Hajime, et al.. (1969). [Coronary perfusion technic with special reference to experimental and clinical data].. PubMed. 22(1). 34–8.

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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