R Ripa

2.9k total citations
115 papers, 2.2k citations indexed

About

R Ripa is a scholar working on Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, R Ripa has authored 115 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Cardiology and Cardiovascular Medicine, 35 papers in Radiology, Nuclear Medicine and Imaging and 26 papers in Molecular Biology. Recurrent topics in R Ripa's work include Cardiac Imaging and Diagnostics (33 papers), Mesenchymal stem cell research (18 papers) and Cerebrovascular and Carotid Artery Diseases (17 papers). R Ripa is often cited by papers focused on Cardiac Imaging and Diagnostics (33 papers), Mesenchymal stem cell research (18 papers) and Cerebrovascular and Carotid Artery Diseases (17 papers). R Ripa collaborates with scholars based in Denmark, United States and Sweden. R Ripa's co-authors include Jens Kastrup, Erik Jørgensen, Andreas Kjær, Yongzhong Wang, Jens Christian Nilsson, Lars Søndergaard, Peer Grande, Hans Erik Johnsen, Jens Jakob Thune and Mandana Haack‐Sørensen and has published in prestigious journals such as Circulation, SHILAP Revista de lepidopterología and Journal of the American College of Cardiology.

In The Last Decade

R Ripa

109 papers receiving 2.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
R Ripa Denmark 27 734 723 667 506 424 115 2.2k
Mercè Roqué Spain 26 826 1.1× 880 1.2× 831 1.2× 265 0.5× 258 0.6× 72 2.9k
Aleksandr Rovner United States 12 457 0.6× 579 0.8× 511 0.8× 435 0.9× 126 0.3× 22 1.5k
Dominique PV de Kleijn Netherlands 16 1.6k 2.2× 600 0.8× 466 0.7× 359 0.7× 86 0.2× 24 2.5k
Stephanie Lehrke Germany 25 684 0.9× 1.4k 1.9× 1.0k 1.5× 589 1.2× 801 1.9× 52 2.7k
Eugenio Quaini Italy 19 1.1k 1.5× 1.6k 2.2× 997 1.5× 196 0.4× 145 0.3× 59 2.8k
Michael A. Kuliszewski Canada 22 860 1.2× 454 0.6× 350 0.5× 163 0.3× 94 0.2× 36 1.9k
Marta Todeschini Italy 24 639 0.9× 506 0.7× 916 1.4× 836 1.7× 71 0.2× 45 3.0k
Mirjam B. Smeets Netherlands 18 1.3k 1.8× 474 0.7× 448 0.7× 249 0.5× 75 0.2× 26 2.4k
Keita Yamasaki Japan 17 589 0.8× 297 0.4× 560 0.8× 136 0.3× 152 0.4× 35 1.7k
Michael Azrin United States 16 645 0.9× 611 0.8× 868 1.3× 117 0.2× 251 0.6× 51 1.7k

Countries citing papers authored by R Ripa

Since Specialization
Citations

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

Fields of papers citing papers by R Ripa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of R Ripa

This figure shows the co-authorship network connecting the top 25 collaborators of R Ripa. A scholar is included among the top collaborators of R Ripa 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 R Ripa. R Ripa 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.
Fosbøl, Marie Øbro, Philip Hasbak, Johan Löfgren, et al.. (2025). First-In-Human Study of [ 64 Cu]Cu-DOTATATE PET/CT in Infective Endocarditis: A Prospective Head-to-Head Comparison With [ 18 F]FDG. Circulation Cardiovascular Imaging. 18(2). e017156–e017156.
2.
Ripa, R, Philip Hasbak, Emilie H. Zobel, et al.. (2023). Increased Subclinical Coronary Artery Pathology in Type 2 Diabetes With Albuminuria. Diabetes. 73(3). 490–496. 3 indexed citations
3.
Jensen, Jacob K., Lars Ringgaard, Bjarke Follin, et al.. (2023). [68Ga]Ga-NODAGA-E[(cRGDyK)]2 angiogenesis PET following myocardial infarction in an experimental rat model predicts cardiac functional parameters and development of heart failure. Journal of Nuclear Cardiology. 30(5). 2073–2084. 7 indexed citations
4.
Laursen, Jens Christian, Emilie H. Zobel, Philip Hasbak, et al.. (2022). In vivo molecular imaging of cardiac angiogenesis in persons with and without type 2 diabetes: A cross‐sectional 68 Ga‐RGD‐PET study. Diabetic Medicine. 40(1). e14960–e14960.
5.
Ripa, R, Emilie H. Zobel, Bernt Johan von Scholten, et al.. (2021). Effect of Liraglutide on Arterial Inflammation Assessed as [ 18 F]FDG Uptake in Patients With Type 2 Diabetes: A Randomized, Double-Blind, Placebo-Controlled Trial. Circulation Cardiovascular Imaging. 14(7). e012174–e012174. 33 indexed citations
6.
Hasbak, Philip, Bernt Johan von Scholten, Jens Christian Laursen, et al.. (2021). Non‐invasive assessment of temporal changes in myocardial microvascular function in persons with type 2 diabetes and healthy controls. Diabetic Medicine. 38(6). e14517–e14517. 4 indexed citations
7.
Zobel, Emilie H., R Ripa, Bernt Johan von Scholten, et al.. (2021). Liraglutide reduces cardiac adipose tissue in type 2 diabetes: A secondary analysis of the LIRAFLAME randomized placebo‐controlled trial. Diabetes Obesity and Metabolism. 23(12). 2651–2659. 10 indexed citations
8.
Pedersen, Sune, Andreas Vegge, R Ripa, et al.. (2019). 18F-FDG PET/MR-imaging in a Göttingen Minipig model of atherosclerosis: Correlations with histology and quantitative gene expression. Atherosclerosis. 285. 55–63. 11 indexed citations
9.
Ghotbi, Adam Ali, Andreas Clemmensen, Kasper Kyhl, et al.. (2017). Rubidium-82 PET imaging is feasible in a rat myocardial infarction model. Journal of Nuclear Cardiology. 26(3). 798–809. 12 indexed citations
10.
Ghotbi, Adam Ali, Andreas Kjær, Lars Nepper‐Christensen, et al.. (2016). Subacute cardiac rubidium-82 positron emission tomography (82Rb-PET) to assess myocardial area at risk, final infarct size, and myocardial salvage after STEMI. Journal of Nuclear Cardiology. 25(3). 970–981. 4 indexed citations
11.
Ripa, R, Andreas Kjær, & Birger Hesse. (2014). Non-Invasive Imaging for Subclinical Coronary Atherosclerosis in Patients with Peripheral Artery Disease. Current Atherosclerosis Reports. 16(6). 7 indexed citations
12.
Ripa, R, Anne Mette Fisker Hag, Anne‐Mette Lebech, et al.. (2013). Feasibility of simultaneous PET/MR of the carotid artery: first clinical experience and comparison to PET/CT. Europe PMC (PubMed Central). 2 indexed citations
13.
Schoos, Mikkel Malby, Lea Munthe‐Fog, Mikkel‐Ole Skjoedt, et al.. (2013). Association between lectin complement pathway initiators, C-reactive protein and left ventricular remodeling in myocardial infarction—A magnetic resonance study. Molecular Immunology. 54(3-4). 408–414. 24 indexed citations
17.
Hedén, Bo, R Ripa, Eva Persson, et al.. (2003). A modified Anderson-Wilkins electrocardiographic acuteness score for anterior or inferior myocardial infarction. American Heart Journal. 146(5). 797–803. 17 indexed citations
18.
Ripa, R, et al.. (1995). [Zinc and diabetes mellitus].. PubMed. 86(10). 415–21. 9 indexed citations
19.
Ripa, R, et al.. (1994). Le mérite et l'amour. PubMed. 67(499). 23–24. 16 indexed citations
20.
Ripa, R & P. Gilli. (1968). [Protease inhibitors (trasylol and epsilon-aminocaproic acid) on human erythrocytic angiotensinase].. PubMed. 44(16). 1297–300. 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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