Margit Zweyer

1.9k total citations
60 papers, 1.6k citations indexed

About

Margit Zweyer is a scholar working on Molecular Biology, Cell Biology and Physiology. According to data from OpenAlex, Margit Zweyer has authored 60 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Molecular Biology, 31 papers in Cell Biology and 21 papers in Physiology. Recurrent topics in Margit Zweyer's work include Muscle Physiology and Disorders (55 papers), Muscle metabolism and nutrition (21 papers) and Adipose Tissue and Metabolism (16 papers). Margit Zweyer is often cited by papers focused on Muscle Physiology and Disorders (55 papers), Muscle metabolism and nutrition (21 papers) and Adipose Tissue and Metabolism (16 papers). Margit Zweyer collaborates with scholars based in Germany, Ireland and United Kingdom. Margit Zweyer's co-authors include Dieter Swandulla, Kay Ohlendieck, A. Wernig, Andrey Irintchev, Paul Dowling, Rustam R. Mundegar, Sandra Murphy, Michael Henry, Paula Meleady and Steven Carberry and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Molecular and Cellular Biology.

In The Last Decade

Margit Zweyer

59 papers receiving 1.5k citations

Peers

Margit Zweyer

PHYSI

SURGE

GENET

Sree Rayavarapu United States

Tomasa Barrientos United States

Sharon A. Coolican United States

Judy U. Earley United States

Michele Hadhazy United States

Kevin I. Watt Australia

Marina Bouché Italy

Pamela Plant Canada

Hala S. Alameddine France

Luca Mendler Hungary

Sree Rayavarapu United States

Margit Zweyer

940 ×0.7

174 ×0.3

280 ×0.6

PHYSI

115 ×0.5

SURGE

111 ×0.5

GENET

Citations per year, relative to Margit Zweyer Margit Zweyer (= 1×) peers Sree Rayavarapu

Countries citing papers authored by Margit Zweyer

Since Specialization

Citations

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

Fields of papers citing papers by Margit Zweyer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Margit Zweyer

This figure shows the co-authorship network connecting the top 25 collaborators of Margit Zweyer. A scholar is included among the top collaborators of Margit Zweyer 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 Margit Zweyer. Margit Zweyer 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

Dowling, Paul, Margit Zweyer, Hemmen Sabir, et al.. (2023). Mass spectrometry-based proteomic characterization of the middle-aged mouse brain for animal model research of neuromuscular diseases. European Journal of Translational Myology. 33(3). 2 indexed citations

Dowling, Paul, et al.. (2023). Extracellular Matrix Proteomics: The mdx-4cv Mouse Diaphragm as a Surrogate for Studying Myofibrosis in Dystrophinopathy. Biomolecules. 13(7). 1108–1108. 9 indexed citations

Murphy, Sandra, Margit Zweyer, Dieter Swandulla, & Kay Ohlendieck. (2022). Bioinformatic Analysis of the Subproteomic Profile of Cardiomyopathic Tissue. Methods in molecular biology. 2596. 377–395. 1 indexed citations

Dowling, Paul, Margit Zweyer, Hemmen Sabir, et al.. (2021). Proteomic profiling of the interface between the stomach wall and the pancreas in dystrophinopathy. European Journal of Translational Myology. 31(1). 14 indexed citations

Dowling, Paul, et al.. (2020). Identification of marker proteins of muscular dystrophy in the urine proteome from the mdx-4cv model of dystrophinopathy. Molecular Omics. 16(3). 268–278. 15 indexed citations

Dowling, Paul, et al.. (2020). Proteomic profiling of fatty acid binding proteins in muscular dystrophy. Expert Review of Proteomics. 17(2). 137–148. 16 indexed citations

Dowling, Paul, Margit Zweyer, Michael Henry, et al.. (2020). Proteome-wide Changes in the mdx-4cv Spleen due to Pathophysiological Cross Talk with Dystrophin-Deficient Skeletal Muscle. iScience. 23(9). 101500–101500. 24 indexed citations

Dowling, Paul, et al.. (2019). Emerging proteomic biomarkers of X-linked muscular dystrophy. Expert Review of Molecular Diagnostics. 19(8). 739–755. 27 indexed citations

Murphy, Sandra, Margit Zweyer, Michael Henry, et al.. (2019). Proteomic profiling of the mouse diaphragm and refined mass spectrometric analysis of the dystrophic phenotype. Journal of Muscle Research and Cell Motility. 40(1). 9–28. 33 indexed citations

10.

Murphy, Sandra, Margit Zweyer, Michael Henry, et al.. (2018). Subproteomic profiling of sarcolemma from dystrophic mdx-4cv skeletal muscle. Data in Brief. 17. 980–993. 6 indexed citations

11.

Murphy, Sandra, Margit Zweyer, Rustam R. Mundegar, Dieter Swandulla, & Kay Ohlendieck. (2018). Proteomic identification of elevated saliva kallikrein levels in the mdx-4cv mouse model of Duchenne muscular dystrophy. Biochemistry and Biophysics Reports. 18. 100541–100541. 15 indexed citations

12.

Murphy, Sandra, Margit Zweyer, Michael Henry, et al.. (2018). Proteomic profiling of liver tissue from the mdx-4cv mouse model of Duchenne muscular dystrophy. Clinical Proteomics. 15(1). 34–34. 26 indexed citations

13.

Murphy, Sandra, Margit Zweyer, Rustam R. Mundegar, Dieter Swandulla, & Kay Ohlendieck. (2018). Dataset on the comparative proteomic profiling of mouse saliva and serum from wild type versus the dystrophic mdx-4cv mouse model of dystrophinopathy. Data in Brief. 21. 1236–1245. 8 indexed citations

14.

Mundegar, Rustam R., Margit Zweyer, & Dieter Swandulla. (2017). Immunofluorescence Microscopy for DIGE-Based Proteomics. Methods in molecular biology. 1664. 301–309. 4 indexed citations

15.

Murphy, Sandra, Margit Zweyer, Michael Henry, et al.. (2015). Label-free mass spectrometric analysis reveals complex changes in the brain proteome from the mdx-4cv mouse model of Duchenne muscular dystrophy. Clinical Proteomics. 12(1). 27–27. 27 indexed citations

16.

Carberry, Steven, Margit Zweyer, Dieter Swandulla, & Kay Ohlendieck. (2013). Comparative proteomic analysis of the contractile-protein-depleted fraction from normal versus dystrophic skeletal muscle. Analytical Biochemistry. 446. 108–115. 34 indexed citations

17.

Lorenzon, Paola, Tiziana Pietrangelo, Ralf B. Schäfer, et al.. (2004). Ageing affects the differentiation potential of human myoblasts. Experimental Gerontology. 39(10). 1545–1554. 52 indexed citations

18.

Wernig, A., Margit Zweyer, & Andrey Irintchev. (2000). Function of skeletal muscle tissue formed after myoblast transplantation into irradiated mouse muscles. The Journal of Physiology. 522(2). 333–345. 56 indexed citations

19.

Irintchev, Andrey, Margit Zweyer, & A. Wernig. (1997). Impaired functional and structural recovery after muscle injury in dystrophic mdx mice. Neuromuscular Disorders. 7(2). 117–125. 38 indexed citations

20.

Irintchev, Andrey, Margit Zweyer, & A. Wernig. (1995). Cellular and molecular reactions in mouse muscles after myoblast implantation. Journal of Neurocytology. 24(4). 319–331. 41 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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