Kaido Kurrikoff

2.3k total citations
45 papers, 1.5k citations indexed

About

Kaido Kurrikoff is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Genetics. According to data from OpenAlex, Kaido Kurrikoff has authored 45 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Molecular Biology, 14 papers in Cellular and Molecular Neuroscience and 10 papers in Genetics. Recurrent topics in Kaido Kurrikoff's work include RNA Interference and Gene Delivery (26 papers), Advanced biosensing and bioanalysis techniques (21 papers) and Virus-based gene therapy research (10 papers). Kaido Kurrikoff is often cited by papers focused on RNA Interference and Gene Delivery (26 papers), Advanced biosensing and bioanalysis techniques (21 papers) and Virus-based gene therapy research (10 papers). Kaido Kurrikoff collaborates with scholars based in Estonia, Sweden and Japan. Kaido Kurrikoff's co-authors include Ülo Langel, Eero Vasar, Taavi Lehto, Piret Arukuusk, Kadi-Liis Veiman, Sulev Kõks, Sirli Raud, Maxime Gestin, Urho Abramov and Tõnis Lehto and has published in prestigious journals such as Scientific Reports, International Journal of Molecular Sciences and Journal of Controlled Release.

In The Last Decade

Kaido Kurrikoff

44 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kaido Kurrikoff Estonia 23 1.1k 260 218 182 155 45 1.5k
Gunnar P.H. Dietz Germany 27 1.6k 1.5× 536 2.1× 291 1.3× 133 0.7× 75 0.5× 48 2.3k
Gary McMaster Switzerland 19 1.7k 1.6× 702 2.7× 274 1.3× 59 0.3× 60 0.4× 31 2.6k
Phillip M. Friden United States 25 791 0.7× 448 1.7× 81 0.4× 317 1.7× 99 0.6× 39 2.2k
Wei Hong China 25 1.0k 0.9× 380 1.5× 315 1.4× 212 1.2× 287 1.9× 43 2.2k
Xiaofen Zhong China 19 1.3k 1.2× 564 2.2× 337 1.5× 173 1.0× 48 0.3× 49 2.3k
A. James Mixson United States 29 1.7k 1.6× 66 0.3× 639 2.9× 302 1.7× 138 0.9× 69 2.7k
Mario Costa Italy 26 929 0.9× 388 1.5× 258 1.2× 48 0.3× 24 0.2× 66 1.9k
Külliki Saar Sweden 9 1.1k 1.0× 192 0.7× 141 0.6× 107 0.6× 139 0.9× 14 1.2k
Renata Battini Italy 21 1.3k 1.2× 186 0.7× 205 0.9× 102 0.6× 20 0.1× 46 1.8k
Thomas J. Novak United States 16 676 0.6× 197 0.8× 153 0.7× 172 0.9× 16 0.1× 22 1.5k

Countries citing papers authored by Kaido Kurrikoff

Since Specialization
Citations

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

Fields of papers citing papers by Kaido Kurrikoff

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kaido Kurrikoff

This figure shows the co-authorship network connecting the top 25 collaborators of Kaido Kurrikoff. A scholar is included among the top collaborators of Kaido Kurrikoff 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 Kaido Kurrikoff. Kaido Kurrikoff 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.
Arukuusk, Piret, et al.. (2023). The Development of Cell-Penetrating Peptides for Efficient and Selective In Vivo Expression of mRNA in Spleen Tissue. Pharmaceutics. 15(3). 952–952. 14 indexed citations
2.
3.
Arukuusk, Piret, et al.. (2022). Predicting Transiently Expressed Protein Yields: Comparison of Transfection Methods in CHO and HEK293. Pharmaceutics. 14(9). 1949–1949. 10 indexed citations
4.
Kurrikoff, Kaido, et al.. (2020). Status update in the use of cell-penetrating peptides for the delivery of macromolecular therapeutics. Expert Opinion on Biological Therapy. 21(3). 361–370. 60 indexed citations
5.
Arukuusk, Piret, et al.. (2019). Enhancement of siRNA transfection by the optimization of fatty acid length and histidine content in the CPP. Biomaterials Science. 7(10). 4363–4374. 33 indexed citations
6.
Kurrikoff, Kaido, et al.. (2019). The future of peptides in cancer treatment. Current Opinion in Pharmacology. 47. 27–32. 64 indexed citations
7.
Arukuusk, Piret, Kaido Kurrikoff, Raivo Raid, et al.. (2017). Formulation of Stable and Homogeneous Cell-Penetrating Peptide NF55 Nanoparticles for Efficient Gene Delivery In Vivo. Molecular Therapy — Nucleic Acids. 10. 28–35. 21 indexed citations
8.
Dowaidar, Moataz, Hani Nasser Abdelhamid, Mattias Hällbrink, et al.. (2017). Magnetic Nanoparticle Assisted Self-assembly of Cell Penetrating Peptides-Oligonucleotides Complexes for Gene Delivery. Scientific Reports. 7(1). 9159–9159. 68 indexed citations
9.
Arukuusk, Piret, Kaido Kurrikoff, Daniel Vasconcelos, et al.. (2016). Optimization of in vivo DNA delivery with NickFect peptide vectors. Journal of Controlled Release. 241. 135–143. 42 indexed citations
10.
Kurrikoff, Kaido, Maxime Gestin, & Ülo Langel. (2015). Recentin vivoadvances in cell-penetrating peptide-assisted drug delivery. Expert Opinion on Drug Delivery. 13(3). 373–387. 105 indexed citations
11.
Kurrikoff, Kaido, et al.. (2015). Galanin receptors as a potential target for neurological disease. Expert Opinion on Therapeutic Targets. 19(12). 1665–1676. 22 indexed citations
12.
Veiman, Kadi-Liis, Kadri Künnapuu, Tõnis Lehto, et al.. (2015). PEG shielded MMP sensitive CPPs for efficient and tumor specific gene delivery in vivo. Journal of Controlled Release. 209. 238–247. 101 indexed citations
13.
Rytkönen, Jussi, Piret Arukuusk, Wujun Xu, et al.. (2013). Porous Silicon–Cell Penetrating Peptide Hybrid Nanocarrier for Intracellular Delivery of Oligonucleotides. Molecular Pharmaceutics. 11(2). 382–390. 20 indexed citations
14.
Lehto, Taavi, Kaido Kurrikoff, & Ülo Langel. (2012). Cell-penetrating peptides for the delivery of nucleic acids. Expert Opinion on Drug Delivery. 9(7). 823–836. 123 indexed citations
15.
Runesson, Johan, et al.. (2012). Novel Galanin Receptor Subtype Specific Ligand in Depression Like Behavior. Neurochemical Research. 38(2). 398–404. 18 indexed citations
16.
Aunapuu, Marina, et al.. (2008). Altered renal morphology in transgenic mice with cholecystokinin overexpression. Transgenic Research. 17(6). 1079–1089. 7 indexed citations
17.
Luuk, Hendrik, Mario Plaas, Sirli Raud, et al.. (2008). Wfs1-deficient mice display impaired behavioural adaptation in stressful environment. Behavioural Brain Research. 198(2). 334–345. 58 indexed citations
18.
Abramov, Urho, Sirli Raud, Jürgen Innos, et al.. (2008). Different housing conditions alter the behavioural phenotype of CCK2 receptor-deficient mice. Behavioural Brain Research. 193(1). 108–116. 20 indexed citations
19.
Abramov, Urho, Sirli Raud, Sulev Kõks, et al.. (2004). Targeted mutation of CCK2 receptor gene antagonises behavioural changes induced by social isolation in female, but not in male mice. Behavioural Brain Research. 155(1). 1–11. 50 indexed citations
20.
Veraksitš, Alar, Kaido Kurrikoff, Sirli Raud, et al.. (2003). Altered pain sensitivity and morphine-induced anti-nociception in mice lacking CCK2 receptors. Psychopharmacology. 166(2). 168–175. 15 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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