Anne Spurkland

12.4k total citations
116 papers, 3.9k citations indexed

About

Anne Spurkland is a scholar working on Immunology, Molecular Biology and Genetics. According to data from OpenAlex, Anne Spurkland has authored 116 papers receiving a total of 3.9k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Immunology, 29 papers in Molecular Biology and 23 papers in Genetics. Recurrent topics in Anne Spurkland's work include T-cell and B-cell Immunology (57 papers), Immune Cell Function and Interaction (40 papers) and Diabetes and associated disorders (17 papers). Anne Spurkland is often cited by papers focused on T-cell and B-cell Immunology (57 papers), Immune Cell Function and Interaction (40 papers) and Diabetes and associated disorders (17 papers). Anne Spurkland collaborates with scholars based in Norway, Sweden and United States. Anne Spurkland's co-authors include Erik Thorsby, Frode Vartdal, Kjersti S. Rønningen, Hanne F. Harbo, Kirsten Muri Boberg, Elisabeth Gulowsen Celius, Gustav Gaudernack, Ludvig M. Sollid, E. Schrumpf and F Vartdal and has published in prestigious journals such as Journal of Biological Chemistry, The Journal of Experimental Medicine and The Journal of Cell Biology.

In The Last Decade

Anne Spurkland

112 papers receiving 3.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Anne Spurkland Norway 35 1.8k 913 777 653 607 116 3.9k
Katherine Siminovitch Canada 26 1.0k 0.6× 917 1.0× 761 1.0× 454 0.7× 331 0.5× 49 3.5k
Miguel Ángel López‐Nevot Spain 38 2.6k 1.4× 945 1.0× 661 0.9× 310 0.5× 926 1.5× 152 4.4k
Pekka Kurki Finland 35 1.1k 0.6× 1.1k 1.2× 443 0.6× 267 0.4× 515 0.8× 95 4.2k
Ola Winqvist Sweden 40 2.1k 1.2× 932 1.0× 749 1.0× 598 0.9× 853 1.4× 128 4.6k
Ann B. Begovich United States 29 3.0k 1.7× 848 0.9× 982 1.3× 217 0.3× 472 0.8× 48 4.6k
Sheri M. Krams United States 40 2.1k 1.2× 1.1k 1.2× 266 0.3× 1.1k 1.6× 1.4k 2.3× 148 5.2k
Michael Geißler Germany 34 818 0.5× 775 0.8× 281 0.4× 581 0.9× 1.5k 2.5× 151 3.5k
Segundo González Spain 44 3.5k 2.0× 845 0.9× 330 0.4× 221 0.3× 1.6k 2.6× 109 5.2k
Kenneth M. Kaufman United States 32 2.2k 1.2× 806 0.9× 511 0.7× 282 0.4× 590 1.0× 87 4.0k
Antonio Puccetti Italy 29 900 0.5× 699 0.8× 189 0.2× 412 0.6× 167 0.3× 83 3.0k

Countries citing papers authored by Anne Spurkland

Since Specialization
Citations

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

Fields of papers citing papers by Anne Spurkland

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anne Spurkland

This figure shows the co-authorship network connecting the top 25 collaborators of Anne Spurkland. A scholar is included among the top collaborators of Anne Spurkland 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 Anne Spurkland. Anne Spurkland 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.
Habtamu, Meseret, et al.. (2020). High-throughput analysis of T cell–monocyte interaction in human tuberculosis. Clinical & Experimental Immunology. 201(2). 187–199. 5 indexed citations
2.
Habtamu, Meseret, et al.. (2018). In vitro analysis of antigen induced T cell-monocyte conjugates by imaging flow cytometry. Journal of Immunological Methods. 460. 93–100. 3 indexed citations
3.
Gustavsen, Marte W., Elisabeth Gulowsen Celius, Anne Spurkland, et al.. (2015). Multiple Sclerosis Risk Allele in CLEC16A Acts as an Expression Quantitative Trait Locus for CLEC16A and SOCS1 in CD4+ T Cells. PLoS ONE. 10(7). e0132957–e0132957. 13 indexed citations
4.
Enqvist, Monika, Eivind Heggernes Ask, Elin Forslund, et al.. (2015). Coordinated Expression of DNAM-1 and LFA-1 in Educated NK Cells. The Journal of Immunology. 194(9). 4518–4527. 73 indexed citations
5.
Sørlie, Morten, Marit E. Jørgensen, Tone Berge, et al.. (2008). Modulation of Lck Function through Multisite Docking to T Cell-specific Adapter Protein. Journal of Biological Chemistry. 283(32). 21909–21919. 22 indexed citations
6.
Sundvold‐Gjerstad, Vibeke, Tomas Mustelin, Tone Berge, et al.. (2005). The C terminus of T cell‐specific adapter protein (TSAd) is necessary for TSAd‐mediated inhibition of Lck activity. European Journal of Immunology. 35(5). 1612–1620. 22 indexed citations
7.
Dai, Ke‐Zheng, Finn–Eirik Johansen, Kristin M. Kolltveit, et al.. (2004). Transcriptional Activation of the SH2D2A Gene Is Dependent on a Cyclic Adenosine 5′-Monophosphate-Responsive Element in the Proximal SH2D2A Promoter. The Journal of Immunology. 172(10). 6144–6151. 13 indexed citations
8.
Etokebe, Godfrey E., Kristine Wiencke, T. Haug, & Anne Spurkland. (2003). The use of modified primer competitors to enhance yields and specificity of HLA class I amplification by polymerase chain reaction (PCR). Tissue Antigens. 61(2). 172–176. 2 indexed citations
9.
10.
Wiencke, Kristine, Anne Spurkland, Erik Schrumpf, & Kirsten Muri Boberg. (2001). Primary sclerosing cholangitis is associated to an extended B8-DR3 haplotype including particular MICA and MICB alleles. Hepatology. 34(4). 625–630. 67 indexed citations
11.
Xu, Chennian, Monica Holmberg, Annette Oturai, et al.. (2001). Linkage analysis suggests a region of importance for multiple sclerosis in 3p14–13. Genes and Immunity. 2(8). 451–454. 3 indexed citations
12.
Masterman, Thomas, Arne Svejgaard, Per Soelberg Sørensen, et al.. (2001). No linkage or association of the nitric oxide synthase genes to multiple sclerosis. Journal of Neuroimmunology. 119(1). 95–100. 13 indexed citations
13.
Sundvold, Vibeke, Knut Martin Torgersen, Nicholas H. Post, et al.. (2000). Cutting Edge: T Cell-Specific Adapter Protein Inhibits T Cell Activation by Modulating Lck Activity. The Journal of Immunology. 165(6). 2927–2931. 46 indexed citations
14.
15.
Saarinen, S., K.M. Boberg, Anne Spurkland, et al.. (1998). HLA class II haplotypes in primary sclerosing cholangitis patients from five ethnic groups.. Hepatology. 28. 1 indexed citations
16.
Boberg, K.M., Giorgia Della Rocca, Anne Spurkland, et al.. (1998). Prognostic indicators of primary sclerosing cholangitis determined by a cox phase-specific time-dependent model.. Hepatology. 28. 1 indexed citations
17.
Гусев, Е. И., М. А. Судомоина, А. Н. Бойко, et al.. (1997). 4-31-11 TNFa1 allele is associated with multiple sclerosis both in Russian and Norwegian caucasians. Journal of the Neurological Sciences. 150. S250–S250. 1 indexed citations
18.
19.
Solberg, Rigmor, Mårten Sandberg, Anne Spurkland, & Tore Jahnsen. (1993). Isolation and Characterization of a Human Pseudogene for the Regulatory Subunit RIα of cAMP-Dependent Protein Kinases and Its Sublocalization on Chromosome 1. Genomics. 15(3). 591–597. 17 indexed citations
20.
Spurkland, Anne, et al.. (1992). HLA-DR and -DQ genotypes of celiac disease patients serologically typed to be non-DR3 or non-DR5/7. Human Immunology. 35(3). 188–192. 99 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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