Akihiro Fukuda

1.2k total citations
61 papers, 831 citations indexed

About

Akihiro Fukuda is a scholar working on Nephrology, Pulmonary and Respiratory Medicine and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Akihiro Fukuda has authored 61 papers receiving a total of 831 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Nephrology, 10 papers in Pulmonary and Respiratory Medicine and 8 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Akihiro Fukuda's work include Renal Diseases and Glomerulopathies (15 papers), Chronic Kidney Disease and Diabetes (12 papers) and Occupational and environmental lung diseases (6 papers). Akihiro Fukuda is often cited by papers focused on Renal Diseases and Glomerulopathies (15 papers), Chronic Kidney Disease and Diabetes (12 papers) and Occupational and environmental lung diseases (6 papers). Akihiro Fukuda collaborates with scholars based in Japan, United States and Russia. Akihiro Fukuda's co-authors include Roger C. Wiggins, Yuji Sato, Larysa Wickman, Jocelyn Wiggins, Su Q. Wang, Mahboob A. Chowdhury, Kerby Shedden, Madhusudan Venkatareddy, Shouichi Fujimoto and Masao Kikuchi and has published in prestigious journals such as Journal of Clinical Oncology, Scientific Reports and Kidney International.

In The Last Decade

Akihiro Fukuda

51 papers receiving 822 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Akihiro Fukuda Japan 16 399 159 123 105 88 61 831
S Arimori Japan 16 227 0.6× 188 1.2× 99 0.8× 113 1.1× 99 1.1× 168 1.3k
Anja Urbschat Germany 17 223 0.6× 202 1.3× 127 1.0× 71 0.7× 25 0.3× 34 808
Ken‐ichi Miyoshi Japan 20 262 0.7× 410 2.6× 195 1.6× 54 0.5× 69 0.8× 63 1.3k
Irina Buhăescu Romania 12 90 0.2× 319 2.0× 132 1.1× 86 0.8× 31 0.4× 18 838
Xiaoming Zha China 17 102 0.3× 251 1.6× 121 1.0× 290 2.8× 74 0.8× 58 902
Vincent Vuiblet France 17 289 0.7× 295 1.9× 177 1.4× 93 0.9× 58 0.7× 49 1.1k
Daisuke Katagiri Japan 17 423 1.1× 193 1.2× 79 0.6× 42 0.4× 17 0.2× 63 884
Tomoaki Nagao Japan 14 118 0.3× 101 0.6× 125 1.0× 35 0.3× 22 0.3× 34 512
Dae Hyun Lee United States 14 176 0.4× 193 1.2× 92 0.7× 81 0.8× 20 0.2× 53 947

Countries citing papers authored by Akihiro Fukuda

Since Specialization
Citations

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

Fields of papers citing papers by Akihiro Fukuda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Akihiro Fukuda

This figure shows the co-authorship network connecting the top 25 collaborators of Akihiro Fukuda. A scholar is included among the top collaborators of Akihiro Fukuda 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 Akihiro Fukuda. Akihiro Fukuda 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
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Uchida, Hiroki, Akihiro Fukuda, Tamio Ueno, et al.. (2024). Development and validation of a machine learning model to predict time to renal replacement therapy in patients with chronic kidney disease. BMC Nephrology. 25(1). 101–101. 3 indexed citations
3.
Tanigawa, Masato, et al.. (2023). Histological classification of Japanese IgA nephropathy with a small number of glomeruli using Bayes’ theorem. Scientific Reports. 13(1). 18663–18663.
4.
Hashimoto, Masaki, Michiko Yuki, Kazuhiro Kitajima, et al.. (2022). Incidence and Risk Factors of Chest Wall Metastasis at Biopsy Sites in Patients with Malignant Pleural Mesothelioma. Cancers. 14(18). 4356–4356. 1 indexed citations
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Sato, Noritaka & Akihiro Fukuda. (2021). Virtual bird's-eye for remote operation of unmanned construction machinery. Proceedings of International Conference on Artificial Life and Robotics. 26. 486–489.
7.
Kamba, Shunsuke, Naoto Tamai, Masakuni Kobayashi, et al.. (2021). Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial. Journal of Gastroenterology. 56(8). 746–757. 77 indexed citations
8.
Ito, Tomohiko, Daisuke Usuda, Susumu Takagi, et al.. (2020). Effects of Tofogliflozin on Cardiac Function in Elderly Patients With Diabetes Mellitus. Journal of Clinical Medicine Research. 12(3). 165–171. 7 indexed citations
9.
Matsumoto, Seiji, Akihiro Fukuda, Masaki Hashimoto, et al.. (2019). The mTOR Signaling Pathway Is Associated With the Prognosis of Malignant Pleural Mesothelioma After Multimodality Therapy. Anticancer Research. 39(11). 6241–6247. 3 indexed citations
10.
Fukuda, Akihiro, Yuji Sato, Masao Kikuchi, et al.. (2019). Podocyte hypertrophic stress and detachment precedes hyperglycemia or albuminuria in a rat model of obesity and type2 diabetes-associated nephropathy. Scientific Reports. 9(1). 18485–18485. 21 indexed citations
11.
Fukunaga, Naoya, et al.. (2019). A case of denosumab-associated membranous nephropathy in a patient with rheumatoid arthritis. CEN Case Reports. 9(1). 1–5. 2 indexed citations
12.
Matsumoto, Seiji, Akihiro Fukuda, Masaki Hashimoto, et al.. (2018). CDK4/6 inhibitor and radiation therapy in malignant pleural mesothelioma.. Journal of Clinical Oncology. 36(15_suppl). e24326–e24326. 6 indexed citations
13.
Fukuda, Akihiro, et al.. (2018). Numerical analysis of three-dimensional acoustic field with background flow using constrained interpolation profile method. Japanese Journal of Applied Physics. 57(7S1). 07LC09–07LC09.
14.
Fukuda, Akihiro, et al.. (2017). Infective endocarditis following tumor necrosis factor-α antagonist therapy for management of psoriatic erythroderma: a case report. Journal of Medical Case Reports. 11(1). 35–35. 4 indexed citations
15.
Ohmi, Masato, Akihiro Fukuda, Jun Miyazu, et al.. (2015). Development of novel high-speed en face optical coherence tomography system using KTN optical beam deflector. Applied Physics Express. 8(2). 27001–27001. 17 indexed citations
16.
Fukuda, Akihiro, Mahboob A. Chowdhury, Madhusudan Venkatareddy, et al.. (2012). Growth-Dependent Podocyte Failure Causes Glomerulosclerosis. Journal of the American Society of Nephrology. 23(8). 1351–1363. 135 indexed citations
17.
Komatsu, Hiroyuki, Shouichi Fujimoto, Seiichiro Hara, et al.. (2009). Recent Therapeutic Strategies Improve Renal Outcome in Patients with IgA Nephropathy. American Journal of Nephrology. 30(1). 19–25. 14 indexed citations
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Fukuda, Akihiro, Yusuke Kobayashi, Tetsutaro Kimachi, & Yoshiji Takemoto. (2003). Synthetic studies on macrolactin A by using a (diene)Fe(CO)3 complex. Tetrahedron. 59(47). 9305–9313. 24 indexed citations
20.
Nishino, Takashi, Akihiro Fukuda, Terukazu Nagumo, Michio Fujihara, & Eisuke Kaji. (1999). Inhibition of the Generation of Thrombin and Factor Xa by a Fucoidan from the Brown Seaweed Ecklonia kurome. Thrombosis Research. 96(1). 37–49. 61 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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