Masaki Kakimoto
- Physiology top 5%
- Molecular Biology
- Endocrinology, Diabetes and Metabolism top 5%
- Clinical Biochemistry top 1%
- Epidemiology
- Co-authors
- Toyoshi InoguchiH NawataTetsuji EtohT. HashimotoMinako ImamuraMasahiro NaruseHong YuTsuyoshi Aoki
- Topics
- Metastasis and carcinoma case studies (6 papers)Gastrointestinal Tumor Research and Treatment (3 papers)SARS-CoV-2 and COVID-19 Research (2 papers)
In The Last Decade
Masaki Kakimoto
19 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 107
- Physiology 490
- Molecular Biology 447
- Endocrinology, Diabetes and Metabolism 291
- Clinical Biochemistry 281
- Epidemiology 246
Countries citing papers authored by Masaki Kakimoto
This map shows the geographic impact of Masaki Kakimoto'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 Masaki Kakimoto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Masaki Kakimoto more than expected).
Fields of papers citing papers by Masaki Kakimoto
This network shows the impact of papers produced by Masaki Kakimoto. 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 Masaki Kakimoto. The network helps show where Masaki Kakimoto may publish in the future.
Co-authorship network of co-authors of Masaki Kakimoto
This figure shows the co-authorship network connecting the top 25 collaborators of Masaki Kakimoto. A scholar is included among the top collaborators of Masaki Kakimoto 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 Masaki Kakimoto. Masaki Kakimoto is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 12 | |
| 3 | 5 | |
| 4 | 0 | |
| 5 | 5 | |
| 6 | 6 | |
| 7 | 48 | |
| 8 | 3 | |
| 9 | 6 | |
| 10 | [Subclavian Artery Hemorrhage Related to Everolimus in a Patient with Recurrent Breast Cancer--A Case Report]. | 2 |
| 11 | [A Case of Long-Term Survival of Advanced Esophageal Basaloid Squamous Carcinoma Invading the Trachea]. | 1 |
| 12 | 2 | |
| 13 | 0 | |
| 14 | [A case of locally recurrent breast cancer difficult to differentiate from nodular fasciitis]. | 1 |
| 15 | 33 | |
| 16 | 11 | |
| 17 | 29 | |
| 18 | 1 | |
| 19 | 0 | |
| 20 | High glucose level and free fatty acid stimulate reactive oxygen species production through protein kinase C--dependent activation of NAD(P)H oxidase in cultured vascular cells.breakdown → | 1267 |
About Masaki Kakimoto
Masaki Kakimoto is a scholar working on Gastroenterology, Pulmonary and Respiratory Medicine and Internal Medicine, having authored 25 papers that have together received 1.4k indexed citations. Recurring topics across this work include Metastasis and carcinoma case studies (6 papers), Gastrointestinal Tumor Research and Treatment (3 papers) and SARS-CoV-2 and COVID-19 Research (2 papers). The work is most often cited by research in Clinical Biochemistry (281 citations), Physiology (490 citations) and Endocrinology, Diabetes and Metabolism (291 citations). Masaki Kakimoto has collaborated with scholars based in Japan, Spain and Vietnam. Frequent co-authors include Toyoshi Inoguchi, H Nawata, Tetsuji Etoh, T. Hashimoto, Minako Imamura, Masahiro Naruse, Hong Yu, Tsuyoshi Aoki, F. Umeda and Pan Li. Their work appears in journals such as Diabetes, Journal of Ethnopharmacology and Cancer Letters.
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.