Kim Masuda

1.7k total citations
17 papers, 1.4k citations indexed

About

Kim Masuda is a scholar working on Pharmacology, Molecular Biology and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Kim Masuda has authored 17 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Pharmacology, 6 papers in Molecular Biology and 6 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Kim Masuda's work include Cannabis and Cannabinoid Research (9 papers), Diet, Metabolism, and Disease (6 papers) and Pancreatic function and diabetes (3 papers). Kim Masuda is often cited by papers focused on Cannabis and Cannabinoid Research (9 papers), Diet, Metabolism, and Disease (6 papers) and Pancreatic function and diabetes (3 papers). Kim Masuda collaborates with scholars based in United States, Italy and Bulgaria. Kim Masuda's co-authors include Benjamin F. Cravatt, Raymond C. Stevens, Michael H. Bracey, Michael A. Hanson, Ku‐Lung Hsu, Alexander Adibekian, Katsunori Tsuboi, Holly Pugh, Nadim Jessani and Heather B. Bradshaw and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Kim Masuda

17 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kim Masuda United States	15	655	621	237	229	153	17	1.4k
Marya Liimatta United States	19	871 1.3×	852 1.4×	296 1.2×	364 1.6×	252 1.6×	23	2.0k
Lee‐Ho Wang United States	15	551 0.8×	725 1.2×	226 1.0×	103 0.4×	165 1.1×	26	1.6k
Armand B. Cognetta United States	15	312 0.5×	728 1.2×	107 0.5×	171 0.7×	81 0.5×	15	1.2k
Chakkodabylu S. Ramesha United States	11	534 0.8×	1.2k 2.0×	242 1.0×	148 0.6×	137 0.9×	13	2.2k
Marc P. Baggelaar Netherlands	16	459 0.7×	281 0.5×	160 0.7×	156 0.7×	87 0.6×	29	815
Anja Rosengarth United States	13	608 0.9×	912 1.5×	365 1.5×	197 0.9×	225 1.5×	15	1.7k
Karen M. Kedzie United States	16	463 0.7×	434 0.7×	149 0.6×	62 0.3×	94 0.6×	20	1.2k
Gregory Murphy United Kingdom	14	224 0.3×	1.6k 2.5×	231 1.0×	255 1.1×	212 1.4×	20	2.1k
Dick Schaap Netherlands	17	210 0.3×	1.8k 3.0×	319 1.3×	128 0.6×	74 0.5×	22	2.2k
Anju Preet United States	15	468 0.7×	639 1.0×	177 0.7×	174 0.8×	123 0.8×	21	1.3k

Countries citing papers authored by Kim Masuda

Since Specialization

Citations

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

Fields of papers citing papers by Kim Masuda

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kim Masuda

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

All Works

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17 of 17 papers shown

Kavanagh, Madeline E., Benjamin D. Horning, Roli Khattri, et al.. (2022). Selective inhibitors of JAK1 targeting an isoform-restricted allosteric cysteine. Nature Chemical Biology. 18(12). 1388–1398. 57 indexed citations

Masuda, Kim, et al.. (2021). From the Core to the Floor—Utilizing a Webinar to Provide Pelvic Health Education. Journal of Womenʼs Health Physical Therapy. 46(2). 95–99. 1 indexed citations

Jing, Hui, Alex Reed, Olesya A. Ulanovskaya, et al.. (2021). Phospholipase Cγ2 regulates endocannabinoid and eicosanoid networks in innate immune cells. Proceedings of the National Academy of Sciences. 118(41). 18 indexed citations

Ichu, Taka-Aki, Alex Reed, Daisuke Ogasawara, et al.. (2020). ABHD12 and LPCAT3 Interplay Regulates a Lyso-phosphatidylserine-C20:4 Phosphatidylserine Lipid Network Implicated in Neurological Disease. Biochemistry. 59(19). 1793–1799. 22 indexed citations

Hsu, Ku‐Lung, Melissa M. Dix, Andreu Viader, et al.. (2014). The hereditary spastic paraplegia-related enzyme DDHD2 is a principal brain triglyceride lipase. Proceedings of the National Academy of Sciences. 111(41). 14924–14929. 129 indexed citations

Hsu, Ku‐Lung, Katsunori Tsuboi, Alexander Adibekian, et al.. (2012). DAGLβ inhibition perturbs a lipid network involved in macrophage inflammatory responses. Nature Chemical Biology. 8(12). 999–1007. 179 indexed citations

Tsuboi, Kazuhito, Toru Uyama, Kim Masuda, et al.. (2012). Endogenous Molecules Stimulating N-Acylethanolamine-Hydrolyzing Acid Amidase (NAAA). ACS Chemical Neuroscience. 3(5). 379–385. 26 indexed citations

Freigang, Stefan, Michele K. McKinney, Philippe Krebs, et al.. (2010). Fatty acid amide hydrolase shapes NKT cell responses by influencing the serum transport of lipid antigen in mice. Journal of Clinical Investigation. 120(6). 1873–1884. 25 indexed citations

Bachovchin, Daniel A., Kim Masuda, Steven J Brown, et al.. (2010). Oxime esters as selective, covalent inhibitors of the serine hydrolase retinoblastoma-binding protein 9 (RBBP9). Bioorganic & Medicinal Chemistry Letters. 20(7). 2254–2258. 25 indexed citations

10.

Bradshaw, Heather B., Neta Rimmerman, Sherry Shu‐Jung Hu, et al.. (2009). The endocannabinoid anandamide is a precursor for the signaling lipid N-arachidonoyl glycine by two distinct pathways. BMC Biochemistry. 10(1). 14–14. 102 indexed citations

11.

Hu, Sherry Shu‐Jung, Heather B. Bradshaw, Susan M. Huang, et al.. (2009). The biosynthesis of N-arachidonoyl dopamine (NADA), a putative endocannabinoid and endovanilloid, via conjugation of arachidonic acid with dopamine. Prostaglandins Leukotrienes and Essential Fatty Acids. 81(4). 291–301. 56 indexed citations

12.

Rimmerman, Neta, Heather B. Bradshaw, Velocity Hughes, et al.. (2008). N-Palmitoyl Glycine, a Novel Endogenous Lipid That Acts As a Modulator of Calcium Influx and Nitric Oxide Production in Sensory Neurons. Molecular Pharmacology. 74(1). 213–224. 73 indexed citations

13.

Mei, Giampiero, Almerinda Di Venere, Valeria Gasperi, et al.. (2006). Closing the Gate to the Active Site. Journal of Biological Chemistry. 282(6). 3829–3836. 13 indexed citations

14.

Jessani, Nadim, Mark Humphrey, W. Hayes McDonald, et al.. (2004). Carcinoma and stromal enzyme activity profiles associated with breast tumor growth in vivo. Proceedings of the National Academy of Sciences. 101(38). 13756–13761. 157 indexed citations

15.

Bracey, Michael H., Michael A. Hanson, Kim Masuda, Raymond C. Stevens, & Benjamin F. Cravatt. (2002). Structural Adaptations in a Membrane Enzyme That Terminates Endocannabinoid Signaling. Science. 298(5599). 1793–1796. 407 indexed citations

16.

Kovacs, Werner J., et al.. (2000). Identification of peroxisomal targeting signals in cholesterol biosynthetic enzymes: AA-CoA thiolase, HMG-CoA synthase, MPPD, and FPP synthase. Journal of Lipid Research. 41(12). 1921–1935. 57 indexed citations

17.

Shackelford, Janis E., et al.. (1997). Characterization of UT2 Cells. Journal of Biological Chemistry. 272(39). 24579–24587. 31 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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