Kozue Uchio‐Yamada

1.3k total citations
39 papers, 1.1k citations indexed

About

Kozue Uchio‐Yamada is a scholar working on Molecular Biology, Nephrology and Physiology. According to data from OpenAlex, Kozue Uchio‐Yamada has authored 39 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Molecular Biology, 12 papers in Nephrology and 9 papers in Physiology. Recurrent topics in Kozue Uchio‐Yamada's work include Renal Diseases and Glomerulopathies (12 papers), Chronic Kidney Disease and Diabetes (5 papers) and CRISPR and Genetic Engineering (4 papers). Kozue Uchio‐Yamada is often cited by papers focused on Renal Diseases and Glomerulopathies (12 papers), Chronic Kidney Disease and Diabetes (5 papers) and CRISPR and Genetic Engineering (4 papers). Kozue Uchio‐Yamada collaborates with scholars based in Japan, United States and China. Kozue Uchio‐Yamada's co-authors include Naoyuki Sato, Ryuichi Morishita, Hiromi Rakugi, Shuko Takeda, Jun Wada, Takanori Kunieda, Daisuke Takeuchi, Hitomi Kurinami, Mitsuru Shinohara and Junichiro Matsuda and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Immunology and PLoS ONE.

In The Last Decade

Kozue Uchio‐Yamada

38 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kozue Uchio‐Yamada Japan 17 423 398 160 102 99 39 1.1k
Andrew D. Nguyen United States 18 298 0.7× 429 1.1× 117 0.7× 52 0.5× 112 1.1× 42 1.2k
Lorena Perrone Italy 22 455 1.1× 742 1.9× 146 0.9× 116 1.1× 37 0.4× 41 1.5k
Victoria Velarde Chile 22 269 0.6× 831 2.1× 81 0.5× 117 1.1× 55 0.6× 46 1.6k
Alissa A. Frame United States 14 363 0.9× 628 1.6× 52 0.3× 115 1.1× 41 0.4× 22 1.2k
Valeriy V. Lyzogubov United States 19 566 1.3× 503 1.3× 187 1.2× 132 1.3× 19 0.2× 36 1.5k
Alain Boom Belgium 21 398 0.9× 625 1.6× 168 1.1× 103 1.0× 21 0.2× 36 1.5k
Takahito Miyake Japan 15 381 0.9× 353 0.9× 110 0.7× 33 0.3× 48 0.5× 40 1.3k
Takashi Ota Japan 16 218 0.5× 478 1.2× 124 0.8× 22 0.2× 70 0.7× 37 1.2k

Countries citing papers authored by Kozue Uchio‐Yamada

Since Specialization
Citations

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

Fields of papers citing papers by Kozue Uchio‐Yamada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kozue Uchio‐Yamada. 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 Kozue Uchio‐Yamada. The network helps show where Kozue Uchio‐Yamada may publish in the future.

Co-authorship network of co-authors of Kozue Uchio‐Yamada

This figure shows the co-authorship network connecting the top 25 collaborators of Kozue Uchio‐Yamada. A scholar is included among the top collaborators of Kozue Uchio‐Yamada 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 Kozue Uchio‐Yamada. Kozue Uchio‐Yamada 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.
Takashima, Shigeo, et al.. (2025). Transcriptional regulation of solute carrier family 6 member 9 gene. Molecular Biology Reports. 52(1). 540–540. 1 indexed citations
2.
Uchio‐Yamada, Kozue, Keiko Yasuda, Kentaro Oh‐hashi, & Noboru MANABE. (2022). Abnormal glomerular basement membrane maturation impairs mesangial cell differentiation during murine postnatal nephrogenesis. American Journal of Physiology-Renal Physiology. 324(1). F124–F134. 1 indexed citations
3.
Uchio‐Yamada, Kozue, et al.. (2018). C1r/C1s deficiency is insufficient to induce murine systemic lupus erythematosus. Genes and Immunity. 20(2). 121–130. 2 indexed citations
4.
Kato, Ryuji, Megumi Matsumoto, Mai Okada, et al.. (2016). Parametric analysis of colony morphology of non-labelled live human pluripotent stem cells for cell quality control. Scientific Reports. 6(1). 34009–34009. 54 indexed citations
5.
Uchio‐Yamada, Kozue, et al.. (2016). Incorrect strain information for mouse cell lines: sequential influence of misidentification on sublines. In Vitro Cellular & Developmental Biology - Animal. 53(3). 225–230. 6 indexed citations
6.
Uchio‐Yamada, Kozue, Jun Wada, Sumie Katayama, et al.. (2013). Tenc1-Deficient Mice Develop Glomerular Disease in a Strain-Specific Manner. Nephron Experimental Nephrology. 123(3-4). 22–33. 19 indexed citations
7.
Kinehara, Masaki, Daiki Tateyama, Mika Suga, et al.. (2013). Protein Kinase C Regulates Human Pluripotent Stem Cell Self-Renewal. PLoS ONE. 8(1). e54122–e54122. 57 indexed citations
8.
Kumagai, A, Nanao Horike, Tatsuya Uebi, et al.. (2011). A Potent Inhibitor of SIK2, 3, 3′, 7-Trihydroxy-4′-Methoxyflavon (4′-O-Methylfisetin), Promotes Melanogenesis in B16F10 Melanoma Cells. PLoS ONE. 6(10). e26148–e26148. 33 indexed citations
9.
Takeda, Shuko, Naoyuki Sato, Kozue Uchio‐Yamada, et al.. (2010). Diabetes-accelerated memory dysfunction via cerebrovascular inflammation and Aβ deposition in an Alzheimer mouse model with diabetes. Proceedings of the National Academy of Sciences. 107(15). 7036–7041. 407 indexed citations
10.
Sato, Naoyuki, et al.. (2010). Possible pathological interaction between diabetes mellitus and Alzheimer's disease. Neuroscience Research. 68. e27–e27. 1 indexed citations
11.
Taguchi, Ayumu, Junichiro Matsuda, Yoko Noguchi, et al.. (2010). Increased globotriaosylceramide levels in a transgenic mouse expressing human  1,4-galactosyltransferase and a mouse model for treating Fabry disease. The Journal of Biochemistry. 149(2). 161–170. 15 indexed citations
12.
13.
Uchio‐Yamada, Kozue, Tomomi Miyamoto, Ichiro Miyoshi, et al.. (2006). Deficiency of the tensin2 gene in the ICGN mouse: an animal model for congenital nephrotic syndrome. Mammalian Genome. 17(5). 407–416. 41 indexed citations
14.
Suzuki, Osamu, Minako Koura, Yoko Noguchi, et al.. (2006). Analyses of the cDNA and genomic DNA sequences encoding the luteinizing hormone β-subunit precursor protein in the rabbit. General and Comparative Endocrinology. 150(3). 514–519. 1 indexed citations
15.
Miyamoto, Yohei, et al.. (2005). Effect of Human Erythropoietin (hEPO) Treatment on Anemia in ICR-derived Glomerulonephritis (ICGN) Mice. EXPERIMENTAL ANIMALS. 54(2). 181–184. 3 indexed citations
16.
Goto, Yasufumi, et al.. (2005). Transforming growth factor-β1 mediated up-regulation of lysyl oxidase in the kidneys of hereditary nephrotic mouse with chronic renal fibrosis. Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin. 447(5). 859–868. 28 indexed citations
17.
Uchio‐Yamada, Kozue, Noboru MANABE, Yasufumi Goto, et al.. (2005). Decreased Expression of Matrix Metalloproteinases and Tissue Inhibitors of Metalloproteinase in the Kidneys of Hereditary Nephrotic (ICGN) Mice. Journal of Veterinary Medical Science. 67(1). 35–41. 16 indexed citations
18.
19.
MANABE, Noboru, et al.. (2001). Localization of Proliferative and Apoptotic Cells in the Kidneys of ICR-Derived Glomerulonephritis(ICGN) Mice.. Journal of Veterinary Medical Science. 63(7). 781–787. 11 indexed citations
20.

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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