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  1. 1
    Academic Journal

    المساهمون: Исследования выполнены за счет субсидий из федерального бюджета Министерства просвещения РФ на финансовое обеспечение выполнения государственного задания №073-00037-2302 от 31.07.2023 г (регистрационный номер 1023012300024-4-1.6.4).

    المصدر: Fundamental and applied research for key propriety areas of bioecology and biotechnology; ; Фундаментальные и прикладные исследования по приоритетным направлениям биоэкологии и биотехнологии

    وصف الملف: text/html

    Relation: info:eu-repo/semantics/altIdentifier/isbn/978-5-907830-38-7; https://phsreda.com/e-articles/10590/Action10590-111738.pdf; Carrier A., Desjobert C., Ponger L. [et al.] DNA methylome combined with chromosome cluster-oriented analysis provides an early signature for cutaneous melanoma aggressiveness. eElife. 2022. 20 (11). 78587.; Cui Y.-Z., Xiao-Yong Man X. Biology of melanocytes in mammals. Morphogenesis and Patterning. Front. Cell Dev. Biol. 2023 Sec. Vol. 11. 2023.; Duan Y., Luo X., Zhao S. [et al.] c-KIT and CD90 are useful markers for predicting prognosis in patients with melanoma. Oncol Let. 2019. 17 (3). P. 2598–2606.; Grichnik J.M., Burch J.A., Burchette J., Shea C.R. The SCF/KIT pathway plays a critical role in the control of normal human melanocyte homeostasis. Journal of Investigative Dermatology. Vol. 111 (2). 1998. P. 233–238.; Jack Longley B., Carter E.L. SCF-KIT pathway in human epidermal melanocyte homeostasis. Journal of Investigative Dermatology. Vol. 113 (1). 1999. P. 139.; Megan Lyle, Georgina V. Long Diagnosis and treatment of KIT-mutant metastatic melanoma. Case Reports. J Clin Oncol. 2013 Sep 10; 31 (26) : 3176–81.; Michalak-Micka K., Buchler V.L., Zapiorkowska-Blumer N. [et al.] Characterization of a melanocyte progenitor population in human interfollicular epidermis. Cell reports. 2022. 38 (9). 110419. https://doi.org/10.1016/j.celrep.2022.110419. EDN: KKVWUZ; Nishikawa S., Kusakabe M., Yoshinaga K. [et al.]. In utero manipulation of coat color formation by a monoclonal anti-c-kit antibody: two distinct waves of c-kit-dependency during melanocyte development. EMBO J. 1991; 10: 2111–2118.; Radu A., Bejenaru C., Tolea I. [et al.]. Immunohistochemical study of CD117 in various cutaneous melanocytic lesions. Exp Ther Med. 2021; 21 (1): 78.; Welch D., Muro M.A., Buonanno M., Brenner D.J. Wavelength-dependent DNA photodamage in a 3-D human skin model over the far-UVC and germicidal UVC wavelength ranges from 215 to 255 nm. Photochem and Photobiol. 2022; 98 (5):1167–1171. https://doi.org/10.1111/php.13602. EDN: ZQVNKM; https://phsreda.com/files/Books/10590/Cover-10590.jpg?req=111738; https://phsreda.com/article/111738/discussion_platform

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  3. 3
    Academic Journal

    المصدر: Research and Practical Medicine Journal; Том 9, № 3 (2022); 129-144 ; Research'n Practical Medicine Journal; Том 9, № 3 (2022); 129-144 ; 2410-1893 ; 10.17709/2410-1893-2022-9-3

    وصف الملف: application/pdf

    Relation: https://www.rpmj.ru/rpmj/article/view/783/501; https://www.rpmj.ru/rpmj/article/downloadSuppFile/783/599; https://www.rpmj.ru/rpmj/article/downloadSuppFile/783/600; https://www.rpmj.ru/rpmj/article/downloadSuppFile/783/601; Состояние онкологической помощи населению России в 2019 году. Под ред. Каприна А. Д., Старинского В. В, Шахзадовой А. О. М.: МНИОИ им. П. А. Герцена − филиал ФГБУ «НМИЦ радиологии» Минздрава России, 2020, 252 с.; Hanna RF, Miloushev VZ, Tang A, Finklestone LA, Brejt SZ, Sandhu RS, et al. Comparative 13-year meta-analysis of the sensitivity and positive predictive value of ultrasound, CT, and MRI for detecting hepatocellular carcinoma. Abdom Radiol (NY). 2016 Jan;41(1):71–90. https://doi.org/10.1007/s00261-015-0592-8; An C, Lee CH, Byun JH, Lee MH, Jeong WK, Choi SH, et al. Intraindividual Comparison between Gadoxetate-Enhanced Magnetic Resonance Imaging and Dynamic Computed Tomography for Characterizing Focal Hepatic Lesions: A Multicenter, Multireader Study. Korean J Radiol. 2019 Dec;20(12):1616–1626. https://doi.org/10.3348/kjr.2019.0363; Martins-Filho SN, Paiva C, Azevedo RS, Alves VAF. Histological Grading of Hepatocellular Carcinoma-A Systematic Review of Literature. Front Med (Lausanne). 2017;4:193. https://doi.org/10.3389/fmed.2017.00193; Okusaka T, Okada S, Ueno H, Ikeda M, Shimada K, Yamamoto J, et al. Satellite lesions in patients with small hepatocellular carcinoma with reference to clinicopathologic features. Cancer. 2002 Nov 1;95(9):1931–1937. https://doi.org/10.1002/cncr.10892; Nishie A, Yoshimitsu K, Okamoto D, Tajima T, Asayama Y, Ishigami K, et al. CT prediction of histological grade of hypervascular hepatocellular carcinoma: utility of the portal phase. Jpn J Radiol. 2013 Feb;31(2):89–98. https://doi.org/10.1007/s11604-012-0149-5; Ломовцева К. Х. Дифференциальная диагностика образований печени солидной структуры: роль диффузионно-взвешенных изображений и гепатоспецифичных контрастных средств: Дисс. … канд. мед. наук. М., 2018, 140 с.; Jeong WK, Jamshidi N, Felker ER, Raman SS, Lu DS. Radiomics and radiogenomics of primary liver cancers. Clin Mol Hepatol. 2019 Mar;25(1):21–29. https://doi.org/10.3350/cmh.2018.1007; Oh J, Lee JM, Park J, Joo I, Yoon JH, Lee DH, et al. Hepatocellular Carcinoma: Texture Analysis of Preoperative Computed Tomography Images Can Provide Markers of Tumor Grade and Disease-Free Survival. Korean J Radiol. 2019 Apr;20(4):569–579. https://doi.org/10.3348/kjr.2018.0501; Mao B, Zhang L, Ning P, Ding F, Wu F, Lu G, et al. Preoperative prediction for pathological grade of hepatocellular carcinoma via machine learning-based radiomics. Eur Radiol. 2020 Dec;30(12):6924–6932. https://doi.org/10.1007/s00330-020-07056-5; Chen W, Zhang T, Xu L, Zhao L, Liu H, Gu LR, et al. Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading. Front Oncol. 2021;11:660509. https://doi.org/10.3389/fonc.2021.660509; Wu M, Tan H, Gao F, Hai J, Ning P, Chen J, et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature. Eur Radiol. 2019 Jun;29(6):2802–2811. https://doi.org/10.1007/s00330-018-5787-2; Geng Z, Zhang Y, Wang S, Li H, Zhang C, Yin S, et al. Radiomics Analysis of Susceptibility Weighted Imaging for Hepatocellular Carcinoma: Exploring the Correlation between Histopathology and Radiomics Features. Magn Reson Med Sci. 2021 Sep 1;20(3):253–263. https://doi.org/10.2463/mrms.mp.2020-0060; Chen W, DelProposto Z, Liu W, Kassir M, Wang Z, Zhao J, et al. Susceptibility-weighted imaging for the noncontrast evaluation of hepatocellular carcinoma: a prospective study with histopathologic correlation. PLoS One. 2014;9(5):e98303. https://doi.org/10.1371/journal.pone.0098303; Yang S, Lin J, Lu F, Han Z, Fu C, Gu H. Use of Ultrasmall Superparamagnetic Iron Oxide Enhanced Susceptibility Weighted Imaging and Mean Vessel Density Imaging to Monitor Antiangiogenic Effects of Sorafenib on Experimental Hepatocellular Carcinoma. Contrast Media Mol Imaging. 2017;2017:9265098. https://doi.org/10.1155/2017/9265098; Zhou W, Zhang L, Wang K, Chen S, Wang G, Liu Z, et al. Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast-enhanced MR images. J Magn Reson Imaging. 2017 May;45(5):1476–1484. https://doi.org/10.1002/jmri.25454; Feng M, Zhang M, Liu Y, Jiang N, Meng Q, Wang J, et al. Texture analysis of MR images to identify the differentiated degree in hepatocellular carcinoma: a retrospective study. BMC Cancer. 2020 Jun 30;20(1):611. https://doi.org/10.1186/s12885-020-07094-8; Yang X, Yuan C, Zhang Y, Wang Z. Magnetic resonance radiomics signatures for predicting poorly differentiated hepatocellular carcinoma: A SQUIRE-compliant study. Medicine (Baltimore). 2021 May 14;100(19):e25838. https://doi.org/10.1097/MD.0000000000025838; Mokrane FZ, Lu L, Vavasseur A, Otal P, Peron JM, Luk L, et al. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules. Eur Radiol. 2020 Jan;30(1):558–570. https://doi.org/10.1007/s00330-019-06347-w; Zhong X, Tang H, Lu B, You J, Piao J, Yang P, et al. Differentiation of Small Hepatocellular Carcinoma From Dysplastic Nodules in Cirrhotic Liver: Texture Analysis Based on MRI Improved Performance in Comparison Over Gadoxetic Acid-Enhanced MR and Diffusion-Weighted Imaging. Front Oncol. 2019;9:1382. https://doi.org/10.3389/fonc.2019.01382; Zhong X, Guan T, Tang D, Li J, Lu B, Cui S, et al. Differentiation of small (≤ 3 cm) hepatocellular carcinomas from benign nodules in cirrhotic liver: the added additive value of MRI-based radiomics analysis to LI-RADS version 2018 algorithm. BMC Gastroenterol. 2021 Apr 7;21(1):155. https://doi.org/10.1186/s12876-021-01710-y; Raman SP, Schroeder JL, Huang P, Chen Y, Coquia SF, Kawamoto S, et al. Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions: generation of a predictive model on the basis of quantitative spatial fre quency measurements--a work in progress. J Comput Assist Tomogr. 2015 Jun;39(3):383–395. https://doi.org/10.1097/RCT.0000000000000217; Stocker D, Marquez HP, Wagner MW, Raptis DA, Clavien PA, Boss A, et al. MRI texture analysis for differentiation of malignant and benign hepatocellular tumors in the non-cirrhotic liver. Heliyon. 2018 Nov;4(11):e00987. https://doi.org/10.1016/j.heliyon.2018.e00987; Wu J, Liu A, Cui J, Chen A, Song Q, Xie L. Radiomics-based classification of hepatocellular carcinoma and hepatic haemangioma on precontrast magnetic resonance images. BMC Med Imaging. 2019 Mar 11;19(1):23. https://doi.org/10.1186/s12880-019-0321-9; Nie P, Yang G, Guo J, Chen J, Li X, Ji Q, et al. A CT-based radiomics nomogram for differentiation of focal nodular hyperplasia from hepatocellular carcinoma in the non-cirrhotic liver. Cancer Imaging. 2020 Feb 24;20(1):20. https://doi.org/10.1186/s40644-020-00297-z; Nie P, Wang N, Pang J, Yang G, Duan S, Chen J, et al. CT-Based Radiomics Nomogram: A Potential Tool for Differentiating Hepatocellular Adenoma From Hepatocellular Carcinoma in the Noncirrhotic Liver. Acad Radiol. 2021 Jun;28(6):799–807. https://doi.org/10.1016/j.acra.2020.04.027; Song S, Li Z, Niu L, Zhou X, Wang G, Gao Y, et al. Hypervascular hepatic focal lesions on dynamic contrast-enhanced CT: preliminary data from arterial phase scans texture analysis for classification. Clin Radiol. 2019 Aug;74(8):653.e11–653.e18. https://doi.org/10.1016/j.crad.2019.05.010; Oyama A, Hiraoka Y, Obayashi I, Saikawa Y, Furui S, Shiraishi K, et al. Hepatic tumor classification using texture and topology analysis of non-contrast-enhanced three-dimensional T1-weighted MR images with a radiomics approach. Sci Rep. 2019 Jun 19;9(1):8764. https://doi.org/10.1038/s41598-019-45283-z; Li Z, Mao Y, Huang W, Li H, Zhu J, Li W, et al. Texture-based classification of different single liver lesion based on SPAIR T2W MRI images. BMC Med Imaging. 2017 Jul 13;17(1):42. https://doi.org/10.1186/s12880-017-0212-x; Liang W, Shao J, Liu W, Ruan S, Tian W, Zhang X, et al. Differentiating Hepatic Epithelioid Angiomyolipoma From Hepatocellular Carcinoma and Focal Nodular Hyperplasia via Radiomics Models. Front Oncol. 2020;10:564307. https://doi.org/10.3389/fonc.2020.564307; Liu X, Khalvati F, Namdar K, Fischer S, Lewis S, Taouli B, et al. Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning? Eur Radiol. 2021 Jan;31(1):244–255. https://doi.org/10.1007/s00330-020-07119-7; Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, et al. Measuring Computed Tomography Scanner Variability of Radiomics Features. Invest Radiol. 2015 Nov;50(11):757–765. https://doi.org/10.1097/RLI.0000000000000180; Hu HT, Shan QY, Chen SL, Li B, Feng ST, Xu EJ, et al. CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners. Radiol Med. 2020 Aug;125(8):697–705. https://doi.org/10.1007/s11547-020-01174-2; Mackin D, Ger R, Dodge C, Fave X, Chi PC, Zhang L, et al. Effect of tube current on computed tomography radiomic features. Sci Rep. 2018 Feb 5;8(1):2354. https://doi.org/10.1038/s41598-018-20713-6; Park HJ, Park B, Lee SS. Radiomics and Deep Learning: Hepatic Applications. Korean J Radiol. 2020 Apr;21(4):387–401. https://doi.org/10.3348/kjr.2019.0752; Li Y, Tan G, Vangel M, Hall J, Cai W. Influence of feature calculating parameters on the reproducibility of CT radiomic features: a thoracic phantom study. Quant Imaging Med Surg. 2020 Sep;10(9):1775–1785. https://doi.org/10.21037/qims-19-921; Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys. 2017 Mar;44(3):1050–1062. https://doi.org/10.1002/mp.12123; Leijenaar RTH, Nalbantov G, Carvalho S, van Elmpt WJC, Troost EGC, Boellaard R, et al. The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep. 2015 Aug 5;5:11075. https://doi.org/10.1038/srep11075; Ng F, Kozarski R, Ganeshan B, Goh V. Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol. 2013 Feb;82(2):342–348. https://doi.org/10.1016/j.ejrad.2012.10.023; Park HJ, Kim JH, Choi SY, Lee ES, Park SJ, Byun JY, et al. Prediction of Therapeutic Response of Hepatocellular Carcinoma to Transcatheter Arterial Chemoembolization Based on Pretherapeutic Dynamic CT and Textural Findings. AJR Am J Roentgenol. 2017 Oct;209(4):W211–W220. https://doi.org/10.2214/AJR.16.17398; Rogers W, Thulasi Seetha S, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A, et al. Radiomics: from qualitative to quantitative imaging. Br J Radiol. 2020 Apr;93(1108):20190948. https://doi.org/10.1259/bjr.20190948; Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020 May;295(2):328–338. https://doi.org/10.1148/radiol.2020191145; https://www.rpmj.ru/rpmj/article/view/783

  4. 4
    Academic Journal

    المساهمون: The study was conducted under the state assignment of the Ministry of Science and Higher Education of the Russian Federation for 2021., Исследование проведено в рамках государственного задания Министерства науки и высшего образования Российской Федерации на 2021 г.

    المصدر: Cancer Urology; Том 17, № 4 (2021); 19-26 ; Онкоурология; Том 17, № 4 (2021); 19-26 ; 1996-1812 ; 1726-9776

    وصف الملف: application/pdf

    Relation: https://oncourology.abvpress.ru/oncur/article/view/1506/1322; https://oncourology.abvpress.ru/oncur/article/downloadSuppFile/1506/1053; Zhang X., Hu B., Sun Y.F. et al. Arsenic trioxide induces differentiation of cancer stem cells in hepatocellular carcinoma through inhibition of LIF/JAK1/STAT3 and NF-kB signaling pathways synergistically. Clin Transl Med 2021;11(2):e335. DOI:10.1002/ctm2.335.; Zhao Z., Gao J., Li C. et al. Reactive oxygen species induce endothelial differentiation of liver cancer stem-like sphere cells through the activation of Akt/ IKK signaling pathway. Oxid Med Cell Longev 2020;2020:1621687. DOI:10.1155/2020/1621687.; Gonzalez-Guerrico A.M., Espinoza I., Schroeder B. et al. Suppression of endogenous lipogenesis induces reversion of the malignant phenotype and normalized differentiation in breast cancer. Oncotarget 2016;7(44):71151-68. DOI:10.18632/oncotarget.9463.; Apanovich N., Peters M., Apanovich P. et al. The genes-candidates for prognostic markers of metastasis by expression level in clear cell renal cell cancer. Diagnostics 2020;10(1):30. DOI:10.3390/diagnostics10010030.; Jogi A., Vaapil M., Johansson M., Pahlman S. Cancer cell differentiation heterogeneity and aggressive behavior in solid tumors. Ups J Med Sci 2012;117(2):217-24. DOI:10.3109/03009734.2012.659294.; Zheng X., Dai F., Feng L. et al. Communication between epithelial-mesenchymal plasticity and cancer stem cells: new insights into cancer progression. Front Oncol 2021;11:617597. DOI:10.3389/fonc.2021.617597.; Apanovich N., Apanovich P., Mansorunov D. et al. The choice of candidates in survival markers based on coordinated gene expression in renal cancer. Front Oncol 2021;11:615787. DOI:10.3389/fonc.2021.615787.; Moein S., Javanmard S.H., Abedi M. et al. Identification of appropriate housekeeping genes for gene expression analysis in long-term hypoxia-treated kidney cells. Adv Biomed Res 2017;6:15. DOI:10.4103/2277-9175.200790.; Rodrigues A.S., Pereira S.L., Ramalho-Santos J. Stem metabolism: Insights from oncometabolism and vice versa. Biochim Biophys Acta Mol Basis Dis 2020;1866(7):165760. DOI:10.1016/j.bbadis.2020.165760.; Mimeault M., Batra S.K. Hypoxiainducing factors as master regulators of stemness properties and altered metabolism of cancer- and metastasisinitiating cells. J Cell Mol Med 2013;17(1):30-54. DOI:10.1111/jcmm.12004.; Capp J.P. Cancer stem cells: from historical roots to a new perspective. J Oncol 2019;2019:5189232. DOI:10.1155/2019/5189232.; Jin X., Jin X., Kim H. Cancer stem cells and differentiation therapy. Tumor Biol 2017;39(10):1010428317729933. DOI:10.1177/1010428317729933.; Riester M., Xu Q., Moreira A. et al. The Warburg effect: persistence of stemcell metabolism in cancers as a failure of differentiation. Ann Oncol 2018;29(1):264-70. DOI:10.1093/annonc/mdx645.; Khan T., Cabral H. Abnormal glycosylation of cancer stem cells and targeting strategies. Front Oncol 2021;11:649338. DOI:10.3389/fonc.2021.649338.; Tanabe S., Quader S., Cabral H., Ono R. Interplay of EMT and CSC in cancer and the potential therapeutic strategies. Front Pharmacol 2020;11:904. DOI:10.3389/fphar.2020.00904.; https://oncourology.abvpress.ru/oncur/article/view/1506

  5. 5
    Academic Journal

    المصدر: Siberian journal of oncology; Том 20, № 3 (2021); 76-81 ; Сибирский онкологический журнал; Том 20, № 3 (2021); 76-81 ; 2312-3168 ; 1814-4861 ; 10.21294/1814-4861-2021-20-3

    وصف الملف: application/pdf

    Relation: https://www.siboncoj.ru/jour/article/view/1812/866; Волков С.В., Лобанов С.Л. Критерии клинической агрессивности колоректального рака. Забайкальский медицинский вестник. 2018; 2: 69–76.; Üçüncü M., Serilmez M., Sarı M., Bademler S., Karabulut S. The Diagnostic Significance of PDGF, EphA7, CCR5, and CCL5 Levels in Colorectal Cancer. Biomolecules. 2019 Sep 9; 9(9): 464. doi:10.3390/biom9090464.; Krzystek-Korpacka M., Zawadzki M., Kapturkiewicz B., Lewandowska P., Bednarz-Misa I., Gorska S., Witkiewicz W., Gamian A. Subsite heterogeneity in the profiles of circulating cytokines in colorectal cancer. Cytokine. 2018 Oct; 110: 435–441. doi:10.1016/j.cyto.2018.05.015.; Lian G., Chen S., Ouyang M., Li F., Chen L., Yang J. Colon Cancer Cell Secretes EGF to Promote M2 Polarization of TAM Through EGFR/PI3K/AKT/mTOR Pathway. Technol Cancer Res Treat. 2019 Jan 1; 18: 1533033819849068. doi:10.1177/1533033819849068.; Herrera A., Herrera M., Guerra-Perez N., Galindo- Pumariño C., Larriba M.J., García-Barberán V., Gil B., Giménez-Moyano S., Ferreiro-Monteagudo R., Veguillas P., Candia A., Peña R., Pinto J., García-Bermejo M.L., Muñoz A., García de Herreros A., Bonilla F., Carrato A., Peña C. Endothelial cell activation on 3D-matrices derived from PDGF-BB-stimulated fibroblasts is mediated by Snail1. Oncogenesis. 2018 Sep 24; 7(9): 76. doi:10.1038/s41389-018-0085-z.; Зибиров Р.Ф., Мозеров С.А. Характеристика клеточного микроокружения опухоли. Онкология. Журнал им. П.А. Герцена. 2018; 2: 67–72.; Cirri P., Chiarugi P. Cancer-associated-fibroblasts and tumour cells: a diabolic liaison driving cancer progression. Cancer Metastasis Rev. 2012 Jun; 31(1–2): 195–208. doi:10.1007/s10555-011-9340-x.; Kamińska K., Szczylik C., Bielecka Z.F., Bartnik E., Porta C., Lian F., Czarnecka A.M. The role of the cell-cell interactions in cancer progression. J Cell Mol Med. 2015 Feb; 19(2): 283–96. doi:10.1111/jcmm.12408.; СветозарскийН.Л., АртифексоваА.А., СветозарскийС.Н. Фактор роста эндотелия сосудов: биологические свойства и практическое значение (обзор литературы). Медицина и образование в Сибири. 2015; 5: 20–32.; Sinha A., Kumar S. Prognostic value of Epidermal growth factor receptor in colorectal carcinoma. Journal of Clinical and Diagnostic Research. 2018 March; 12(3): 1–4. doi:10.7860/JCDR/2018/34864.11234.; Mouzakiti A., Nastos C., Vlachodimitropoulos D., Gennatas C., Kondi-Pafiti A., Voros D. Prognostic significance of EGFR and COX-2 expression in colorectal cancer and their association. A study in Greek population. J BUON. 2018 Jan-Feb; 23(1): 23–28.; https://www.siboncoj.ru/jour/article/view/1812

  6. 6
    Academic Journal

    المساهمون: The study was supported by the grant from the President of the Russian Federation for state support of young Russian scientists MK-6143.2018.7 (agreement No. 075-02-2018-547), Работа выполнена при финансовой поддержке гранта президента Российской Федерации для государственной поддержки молодых российских ученых МК-6143.2018.7 (соглашение № 075-02-2018-547)

    المصدر: Advances in Molecular Oncology; Том 6, № 3 (2019); 49-56 ; Успехи молекулярной онкологии; Том 6, № 3 (2019); 49-56 ; 2413-3787 ; 2313-805X ; 10.17650/2313-805X-2019-6-3

    وصف الملف: application/pdf

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    المصدر: Scientific look into the future; No. 13-01 (2019); 119-123 ; Научный взгляд в будущее; № 13-01 (2019); 119-123 ; Науковий погляд у майбутнє; № 13-01 (2019); 119-123 ; 2415-7538 ; 2415-766X

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    المصدر: Ukrainian Journal of Pediatric Endocrinology; № 1 (2020); 5—17
    Украинский журнал детской эндокринологии; № 1 (2020); 5—17
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    المؤلفون: Syvolap, V. D., Zemlyaniy, Ya. V.

    المصدر: Zaporozhye мedical journal; No. 5 (2016): Zaporozhye medical journal ; Запорожский медицинский журнал; № 5 (2016): Запорізький медичний журнал ; Запорізький медичний журнал; № 5 (2016): Запорізький медичний журнал ; 2310-1210 ; 2306-4145

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    المصدر: Cancer Urology; Том 11, № 1 (2015); 30-40 ; Онкоурология; Том 11, № 1 (2015); 30-40 ; 1996-1812 ; 1726-9776 ; 10.17650/1726-9776-2015-11-1

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