Igor A. Iashchishyn

575 total citations
17 papers, 348 citations indexed

About

Igor A. Iashchishyn is a scholar working on Molecular Biology, Physiology and Neurology. According to data from OpenAlex, Igor A. Iashchishyn has authored 17 papers receiving a total of 348 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 9 papers in Physiology and 4 papers in Neurology. Recurrent topics in Igor A. Iashchishyn's work include S100 Proteins and Annexins (10 papers), Alzheimer's disease research and treatments (9 papers) and Silk-based biomaterials and applications (3 papers). Igor A. Iashchishyn is often cited by papers focused on S100 Proteins and Annexins (10 papers), Alzheimer's disease research and treatments (9 papers) and Silk-based biomaterials and applications (3 papers). Igor A. Iashchishyn collaborates with scholars based in Sweden, Lithuania and Croatia. Igor A. Iashchishyn's co-authors include Ludmilla A. Morozova‐Roche, Vytautas Smirnovas, Chao Wang, Roman Moskalenko, Darius Šulskis, Gábor G. Kovács, István Horváth, Jonathan Pansieri, Astrid Gräslund and Sebastian K.T.S. Wärmländer and has published in prestigious journals such as Advanced Functional Materials, Scientific Reports and ACS Applied Materials & Interfaces.

In The Last Decade

Igor A. Iashchishyn

17 papers receiving 345 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Igor A. Iashchishyn Sweden 12 216 153 71 39 34 17 348
Leonardo M. Cortez Canada 13 320 1.5× 137 0.9× 67 0.9× 16 0.4× 73 2.1× 29 542
Fabrizio Chiti Italy 8 268 1.2× 182 1.2× 114 1.6× 48 1.2× 20 0.6× 11 410
Ranjeet Kumar Sweden 13 176 0.8× 130 0.8× 97 1.4× 26 0.7× 24 0.7× 32 487
Vijayaraghavan Rangachari United States 16 383 1.8× 279 1.8× 93 1.3× 79 2.0× 42 1.2× 34 567
Nitu Singh India 11 194 0.9× 64 0.4× 98 1.4× 19 0.5× 34 1.0× 27 364
Hanna Willander Sweden 8 251 1.2× 163 1.1× 19 0.3× 26 0.7× 24 0.7× 8 431
Christopher Kazu Williams United States 9 151 0.7× 191 1.2× 84 1.2× 27 0.7× 32 0.9× 17 372
Daniel T. Ladror United States 10 167 0.8× 133 0.9× 141 2.0× 21 0.5× 15 0.4× 14 401
Tomasz Borowik Poland 10 242 1.1× 170 1.1× 32 0.5× 87 2.2× 11 0.3× 15 411
Phoebe S. Tsoi United States 8 320 1.5× 79 0.5× 68 1.0× 11 0.3× 15 0.4× 17 446

Countries citing papers authored by Igor A. Iashchishyn

Since Specialization
Citations

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

Fields of papers citing papers by Igor A. Iashchishyn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Igor A. Iashchishyn. 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 Igor A. Iashchishyn. The network helps show where Igor A. Iashchishyn may publish in the future.

Co-authorship network of co-authors of Igor A. Iashchishyn

This figure shows the co-authorship network connecting the top 25 collaborators of Igor A. Iashchishyn. A scholar is included among the top collaborators of Igor A. Iashchishyn 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 Igor A. Iashchishyn. Igor A. Iashchishyn 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.
Iashchishyn, Igor A., Darius Šulskis, Jonathan D. Gilthorpe, et al.. (2024). ApoE Isoforms Inhibit Amyloid Aggregation of Proinflammatory Protein S100A9. International Journal of Molecular Sciences. 25(4). 2114–2114. 1 indexed citations
2.
Wang, Han, Yu Wang, Axel Leppert, et al.. (2024). Spiders Use Structural Conversion of Globular Amyloidogenic Domains to Make Strong Silk Fibers. Advanced Functional Materials. 34(23). 8 indexed citations
3.
Wang, Han, Yu Wang, Axel Leppert, et al.. (2024). Spiders Use Structural Conversion of Globular Amyloidogenic Domains to Make Strong Silk Fibers (Adv. Funct. Mater. 23/2024). Advanced Functional Materials. 34(23). 3 indexed citations
4.
Wang, Yu, Hairui Yu, Ruifang Liu, et al.. (2023). Spider Silk Protein Forms Amyloid‐Like Nanofibrils through a Non‐Nucleation‐Dependent Polymerization Mechanism. Small. 19(46). e2304031–e2304031. 13 indexed citations
5.
Pansieri, Jonathan, Igor A. Iashchishyn, Suman Paul, et al.. (2023). Residue-specific binding of Ni(II) ions influences the structure and aggregation of amyloid beta (Aβ) peptides. Scientific Reports. 13(1). 3341–3341. 9 indexed citations
6.
Majda, Mateusz, Liudmila Kozlova, Alicja Banasiak, et al.. (2021). Elongation of wood fibers combines features of diffuse and tip growth. New Phytologist. 232(2). 673–691. 16 indexed citations
7.
Leri, Manuela, Himanshu Chaudhary, Igor A. Iashchishyn, et al.. (2021). Natural Compound from Olive Oil Inhibits S100A9 Amyloid Formation and Cytotoxicity: Implications for Preventing Alzheimer’s Disease. ACS Chemical Neuroscience. 12(11). 1905–1918. 24 indexed citations
8.
Iashchishyn, Igor A., Nina V. Romanova, Greta Musteikytė, et al.. (2021). Co-Aggregation of S100A9 with DOPA and Cyclen-Based Compounds Manifested in Amyloid Fibril Thickening without Altering Rates of Self-Assembly. International Journal of Molecular Sciences. 22(16). 8556–8556. 2 indexed citations
9.
Chaudhary, Himanshu, Igor A. Iashchishyn, Nina V. Romanova, et al.. (2021). Polyoxometalates as Effective Nano-inhibitors of Amyloid Aggregation of Pro-inflammatory S100A9 Protein Involved in Neurodegenerative Diseases. ACS Applied Materials & Interfaces. 13(23). 26721–26734. 21 indexed citations
10.
Pansieri, Jonathan, Igor A. Iashchishyn, Mantas Mališauskas, et al.. (2020). Templating S100A9 amyloids on Aβ fibrillar surfaces revealed by charge detection mass spectrometry, microscopy, kinetic and microfluidic analyses. Chemical Science. 11(27). 7031–7039. 25 indexed citations
11.
Jakubec, Martin, et al.. (2020). Cholesterol‐containing lipid nanodiscs promote an α‐synuclein binding mode that accelerates oligomerization. FEBS Journal. 288(6). 1887–1905. 33 indexed citations
12.
Wang, Chao, Igor A. Iashchishyn, Vito Foderà, et al.. (2019). Proinflammatory and amyloidogenic S100A9 induced by traumatic brain injury in mouse model. Neuroscience Letters. 699. 199–205. 16 indexed citations
13.
Pansieri, Jonathan, Igor A. Iashchishyn, Mazin Magzoub, et al.. (2019). Pro-Inflammatory S100A9 Protein Aggregation Promoted by NCAM1 Peptide Constructs. ACS Chemical Biology. 14(7). 1410–1417. 12 indexed citations
14.
Horváth, István, Igor A. Iashchishyn, Roman Moskalenko, et al.. (2018). Co-aggregation of pro-inflammatory S100A9 with α-synuclein in Parkinson’s disease: ex vivo and in vitro studies. Journal of Neuroinflammation. 15(1). 172–172. 54 indexed citations
15.
Iashchishyn, Igor A., M. A. Gruden, Roman Moskalenko, et al.. (2018). Intranasally Administered S100A9 Amyloids Induced Cellular Stress, Amyloid Seeding, and Behavioral Impairment in Aged Mice. ACS Chemical Neuroscience. 9(6). 1338–1348. 12 indexed citations
16.
Wang, Chao, Igor A. Iashchishyn, Jonathan Pansieri, et al.. (2018). S100A9-Driven Amyloid-Neuroinflammatory Cascade in Traumatic Brain Injury as a Precursor State for Alzheimer’s Disease. Scientific Reports. 8(1). 12836–12836. 49 indexed citations
17.
Iashchishyn, Igor A., et al.. (2017). Finke–Watzky Two-Step Nucleation–Autocatalysis Model of S100A9 Amyloid Formation: Protein Misfolding as “Nucleation” Event. ACS Chemical Neuroscience. 8(10). 2152–2158. 50 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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