Kai Pinkernell

1.3k total citations
21 papers, 1.0k citations indexed

About

Kai Pinkernell is a scholar working on Molecular Biology, Surgery and Biomaterials. According to data from OpenAlex, Kai Pinkernell has authored 21 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 9 papers in Surgery and 7 papers in Biomaterials. Recurrent topics in Kai Pinkernell's work include Tissue Engineering and Regenerative Medicine (8 papers), Electrospun Nanofibers in Biomedical Applications (7 papers) and Mesenchymal stem cell research (6 papers). Kai Pinkernell is often cited by papers focused on Tissue Engineering and Regenerative Medicine (8 papers), Electrospun Nanofibers in Biomedical Applications (7 papers) and Mesenchymal stem cell research (6 papers). Kai Pinkernell collaborates with scholars based in Germany, United States and Norway. Kai Pinkernell's co-authors include Eckhard Alt, Yao‐Hua Song, Xiaowen Bai, Christian Valina, Richard J. Campeau, Thierry H. Le Jemtel, Said Hamid Sadat, Hai-Chien Kuo, John K. Fraser and Marc H. Hedrick and has published in prestigious journals such as Blood, Biochemical and Biophysical Research Communications and European Heart Journal.

In The Last Decade

Kai Pinkernell

20 papers receiving 999 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kai Pinkernell Germany 13 603 576 388 292 135 21 1.0k
Takafumi Fujii Japan 10 713 1.2× 642 1.1× 444 1.1× 312 1.1× 236 1.7× 17 1.3k
Zeeshan Pasha United States 14 512 0.8× 498 0.9× 636 1.6× 231 0.8× 133 1.0× 16 1.2k
Lindsay Heyd United States 6 593 1.0× 580 1.0× 576 1.5× 266 0.9× 99 0.7× 6 1.0k
Jin-Qiang Kuang Canada 12 633 1.0× 703 1.2× 271 0.7× 313 1.1× 85 0.6× 13 1.0k
Zhuo Sun Canada 12 412 0.7× 427 0.7× 367 0.9× 184 0.6× 141 1.0× 21 959
Amitabh C. Pandey United States 10 799 1.3× 478 0.8× 307 0.8× 108 0.4× 83 0.6× 41 1.3k
Carole Manneville Switzerland 4 792 1.3× 547 0.9× 447 1.2× 319 1.1× 49 0.4× 4 1.2k
Akihito Mikamo Japan 17 368 0.6× 601 1.0× 385 1.0× 198 0.7× 265 2.0× 69 1.1k
Xinyang Hu China 9 458 0.8× 377 0.7× 376 1.0× 182 0.6× 55 0.4× 13 863
Sebastian Gehmert Germany 20 576 1.0× 715 1.2× 315 0.8× 250 0.9× 37 0.3× 53 1.4k

Countries citing papers authored by Kai Pinkernell

Since Specialization
Citations

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

Fields of papers citing papers by Kai Pinkernell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kai Pinkernell

This figure shows the co-authorship network connecting the top 25 collaborators of Kai Pinkernell. A scholar is included among the top collaborators of Kai Pinkernell 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 Kai Pinkernell. Kai Pinkernell 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.
2.
Fløisand, Yngvar, Mats Remberger, Iris Bigalke, et al.. (2023). WT1 and PRAME RNA-loaded dendritic cell vaccine as maintenance therapy in de novo AML after intensive induction chemotherapy. Leukemia. 37(9). 1842–1849. 16 indexed citations
3.
Heuser, Michael, Felicitas Thol, Adrian Schwarzer, et al.. (2021). Allogeneic, CD34 +, Umbilical Cordblood-Derived NK Cell Adoptive Immunotherapy for the Treatment of Acute Myeloid Leukemia Patients with Measurable Residual Disease. Blood. 138(Supplement 1). 1745–1745. 4 indexed citations
4.
Fløisand, Yngvar, Iris Bigalke, Dag Josefsen, et al.. (2020). A WT-1 and PRAME "Fast-DC" Immunotherapy As a Potential Post-Remission Strategy for AML. Blood. 136(Supplement 1). 11–11. 1 indexed citations
5.
Raffegerst, Silke, Yngvar Fløisand, Dag Josefsen, et al.. (2019). DC Vaccination Induces Antigen Specific Immune Responses in AML Patients: A 1-Year Interim Assessment. Blood. 134(Supplement_1). 3923–3923. 5 indexed citations
6.
7.
Song, Yao‐Hua, Kai Pinkernell, & Eckhard Alt. (2011). Stem cell induced cardiac regeneration: Fusion/mitochondrial exchange and/or transdifferentiation?. Cell Cycle. 10(14). 2281–2286. 19 indexed citations
8.
Zhu, Min, Yan Chen, R. Schreiber, et al.. (2010). Supplementation of Fat Grafts With Adipose-Derived Regenerative Cells Improves Long-Term Graft Retention. Annals of Plastic Surgery. 64(2). 222–228. 224 indexed citations
9.
Zheng, Feng, Joey Ting, Zeni Alfonso, et al.. (2010). Fresh and cryopreserved, uncultured adipose tissue-derived stem and regenerative cells ameliorate ischemia–reperfusion-induced acute kidney injury. Nephrology Dialysis Transplantation. 25(12). 3874–3884. 76 indexed citations
10.
Alt, Eckhard, Kai Pinkernell, Michael Coleman, et al.. (2009). Effect of freshly isolated autologous tissue resident stromal cells on cardiac function and perfusion following acute myocardial infarction. International Journal of Cardiology. 144(1). 26–35. 30 indexed citations
11.
Phillips, M. Ian, Yaoliang Tang, & Kai Pinkernell. (2008). Stem Cell Therapy for Heart Failure: the Science and Current Progress. Future Cardiology. 4(3). 285–298. 6 indexed citations
12.
Valina, Christian, Kai Pinkernell, Yao‐Hua Song, et al.. (2007). Intracoronary administration of autologous adipose tissue-derived stem cells improves left ventricular function, perfusion, and remodelling after acute myocardial infarction. European Heart Journal. 28(21). 2667–2677. 308 indexed citations
13.
Song, Yao‐Hua, Sebastian Gehmert, Kai Pinkernell, et al.. (2007). VEGF is critical for spontaneous differentiation of stem cells into cardiomyocytes. Biochemical and Biophysical Research Communications. 354(4). 999–1003. 103 indexed citations
14.
Bai, Xiaowen, Kai Pinkernell, Yao‐Hua Song, et al.. (2006). Genetically selected stem cells from human adipose tissue express cardiac markers. Biochemical and Biophysical Research Communications. 353(3). 665–671. 55 indexed citations
15.
Duckers, Henricus J., et al.. (2006). The Bedside Celution system for isolation of adipose derived regenerative cells.. PubMed. 2(3). 395–8. 19 indexed citations
16.
Kerut, Edmund Kenneth, et al.. (2004). Technique and Imaging for Transthoracic Echocardiography of the Laboratory Pig. Echocardiography. 21(5). 439–442. 18 indexed citations
17.
18.
Skaletz‐Rorowski, Adriane, et al.. (2001). Lovastatin blocks basic fibroblast growth factor-induced mitogen-activated protein kinase signaling in coronary smooth muscle cells via phosphatase inhibition. European Journal of Cell Biology. 80(3). 207–212. 12 indexed citations
19.
Laugwitz, Karl‐Ludwig, Alessandra Moretti, Hans‐Jörg Weig, et al.. (2001). Blocking Caspase-Activated Apoptosis Improves Contractility in Failing Myocardium. Human Gene Therapy. 12(17). 2051–2063. 72 indexed citations
20.
Skaletz‐Rorowski, Adriane, et al.. (1999). Protein Kinase C Mediates Basic Fibroblast Growth Factor–Induced Proliferation Through Mitogen-Activated Protein Kinase in Coronary Smooth Muscle Cells. Arteriosclerosis Thrombosis and Vascular Biology. 19(7). 1608–1614. 20 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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