Heather Fairfield

1.7k total citations
44 papers, 1.0k citations indexed

About

Heather Fairfield is a scholar working on Oncology, Molecular Biology and Hematology. According to data from OpenAlex, Heather Fairfield has authored 44 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Oncology, 20 papers in Molecular Biology and 15 papers in Hematology. Recurrent topics in Heather Fairfield's work include Multiple Myeloma Research and Treatments (13 papers), Chemokine receptors and signaling (10 papers) and Peroxisome Proliferator-Activated Receptors (6 papers). Heather Fairfield is often cited by papers focused on Multiple Myeloma Research and Treatments (13 papers), Chemokine receptors and signaling (10 papers) and Peroxisome Proliferator-Activated Receptors (6 papers). Heather Fairfield collaborates with scholars based in United States, Australia and Denmark. Heather Fairfield's co-authors include Michaela R. Reagan, Carolyne Falank, Clifford J. Rosen, Michelle M. McDonald, Mariah Farrell, Samantha Costa, Victoria DeMambro, David E. Bergstrom, Jessica A. Pettitt and Calvin Vary and has published in prestigious journals such as Journal of Biological Chemistry, Blood and PLoS ONE.

In The Last Decade

Heather Fairfield

43 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Heather Fairfield United States 17 412 327 326 161 147 44 1.0k
Thomas H. Ambrosi United States 10 408 1.0× 194 0.6× 170 0.5× 135 0.8× 308 2.1× 22 1.0k
Keertik Fulzele United States 17 997 2.4× 331 1.0× 524 1.6× 443 2.8× 195 1.3× 37 1.9k
Anne‐Marie Jank Germany 4 265 0.6× 162 0.5× 118 0.4× 90 0.6× 183 1.2× 4 835
Antonia Graja Germany 10 335 0.8× 162 0.5× 107 0.3× 92 0.6× 188 1.3× 10 913
Tami Kobayashi Japan 20 501 1.2× 95 0.3× 278 0.9× 251 1.6× 82 0.6× 45 1.1k
Abbas Jafari Denmark 17 431 1.0× 63 0.2× 236 0.7× 103 0.6× 156 1.1× 35 823
Yuiko Sato Japan 23 649 1.6× 99 0.3× 364 1.1× 321 2.0× 91 0.6× 59 1.4k
Hina Takano Japan 18 403 1.0× 597 1.8× 225 0.7× 77 0.5× 291 2.0× 45 1.4k
Hideki Tsuboi Japan 19 362 0.9× 125 0.4× 260 0.8× 374 2.3× 66 0.4× 74 1.2k

Countries citing papers authored by Heather Fairfield

Since Specialization
Citations

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

Fields of papers citing papers by Heather Fairfield

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Heather Fairfield

This figure shows the co-authorship network connecting the top 25 collaborators of Heather Fairfield. A scholar is included among the top collaborators of Heather Fairfield 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 Heather Fairfield. Heather Fairfield 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.
Murphy, Connor, Heather Fairfield, Victoria DeMambro, et al.. (2025). Inhibition of acyl‐ CoA synthetase long‐chain isozymes decreases multiple myeloma cell proliferation and causes mitochondrial dysfunction. Molecular Oncology. 19(6). 1687–1706. 3 indexed citations
2.
Fairfield, Heather, et al.. (2024). Adipocytes and metabolism: Contributions to multiple myeloma. Journal of bone oncology. 46. 100609–100609. 4 indexed citations
3.
Qiang, Ya‐Wei, et al.. (2024). Fatty Acid Binding Protein 6 (FABP6) Represents a Novel Target for Multiple Myeloma. Blood. 144(Supplement 1). 4649–4649.
4.
Fairfield, Heather, et al.. (2023). Obesity and myeloma: Clinical and mechanistic contributions to disease progression. Frontiers in Endocrinology. 14. 1118691–1118691. 15 indexed citations
5.
Farrell, Mariah, Heather Fairfield, Connor Murphy, et al.. (2023). Targeting the fatty acid binding proteins disrupts multiple myeloma cell cycle progression and MYC signaling. eLife. 12. 25 indexed citations
6.
Fairfield, Heather, et al.. (2023). Development and characterization of three cell culture systems to investigate the relationship between primary bone marrow adipocytes and myeloma cells. Frontiers in Oncology. 12. 912834–912834. 8 indexed citations
7.
Sundberg, John P., Hannah Galantino‐Homer, Heather Fairfield, et al.. (2022). Witch Nails (Krt90whnl): A spontaneous mouse mutation affecting nail growth and development. PLoS ONE. 17(11). e0277284–e0277284. 2 indexed citations
8.
Fairfield, Heather, Samantha Costa, Carolyne Falank, et al.. (2021). Multiple Myeloma Cells Alter Adipogenesis, Increase Senescence-Related and Inflammatory Gene Transcript Expression, and Alter Metabolism in Preadipocytes. Frontiers in Oncology. 10. 584683–584683. 32 indexed citations
9.
Jafari, Abbas, Heather Fairfield, Thomas Levin Andersen, & Michaela R. Reagan. (2021). Myeloma-bone marrow adipocyte axis in tumour survival and treatment response. British Journal of Cancer. 125(6). 775–777. 15 indexed citations
10.
Costa, Samantha, Heather Fairfield, Mariah Farrell, et al.. (2021). Sclerostin antibody increases trabecular bone and bone mechanical properties by increasing osteoblast activity damaged by whole-body irradiation in mice. Bone. 147. 115918–115918. 5 indexed citations
11.
Fairfield, Heather, Carolyne Falank, Mariah Farrell, et al.. (2018). Development of a 3D bone marrow adipose tissue model. Bone. 118. 77–88. 53 indexed citations
12.
Fairfield, Heather, et al.. (2017). Myeloma-Associated Adipocytes Exhibit Reduced Adipogenic Gene Expression and Delipidation. Blood. 130. 1768–1768. 2 indexed citations
13.
Falank, Carolyne, Heather Fairfield, Mariah Farrell, & Michaela R. Reagan. (2017). New Bone Cell Type Identified as Driver of Drug Resistance in Multiple Myeloma: The Bone Marrow Adipocyte. Blood. 130. 122–122. 10 indexed citations
14.
Fairfield, Heather, Clifford J. Rosen, & Michaela R. Reagan. (2017). Connecting Bone and Fat: the Potential Role for Sclerostin. PubMed. 3(2). 114–121. 40 indexed citations
15.
Falank, Carolyne, Heather Fairfield, & Michaela R. Reagan. (2017). Reflections on Cancer in the Bone Marrow: Adverse Roles of Adipocytes. PubMed. 3(4). 254–262. 8 indexed citations
16.
Falank, Carolyne, Heather Fairfield, & Michaela R. Reagan. (2016). Signaling Interplay between Bone Marrow Adipose Tissue and Multiple Myeloma cells. Frontiers in Endocrinology. 7. 67–67. 64 indexed citations
17.
Tomberg, Kärt, Rami Khoriaty, Randal J. Westrick, et al.. (2016). Spontaneous 8bp Deletion in Nbeal2 Recapitulates the Gray Platelet Syndrome in Mice. PLoS ONE. 11(3). e0150852–e0150852. 8 indexed citations
18.
Noguchi, Junko, Michiko Hirose, Shimpei Kajita, et al.. (2013). A Mutation in the Nuclear Pore Complex Gene Tmem48 Causes Gametogenesis Defects in Skeletal Fusions with Sterility (sks) Mice. Journal of Biological Chemistry. 288(44). 31830–31841. 13 indexed citations
19.
Flaherty, John P., et al.. (2010). Molecular characterization of an allelic series of mutations in the mouse Nox3 gene. Mammalian Genome. 22(3-4). 156–169. 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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