A. S. Khalansky

2.1k total citations
25 papers, 1.7k citations indexed

About

A. S. Khalansky is a scholar working on Biomaterials, Biomedical Engineering and Genetics. According to data from OpenAlex, A. S. Khalansky has authored 25 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Biomaterials, 13 papers in Biomedical Engineering and 10 papers in Genetics. Recurrent topics in A. S. Khalansky's work include Nanoparticle-Based Drug Delivery (17 papers), Glioma Diagnosis and Treatment (10 papers) and Nanoplatforms for cancer theranostics (9 papers). A. S. Khalansky is often cited by papers focused on Nanoparticle-Based Drug Delivery (17 papers), Glioma Diagnosis and Treatment (10 papers) and Nanoplatforms for cancer theranostics (9 papers). A. S. Khalansky collaborates with scholars based in Russia, Germany and United Kingdom. A. S. Khalansky's co-authors include Svetlana Gelperina, Jörg Kreuter, Reiner Uhl, J. Kreuter, Olga Maksimenko, Igor Skidan, З. С. Смирнова, Severin Se, Telli Hekmatara and David J. Begley and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

A. S. Khalansky

23 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
A. S. Khalansky Russia 14 1.1k 626 596 448 199 25 1.7k
Guangzhi Gu China 14 1.1k 1.0× 681 1.1× 1.0k 1.7× 305 0.7× 76 0.4× 15 1.9k
Igor Skidan United States 9 588 0.5× 293 0.5× 510 0.9× 247 0.6× 86 0.4× 10 1.2k
Jianhua Zhu China 23 1.1k 1.0× 829 1.3× 904 1.5× 360 0.8× 51 0.3× 43 2.2k
Xiyang Sun China 20 582 0.5× 653 1.0× 831 1.4× 242 0.5× 65 0.3× 32 2.0k
В Е Петров Russia 7 922 0.8× 356 0.6× 571 1.0× 531 1.2× 39 0.2× 13 1.6k
Daniela Belletti Italy 21 663 0.6× 377 0.6× 627 1.1× 306 0.7× 44 0.2× 56 1.7k
Telli Hekmatara Germany 6 663 0.6× 316 0.5× 395 0.7× 254 0.6× 53 0.3× 7 1.0k
Yuyang Kuang China 18 783 0.7× 501 0.8× 1.0k 1.8× 128 0.3× 47 0.2× 21 1.6k
Linwei Lu China 20 555 0.5× 528 0.8× 738 1.2× 89 0.2× 81 0.4× 42 1.4k

Countries citing papers authored by A. S. Khalansky

Since Specialization
Citations

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

Fields of papers citing papers by A. S. Khalansky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of A. S. Khalansky

This figure shows the co-authorship network connecting the top 25 collaborators of A. S. Khalansky. A scholar is included among the top collaborators of A. S. Khalansky 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 A. S. Khalansky. A. S. Khalansky 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.
Gulyaev, М. V., et al.. (2025). The Original Mouse Models of Glioblastoma: Analysis of Pathophysiological Characteristics of Transplanted Tumor Tissue. Sovremennye tehnologii v medicine. 17(5). 50–50. 1 indexed citations
2.
Алексеева, А. И., et al.. (2024). The Effect of Therapy Regimen on Antitumor Efficacy of the Nanosomal Doxorubicin against Rat Glioblastoma 101.8. Bulletin of Experimental Biology and Medicine. 176(5). 697–702. 2 indexed citations
3.
4.
Алексеева, А. И., et al.. (2022). Nitric oxide donor nitrosorbide potentiates the antitumor effect of doxorubicin against experimental glioblastoma. Burdenko s Journal of Neurosurgery. 86(1). 66–66. 3 indexed citations
5.
Maksimenko, Olga, Julia Malinovskaya, Е. В. Шипуло, et al.. (2019). Doxorubicin-loaded PLGA nanoparticles for the chemotherapy of glioblastoma: Towards the pharmaceutical development. International Journal of Pharmaceutics. 572. 118733–118733. 97 indexed citations
6.
Postovalova, Ekaterina, et al.. (2018). Drug-Induced Pathomorphosis of Glioblastoma 101.8 in Wistar Rats Treated with Doxorubicin Bound to Poly(lactide-co-glycolide) Nanoparticles. Sovremennye tehnologii v medicine. 10(4). 105–105. 6 indexed citations
7.
Dudenkova, Varvara V., Konstantin S. Yashin, Elena B. Kiseleva, et al.. (2016). Multiphoton Tomography and Cross-Polarization Optical Coherence Tomography for Diagnosing Brain Gliomas: Pilot Study. Sovremennye tehnologii v medicine. 8(4). 64–75. 3 indexed citations
8.
Баклаушев, Владимир П., N. V. Nukolova, A. S. Khalansky, et al.. (2014). Treatment of glioma by cisplatin-loaded nanogels conjugated with monoclonal antibodies against Cx43 and BSAT1. Drug Delivery. 22(3). 276–285. 60 indexed citations
9.
Khalansky, A. S., Svetlana Gelperina, Olga Maksimenko, et al.. (2011). Efficient Chemotherapy of Rat Glioblastoma Using Doxorubicin-Loaded PLGA Nanoparticles with Different Stabilizers. PLoS ONE. 6(5). e19121–e19121. 133 indexed citations
10.
Khalansky, A. S., Christian Bernreuther, Martin Michaelis, et al.. (2011). Treatment of glioblastoma with poly(isohexyl cyanoacrylate) nanoparticles. International Journal of Pharmaceutics. 415(1-2). 244–251. 38 indexed citations
11.
Khalansky, A. S., et al.. (2011). Kinetics of transport of doxorubicin bound to nanoparticles across the blood–brain barrier. Journal of Controlled Release. 154(1). 103–107. 84 indexed citations
12.
Hekmatara, Telli, Christian Bernreuther, A. S. Khalansky, et al.. (2009). Efficient systemic therapy of rat glioblastoma by nanoparticle-bound doxorubicin is due to antiangiogenic effects. Clinical Neuropathology. 28(5). 153–164. 48 indexed citations
13.
Gelperina, Svetlana, Olga Maksimenko, A. S. Khalansky, et al.. (2009). Drug delivery to the brain using surfactant-coated poly(lactide-co-glycolide) nanoparticles: Influence of the formulation parameters. European Journal of Pharmaceutics and Biopharmaceutics. 74(2). 157–163. 240 indexed citations
14.
Khalansky, A. S., Telli Hekmatara, Rainer Müller, et al.. (2006). Chemotherapy of brain tumour using doxorubicin bound to surfactant-coated poly(butyl cyanoacrylate) nanoparticles: Revisiting the role of surfactants. Journal of Controlled Release. 117(1). 51–58. 249 indexed citations
15.
Gelperina, Svetlana, et al.. (2006). Influence of surfactants, polymer and doxorubicin loading on the anti-tumour effect of poly(butyl cyanoacrylate) nanoparticles in a rat glioma model. Journal of Microencapsulation. 23(5). 582–592. 85 indexed citations
16.
17.
Steiniger, Sebastian C.J., Jörg Kreuter, A. S. Khalansky, et al.. (2004). Chemotherapy of glioblastoma in rats using doxorubicin‐loaded nanoparticles. International Journal of Cancer. 109(5). 759–767. 341 indexed citations
18.
Gelperina, Svetlana, A. S. Khalansky, Igor Skidan, et al.. (2002). Toxicological studies of doxorubicin bound to polysorbate 80-coated poly(butyl cyanoacrylate) nanoparticles in healthy rats and rats with intracranial glioblastoma. Toxicology Letters. 126(2). 131–141. 140 indexed citations
19.
20.
Khalansky, A. S., et al.. (1984). Specific binding of [3H]diazepam in mouse glioblastoma the influence of clonazepam and Ro 5–4864 on [3H]diazepam binding. Neuroscience Letters. 52(3). 259–262. 4 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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