Maaike de Bie

558 total citations
17 papers, 321 citations indexed

About

Maaike de Bie is a scholar working on Pathology and Forensic Medicine, Genetics and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Maaike de Bie has authored 17 papers receiving a total of 321 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Pathology and Forensic Medicine, 6 papers in Genetics and 6 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Maaike de Bie's work include Chronic Lymphocytic Leukemia Research (6 papers), Acute Lymphoblastic Leukemia research (6 papers) and Immunodeficiency and Autoimmune Disorders (5 papers). Maaike de Bie is often cited by papers focused on Chronic Lymphocytic Leukemia Research (6 papers), Acute Lymphoblastic Leukemia research (6 papers) and Immunodeficiency and Autoimmune Disorders (5 papers). Maaike de Bie collaborates with scholars based in Netherlands, Germany and Poland. Maaike de Bie's co-authors include Vincent H. J. van der Velden, Jacques J. M. van Dongen, Jane S.A. Voerman, Jeltje F. de Vries, Elisabeth R. van Wering, Monique L. den Boer, C. Michel Zwaan, Tomasz Szczepański, Patricia G. Hoogeveen and Daniëlle C.H. Jacobs and has published in prestigious journals such as Blood, PLoS ONE and Journal of Allergy and Clinical Immunology.

In The Last Decade

Maaike de Bie

14 papers receiving 315 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maaike de Bie Netherlands 9 199 127 103 86 56 17 321
Sitaram Ghogale India 12 197 1.0× 174 1.4× 71 0.7× 95 1.1× 47 0.8× 29 320
Sharon Bergeron United States 8 152 0.8× 57 0.4× 98 1.0× 71 0.8× 60 1.1× 10 291
Kayla L. Nguyen United States 4 310 1.6× 161 1.3× 37 0.4× 140 1.6× 71 1.3× 10 426
Hui-liang Xue China 10 184 0.9× 158 1.2× 39 0.4× 60 0.7× 138 2.5× 54 395
Michele Pizzuti Italy 9 71 0.4× 140 1.1× 121 1.2× 108 1.3× 19 0.3× 15 333
Edward Kwan Australia 10 213 1.1× 132 1.0× 60 0.6× 87 1.0× 57 1.0× 20 337
Lucie Šrámková Czechia 11 347 1.7× 308 2.4× 39 0.4× 137 1.6× 66 1.2× 34 481
Sung-Nam Lim South Korea 9 51 0.3× 100 0.8× 79 0.8× 80 0.9× 43 0.8× 36 262
Sherry Pierce United States 4 222 1.1× 262 2.1× 38 0.4× 72 0.8× 104 1.9× 5 342
A von Stackelberg Germany 7 161 0.8× 120 0.9× 27 0.3× 53 0.6× 43 0.8× 13 246

Countries citing papers authored by Maaike de Bie

Since Specialization
Citations

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

Fields of papers citing papers by Maaike de Bie

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maaike de Bie. 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 Maaike de Bie. The network helps show where Maaike de Bie may publish in the future.

Co-authorship network of co-authors of Maaike de Bie

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

All Works

17 of 17 papers shown
1.
Zhou, Zijun, Virgil A. S. H. Dalm, Fabian Kaiser, et al.. (2025). Novel STAT3 Y360C Gain-of-function Variant Underlies Immune Dysregulation and Aberrancy in Mitochondrial Dynamics. Immune Network. 25(2). e18–e18. 1 indexed citations
2.
Körholz, Julia, Virgil A. S. H. Dalm, Maaike de Bie, et al.. (2025). IKBKB gain of function: An inborn error with clinical heterogeneity progressing toward combined immunodeficiency. Journal of Allergy and Clinical Immunology. 156(2). 279–293.
3.
Bie, Maaike de, et al.. (2025). ZBTB7A regulates CD95-mediated cell growth in colorectal cancer cell lines. PLoS ONE. 20(9). e0329958–e0329958.
4.
5.
Bie, Maaike de, Roosmarijn C. Drexhage, Sharon Veenbergen, et al.. (2024). Clinical performance of a novel and rapid bioassay for detection of thyroid-stimulating immunoglobulins in Graves’ orbitopathy patients: a comparison with two commonly used immunoassays. Frontiers in Endocrinology. 15. 1469179–1469179. 2 indexed citations
6.
7.
Zhou, Zijun, Peter J. van der Spek, Sigrid Swagemakers, et al.. (2024). A patient-based murine model recapitulates human STAT3 gain-of-function syndrome. Clinical Immunology. 266. 110312–110312. 2 indexed citations
8.
Drexhage, Hemmo A., Annemarie Wijkhuijs, Corine H. GeurtsvanKessel, et al.. (2023). Immunological profiling in long COVID: overall low grade inflammation and T-lymphocyte senescence and increased monocyte activation correlating with increasing fatigue severity. Frontiers in Immunology. 14. 1254899–1254899. 30 indexed citations
9.
Litjens, Nicolle H. R., Anton W. Langerak, Mariska Klepper, et al.. (2020). Validation of a Combined Transcriptome and T Cell Receptor Alpha/Beta (TRA/TRB) Repertoire Assay at the Single Cell Level for Paucicellular Samples. Frontiers in Immunology. 11. 1999–1999. 1 indexed citations
10.
Meijers, Ruud W. J., Leticia G. León, Maaike de Bie, et al.. (2019). Responsiveness of chronic lymphocytic leukemia cells to B-cell receptor stimulation is associated with low expression of regulatory molecules of the nuclear factor-κB pathway. Haematologica. 105(1). 182–192. 3 indexed citations
11.
12.
Velden, Vincent H. J. van der, Juan Flores‐Montero, Martín Pérez‐Andrés, et al.. (2017). Optimization and testing of dried antibody tube: The EuroFlow LST and PIDOT tubes as examples. Journal of Immunological Methods. 475. 112287–112287. 25 indexed citations
13.
Velden, Vincent H. J. van der, Jeltje F. de Vries, Válerie de Haas, et al.. (2015). New cellular markers at diagnosis are associated with isolated central nervous system relapse in paediatric B‐cell precursor acute lymphoblastic leukaemia. British Journal of Haematology. 172(5). 769–781. 35 indexed citations
14.
Vries, Jeltje F. de, C. Michel Zwaan, Maaike de Bie, et al.. (2011). The novel calicheamicin-conjugated CD22 antibody inotuzumab ozogamicin (CMC-544) effectively kills primary pediatric acute lymphoblastic leukemia cells. Leukemia. 26(2). 255–264. 92 indexed citations
15.
Velden, Vincent H. J. van der, Maaike de Bie, Elisabeth R. van Wering, & Jacques J. M. van Dongen. (2006). Immunoglobulin light chain gene rearrangements in precursor-B-acute lymphoblastic leukemia: characteristics and applicability for the detection of minimal residual disease.. PubMed. 91(5). 679–82. 17 indexed citations
16.
Velden, Vincent H. J. van der, Monika Brüggemann, Patricia G. Hoogeveen, et al.. (2004). TCRB gene rearrangements in childhood and adult precursor-B-ALL: frequency, applicability as MRD-PCR target, and stability between diagnosis and relapse. Leukemia. 18(12). 1971–1980. 35 indexed citations
17.
Szczepański, Tomasz, Vincent H. J. van der Velden, Patricia G. Hoogeveen, et al.. (2003). Vδ2-Jα rearrangements are frequent in precursor-B–acute lymphoblastic leukemia but rare in normal lymphoid cells. Blood. 103(10). 3798–3804. 46 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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