Maaike van Putten

2.3k total citations
69 papers, 1.6k citations indexed

About

Maaike van Putten is a scholar working on Molecular Biology, Physiology and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Maaike van Putten has authored 69 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 63 papers in Molecular Biology, 24 papers in Physiology and 11 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Maaike van Putten's work include Muscle Physiology and Disorders (61 papers), Adipose Tissue and Metabolism (19 papers) and RNA Research and Splicing (11 papers). Maaike van Putten is often cited by papers focused on Muscle Physiology and Disorders (61 papers), Adipose Tissue and Metabolism (19 papers) and RNA Research and Splicing (11 papers). Maaike van Putten collaborates with scholars based in Netherlands, United States and United Kingdom. Maaike van Putten's co-authors include Annemieke Aartsma‐Rus, Peter A.C. ’t Hoen, Margriet Hulsker, Vishna Devi Nadarajah, Jaap J. Plomp, E. Niks, Jan J.G.M. Verschuuren, Johan T. den Dunnen, Gert‐Jan B. van Ommen and Louise van der Weerd and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Maaike van Putten

63 papers receiving 1.6k 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 van Putten Netherlands 23 1.3k 401 241 212 200 69 1.6k
Glen B. Banks United States 21 1.0k 0.8× 235 0.6× 207 0.9× 163 0.8× 235 1.2× 28 1.2k
Humberto Santo Neto Brazil 23 893 0.7× 396 1.0× 210 0.9× 131 0.6× 221 1.1× 71 1.4k
A. Reghan Foley United States 17 869 0.7× 472 1.2× 156 0.6× 163 0.8× 196 1.0× 57 1.5k
Angelika Paul New Zealand 10 1.2k 0.9× 602 1.5× 172 0.7× 146 0.7× 172 0.9× 11 1.7k
Iain W. McKinnell United Kingdom 22 1.7k 1.3× 327 0.8× 272 1.1× 92 0.4× 231 1.2× 29 2.1k
Maria Júlia Marques Brazil 20 737 0.6× 319 0.8× 82 0.3× 88 0.4× 268 1.3× 57 1.1k
Christophe Chanoine France 25 1.1k 0.8× 151 0.4× 170 0.7× 163 0.8× 139 0.7× 60 1.4k
Robert B. White Australia 15 1.1k 0.8× 260 0.6× 146 0.6× 68 0.3× 146 0.7× 32 1.3k
Toshiyuki Kumagai Japan 23 852 0.7× 152 0.4× 286 1.2× 103 0.5× 345 1.7× 60 1.5k
Paschalis Kratsios United States 18 1.0k 0.8× 210 0.5× 130 0.5× 69 0.3× 272 1.4× 38 1.6k

Countries citing papers authored by Maaike van Putten

Since Specialization
Citations

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

Fields of papers citing papers by Maaike van Putten

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maaike van Putten

This figure shows the co-authorship network connecting the top 25 collaborators of Maaike van Putten. A scholar is included among the top collaborators of Maaike van Putten 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 van Putten. Maaike van Putten 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.
Ma, Shuang‐Gang, Talia Gileadi, Maaike van Putten, et al.. (2025). Regional Expression of Dystrophin Gene Transcripts and Proteins in the Mouse Brain. Cells. 14(18). 1441–1441.
2.
3.
Suidgeest, Ernst, Jessica C. de Greef, Louise van der Weerd, et al.. (2024). A Natural History Study of Hindlimb Physiology and Histopathology in a Heterozygous OPMD Mouse Model. SHILAP Revista de lepidopterología. 7(2). 107–116.
4.
O’Reilly, Daniel, David van de Vijver, Ingrid E.C. Verhaart, et al.. (2023). Challenges of Assessing Exon 53 Skipping of the Human DMD Transcript with Locked Nucleic Acid-Modified Antisense Oligonucleotides in a Mouse Model for Duchenne Muscular Dystrophy. Nucleic Acid Therapeutics. 33(6). 348–360. 7 indexed citations
5.
Gileadi, Talia, Luis Garcı́a, Vincent P. Kelly, et al.. (2023). Networking to Optimize Dmd exon 53 Skipping in the Brain of mdx52 Mouse Model. Biomedicines. 11(12). 3243–3243. 6 indexed citations
6.
Abdelaal, Tamim, et al.. (2023). Spatial transcriptomics reveal markers of histopathological changes in Duchenne muscular dystrophy mouse models. Nature Communications. 14(1). 4909–4909. 24 indexed citations
7.
Cameron, Donnie, Tooba Abbassi‐Daloii, Zaïda Koeks, et al.. (2023). Diffusion‐tensor magnetic resonance imaging captures increased skeletal muscle fibre diameters in Becker muscular dystrophy. Journal of Cachexia Sarcopenia and Muscle. 14(3). 1546–1557. 17 indexed citations
8.
Plomp, Jaap J., et al.. (2022). The therapeutic potential of soluble activin type IIB receptor treatment in a limb girdle muscular dystrophy type 2D mouse model. Neuromuscular Disorders. 32(5). 419–435. 1 indexed citations
9.
Parmar, Rubina, Klaus Charissé, Laura Sepp‐Lorenzino, et al.. (2022). Efficient Downregulation of Alk4 in Skeletal Muscle After Systemic Treatment with Conjugated siRNAs in a Mouse Model for Duchenne Muscular Dystrophy. Nucleic Acid Therapeutics. 33(1). 26–34. 11 indexed citations
10.
Lyu, Pin, Kyung Whan Yoo, Manish Yadav, et al.. (2020). Sensitive and reliable evaluation of single-cut sgRNAs to restore dystrophin by a GFP-reporter assay. PLoS ONE. 15(9). e0239468–e0239468. 8 indexed citations
11.
Aartsma‐Rus, Annemieke & Maaike van Putten. (2019). The use of genetically humanized animal models for personalized medicine approaches. Disease Models & Mechanisms. 13(2). 22 indexed citations
12.
13.
Hulsker, Margriet, et al.. (2018). Voluntary exercise improves muscle function and does not exacerbate muscle and heart pathology in aged Duchenne muscular dystrophy mice. Journal of Molecular and Cellular Cardiology. 125. 29–38. 17 indexed citations
14.
Putten, Maaike van, et al.. (2018). Low dystrophin levels are insufficient to normalize the neuromuscular synaptic abnormalities of mdx mice. Neuromuscular Disorders. 28(5). 427–442. 14 indexed citations
15.
Putten, Maaike van, et al.. (2016). Characterization of neuromuscular synapse function abnormalities in multiple Duchenne muscular dystrophy mouse models. European Journal of Neuroscience. 43(12). 1623–1635. 56 indexed citations
16.
Lucassen, Eliane A., Claudia P. Coomans, Maaike van Putten, et al.. (2016). Environmental 24-hr Cycles Are Essential for Health. Current Biology. 26(14). 1843–1853. 101 indexed citations
17.
Putten, Maaike van, et al.. (2015). Evaluation of 2’-Deoxy-2’-fluoro Antisense Oligonucleotides for Exon Skipping in Duchenne Muscular Dystrophy. Molecular Therapy — Nucleic Acids. 4. e265–e265. 20 indexed citations
18.
Aartsma‐Rus, Annemieke & Maaike van Putten. (2014). Assessing Functional Performance in the <em>Mdx</em> Mouse Model. Journal of Visualized Experiments. 135 indexed citations
19.
Putten, Maaike van, Margriet Hulsker, Vishna Devi Nadarajah, et al.. (2012). The Effects of Low Levels of Dystrophin on Mouse Muscle Function and Pathology. PLoS ONE. 7(2). e31937–e31937. 89 indexed citations
20.
Dooren, Tom J. M. Van, et al.. (2010). HANDEDNESS AND ASYMMETRY IN SCALE-EATING CICHLIDS: ANTISYMMETRIES OF DIFFERENT STRENGTH. Evolution. 64(7). 2159–65. 35 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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