Marc H. De Baets

5.8k total citations · 2 hit papers
87 papers, 4.3k citations indexed

About

Marc H. De Baets is a scholar working on Neurology, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Marc H. De Baets has authored 87 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Neurology, 25 papers in Radiology, Nuclear Medicine and Imaging and 19 papers in Molecular Biology. Recurrent topics in Marc H. De Baets's work include Myasthenia Gravis and Thymoma (46 papers), Peripheral Neuropathies and Disorders (37 papers) and Monoclonal and Polyclonal Antibodies Research (24 papers). Marc H. De Baets is often cited by papers focused on Myasthenia Gravis and Thymoma (46 papers), Peripheral Neuropathies and Disorders (37 papers) and Monoclonal and Polyclonal Antibodies Research (24 papers). Marc H. De Baets collaborates with scholars based in Netherlands, Belgium and United States. Marc H. De Baets's co-authors include Mario Losen, Pilar Martínez‐Martínez, Mónica Galli, Tiziano Barbui, R.F.A. Zwaal, Edouard M. Bevers, H.C. Hemker, Cecile Maassen, Paul Comfurius and Jan J.G.M. Verschuuren and has published in prestigious journals such as Science, The Lancet and Journal of Biological Chemistry.

In The Last Decade

Marc H. De Baets

87 papers receiving 4.2k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Anticardiolipin antibodies (ACA) directed not to cardiolipin but to a plasma protein cofactor

1990 1.0k citations Marc H. De Baets et al. profile →
Anti-Inflammatory Activity of Human IgG4 Antibodies by Dynamic Fab Arm Exchange

2007 707 citations Mario Losen, Pilar Martínez‐Martínez et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Marc H. De Baets Netherlands	31	1.4k	1.2k	1.0k	893	775	87	4.3k
Ann Kari Lefvert Sweden	36	1.3k 0.9×	727 0.6×	553 0.5×	591 0.7×	1.9k 2.4×	125	4.4k
Florian Deisenhammer Austria	43	2.8k 1.9×	1.7k 1.4×	793 0.8×	1.1k 1.3×	1.6k 2.1×	225	7.2k
Jan Lycke Sweden	43	1.8k 1.3×	876 0.7×	227 0.2×	1.1k 1.3×	1.1k 1.4×	148	6.3k
Katherine Miszkiel United Kingdom	29	1.3k 0.9×	945 0.8×	514 0.5×	499 0.6×	665 0.9×	47	4.4k
Bibiana Bielekova United States	38	856 0.6×	756 0.6×	436 0.4×	1.1k 1.2×	3.1k 4.0×	103	6.0k
Paolo A. Muraro United Kingdom	41	1.1k 0.7×	849 0.7×	283 0.3×	803 0.9×	1.9k 2.5×	128	5.9k
Anders Svenningsson Sweden	38	1.7k 1.2×	620 0.5×	261 0.2×	862 1.0×	1.2k 1.5×	110	5.4k
Martin S. Weber Germany	41	1.1k 0.8×	640 0.5×	398 0.4×	967 1.1×	2.6k 3.3×	150	5.4k
Robert M. Verdijk Netherlands	36	478 0.3×	420 0.3×	446 0.4×	1.2k 1.4×	1.4k 1.8×	194	4.7k
John N. Whitaker United States	40	1.2k 0.9×	669 0.5×	536 0.5×	1.8k 2.0×	1.3k 1.7×	160	5.4k

Countries citing papers authored by Marc H. De Baets

Since Specialization

Citations

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

Fields of papers citing papers by Marc H. De Baets

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marc H. De Baets. 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 Marc H. De Baets. The network helps show where Marc H. De Baets may publish in the future.

Co-authorship network of co-authors of Marc H. De Baets

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

All Works

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20 of 20 papers shown

Mané-Damas, Marina, Abhishek Saxena, Gisela Nogales‐Gadea, et al.. (2025). Efficacy of MLN9708 (ixazomib) in experimental autoimmune myasthenia gravis and in anti-AChR producing primary thymic cell cultures from myasthenia gravis patients. Frontiers in Immunology. 16. 1521432–1521432. 1 indexed citations

Rennspiess, Dorit, Myrurgia A. Abdul‐Hamid, Monique Hochstenbag, et al.. (2015). Detection of Human Polyomavirus 7 in Human Thymic Epithelial Tumors. Journal of Thoracic Oncology. 10(2). 360–366. 40 indexed citations

Abdul‐Hamid, Myrurgia A., Monique Hochstenbag, Anne‐Marie C. Dingemans, et al.. (2015). Expression of pRb and p16INK4 in human thymic epithelial tumors in relation to the presence of human polyomavirus 7. Diagnostic Pathology. 10(1). 201–201. 8 indexed citations

Verschuuren, Jan J.G.M., Maartje G. Huijbers, Jaap J. Plomp, et al.. (2013). Pathophysiology of myasthenia gravis with antibodies to the acetylcholine receptor, muscle-specific kinase and low-density lipoprotein receptor-related protein 4. Autoimmunity Reviews. 12(9). 918–923. 128 indexed citations

Martínez‐Martínez, Pilar, Peter Molenaar, Mario Losen, et al.. (2013). Autoantibodies to neurotransmitter receptors and ion channels: from neuromuscular to neuropsychiatric disorders. Frontiers in Genetics. 4. 181–181. 11 indexed citations

Bosma, Madeleen, Matthijs K. C. Hesselink, Lauren M. Sparks, et al.. (2012). Perilipin 2 Improves Insulin Sensitivity in Skeletal Muscle Despite Elevated Intramuscular Lipid Levels. Diabetes. 61(11). 2679–2690. 123 indexed citations

Cufi, Perrine, Nadine Dragin, Julia Miriam Weiss, et al.. (2012). Implication of double‐stranded RNA signaling in the etiology of autoimmune myasthenia gravis. Annals of Neurology. 73(2). 281–293. 71 indexed citations

Gomez, Alejandro M., Nick Willcox, Peter Molenaar, et al.. (2012). Targeting plasma cells with proteasome inhibitors: possible roles in treating myasthenia gravis?. Annals of the New York Academy of Sciences. 1274(1). 48–59. 34 indexed citations

Gomez, Alejandro M., Kathleen Vrolix, Pilar Martínez‐Martínez, et al.. (2011). Proteasome Inhibition with Bortezomib Depletes Plasma Cells and Autoantibodies in Experimental Autoimmune Myasthenia Gravis. The Journal of Immunology. 186(4). 2503–2513. 110 indexed citations

10.

Timmers, Silvie, Johan de Vogel‐van den Bosch, Matthijs K. C. Hesselink, et al.. (2011). Paradoxical Increase in TAG and DAG Content Parallel the Insulin Sensitizing Effect of Unilateral DGAT1 Overexpression in Rat Skeletal Muscle. PLoS ONE. 6(1). e14503–e14503. 39 indexed citations

11.

Martínez‐Martínez, Pilar, Mario Losen, Marc H. De Baets, et al.. (2009). The Effect of Plasma From Muscle-Specific Tyrosine Kinase Myasthenia Patients on Regenerating Endplates. American Journal Of Pathology. 175(4). 1536–1544. 32 indexed citations

12.

Mencarelli, Chiara, Mario Losen, Juan Saus, et al.. (2009). The expression of the Goodpasture antigen-binding protein (ceramide transporter) in adult rat brain. Journal of Chemical Neuroanatomy. 38(2). 97–105. 10 indexed citations

13.

Hesselink, Matthijs K. C., Gert Schaart, Esther Moonen‐Kornips, et al.. (2004). DGAT1 overexpression in muscle by in vivo DNA electroporation increases intramyocellular lipid content. Journal of Lipid Research. 46(2). 230–236. 40 indexed citations

14.

Verschuuren, Jan J.G.M., et al.. (1998). Selection of recombinant anti-HuD Fab fragments from a phage display antibody library of a lung cancer patient with paraneoplastic encephalomyelitis. Journal of Neuroimmunology. 82(2). 200–209. 31 indexed citations

15.

Baets, Marc H. De, et al.. (1997). Human anti-nicotinic acetylcholine receptor recombinant Fab fragments isolated from thymus-derived phage display libraries from myasthenia gravis patients reflect predominant specificities in serum and block the action of pathogenic serum antibodies. The Journal of Immunology. 158(4). 1919–1929. 110 indexed citations

16.

Graus, Y., Peter J.C. van Breda Vriesman, & Marc H. De Baets. (1993). Characterization of anti-acetylcholine receptor (AChR) antibodies from mice differing in susceptibility for experimental autoimmune myasthenia gravis (EAMG). Clinical & Experimental Immunology. 92(3). 506–513. 25 indexed citations

17.

Verschuuren, Jan J.G.M., et al.. (1991). Paratope- and framework-related cross-reactive idiotopes on anti-acetylcholine receptor antibodies. The Journal of Immunology. 146(3). 941–948. 13 indexed citations

18.

Verschuuren, Jan J.G.M., Frank Spaans, & Marc H. De Baets. (1990). Single‐fiber electromyography in experimental autoimmune myasthenia gravis. Muscle & Nerve. 13(6). 485–492. 29 indexed citations

19.

Kabel, Peter, et al.. (1990). Dendritic cells and class II MHC expression on thyrocytes during the autoimmune thyroid disease of the BB rat. Clinical Immunology and Immunopathology. 55(1). 9–22. 67 indexed citations

20.

Baets, Marc H. De. (1988). Specificities of autoantibodies in autoimmune receptor diseases. Immunologic Research. 7(3). 218–231. 1 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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