Jürgen Riedl

1.3k total citations
20 papers, 860 citations indexed

About

Jürgen Riedl is a scholar working on Genetics, Hematology and Molecular Biology. According to data from OpenAlex, Jürgen Riedl has authored 20 papers receiving a total of 860 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Genetics, 9 papers in Hematology and 5 papers in Molecular Biology. Recurrent topics in Jürgen Riedl's work include Iron Metabolism and Disorders (7 papers), Hemoglobinopathies and Related Disorders (7 papers) and Erythropoietin and Anemia Treatment (6 papers). Jürgen Riedl is often cited by papers focused on Iron Metabolism and Disorders (7 papers), Hemoglobinopathies and Related Disorders (7 papers) and Erythropoietin and Anemia Treatment (6 papers). Jürgen Riedl collaborates with scholars based in Netherlands, United States and Gabon. Jürgen Riedl's co-authors include Johannes L. Bos, Bas Ponsioen, Kees Jalink, Jun Zhao, Fried Zwartkruis, Wouter H. Moolenaar, Manuela Zaccolo, Sam Machin, Holger Rehmann and Gina Zini and has published in prestigious journals such as Journal of Biological Chemistry, Biochemical Journal and Journal of Cell Science.

In The Last Decade

Jürgen Riedl

20 papers receiving 844 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jürgen Riedl Netherlands 12 480 164 137 96 91 20 860
Timothy J. Martins United States 14 1.6k 3.2× 187 1.1× 125 0.9× 16 0.2× 54 0.6× 29 1.9k
Reinhold Nafe Germany 13 166 0.3× 244 1.5× 36 0.3× 28 0.3× 24 0.3× 45 840
Felix Graßmann Germany 25 756 1.6× 61 0.4× 55 0.4× 83 0.9× 96 1.1× 68 1.9k
Susan Sheehan United States 22 467 1.0× 125 0.8× 42 0.3× 46 0.5× 46 0.5× 38 1.6k
Monika Fleckenstein Germany 45 2.1k 4.4× 88 0.5× 119 0.9× 33 0.3× 209 2.3× 137 6.7k
Richard C. McEachin United States 20 661 1.4× 73 0.4× 74 0.5× 100 1.0× 57 0.6× 30 1.2k
Jonathan C. Henriksen United States 14 227 0.5× 135 0.8× 15 0.1× 29 0.3× 50 0.5× 19 819
Dabin Jeong South Korea 6 876 1.8× 65 0.4× 28 0.2× 12 0.1× 51 0.6× 11 1.3k
Catherine A. Cukras United States 32 1.3k 2.8× 52 0.3× 193 1.4× 31 0.3× 102 1.1× 128 3.0k

Countries citing papers authored by Jürgen Riedl

Since Specialization
Citations

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

Fields of papers citing papers by Jürgen Riedl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jürgen Riedl

This figure shows the co-authorship network connecting the top 25 collaborators of Jürgen Riedl. A scholar is included among the top collaborators of Jürgen Riedl 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 Jürgen Riedl. Jürgen Riedl 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.
Riedl, Jürgen, et al.. (2021). The accuracy of mean corpuscular volume guided anaemia classification in primary care. Family Practice. 38(6). 735–739. 4 indexed citations
2.
Riedl, Jürgen, et al.. (2020). A new diagnostic work-up for defining anemia etiologies: a cohort study in patients ≥ 50 years in general practices. BMC Family Practice. 21(1). 167–167. 6 indexed citations
3.
Kratz, Alexander, Szu‐Hee Lee, Gina Zini, et al.. (2019). Digital morphology analyzers in hematology: ICSH review and recommendations. International Journal of Laboratory Hematology. 41(4). 437–447. 106 indexed citations
4.
Rab, Minke A.E., Brigitte A. van Oirschot, Osheiza Abdulmalik, et al.. (2019). Rapid and reproducible characterization of sickling during automated deoxygenation in sickle cell disease patients. American Journal of Hematology. 94(5). 575–584. 43 indexed citations
5.
Riedl, Jürgen, et al.. (2019). Long-term outcomes in patients newly diagnosed with iron deficiency anaemia in general practice: a retrospective cohort study. BMJ Open. 9(11). e032930–e032930. 7 indexed citations
6.
Hofland, Tom, Renate de Boer, Iris de Weerdt, et al.. (2019). Natural Killer Cell Hypo-responsiveness in Chronic Lymphocytic Leukemia can be Circumvented In Vitro by Adequate Activating Signaling. HemaSphere. 3(6). e308–e308. 20 indexed citations
7.
Riedl, Jürgen, et al.. (2018). Diagnostics in anaemia of chronic disease in general practice: a real-world retrospective cohort study. BJGP Open. 2(3). bjgpopen18X101597–bjgpopen18X101597. 8 indexed citations
8.
Kip, Michelle M. A., Jürgen Riedl, Joost van Rosmalen, et al.. (2018). The effectiveness of a routine versus an extensive laboratory analysis in the diagnosis of anaemia in general practice. Annals of Clinical Biochemistry International Journal of Laboratory Medicine. 55(5). 535–542. 7 indexed citations
9.
10.
Huisjes, Rick, Wouter W. van Solinge, Mark‐David Levin, Richard van Wijk, & Jürgen Riedl. (2017). Digital microscopy as a screening tool for the diagnosis of hereditary hemolytic anemia. International Journal of Laboratory Hematology. 40(2). 159–168. 20 indexed citations
11.
Riedl, Jürgen, et al.. (2016). Prevalence of potential underlying aetiology of macrocytic anaemia in Dutch general practice. BMC Family Practice. 17(1). 113–113. 15 indexed citations
12.
Riedl, Jürgen, et al.. (2015). Interlaboratory Reproducibility of Blood Morphology Using the Digital Microscope. SLAS TECHNOLOGY. 20(6). 670–675. 16 indexed citations
13.
Riedl, Jürgen, et al.. (2015). Automated Detection and Classification of Schistocytes by a Novel Red Blood Cell Module Using Digital Imaging/Microscopy. Journal of Hematology. 4(2). 184–186. 4 indexed citations
14.
Riedl, Jürgen, et al.. (2010). Automated morphological analysis of cells in body fluids by the digital microscopy system DM96. Journal of Clinical Pathology. 63(6). 538–543. 14 indexed citations
15.
Vliem, Marjolein J., Bas Ponsioen, Frank Schwede, et al.. (2008). 8‐pCPT‐2′‐O‐Me‐cAMP‐AM: An Improved Epac‐Selective cAMP Analogue. ChemBioChem. 9(13). 2052–2054. 97 indexed citations
16.
Broeke, Jurjen H., Ineke M. Dijkstra, Ali Taylan Cemgil, et al.. (2008). Automated quantification of cellular traffic in living cells. Journal of Neuroscience Methods. 178(2). 378–384. 9 indexed citations
17.
Riedl, Jürgen, Dominique T. Brandt, Eduard Batlle, et al.. (2005). Down-regulation of Rap1 activity is involved in ephrinB1-induced cell contraction. Biochemical Journal. 389(2). 465–469. 17 indexed citations
18.
Zhang, Zhongchun, Holger Rehmann, Leo Price, Jürgen Riedl, & Johannes L. Bos. (2005). AF6 Negatively Regulates Rap1-induced Cell Adhesion. Journal of Biological Chemistry. 280(39). 33200–33205. 40 indexed citations
19.
Ponsioen, Bas, Jun Zhao, Jürgen Riedl, et al.. (2004). Detecting cAMP‐induced Epac activation by fluorescence resonance energy transfer: Epac as a novel cAMP indicator. EMBO Reports. 5(12). 1176–1180. 353 indexed citations
20.
Heuvel, A. Pieter J. van den, Alida M.M. de Vries-Smits, Pascale C. van Weeren, et al.. (2002). Binding of protein kinase B to the plakin family member periplakin. Journal of Cell Science. 115(20). 3957–3966. 72 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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