Ingo Uphues

937 total citations
17 papers, 617 citations indexed

About

Ingo Uphues is a scholar working on Molecular Biology, Surgery and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Ingo Uphues has authored 17 papers receiving a total of 617 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 9 papers in Surgery and 5 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Ingo Uphues's work include Metabolism, Diabetes, and Cancer (11 papers), Pancreatic function and diabetes (9 papers) and Cellular transport and secretion (4 papers). Ingo Uphues is often cited by papers focused on Metabolism, Diabetes, and Cancer (11 papers), Pancreatic function and diabetes (9 papers) and Cellular transport and secretion (4 papers). Ingo Uphues collaborates with scholars based in Germany, France and Austria. Ingo Uphues's co-authors include Jürgen Eckel, Thomas Kolter, Bruno Goud, H. Reinauer, Anke M. Schulte, Werner Kramer, Ronan Roussel, Hervé Le Stunff, Ioannis Xénarios and Pedro Marques‐Vidal and has published in prestigious journals such as Journal of Biological Chemistry, The Journal of Clinical Endocrinology & Metabolism and Diabetes.

In The Last Decade

Ingo Uphues

17 papers receiving 610 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ingo Uphues Germany 10 404 206 174 170 103 17 617
Rodolphe Dusaulcy Switzerland 12 384 1.0× 164 0.8× 106 0.6× 177 1.0× 63 0.6× 20 658
Yukiko Kanda Japan 12 209 0.5× 304 1.5× 304 1.7× 125 0.7× 51 0.5× 20 611
Joan Helmering United States 9 342 0.8× 88 0.4× 139 0.8× 178 1.0× 44 0.4× 11 581
Kwan Yi Chu Australia 15 277 0.7× 396 1.9× 227 1.3× 131 0.8× 92 0.9× 18 797
Murthy S.R. Madiraju Canada 3 309 0.8× 219 1.1× 169 1.0× 157 0.9× 26 0.3× 5 533
T. Shirotani Japan 12 247 0.6× 233 1.1× 164 0.9× 103 0.6× 35 0.3× 14 488
Emilia Zmuda‐Trzebiatowska Sweden 8 302 0.7× 108 0.5× 70 0.4× 119 0.7× 33 0.3× 9 453
E. Seffer Germany 12 400 1.0× 195 0.9× 159 0.9× 147 0.9× 24 0.2× 16 575
Elodie M. Varin Canada 15 325 0.8× 358 1.7× 509 2.9× 152 0.9× 48 0.5× 18 885
Kevin Vivot France 14 279 0.7× 275 1.3× 183 1.1× 113 0.7× 19 0.2× 19 625

Countries citing papers authored by Ingo Uphues

Since Specialization
Citations

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

Fields of papers citing papers by Ingo Uphues

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ingo Uphues

This figure shows the co-authorship network connecting the top 25 collaborators of Ingo Uphues. A scholar is included among the top collaborators of Ingo Uphues 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 Ingo Uphues. Ingo Uphues 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.
Weber, Alexander, Alexander Pautsch, Ingo Uphues, et al.. (2024). Discovery of BI-9787, a potent zwitterionic ketohexokinase inhibitor with oral bioavailability. Bioorganic & Medicinal Chemistry Letters. 112. 129930–129930. 3 indexed citations
2.
Thomas, Leo, Eric Martel, Wolfgang Rist, et al.. (2024). The dual GCGR/GLP‐1R agonist survodutide: Biomarkers and pharmacological profiling for clinical candidate selection. Diabetes Obesity and Metabolism. 26(6). 2368–2378. 8 indexed citations
3.
Bauer, Margit, Helmut Romig, Alexander Weber, et al.. (2023). Crystal structures of human and mouse ketohexokinase provide a structural basis for species- and isoform-selective inhibitor design. Acta Crystallographica Section D Structural Biology. 79(10). 871–880. 3 indexed citations
4.
Thomas, Leo, Peter Haebel, Eric J. Simon, et al.. (2022). BI 456906: Discovery and preclinical pharmacology of a novel GCGR/GLP-1R dual agonist with robust anti-obesity efficacy. Molecular Metabolism. 66. 101633–101633. 81 indexed citations
5.
Wigger, Leonore, Céline Cruciani‐Guglielmacci, Jessica Denom, et al.. (2017). Plasma Dihydroceramides Are Diabetes Susceptibility Biomarker Candidates in Mice and Humans. Cell Reports. 18(9). 2269–2279. 173 indexed citations
6.
Chen, Chunguang, Christian M. Cohrs, Julie A. Chouinard, et al.. (2016). Alterations in β-Cell Calcium Dynamics and Efficacy Outweigh Islet Mass Adaptation in Compensation of Insulin Resistance and Prediabetes Onset. Diabetes. 65(9). 2676–2685. 53 indexed citations
7.
Masjkur, Jimmy, Steven Poser, Polyxeni Nikolakopoulou, et al.. (2014). Hes3 Is Expressed in the Adult Pancreatic Islet and Regulates Gene Expression, Cell Growth, and Insulin Release. Journal of Biological Chemistry. 289(51). 35503–35516. 9 indexed citations
8.
Uphues, Ingo, et al.. (2012). Regulation of subcellular distribution of GLUT4 in cardiomyocytes: Rab4A reduces basal glucose transport and augments insulin responsiveness. Experimental and Clinical Endocrinology & Diabetes. 108(1). 26–36. 5 indexed citations
9.
Kessler, Alexandra, et al.. (2001). Diversification of cardiac insulin signaling involves the p85α/β subunits of phosphatidylinositol 3-kinase. American Journal of Physiology-Endocrinology and Metabolism. 280(1). E65–E74. 34 indexed citations
10.
11.
Eckel, Jürgen, et al.. (2000). Cardiac insulin resistance is associated with an impaired recruitment of phosphatidylinositol 3-kinase to GLUT4 vesicles. International Journal of Obesity. 24(S2). S120–S121. 5 indexed citations
12.
Kolter, Thomas, Ingo Uphues, & Jürgen Eckel. (1997). Molecular analysis of insulin resistance in isolated ventricular cardiomyocytes of obese Zucker rats. American Journal of Physiology-Endocrinology and Metabolism. 273(1). E59–E67. 71 indexed citations
13.
Uphues, Ingo, Yijuang Chern, & Jürgen Eckel. (1995). Insulin‐dependent translocation of the small GTP‐binding protein rab3C in cardiac muscle: studies on insulin‐resistant Zucker rats. FEBS Letters. 377(2). 109–112. 10 indexed citations
14.
Uphues, Ingo, Thomas Kolter, Bruno Goud, & Jürgen Eckel. (1995). Failure of insulin-regulated recruitment of the glucose transporter GLUT4 in cardiac muscle of obese Zucker rats is associated with alterations of small-molecular-mass GTP-binding proteins. Biochemical Journal. 311(1). 161–166. 49 indexed citations
15.
Russ, Martina, et al.. (1994). Photoaffinity labelling of cardiac membrane GTP‐binding proteins in response to insulin. European Journal of Biochemistry. 219(1-2). 325–330. 11 indexed citations
16.
Uphues, Ingo, Thomas Kolter, Bruno Goud, & Jürgen Eckel. (1994). Insulin-induced translocation of the glucose transporter GLUT4 in cardiac muscle: studies on the role of small-molecular-mass GTP-binding proteins. Biochemical Journal. 301(1). 177–182. 48 indexed citations
17.
Kolter, Thomas, et al.. (1992). Contraction-induced translocation of the glucose transporter Glut4 in isolated ventricular cardiomyocytes. Biochemical and Biophysical Research Communications. 189(2). 1207–1214. 49 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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