Ronald K. June

1.7k total citations
78 papers, 1.2k citations indexed

About

Ronald K. June is a scholar working on Rheumatology, Biomedical Engineering and Molecular Biology. According to data from OpenAlex, Ronald K. June has authored 78 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 62 papers in Rheumatology, 24 papers in Biomedical Engineering and 23 papers in Molecular Biology. Recurrent topics in Ronald K. June's work include Osteoarthritis Treatment and Mechanisms (61 papers), Lower Extremity Biomechanics and Pathologies (18 papers) and Metabolomics and Mass Spectrometry Studies (16 papers). Ronald K. June is often cited by papers focused on Osteoarthritis Treatment and Mechanisms (61 papers), Lower Extremity Biomechanics and Pathologies (18 papers) and Metabolomics and Mass Spectrometry Studies (16 papers). Ronald K. June collaborates with scholars based in United States, Spain and Netherlands. Ronald K. June's co-authors include David P. Fyhrie, Steven F. Dowdy, Jacob M. Gump, Brian Bothner, Rachel A. Rawle, Jonathan K. Hilmer, Mark Greenwood, Erik Adams, Timothy M. Griffin and Ru Liu‐Bryan and has published in prestigious journals such as Journal of the American Chemical Society, Journal of Biological Chemistry and SHILAP Revista de lepidopterología.

In The Last Decade

Ronald K. June

75 papers receiving 1.1k citations

Peers

Ronald K. June

RHEUM

SURGE

PHARM

Rui Wu China

Takayuki Nishiyama Japan

Takumi Matsumoto Japan

Yulia Merkher Israel

Deting Xue China

Ke Lu China

Xiaoqun Li China

Harish K. Datta United Kingdom

Xintao Zhang China

Veronika Barešová Czechia

Rui Wu China

Ronald K. June

324 ×0.5

RHEUM

815 ×1.9

181 ×1.0

SURGE

262 ×1.5

102 ×0.8

PHARM

Citations per year, relative to Ronald K. June Ronald K. June (= 1×) peers Rui Wu

Countries citing papers authored by Ronald K. June

Since Specialization

Citations

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

Fields of papers citing papers by Ronald K. June

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ronald K. June. 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 Ronald K. June. The network helps show where Ronald K. June may publish in the future.

Co-authorship network of co-authors of Ronald K. June

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Brahmachary, Priyanka, et al.. (2025). Metabolomic Profiling and Characterization of a Novel 3D Culture System for Studying Chondrocyte Mechanotransduction. Cellular and Molecular Bioengineering. 18(6). 589–609.

Brahmachary, Priyanka, et al.. (2025). Chondrocytes Embedded in Agarose Generate Distinct Metabolic Heat Profiles Based on Media Carbon Sources. Annals of Biomedical Engineering. 53(9). 2071–2079. 1 indexed citations

Owkes, Mark, et al.. (2024). Heat conduction simulation of chondrocyte-embedded agarose gels suggests negligible impact of viscoelastic dissipation on temperature change. Journal of Biomechanics. 176. 112307–112307. 4 indexed citations

Bothner, Brian, et al.. (2024). Metabolomic profiles of cartilage and bone reflect tissue type, radiography-confirmed osteoarthritis, and spatial location within the joint. Biochemical and Biophysical Research Communications. 703. 149683–149683. 4 indexed citations

Brahmachary, Priyanka, et al.. (2024). Metabolomic Profiles and Pathways in Osteoarthritic Human Cartilage: A Comparative Analysis with Healthy Cartilage. Metabolites. 14(4). 183–183. 5 indexed citations

Heveran, Chelsea M., et al.. (2024). Osteochondral fluid transport in an ex vivo system. Osteoarthritis and Cartilage. 32(7). 907–911. 3 indexed citations

Bothner, Brian, et al.. (2024). The metabolome of male and female individuals with knee osteoarthritis is influenced by 18-months of weight loss intervention: the IDEA trial. BMC Musculoskeletal Disorders. 25(1). 1057–1057. 2 indexed citations

Ramos, Y.F., Sarah J. Rice, Shabana Amanda Ali, et al.. (2024). Evolution and advancements in genomics and epigenomics in OA research: How far we have come. Osteoarthritis and Cartilage. 32(7). 858–868. 13 indexed citations

Brahmachary, Priyanka, et al.. (2023). Pericellular Matrix Formation and Atomic Force Microscopy of Single Primary Human Chondrocytes Cultured in Alginate Microgels. Advanced Biology. 8(1). e2300268–e2300268. 2 indexed citations

10.

Farooq, Muhammad, Kelsey H. Collins, Annemarie Lang, et al.. (2023). Three decades of advancements in osteoarthritis research: insights from transcriptomic, proteomic, and metabolomic studies. Osteoarthritis and Cartilage. 32(4). 385–397. 27 indexed citations

11.

Satalich, James, et al.. (2023). Metabolic phenotypes reflect patient sex and injury status: A cross-sectional analysis of human synovial fluid. Osteoarthritis and Cartilage. 32(9). 1074–1083. 9 indexed citations

12.

Greenwood, Mark, et al.. (2023). Metabolomic profiling to identify early urinary biomarkers and metabolic pathway alterations in autosomal dominant polycystic kidney disease. American Journal of Physiology-Renal Physiology. 324(6). F590–F602. 6 indexed citations

13.

Brahmachary, Priyanka, et al.. (2022). Correlations between metabolites in the synovial fluid and serum: A mouse injury study. Journal of Orthopaedic Research®. 40(12). 2792–2802. 9 indexed citations

14.

Christiansen, Bernd, et al.. (2021). The microbiome mediates epiphyseal bone loss and metabolomic changes after acute joint trauma in mice. Osteoarthritis and Cartilage. 29(6). 882–893. 18 indexed citations

15.

Thompson, Matthew A., et al.. (2020). The TAT Protein Transduction Domain as an Intra-Articular Drug Delivery Technology. Cartilage. 13(2_suppl). 1637S–1645S. 5 indexed citations

16.

Mumey, Brendan, et al.. (2018). Physiological dynamic compression regulates central energy metabolism in primary human chondrocytes. Biomechanics and Modeling in Mechanobiology. 18(1). 69–77. 26 indexed citations

17.

Haudenschild, Dominik R., et al.. (2018). Inhibition of early response genes prevents changes in global joint metabolomic profiles in mouse post-traumatic osteoarthritis. Osteoarthritis and Cartilage. 27(3). 504–512. 19 indexed citations

18.

June, Ronald K., et al.. (2017). A Comparison of Shear- and Compression-induced Mechanotransduction in Chondrocytes. Osteoarthritis and Cartilage. 25. S278–S279. 1 indexed citations

19.

Hamil, Alexander, Dominik R. Haudenschild, Steven F. Dowdy, & Ronald K. June. (2015). Evidence of in vivo drug delivery via the tat protein transduction domain. Osteoarthritis and Cartilage. 23. A404–A405. 1 indexed citations

20.

Hilmer, Jonathan K., et al.. (2014). Candidate mediators of chondrocyte mechanotransduction via targeted and untargeted metabolomic measurements. Archives of Biochemistry and Biophysics. 545. 116–123. 26 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.

Explore authors with similar magnitude of impact