Н. Е. Кушлинский

2.6k total citations
275 papers, 1.8k citations indexed

About

Н. Е. Кушлинский is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Н. Е. Кушлинский has authored 275 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 138 papers in Molecular Biology, 89 papers in Cancer Research and 86 papers in Oncology. Recurrent topics in Н. Е. Кушлинский's work include Cancer-related molecular mechanisms research (41 papers), MicroRNA in disease regulation (26 papers) and Protease and Inhibitor Mechanisms (22 papers). Н. Е. Кушлинский is often cited by papers focused on Cancer-related molecular mechanisms research (41 papers), MicroRNA in disease regulation (26 papers) and Protease and Inhibitor Mechanisms (22 papers). Н. Е. Кушлинский collaborates with scholars based in Russia, United Kingdom and United States. Н. Е. Кушлинский's co-authors include Э. А. Брага, М. В. Фридман, Е. С. Герштейн, Alexey A. Dmitriev, Е. А. Филиппова, N. V. Lyubimova, В. И. Логинов, И. В. Пронина, T. P. Kazubskaya and М. Л. Филипенко and has published in prestigious journals such as SHILAP Revista de lepidopterología, Analytical Biochemistry and International Journal of Molecular Sciences.

In The Last Decade

Н. Е. Кушлинский

246 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Н. Е. Кушлинский Russia 20 1.1k 854 473 301 139 275 1.8k
Bin Shi China 10 1.2k 1.1× 606 0.7× 852 1.8× 271 0.9× 219 1.6× 16 2.1k
Rafael Malagoli Rocha Brazil 18 1.5k 1.4× 1.0k 1.2× 639 1.4× 244 0.8× 138 1.0× 49 2.2k
Jianyong Zheng China 26 886 0.8× 595 0.7× 428 0.9× 233 0.8× 173 1.2× 86 1.6k
Mattia Lauriola Italy 21 1.5k 1.4× 723 0.8× 484 1.0× 177 0.6× 182 1.3× 45 2.2k
Zhongyu Yuan China 23 743 0.7× 610 0.7× 808 1.7× 356 1.2× 182 1.3× 111 1.8k
Hai Wu China 26 1.1k 1.0× 645 0.8× 301 0.6× 157 0.5× 127 0.9× 62 1.7k
Jing Huang China 23 934 0.9× 634 0.7× 412 0.9× 159 0.5× 117 0.8× 81 1.6k
Marı́a Victoria González Spain 22 911 0.8× 348 0.4× 396 0.8× 234 0.8× 127 0.9× 47 1.6k
Hervé Sartelet France 23 807 0.7× 377 0.4× 527 1.1× 224 0.7× 246 1.8× 82 1.7k
Juxiang Chen China 29 1.5k 1.4× 864 1.0× 496 1.0× 544 1.8× 196 1.4× 76 2.6k

Countries citing papers authored by Н. Е. Кушлинский

Since Specialization
Citations

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

Fields of papers citing papers by Н. Е. Кушлинский

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Н. Е. Кушлинский. 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 Н. Е. Кушлинский. The network helps show where Н. Е. Кушлинский may publish in the future.

Co-authorship network of co-authors of Н. Е. Кушлинский

This figure shows the co-authorship network connecting the top 25 collaborators of Н. Е. Кушлинский. A scholar is included among the top collaborators of Н. Е. Кушлинский 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 Н. Е. Кушлинский. Н. Е. Кушлинский 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
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Burdennyy, A. M., et al.. (2024). Hypermethylation in Ovarian Cancer of Long Noncoding RNA Genes: HOTAIR, GAS5, LINC00472, LINC00886, TUG1. Russian Journal of Genetics. 60(5). 665–675. 1 indexed citations
3.
Брага, Э. А., A. M. Burdennyy, Л. А. Урошлев, et al.. (2024). Ten Hypermethylated lncRNA Genes Are Specifically Involved in the Initiation, Progression, and Lymphatic and Peritoneal Metastasis of Epithelial Ovarian Cancer. International Journal of Molecular Sciences. 25(21). 11843–11843. 1 indexed citations
4.
Кушлинский, Н. Е., et al.. (2024). The content of soluble forms of galectins -1, -3, -4, -7, -9 in patients with renal cell cancer of various morphological types. SHILAP Revista de lepidopterología. 52(3). 107–119.
5.
Ворохобина, Н. В., et al.. (2023). The relationship between urine steroid metabolome and the course of adrenocortical cancer. SHILAP Revista de lepidopterología. 51(3). 143–153.
6.
Ворохобина, Н. В., et al.. (2023). Prognostic markers of recurrence in adrenocortical carcinoma patients after surgery. HERALD of North-Western State Medical University named after I I Mechnikov. 15(2). 57–67.
7.
Короткова, Е. А., et al.. (2023). Galectin-3 and matrix metalloproteinases 2 and 9 in peripheral blood of gastric cancer patients. SHILAP Revista de lepidopterología. 51(1). 23–31. 1 indexed citations
8.
Ковалева, О. В., et al.. (2022). DIAGNOSTIC AND PROGNOSTIC POTENTIAL OF THE RESIDENT NON-SMALL CELL LUNG CANCER MICROBIOME. Russian Clinical Laboratory Diagnostics. 67(8). 458–462. 4 indexed citations
9.
Логинов, В. И., И. В. Пронина, Е. А. Филиппова, et al.. (2022). Aberrant Methylation of 20 miRNA Genes Specifically Involved in Various Steps of Ovarian Carcinoma Spread: From Primary Tumors to Peritoneal Macroscopic Metastases. International Journal of Molecular Sciences. 23(3). 1300–1300. 19 indexed citations
10.
Ковалева, О. В., et al.. (2022). Prognostic significance of sPD-1/sPD-L1 in renal cancer depending on the phenotype of tumor and stromal cells. Cancer Urology. 18(2). 17–28. 1 indexed citations
11.
Герштейн, Е. С., et al.. (2022). Soluble forms of PD-1/PD-L immune checkpoint receptor and ligand in blood serum of breast cancer patients: association with clinical pathologic factors and molecular type of the tumor. Russian Clinical Laboratory Diagnostics. 67(2). 76–80. 2 indexed citations
12.
Кушлинский, Н. Е., et al.. (2021). Prognostic significance of soluble forms of immune checkpoint PD-1/PDL1 receptor and ligand in blood plasma of gastric cancer patients. Russian Clinical Laboratory Diagnostics. 66(3). 139–146. 6 indexed citations
13.
Ворохобина, Н. В., et al.. (2021). Study of urine steroid profiles by gas chromatography-mass spectrometry in patients with adrenocortical cancer in the course of treatment. Almanac of Clinical Medicine. 49(4). 277–284. 1 indexed citations
14.
Герштейн, Е. С., et al.. (2021). Prognostic significance of the TNM system criteria, levels of serum insulin-like growth factors and their transport proteins, VEGF and MMP-7 in colorectal cancer. Russian Clinical Laboratory Diagnostics. 66(8). 459–464. 2 indexed citations
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Герштейн, Е. С., et al.. (2020). SOLUBLE FORMS OF PD-1 AND PD-L1 IN BLOOD PLASMA OF GASTRIC CANCER PATIENTS AND THEIR ASSOCIATIONS WITH CLINICAL AND MORPHOLOGICAL CHARACTERISTICS OF THE DISEASE. Russian Clinical Laboratory Diagnostics. 65(6). 347–352. 6 indexed citations
18.
Кушлинский, Н. Е., et al.. (2019). Soluble forms of the immune check-point receptor PD-1 and its ligand PD-L1 in blood serum of patients with renal cell carcinoma: clinical and pathologic correlations. SHILAP Revista de lepidopterología. 15(1). 15–22. 7 indexed citations
19.
Кушлинский, Н. Е., et al.. (2018). Modern approaches to kidney cancer immunotherapy. Cancer Urology. 14(2). 54–67. 13 indexed citations
20.
Брага, Э. А., М. В. Фридман, & Н. Е. Кушлинский. (2017). Molecular mechanisms of ovarian carcinoma metastasis: Key genes and regulatory microRNAs. Biochemistry (Moscow). 82(5). 529–541. 29 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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