Kengo Maeda

3.9k total citations
128 papers, 2.6k citations indexed

About

Kengo Maeda is a scholar working on Molecular Biology, Neurology and Physiology. According to data from OpenAlex, Kengo Maeda has authored 128 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Molecular Biology, 26 papers in Neurology and 26 papers in Physiology. Recurrent topics in Kengo Maeda's work include Pain Mechanisms and Treatments (15 papers), Botulinum Toxin and Related Neurological Disorders (12 papers) and Nerve injury and regeneration (9 papers). Kengo Maeda is often cited by papers focused on Pain Mechanisms and Treatments (15 papers), Botulinum Toxin and Related Neurological Disorders (12 papers) and Nerve injury and regeneration (9 papers). Kengo Maeda collaborates with scholars based in Japan, United States and United Kingdom. Kengo Maeda's co-authors include Hitoshi Yasuda, Toyoaki Murohara, Atsunori Kashiwagi, Mitsuru Sanada, Masahide Takahashi, Ryuichi Kikkawa, Atsushi Enomoto, Takahisa Kondo, Naoya Asai and Masahiko Terada and has published in prestigious journals such as The Journal of Immunology, Nature Cell Biology and Circulation Research.

In The Last Decade

Kengo Maeda

127 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kengo Maeda Japan 27 835 624 401 296 293 128 2.6k
Matthias Orth Germany 23 875 1.0× 1.1k 1.8× 455 1.1× 130 0.4× 299 1.0× 90 2.8k
Yuichi NAKAZATO Japan 29 1.1k 1.3× 1.1k 1.8× 250 0.6× 466 1.6× 251 0.9× 104 2.9k
Erik N.T.P. Bakker Netherlands 30 559 0.7× 585 0.9× 702 1.8× 546 1.8× 474 1.6× 85 2.7k
Toke Bek Denmark 42 1.3k 1.6× 698 1.1× 327 0.8× 521 1.8× 258 0.9× 271 5.8k
Satoru Ito Japan 34 592 0.7× 766 1.2× 220 0.5× 600 2.0× 128 0.4× 132 3.6k
Louis G. D’Alecy United States 35 967 1.2× 904 1.4× 453 1.1× 628 2.1× 547 1.9× 128 3.8k
Kunihiko Tanaka Japan 29 869 1.0× 542 0.9× 351 0.9× 195 0.7× 100 0.3× 143 2.8k
Kai‐Yuan Tzen Taiwan 31 592 0.7× 383 0.6× 443 1.1× 182 0.6× 571 1.9× 159 2.9k
Takashi Kanda Japan 30 621 0.7× 336 0.5× 433 1.1× 666 2.3× 517 1.8× 284 3.3k
Carlo Serra Switzerland 29 1.1k 1.3× 298 0.5× 225 0.6× 160 0.5× 308 1.1× 163 2.8k

Countries citing papers authored by Kengo Maeda

Since Specialization
Citations

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

Fields of papers citing papers by Kengo Maeda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kengo Maeda

This figure shows the co-authorship network connecting the top 25 collaborators of Kengo Maeda. A scholar is included among the top collaborators of Kengo Maeda 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 Kengo Maeda. Kengo Maeda 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.
Ishibashi, Akihiro, Kengo Maeda, & Takashi Okamura. (2023). Semiclassical Einstein equations from holography and boundary dynamics. Journal of High Energy Physics. 2023(5). 3 indexed citations
2.
Maeda, Kengo. (2019). Akinetopsia on Driving. Journal of Stroke and Cerebrovascular Diseases. 28(7). e102–e103. 2 indexed citations
3.
Kobayashi, Koichi S., Kengo Maeda, Mikito Takefuji, et al.. (2017). Dynamics of angiogenesis in ischemic areas of the infarcted heart. Scientific Reports. 7(1). 7156–7156. 80 indexed citations
4.
Yamamura, Yumiko, Naoya Asai, Atsushi Enomoto, et al.. (2015). Akt–Girdin Signaling in Cancer-Associated Fibroblasts Contributes to Tumor Progression. Cancer Research. 75(5). 813–823. 100 indexed citations
5.
Kanazaki, Masahiro, et al.. (2015). Efficient Global Optimization Applied to Multi-Objective Design Optimization of Lift Creating Cylinder Using Plasma Actuators. 2 indexed citations
6.
Ota, Tomoyuki, Hideki Ishii, Susumu Suzuki, et al.. (2014). Relation Between Paradoxical Decrease in High-Density Lipoprotein Cholesterol Levels After Statin Therapy and Adverse Cardiovascular Events in Patients With Acute Myocardial Infarction. The American Journal of Cardiology. 115(4). 411–416. 9 indexed citations
7.
Ishii, Hideki, Satoshi Ichimiya, Masaaki Kanashiro, et al.. (2013). Renal Dysfunction and Atherosclerosis of the Neointima following Bare Metal Stent Implantation. American Journal of Nephrology. 38(1). 58–65. 5 indexed citations
8.
Okumura, Naoki, Takahisa Kondo, Kunihiro Matsushita, et al.. (2013). Associations of proteinuria and the estimated glomerular filtration rate with incident hypertension in young to middle-aged Japanese males. Preventive Medicine. 60. 48–54. 11 indexed citations
9.
Asai, Naoya, Atsushi Enomoto, Yoshiyuki Kawamoto, et al.. (2011). Protective role of Gipie, a Girdin family protein, in endoplasmic reticulum stress responses in endothelial cells. Molecular Biology of the Cell. 22(6). 736–747. 26 indexed citations
10.
Matsushita, Kunihiro, Takashi Muramatsu, Takahisa Kondo, et al.. (2010). Rationale and design of the NAGOYA HEART Study: Comparison between valsartan and amlodipine regarding morbidity and mortality in patients with hypertension and glucose intolerance. Journal of Cardiology. 56(1). 111–117. 10 indexed citations
11.
Maeda, Kengo, Takahiro Ito, Nobuhiro Ogawa, et al.. (2009). A case of agrammatism due to cerebral infarction of the middle-lower part of the right precentral gyrus. Rinsho Shinkeigaku. 49(7). 414–418. 5 indexed citations
12.
Ohara, Takeshi, Kengo Maeda, Yushi Hirota, et al.. (2007). Effects of pioglitazone and metformin on intracellular lipid content in liver and skeletal muscle of individuals with type 2 diabetes mellitus. Metabolism. 56(10). 1418–1424. 76 indexed citations
13.
Asai, Naoya, Mayumi Jijiwa, Atsushi Enomoto, et al.. (2006). RET receptor signaling: Dysfunction in thyroid cancer and Hirschsprung's disease. Pathology International. 56(4). 164–172. 59 indexed citations
14.
Ohara, Takeshi, Yushi Hirota, Kengo Maeda, et al.. (2005). Association of the −112A > C polymorphism of the uncoupling protein 1 gene with insulin resistance in Japanese individuals with type 2 diabetes. Biochemical and Biophysical Research Communications. 339(4). 1212–1216. 12 indexed citations
15.
Maeda, Kengo, Keiko Ishihara, Kazuaki Miyake, et al.. (2005). Inverse correlation between serum adiponectin concentration and hepatic lipid content in Japanese with type 2 diabetes. Metabolism. 54(6). 775–780. 17 indexed citations
16.
17.
Yasuda, Hitoshi, Masahiko Terada, Kengo Maeda, et al.. (2003). Diabetic neuropathy and nerve regeneration. Progress in Neurobiology. 69(4). 229–285. 202 indexed citations
18.
Fernyhough, Paul, Kengo Maeda, & David R. Tomlinson. (1996). Brain-Derived Neurotrophic Factor mRNA Levels Are Up-Regulated in Hindlimb Skeletal Muscle of Diabetic Rats: Effect of Denervation. Experimental Neurology. 141(2). 297–303. 16 indexed citations
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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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