James G. Burchfield

2.9k total citations
51 papers, 1.7k citations indexed

About

James G. Burchfield is a scholar working on Molecular Biology, Surgery and Physiology. According to data from OpenAlex, James G. Burchfield has authored 51 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Molecular Biology, 17 papers in Surgery and 15 papers in Physiology. Recurrent topics in James G. Burchfield's work include Metabolism, Diabetes, and Cancer (23 papers), Pancreatic function and diabetes (16 papers) and Adipose Tissue and Metabolism (14 papers). James G. Burchfield is often cited by papers focused on Metabolism, Diabetes, and Cancer (23 papers), Pancreatic function and diabetes (16 papers) and Adipose Tissue and Metabolism (14 papers). James G. Burchfield collaborates with scholars based in Australia, United Kingdom and United States. James G. Burchfield's co-authors include David E. James, Daniel J. Fazakerley, Jacqueline Stöckli, Pengyi Yang, Trevor J. Biden, Carsten Schmitz‐Peiffer, William E. Hughes, Benjamin L. Parker, Sean J. Humphrey and James R. Krycer and has published in prestigious journals such as Journal of Biological Chemistry, Nature Communications and The EMBO Journal.

In The Last Decade

James G. Burchfield

51 papers receiving 1.7k citations

Peers

James G. Burchfield
James R. Krycer Australia
Kyle S. McCommis United States
Zhiqiang Li United States
Merrie Mosedale United States
Will A. Coumans Netherlands
James G. Burchfield
Citations per year, relative to James G. Burchfield James G. Burchfield (= 1×) peers Shuai Chen

Countries citing papers authored by James G. Burchfield

Since Specialization
Citations

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

Fields of papers citing papers by James G. Burchfield

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James G. Burchfield. 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 James G. Burchfield. The network helps show where James G. Burchfield may publish in the future.

Co-authorship network of co-authors of James G. Burchfield

This figure shows the co-authorship network connecting the top 25 collaborators of James G. Burchfield. A scholar is included among the top collaborators of James G. Burchfield 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 James G. Burchfield. James G. Burchfield 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.
Burchfield, James G., Alexis Díaz‐Vegas, & David E. James. (2025). The insulin signalling network. Nature Metabolism. 7(9). 1745–1764. 2 indexed citations
2.
Díaz‐Vegas, Alexis, Kristen C. Cooke, Belinda Yau, et al.. (2024). Deletion of miPEP in adipocytes protects against obesity and insulin resistance by boosting muscle metabolism. Molecular Metabolism. 86. 101983–101983. 4 indexed citations
3.
Díaz‐Vegas, Alexis, Søren Madsen, Kristen C. Cooke, et al.. (2023). Mitochondrial electron transport chain, ceramide, and coenzyme Q are linked in a pathway that drives insulin resistance in skeletal muscle. eLife. 12. 17 indexed citations
4.
Díaz‐Vegas, Alexis, Søren Madsen, Kristen C. Cooke, et al.. (2023). Mitochondrial electron transport chain, ceramide, and coenzyme Q are linked in a pathway that drives insulin resistance in skeletal muscle. eLife. 12. 23 indexed citations
5.
Madsen, Søren, Stewart W. C. Masson, Kristen C. Cooke, et al.. (2023). Dual Tracer Test to Measure Tissue-Specific Insulin Action in Individual Mice Identifies In Vivo Insulin Resistance Without Fasting Hyperinsulinemia. Diabetes. 73(3). 359–373. 6 indexed citations
6.
Díaz‐Vegas, Alexis, Dougall M. Norris, Kristen C. Cooke, et al.. (2022). A high-content endogenous GLUT4 trafficking assay reveals new aspects of adipocyte biology. Life Science Alliance. 6(1). e202201585–e202201585. 11 indexed citations
7.
Zhang, Zhe, Wei Li, Guang Yang, et al.. (2020). CASK modulates the assembly and function of the Mint1/Munc18-1 complex to regulate insulin secretion. Cell Discovery. 6(1). 92–92. 56 indexed citations
8.
Krycer, James R., Alexis Díaz‐Vegas, Kristen C. Cooke, et al.. (2019). Mitochondrial oxidants, but not respiration, are sensitive to glucose in adipocytes. Journal of Biological Chemistry. 295(1). 99–110. 19 indexed citations
9.
Kearney, Alison L., Kristen C. Cooke, Dougall M. Norris, et al.. (2019). Serine 474 phosphorylation is essential for maximal Akt2 kinase activity in adipocytes. Journal of Biological Chemistry. 294(45). 16729–16739. 28 indexed citations
10.
Norris, Dougall M., Pengyi Yang, James R. Krycer, et al.. (2017). An improved Akt reporter reveals intra- and inter-cellular heterogeneity and oscillations in signal transduction. Journal of Cell Science. 130(16). 2757–2766. 15 indexed citations
11.
George, Mitchell J., James G. Burchfield, B Macfarlane, et al.. (2017). Multiplate and TEG platelet mapping in a population of severely injured trauma patients. Transfusion Medicine. 28(3). 224–230. 21 indexed citations
12.
Parker, Benjamin L., James G. Burchfield, Daniel Clayton, et al.. (2017). Multiplexed Temporal Quantification of the Exercise-regulated Plasma Peptidome. Molecular & Cellular Proteomics. 16(12). 2055–2068. 52 indexed citations
13.
Hoffman, Nolan J., Benjamin L. Parker, Rima Chaudhuri, et al.. (2015). Global Phosphoproteomic Analysis of Human Skeletal Muscle Reveals a Network of Exercise-Regulated Kinases and AMPK Substrates. Cell Metabolism. 22(5). 922–935. 320 indexed citations
14.
Hoffman, Nolan J., Benjamin L. Parker, Rima Chaudhuri, et al.. (2015). Global Phosphoproteomic Analysis of Human Skeletal Muscle Reveals a Network of Exercise-Regulated Kinases and AMPK Substrates. Cell Metabolism. 22(5). 948–948. 8 indexed citations
15.
Gribben, Christopher, James G. Burchfield, Kristen C. Thomas, et al.. (2014). The Role of the Niemann-Pick Disease, Type C1 Protein in Adipocyte Insulin Action. PLoS ONE. 9(4). e95598–e95598. 17 indexed citations
16.
Li, Jialin, James Cantley, James G. Burchfield, et al.. (2014). DOC2 isoforms play dual roles in insulin secretion and insulin-stimulated glucose uptake. Diabetologia. 57(10). 2173–2182. 29 indexed citations
17.
Iismaa, Siiri E., Mark Aplin, Sara Holman, et al.. (2013). Glucose Homeostasis in Mice Is Transglutaminase 2 Independent. PLoS ONE. 8(5). e63346–e63346. 22 indexed citations
18.
Frangioudakis, Georgia, James G. Burchfield, Gregory J. Cooney, et al.. (2009). Diverse roles for protein kinase C δ and protein kinase C ε in the generation of high-fat-diet-induced glucose intolerance in mice: regulation of lipogenesis by protein kinase C δ. Diabetologia. 52(12). 2616–2620. 49 indexed citations
19.
Cazzolli, Rosanna, Todd W. Mitchell, James G. Burchfield, et al.. (2007). Dilinoleoyl-phosphatidic acid mediates reduced IRS-1 tyrosine phosphorylation in rat skeletal muscle cells and mouse muscle. Diabetologia. 50(8). 1732–1742. 18 indexed citations
20.
Burchfield, James G., William E. Hughes, Valerie C. Wasinger, et al.. (2004). Akt Mediates Insulin-stimulated Phosphorylation of Ndrg2. Journal of Biological Chemistry. 279(18). 18623–18632. 71 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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