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1Academic Journal
Πηγή: Ukrainian Neurosurgical Journal; Vol. 31 No. 3 (2025); 63-67
Ukrainian Neurosurgical Journal; Том 31 № 3 (2025); 63-67Θεματικοί όροι: пухлина ЦНС, intracranial mesenchymal tumor, ангіоматоїдна фіброзна гістіоцитома, FET::CREB fusion, молекулярная диагностика, молекулярна діагностика, внутричерепная мезенхимальная опухоль, слияние FET::CREB, molecular diagnostics, внутрішньочерепна мезенхімальна пухлина, злиття FET::CREB, angiomatoid fibrous histiocytoma, EWSR1::CREB, ангиоматоидная фиброзная гистиоцитома, опухоль ЦНС, CNS tumor
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Σύνδεσμος πρόσβασης: https://theunj.org/article/view/328774
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2Academic Journal
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3Academic Journal
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4Book
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5Academic Journal
Συγγραφείς: Mudunov A.M., Khabazova A.M., Pak M.B., Berelavichus S.V., Chen H.
Πηγή: Head and Neck Tumors; Vol 15, No 3 (2025); 115-123 ; Опухоли головы и шеи; Vol 15, No 3 (2025); 115-123 ; 2411-4634 ; 2222-1468
Θεματικοί όροι: nasal cancer, comprehensive genomic profiling, extensive molecular t argeted therapy, immunotherapy, anti-pD-1 checkpoint inhibitors, mutation in the PTCH1 gene, Hedgehog signaling pathway, рак слизистой оболочки полости носа, комплексное геномное профилирование, расширенное молекулярно-генетическое исследование, молекулярная диагностика рака слизистой оболочки полости носа, таргетная терапия, иммунотерапия, анти-pD-1-ингибиторы контрольных точек, мутация в гене PTCH1, сигнальный путь Hedgehog
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Relation: https://ogsh.abvpress.ru/jour/article/view/1110/694; https://ogsh.abvpress.ru/jour/article/view/1110/695; https://ogsh.abvpress.ru/jour/article/view/1110
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6Academic Journal
Συγγραφείς: Илхомовна , Махмудова Зарина, Алламбергеновна, Куандикова Умида, Билаловна , Жалгасова Мунира, Давроновна, Ембергенова Муниса
Πηγή: World of Medicine : Journal of Biomedical Sciences; Vol. 2 No. 1 (2025): World of Medicine : Journal of Biomedical Sciences; 37-41 ; 2960-9356
Θεματικοί όροι: ИТ-инструменты, КТ, МРТ или УЗИ, ДНК, Молекулярная диагностика, Аристотеля, микроскопа
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7Academic Journal
Συγγραφείς: A. O. Morozov, A. K. Bazarkin, S. V. Vovdenko, M. S. Taratkin, M. S. Balashova, D. V. Enikeev, А. О. Морозов, А. К. Базаркин, С. В. Вовденко, М. С. Тараткин, М. С. Балашова, Д. В. Еникеев
Πηγή: Urology Herald; Том 12, № 1 (2024); 117-130 ; Вестник урологии; Том 12, № 1 (2024); 117-130 ; 2308-6424 ; 10.21886/2308-6424-2024-12-1
Θεματικοί όροι: обзор литературы, machine learning, prostate cancer, molecular diagnostics, genetical diagnostics, review, машинное обучение, рак простаты, молекулярная диагностика, генетическая диагностика
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Relation: https://www.urovest.ru/jour/article/view/836/540; Barros-Silva D, Costa-Pinheiro P, Duarte H, Sousa EJ, Evangelista AF, Graça I, Carneiro I, Martins AT, Oliveira J, Carvalho AL, Marques MM, Henrique R, Jerónimo C. MicroRNA-27a-5p regulation by promoter methylation and MYC signaling in prostate carcinogenesis. Cell Death Dis. 2018;9(2):167. DOI:10.1038/s41419-017-0241-y; Zhou K, Arslanturk S, Craig DB, Heath E, Draghici S. Discovery of primary prostate cancer biomarkers using cross cancer learning. Sci Rep. 2021;11(1):10433. DOI:10.1038/s41598-021-89789-x; Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:S36-S40. DOI:10.1016/j.metabol.2017.01.011; Tizhoosh HR, Pantanowitz L. Artificial Intelligence and Digital Pathology: Challenges and Opportunities. J Pathol Inform. 2018;9:38. DOI:10.4103/jpi.jpi_53_18; Ramesh AN, Kambhampati C, Monson JR, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004;86(5):334-8. DOI:10.1308/147870804290; Akkus Z, Cai J, Boonrod A, Zeinoddini A, Weston AD, Philbrick KA, Erickson BJ. A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow. J Am Coll Radiol. 2019;16(9 Pt B):1318-1328. DOI:10.1016/j.jacr.2019.06.004; Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73-81. DOI:10.1080/13645706.2019.1575882; Niel O, Bastard P. Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives. Am J Kidney Dis. 2019;74(6):803-810. DOI:10.1053/j.ajkd.2019.05.020; Ayyad SM, Shehata M, Shalaby A, Abou El-Ghar M, Ghazal M, El-Melegy M, Abdel-Hamid NB, Labib LM, Ali HA, ElBaz A. Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey. Sensors (Basel). 2021;21(8):2586. DOI:10.3390/s21082586; Тимофеева Е.Ю., Азильгареева К.Р., Морозов А.О., Тараткин М.С., Еникеев Д.В. Использование искусственного интеллекта в диагностике, лечении и наблюдении за пациентами с раком почки. Вестник урологии. 2023;11(3):142-148. DOI:10.21886/2308-6424-2023-11-3-142-148; Rajwa P, Schuettfort VM, Quhal F, Mori K, Katayama S, Laukhtina E, Pradere B, Motlagh RS, Mostafaei H, Grossmann NC, Aulitzky A, Paradysz A, Karakiewicz PI, Fajkovic H, Zimmermann K, Heidenreich A, Gontero P, Shariat SF. Role of systemic immune-inflammation index in patients treated with salvage radical prostatectomy. World J Urol. 2021;39(10):3771-3779. DOI:10.1007/s00345-021-03715-4; Yanagisawa T, Kawada T, Rajwa P, Mostafaei H, Motlagh RS, Quhal F, Laukhtina E, König F, Pallauf M, Pradere B, Karakiewicz PI, Nyirady P, Kimura T, Egawa S, Shariat SF. Sequencing impact and prognostic factors in metastatic castration-resistant prostate cancer patients treated with cabazitaxel: A systematic review and meta-analysis. Urol Oncol. 2023;41(4):177-191. DOI:10.1016/j.urolonc.2022.06.018; Enikeev D, Morozov A, Babaevskaya D, Bazarkin A, Malavaud B. A Systematic Review of Circulating Tumor Cells Clinical Application in Prostate Cancer Diagnosis. Cancers (Basel). 2022;14(15):3802. DOI:10.3390/cancers14153802; Reichl F, Muhr D, Rebhan K, Kramer G, Shariat SF, Singer CF, Tan YY. Cancer Spectrum, Family History of Cancer and Overall Survival in Men with Germline BRCA1 or BRCA2 Mutations. J Pers Med. 2021;11(9):917. DOI:10.3390/jpm11090917; Perera M, Mirchandani R, Papa N, Breemer G, Effeindzourou A, Smith L, Swindle P, Smith E. PSA-based machine learning model improves prostate cancer risk stratification in a screening population. World J Urol. 2021;39(6):1897-1902. DOI:10.1007/s00345-020-03392-9; Rodrigues VC, Soares JC, Soares AC, Braz DC, Melendez ME, Ribas LC, Scabini LFS, Bruno OM, Carvalho AL, Reis RM, Sanfelice RC, Oliveira ON Jr. Electrochemical and optical detection and machine learning applied to images of genosensors for diagnosis of prostate cancer with the biomarker PCA3. Talanta. 2021;222:121444. DOI:10.1016/j.talanta.2020.121444; Cario CL, Chen E, Leong L, Emami NC, Lopez K, Tenggara I, Simko JP, Friedlander TW, Li PS, Paris PL, Carroll PR, Witte JS. A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer. BMC Cancer. 2020;20(1):820. DOI:10.1186/s12885-020-07318-x; Gumaei A, Sammouda R, Al-Rakhami M, AlSalman H, ElZaart A. Feature selection with ensemble learning for prostate cancer diagnosis from microarray gene expression. Health Informatics J. 2021;27(1):1460458221989402. DOI:10.1177/1460458221989402; Alshareef AM, Alsini R, Alsieni M, Alrowais F, Marzouk R, Abunadi I, Nemri N. Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression. J Healthc Eng. 2022;2022:7364704. DOI:10.1155/2022/7364704; Penney KL, Tyekucheva S, Rosenthal J, El Fandy H, Carelli R, Borgstein S, Zadra G, Fanelli GN, Stefanizzi L, Giunchi F, Pomerantz M, Peisch S, Coulson H, Lis R, Kibel AS, Fiorentino M, Umeton R, Loda M. Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum. Mol Cancer Res. 2021;19(3):475-484. DOI:10.1158/1541-7786.MCR-20-0548; Pachynski RK, Kim EH, Miheecheva N, Kotlov N, Ramachandran A, Postovalova E, Galkin I, Svekolkin V, Lyu Y, Zou Q, Cao D, Gaut J, Ippolito JE, Bagaev A, Bruttan M, Gancharova O, Nomie K, Tsiper M, Andriole GL, Ataullakhanov R, Hsieh JJ. Single-cell Spatial Proteomic Revelations on the Multiparametric MRI Heterogeneity of Clinically Significant Prostate Cancer. Clin Cancer Res. 2021;27(12):3478-3490. DOI:10.1158/1078-0432.CCR-20-4217; Cosma G, McArdle SE, Foulds GA, Hood SP, Reeder S, Johnson C, Khan MA, Pockley AG. Prostate Cancer: Early Detection and Assessing Clinical Risk Using Deep Machine Learning of High Dimensional Peripheral Blood Flow Cytometric Phenotyping Data. Front Immunol. 2021;12:786828. DOI:10.3389/fimmu.2021.786828; Dadhania V, Gonzalez D, Yousif M, Cheng J, Morgan TM, Spratt DE, Reichert ZR, Mannan R, Wang X, Chinnaiyan A, Cao X, Dhanasekaran SM, Chinnaiyan AM, Pantanowitz L, Mehra R. Leveraging artificial intelligence to predict ERG gene fusion status in prostate cancer. BMC Cancer. 2022;22(1):494. DOI:10.1186/s12885-022-09559-4; Li R, Zhu J, Zhong WD, Jia Z. Comprehensive Evaluation of Machine Learning Models and Gene Expression Signatures for Prostate Cancer Prognosis Using Large Population Cohorts. Cancer Res. 2022;82(9):1832-1843. DOI:10.1158/0008-5472.CAN-21-3074; Williams C, Khondakar NR, Daneshvar MA, O'Connor LP, Gomella PT, Mehralivand S, Yerram NK, Egan J, Gurram S, Rompré-Brodeur A, Webster BR, Owens-Walton J, Parnes H, Merino MJ, Wood BJ, Choyke P, Turkbey B, Pinto PA. The Risk of Prostate Cancer Progression in Active Surveillance Patients with Bilateral Disease Detected by Combined Magnetic Resonance Imaging-Fusion and Systematic Biopsy. J Urol. 2021;206(5):1157-1165. DOI:10.1097/JU.0000000000001941; Hamzeh O, Alkhateeb A, Zheng J, Kandalam S, Rueda L. Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data. BMC Bioinformatics. 2020;21(Suppl 2):78. DOI:10.1186/s12859-020-3345-9; Belinky F, Nativ N, Stelzer G, Zimmerman S, Iny Stein T, Safran M, Lancet D. PathCards: multi-source consolidation of human biological pathways. Database (Oxford). 2015;2015:bav006. DOI:10.1093/database/bav006; Guo H, Zhang Z, Wang Y, Xue S. Identification of crucial genes and pathways associated with prostate cancer in multiple databases. J Int Med Res. 2021;49(6):3000605211016624. DOI:10.1177/03000605211016624; Shamsara E, Shamsara J. Bioinformatics analysis of the genes involved in the extension of prostate cancer to adjacent lymph nodes by supervised and unsupervised machine learning methods: The role of SPAG1 and PLEKHF2. Genomics. 2020;112(6):3871-3882. DOI:10.1016/j.ygeno.2020.06.035; Xue J, Pu Y, Smith J, Gao X, Wang C, Wu B. Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods. Sci Rep. 2021;11(1):2282. DOI:10.1038/s41598-021-81945-7; Mansinho A, Macedo D, Fernandes I, Costa L. CastrationResistant Prostate Cancer: Mechanisms, Targets and Treatment. Adv Exp Med Biol. 2018;1096:117-133. DOI:10.1007/978-3-319-99286-0_7; Lin E, Hahn AW, Nussenzveig RH, Wesolowski S, Sayegh N, Maughan BL, McFarland T, Rathi N, Sirohi D, Sonpavde G, Swami U, Kohli M, Rich T, Sartor O, Yandell M, Agarwal N. Identification of Somatic Gene Signatures in Circulating Cell-Free DNA Associated with Disease Progression in Metastatic Prostate Cancer by a Novel Machine Learning Platform. Oncologist. 2021;26(9):751-760. DOI:10.1002/onco.13869; Paul N, Carabet LA, Lallous N, Yamazaki T, Gleave ME, Rennie PS, Cherkasov A. Cheminformatics Modeling of Adverse Drug Responses by Clinically Relevant Mutants of Human Androgen Receptor. J Chem Inf Model. 2016;56(12):2507-2516. DOI:10.1021/acs.jcim.6b00400; Bruce CL, Melville JL, Pickett SD, Hirst JD. Contemporary QSAR classifiers compared. J Chem Inf Model. 2007;47(1):219-27. DOI:10.1021/ci600332j; Wan Q, Pal R. An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge. PLoS One. 2014;9(6):e101183. 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DOI:10.3390/ijms21165847; Morozov A, Taratkin M, Bazarkin A, Rivas JG, Puliatti S, Checcucci E, Belenchon IR, Kowalewski KF, Shpikina A, Singla N, Teoh JYC, Kozlov V, Rodler S, Piazza P, Fajkovic H, Yakimov M, Abreu AL, Cacciamani GE, Enikeev D; Young Academic Urologists (YAU) Working Group in Uro-technology of the European Association of Urology. A systematic review and meta-analysis of artificial intelligence diagnostic accuracy in prostate cancer histology identification and grading. Prostate Cancer Prostatic Dis. 2023;26(4):681-692. DOI:10.1038/s41391-023-00673-3; Kowalewski KF, Egen L, Fischetti CE, Puliatti S, Juan GR, Taratkin M, Ines RB, Sidoti Abate MA, Mühlbauer J, Wessels F, Checcucci E, Cacciamani G; Young Academic Urologists (YAU)-Urotechnology-Group. Artificial intelligence for renal cancer: From imaging to histology and beyond. Asian J Urol. 2022;9(3):243-252. DOI:10.1016/j.ajur.2022.05.003; Zeng J, Cheng Q, Zhang D, Fan M, Shi C, Luo L. Diagnostic Ability of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Prostate Cancer and Clinically Significant Prostate Cancer in Equivocal Lesions: A Systematic Review and Meta-Analysis. Front Oncol. 2021;11:620628. DOI:10.3389/fonc.2021.620628; https://www.urovest.ru/jour/article/view/836
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8Academic Journal
Συγγραφείς: D. E. Reingardt, Yu. V. Ostankova, L. V. Lyalina, E. V. Anufrieva, A. V. Semenov, Areg A. Totolian, Д. Э. Рейнгардт, Ю. В. Останкова, Л. B. Лялина, Е. В. Ануфриева, А. В. Семенов, Арег А. Тотолян
Πηγή: HIV Infection and Immunosuppressive Disorders; Том 15, № 4 (2023); 86-93 ; ВИЧ-инфекция и иммуносупрессии; Том 15, № 4 (2023); 86-93 ; 2077-9828 ; 10.22328/2077-9828-2023-15-4
Θεματικοί όροι: молекулярная диагностика, direct acting antivirals (DAAs), drug resistance, genotypes, mutations, molecular diagnostics, препараты прямого противовирусного действия (ПППД), лекарственная устойчивость, генотипы, мутации
Περιγραφή αρχείου: application/pdf
Relation: https://hiv.bmoc-spb.ru/jour/article/view/855/574; World Health Organization. Hepatitis C. Key facts. http://www.who.int/news-room/fact-sheets/detail/hepatitis-c Access date: 15.11.2023.; World Health Organization. Global health sector strategy on viral hepatitis, 2016–2021: towards ending viral hepatitis. 2016. Available at: http://apps.who.int/iris/bitstream/10665/246177/1/WHO-HIV-2016.06-eng.pdf. Access date: 13.11.2023.; Bertino G., Ardiri A., Proiti M., Rigano G., Frazzetto E., Demma S., Ruggeri MI., Scuderi L., Malaguarnera G., Bertino N., Rapisarda V., Di Carlo I., Toro A., Salomone F., Malaguarnera M., Bertino E, Malaguarnera M. Chronic hepatitis C: This and the new era of treatment // World J. Hepatol. 2016. Vol. 8, No. 2. Р. 92–106. doi:10.4254/wjh.v8.i2.92.; Jia Y., Yue W., Gao Q., Tao R., Zhang Y., Fu X. et al. Characterization of a novel hepatitis c subtype, 6xj, and its consequences for direct-acting antiviral treatment in yunnan, China // Microbiol. Spectr. 2021. Vol. 9, No. 1. Р. e0029721. doi:10.1128/Spectrum.00297-21.; Хорькова Е.В., Лялина Л.В., Микаилова О.М., Ковеленов А.Ю., Останкова Ю.В., Валутите Д.Э., Стасишкис Т.А., Цветков В.В., Новак К.Е., Ришняк О.Ю., Крицкая И.В., Буц Л.В., Тягунов Д.С. Актуальные вопросы эпидемиологического надзора за хроническими вирусными гепатитами B, C, D и гепатоцеллюлярной карциномой на региональном уровне. Здоровье населения и среда обитания // ЗНиСО. 2021. Т. 29, № 8. С. 76–84. doi:10.35627/2219-5238/2021-29-8-76-84.; Fried M.W., Shiffman M.L., Reddy K.R., Smith C., Marinos G., Gonçales F.L. Jr, Häussinger D., Diago M., Carosi G., Dhumeaux D., Craxi A., Lin A., Hoffman J., Yu J. Peginterferon alfa-2a plus ribavirin for chronic hepatitis C virus infection // N Engl J Med. 2002. Vol. 347(13. Р. 975– 982. doi:10.1056/NEJMoa020047.; Davoodi L., Masoum B., Moosazadeh M., Jafarpour H., Haghshenas M.R., Mousavi T. Psychiatric side effects of pegylated interferonand ribavirin therapy in Iranian patients with chronic hepatitis C: A meta-analysis // Exp. Ther. Med. 2018. Vol. 16, Nо. 2. Р. 971–978. doi:10.3892/etm.2018.6255.; Soriano V., Vispo E., Poveda E., Labarga P., Martin-Carbonero L., Fernandez-Montero J.V., Barreiro P. Directly acting antivirals against hepatitis C virus // J. Antimicrob. Chemother. 2011. Vol. 66, Nо. 8. Р. 1673–1686. doi:10.1093/jac/dkr215.; Aghemo A., De Francesco R. New horizons in hepatitis C antiviral therapy with direct-acting antivirals // Hepatology. 2013. Vol. 58, Nо. 1. Р. 428–438.; Milani A., Basimi P., Agi E., Bolhassani A. Pharmaceutical Approaches for Treatment of Hepatitis C virus // Curr. Pharm. Des. 2020. Vol. 26, Nо. 34. Р. 4304–4314. doi:10.2174/1381612826666200509233215.; Simmonds P. The origin of hepatitis C virus // Curr. Top. Microbiol Immunol. 2013. Vol. 369. Р. 1–15. doi:10.1007/978-3-642-27340-7_1.; Kanwal F., Kramer J.R., Ilyas J., Duan Z., El-Serag H.B. HCV genotype 3 is associated with an increased risk of cirrhosis and hepatocellular cancer in a national sample of U.S. Veterans with HCV // Hepatology. 2014. Vol. 60, Nо. 1. Р. 98–105. doi:10.1002/hep.27095.; Останкова Ю.В., Валутите Д.Э., Зуева Е.Б., Серикова Е.Н., Щемелев А.Н., Boumbaly S., Balde T.A., Семенов А.В. Первичные мутации лекарственной устойчивости вируса гепатита C у пациентов с впервые выявленной ВИЧ-инфекцией // Проблемы особо опасных инфекций. 2020. № 3. С. 97–105. doi:10.21055/0370-1069-2020-3-97-105.; Кичатова В.С., Кюрегян К.К. Современный взгляд на резистентность к препаратам прямого противовирусного действия при лечении вирусного гепатита С // Инфекционные болезни: Новости. Мнения. Обучение. 2019. Т. 8, № 2. С. 64–71. doi:10.24411/2305-3496-2019-12009.; Jackowiak P., Kuls K., Budzko L., Mania A., Figlerowicz M., Figlerowicz M. 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9Academic Journal
Συγγραφείς: Mahmoudi Niloufar, Pakina E.N., Limantceva L.A., Ivanov A.V.
Πηγή: RUDN Journal of Agronomy and Animal Industries, Vol 15, Iss 4, Pp 353-362 (2020)
Θεματικοί όροι: 0301 basic medicine, ditylenchus destructor, 0303 health sciences, ITS-rRNA, ITS-рРНК, вредитель растений, Agriculture, Ditylenchus destructor, молекулярная диагностика, 15. Life on land, its-rrna, molecular diagnostics, plant pest, 03 medical and health sciences, стеблевая картофельная нематода, pcr-rflp, potato rot nematode
Σύνδεσμος πρόσβασης: http://agrojournal.rudn.ru/agronomy/article/download/19603/16255
https://doaj.org/article/5d6ecdeb635d49dd9895e8d437d2c9fd
http://agrojournal.rudn.ru/agronomy/article/viewFile/19603/16255
https://cyberleninka.ru/article/n/diagnosis-of-potato-rot-nematode-ditylenchus-destructor-using-pcr-rflp -
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11Report
Θεματικοί όροι: нейровизуализация, neuroimaging, immunomodulators, nervous system, молекулярная диагностика, treatment outcomes, molecular diagnostics, терапевтические стратегии, biological therapy, аутоиммунные заболевания, нервная система, результаты лечения, autoimmune diseases, иммуномодуляторы, therapeutic strategies, биологическая терапия
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12Academic Journal
Συγγραφείς: S. E. Titov, S. A. Lukyanov, S. V. Sergiyko, Yu. A. Veryaskina, T. E. Ilyina, E. S. Kozorezov, S. L. Vorobyov, С. Е. Титов, С. А. Лукьянов, С. В. Сергийко, Ю. А. Веряскина, Т. Е. Ильина, Е. С. Козорезова, С. Л. Воробьев
Συνεισφορές: The research was carried out within the framework of the state task (Unified State information system for accounting of research, development and technological works for civil purposes, No. 121040100268-9) and at the expense of a grant from the Russian Science Foundation (grant No. 20-14-00074-P)., Исследование выполнено в рамках государственного задания (Единая государственная информационная система учета научно-исследовательских, опытно-конструкторских и технологических работ гражданского назначения, № 121040100268-9) и за счет гранта Российского научного фонда (грант № 20-14-00074-П)
Πηγή: Head and Neck Tumors (HNT); Том 13, № 3 (2023); 10-23 ; Опухоли головы и шеи; Том 13, № 3 (2023); 10-23 ; 2411-4634 ; 2222-1468 ; 10.17650/2222-1468-2023-13-3
Θεματικοί όροι: фолликулярная аденома щитовидной железы, микроРНК, молекулярная диагностика, массовое параллельное секвенирование, follicular thyroid adenoma, microRNA, molecular diagnostics, mass parallel sequencing
Περιγραφή αρχείου: application/pdf
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DOI:10.1097/SLA.0000000000003580; Silaghi C.A., Lozovanu V., Georgescu C.E. et al. Thyroseq v3, Afirma GSC, and microRNA panels versus previous molecular tests in the preoperative diagnosis of indeterminate thyroid nodules: a systematic review and meta-analysis. Front Endocrinol (Lausanne) 2021;12:649522. DOI:10.3389/fendo.2021.649522; Wang M.M., Beckett K., Douek M. et al. Diagnostic value of molecular testing in sonographically suspicious thyroid nodules. J Endocr Soc 2020;4(9):bvaa081. DOI:10.1210/jendso/bvaa081; Azizi G., Keller J.M., Mayo M.L. et al. Shear wave elastography and Afirma™ gene expression classifier in thyroid nodules with indeterminate cytology: a comparison study. Endocrine 2018;59(3):573–84. DOI:10.1007/s12020-017-1509-9; Patel K.N., Angell T.E., Babiarz J. et al. Performance of a genomic sequencing classifier for the preoperative diagnosis of cytologically indeterminate thyroid nodules. JAMA Surg 2018;153(9):817–24. DOI:10.1001/jamasurg.2018.1153; Титов С.Е., Лукьянов С.А., Козорезова Е.С. и др. Валидация дооперационной диагностики злокачественных опухолей щитовидной железы с помощью молекулярного классификатора. Вопросы онкологии 2022;68(6):741–51. DOI:10.37469/0507-3758-2022-68-6-741-751; Xing M., Liu R., Liu X. et al. BRAF V600E and TERT promoter mutations cooperatively identify the most aggressive papillary thyroid cancer with highest recurrence. J Clin Oncol 2014;32(25):2718–26. DOI:10.1200/JCO.2014.55.5094; Xing M. Clinical utility of RAS mutations in thyroid cancer: a blurred picture now emerging clearer. BMC Med 2016;14:12. DOI:10.1186/s12916-016-0559-9; Song Y.S., Park Y.J. Genomic characterization of differentiated thyroid carcinoma. Endocrinol Metab (Seoul) 2019;34(1):1–10. DOI:10.3803/EnM.2019.34.1.1; De Martino M., Esposito F., Capone M. et al. Noncoding RNAs in thyroid-follicular-cell-derived carcinomas. Cancers (Basel) 2022;14(13):3079. DOI:10.3390/cancers14133079; Macfarlane L.A., Murphy P.R. MicroRNA: biogenesis, function and role in cancer. Curr Genomics 2010;11(7):537–61. DOI:10.2174/138920210793175895; Santiago K., Chen Wongworawat Y., Khan S. Differential microRNA-signatures in thyroid cancer subtypes. J Oncol 2020;2020:2052396. DOI:10.1155/2020/2052396; Wojtas B., Ferraz C., Stokowy T. et al. Differential miRNA expression defines migration and reduced apoptosis in follicular thyroid carcinomas. Mol Cell Endocrinol 2014;388(1–2):1–9. DOI:10.1016/j.mce.2014.02.011; Stokowy T., Wojtaś B., Fujarewicz K. et al. miRNAs with the potential to distinguish follicular thyroid carcinomas from benign follicular thyroid tumors: results of a meta-analysis. Horm Metab Res 2014;46(3):171–80. DOI:10.1055/s-0033-1363264; Weber F., Teresi R.E., Broelsch C.E. et al. A limited set of human microRNA is deregulated in follicular thyroid carcinoma. J Clin Endocrinol Metab 2006;91(9):3584–91. DOI:10.1210/jc.2006-0693; Dom G., Frank S., Floor S. et al. Thyroid follicular adenomas and carcinomas: molecular profiling provides evidence for a continuous evolution. Oncotarget 2017;9(12):10343–59. DOI:10.18632/oncotarget.23130; Titov S., Demenkov P.S., Lukyanov S.A. et al. Preoperative detection of malignancy in fine-needle aspiration cytology (FNAC) smears with indeterminate cytology (Bethesda III, IV) by a combined molecular classifier. J Clin Pathol 2020;73(11):722–7. DOI:10.1136/jclinpath-2020-206445; Titov S.E., Kozorezova E.S., Demenkov P.S. et al. Preoperative typing of thyroid and parathyroid tumors with a combined molecular classifier. Cancers 2021;13(2):237. DOI:10.3390/cancers13020237; Andrews S. FastQC: a quality control tool for high throughput sequence data. Available at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/; Titov S.E., Ivanov M.K., Karpinskaya E.V. et al. miRNA profiling, detection of BRAF V600E mutation and RET-PTC1 translocation in patients from Novosibirsk oblast (Russia) with different types of thyroid tumors. BMC Cancer 2016;16:201. DOI:10.1186/s12885-016-2240-2; Chen C., Ridzon D.A., Broomer A.J. et al. Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res 2005;33(20):e179. DOI:10.1093/nar/gni178; Livak K.J., Schmittgen T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCt method. Methods 2001;25(4):402–8. DOI:10.1006/meth.2001.1262; Mercaldo N.D., Lau K.F., Zhou X.H. Confidence intervals for predictive values with an emphasis to case-control studies. Stat Med 2007;26(20):2170–83. DOI:10.1002/sim.2677; Pérez-Ortiz M., Torres-Jiménez M., Gutiérrez P.A. et al. Fisher score-based feature selection for ordinal classification: a social survey on subjective well-being. In: Hybrid Artificial Intelligent Systems. Ed. by F. Martínez-Álvarez, A. Troncoso, H. Quintián, E. Corchado. HAIS 2016. Lecture Notes in Computer Science. Vol. 9648. Springer, Cham. DOI:10.1007/978-3-319-32034-2_50; Kononenko I., Šimec E., Robnik-Sikonja M. Overcoming the myopia of inductive learning algorithms with RELIEFF. Applied Intelligence 1997;7(1):39–55. DOI:10.1023/A:1008280620621; Li J., Cheng K., Wang S. et al. Feature selection. ACM Computing Surveys 2017;50(6):1–45. DOI:10.1145/3136625; Bylesjö M., Rantalainen M., Cloarec O. et al. OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J Chemometrics 2006;20(8–10):341–51. DOI:10.1002/cem.1006; Thevenot E., Roux A., Xu Y. et al. Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. J Proteome Res 2015;14(8):3322–35. DOI:10.1021/acs.jproteome.5b00354; Tenenhaus M. La raegression PLS. Paris, Editions Technip, 1998.; Ricco R. TANAGRA: a free software for research and academic purposes. Proceedings of EGC’2005, RNTI-E-3. (In French). Available at: https://www.researchgate.net/publication/220786300_TANAGRA_un_logiciel_gratuit_pour_l'enseignement_et_la_recherche.; Quinlan J.R. C4.5: programs for machine learning. San Francisco: Morgan Kaufmann Publishers Inc; 1993.; Зиновьев А.Ю. Визуализация многомерных данных. Красноярск: Издательство КГТУ, 2000.; McHenry C.R., Phitayakorn R. Follicular adenoma and carcinoma of the thyroid gland. Oncologist 2011;16(5):585–93. DOI:10.1634/theoncologist.2010-0405; Valderrabano P., Leon M.E., Centeno B.A. et al. Institutional prevalence of malignancy of indeterminate thyroid cytology is necessary but insufficient to accurately interpret molecular marker tests. Eur J Endocrinol 2016;174(5):621–9. DOI:10.1530/EJE-15-1163; Rosai J., Kuhn E., Carcangiu M.L. Pitfalls in thyroid tumour pathology. Histopathology 2006;49:107–20. DOI:10.1111/j.1365-2559.2006.02451.x; Franc B., de la Salmonière P., Lange F. et al. Interobserver and intraobserver reproducibility in the histopathology of follicular thyroid carcinoma. Hum Pathol 2003;34(11):1092–100. DOI:10.1016/s0046-8177(03)00403-9; Cipriani N.A., Nagar S., Kaplan S.P. et al. Follicular thyroid carcinoma: how have histologic diagnoses changed in the last halfcentury and what are the prognostic implications? Thyroid 2015;25(11):1209–16. DOI:10.1089/thy.2015.0297; https://ogsh.abvpress.ru/jour/article/view/910
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13Academic Journal
Συγγραφείς: E. I. Petrova, L. V. Olkhova, S. A. Galstyan, E. N. Telysheva, O. G. Zheludkova, M. V. Ryzhova, Е. И. Петрова, Л. В. Ольхова, С. А. Галстян, Е. Н. Телышева, О. Г. Желудкова, М. В. Рыжова
Συνεισφορές: The study was financially supported by the Ministry of Science and Higher Education of the Russian Federation (Agreement No. 075 15 2021 1343) “Development of a bioresource collection of tumors of the human nervous system with molecular genetic certification for personalized treatment of patients with neuro-oncological diseases”., Работа выполнена при поддержке гранта Министерства образования и науки № 075-15-2021-1343 «Развитие биоресурсной коллекции опухолей нервной системы человека с молекулярно-генетической паспортизацией для персонифицированного лечения пациентов с нейроонкологическими заболеваниями».
Πηγή: Russian Journal of Pediatric Hematology and Oncology; Том 10, № 3 (2023); 15-21 ; Российский журнал детской гематологии и онкологии (РЖДГиО); Том 10, № 3 (2023); 15-21 ; 2413-5496 ; 2311-1267
Θεματικοί όροι: Illumina EPIC Methylation microarray, epigenetics of CNS tumors, molecular diagnostics, DNA methylation, cell type deconvolution, эпигенетика опухолей центральной нервной системы, молекулярная диагностика, метилирование ДНК, деконволюция клеточных типов
Περιγραφή αρχείου: application/pdf
Relation: https://journal.nodgo.org/jour/article/view/959/843; Ostrom Q.T., Price M., Neff C., Cioffi G., Waite K.A., Kruchko C., Barnholtz-Sloan J.S. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015–2019. Neuro Oncol. 2022;24(Suppl 5):v1–v95. doi:10.1093/neuonc/noac202.; Louis D.N., Ohgaki H., Wiestler O.D., Cavenee W.K. World Health Organization Classification of Tumours of the Central Nervous System. 4th ed., updated ed. Lyon: International Agency for Research on Cancer; 2016.; WHO Classification of Tumours Editorial Board. World Health Organization Classification of Tumours of the Central Nervous System. 5th ed. Lyon: International Agency for Research on Cancer; 2021.; Sturm D., Witt H., Hovestadt V., Khuong-Quang D.A., Jones D.T., Konermann C., Pfaff E., Tönjes M., Sill M., Bender S., Kool M., Zapatka M., Becker N., Zucknick M., Hielscher T., Liu X.Y., Fontebasso A.M., Ryzhova M., Albrecht S., Jacob K., Wolter M., Ebinger M., Schuhmann M.U., van Meter T., Frühwald M.C., Hauch H., Pekrun A., Radlwimmer B., Niehues T., von Komorowski G., Dürken M., Kulozik A.E., Madden J., Donson A., Foreman N.K., Drissi R., Fouladi M., Scheurlen W., von Deimling A., Monoranu C., Roggendorf W., Herold-Mende C., Unterberg A., Kramm C.M., Felsberg J., Hartmann C., Wiestler B., Wick W., Milde T., Witt O., Lindroth A.M., Schwartzentruber J., Faury D., Fleming A., Zakrzewska M., Liberski P.P., Zakrzewski K., Hauser P., Garami M., Klekner A., Bognar L., Morrissy S., Cavalli F., Taylor M.D., van Sluis P., Koster J., Versteeg R., Volckmann R., Mikkelsen T., Aldape K., Reifenberger G., Collins V.P., Majewski J., Korshunov A., Lichter P., Plass C., Jabado N., Pfister S.M. Hotspot mutations in H3F3A and IDH1 define distinct epigenetic and biological subgroups of glioblastoma. Cancer Cell. 2012;22(4):425–37. doi:10.1016/j.ccr.2012.08.024.; Capper D., Jones D.T.W., Sill M., Hovestadt V., Schrimpf D., Sturm D., Koelsche C., Sahm F., Chavez L., Reuss D.E., Kratz A., Wefers A.K., Huang K., Pajtler K.W., Schweizer L., Stichel D., Olar A., Engel N.W., Lindenberg K., Harter P.N., Braczynski A.K., Plate K.H., Dohmen H., Garvalov B.K., Coras R., Hölsken A., Hewer E., Bewerunge-Hudler M., Schick M., Fischer R., Beschorner R., Schittenhelm J., Staszewski O., Wani K., Varlet P., Pages M., Temming P., Lohmann D., Selt F., Witt H., Milde T., Witt O., Aronica E., Giangaspero F., Rushing E., Scheurlen W., Geisenberger C., Rodriguez F.J., Becker A., Preusser M., Haberler C., Bjerkvig R., Cryan J., Farrell M., Deckert M., Hench J., Frank S., Serrano J., Kannan K., Tsirigos A., Brück W., Hofer S., Brehmer S., Seiz-Rosenhagen M., Hänggi D., Hans V., Rozsnoki S., Hansford J.R., Kohlhof P., Kristensen B.W., Lechner M., Lopes B., Mawrin C., Ketter R., Kulozik A., Khatib Z., Heppner F., Koch A., Jouvet A., Keohane C., Mühleisen H., Mueller W., Pohl U., Prinz M., Benner A., Zapatka M., Gottardo N.G., Driever P.H., Kramm C.M., Müller H.L., Rutkowski S., von Hoff K., Frühwald M.C., Gnekow A., Fleischhack G., Tippelt S., Calaminus G., Monoranu C.M., Perry A., Jones C., Jacques T.S., Radlwimmer B., Gessi M., Pietsch T., Schramm J., Schackert G., Westphal M., Reifenberger G., Wesseling P., Weller M., Collins V.P., Blümcke I., Bendszus M., Debus J., Huang A., Jabado N., Northcott P.A., Paulus W., Gajjar A., Robinson G.W., Taylor M.D., Jaunmuktane Z., Ryzhova M., Platten M., Unterberg A., Wick W., Karajannis M.A., Mittelbronn M., Acker T., Hartmann C., Aldape K., Schüller U., Buslei R., Lichter P., Kool M., Herold-Mende C., Ellison D.W., Hasselblatt M., Snuderl M., Brandner S., Korshunov A., von Deimling A., Pfister S.M. DNA methylation-based classification of central nervous system tumours. Nature. 2018;555(7697):469–74. doi:10.1038/nature26000.; Capper D., Stichel D., Sahm F., Jones D.T.W., Schrimpf D., Sill M., Schmid S., Hovestadt V., Reuss D.E., Koelsche C., Reinhardt A., Wefers A.K., Huang K., Sievers P., Ebrahimi A., Schöler A., Teichmann D., Koch A., Hänggi D., Unterberg A., Platten M., Wick W., Witt O., Milde T., Korshunov A., Pfister S.M., von Deimling A. Practical implementation of DNA methylation and copy-number-based CNS tumor diagnostics: the Heidelberg experience. Acta Neuropathol. 2018;136(2):181–210. doi:10.1007/s00401-018-1879-y.; Рыжова М.В., Галстян С.А., Телышева Е.Н. Значение оценки метилирования ДНК в морфологической диагностике опухолей ЦНС. Архив патологии. 2022;84(3):65–75. doi:10.17116/patol20228403165.; Рыжова М.В., Телышева Е.Н., Шайхаев Е.Г., Старовойтов Д.В., Котельникова А.О., Галстян С.А., Оконечников К.В. Современные диагностические возможности молекулярного исследования опухолей мозга в центре нейрохирургии им. акад. Н.Н. Бурденко. Журнал Вопросы нейрохирургии им. Н.Н. Бурденко. 2021;85(6):98–101. doi:10.17116/neiro20218506192.; Петрова Е.И., Галстян С.А., Телышева Е.Н., Рыжова М.В. Визуализация результатов анализа структуры метилирования ДНК как инструмент контроля качества молекулярной классификации опухолей ЦНС. Российский нейрохирургический журнал им. проф. А.Л. Поленова. 2022;14(4):64–70. doi:10.56618/20712693_2022_14_4_64.; Aryee M.J., Jaffe A.E., Corrada-Bravo H., Ladd-Acosta C., Feinberg A.P., Hansen K.D., Irizarry R.A. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–9. doi:10.1093/bioinformatics/btu049.; Mansell G., Gorrie-Stone T.J., Bao Y., Kumari M., Schalkwyk L.S., Mill J., Hannon E. Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array. BMC Genomics. 2019;20(1):366. doi:10.1186/s12864-019-5761-7.; Bady P., Sciuscio D., Diserens A.C., Bloch J., van den Bent M.J., Marosi C., Dietrich P.Y., Weller M., Mariani L., Heppner F.L., Mcdonald D.R., Lacombe D., Stupp R., Delorenzi M., Hegi M.E. MGMT methylation analysis of glioblastoma on the Infinium methylation BeadChip identifies two distinct CpG regions associated with gene silencing and outcome, yielding a prediction model for comparisons across datasets, tumor grades, and CIMP-status. Acta Neuropathol. 2012;124(4):547–60. doi:10.1007/s00401-012-1016-2. Erratum in: Acta Neuropathol. 2013;126(1):159.; Teschendorff A.E., Breeze C.E., Zheng S.C., Beck S. A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies. BMC Bioinformatics. 2017;18(1):105. doi:10.1186/s12859-017-1511-5.; Grabovska Y., Mackay A., O’Hare P., Crosier S., Finetti M., Schwalbe E.C., Pickles J.C., Fairchild A.R., Avery A., Cockle J., Hill R., Lindsey J., Hicks D., Kristiansen M., Chalker J., Anderson J., Hargrave D., Jacques T.S., Straathof K., Bailey S., Jones C., Clifford S.C., Williamson D. Pediatric pan-central nervous system tumor analysis of immune-cell infiltration identifies correlates of antitumor immunity. Nat Commun. 2020;11(1):4324. doi:10.1038/s41467-020-18070-y.; Chen Z., Hambardzumyan D. Immune Microenvironment in Glioblastoma Subtypes. Front Immunol. 2018;9:1004. doi:10.3389/fimmu.2018.01004.; Mo F., Pellerino A., Soffietti R., Rudà R. Blood-Brain Barrier in Brain Tumors: Biology and Clinical Relevance. Int J Mol Sci. 2021;22(23):12654. doi:10.3390/ijms222312654.; Han S., Ma E., Wang X., Yu C., Dong T., Zhan W., Wei X., Liang G., Feng S. Rescuing defective tumor-infiltrating T-cell proliferation in glioblastoma patients. Oncol Lett. 2016;12(4):2924–9. doi:10.3892/ol.2016.4944.; Zhai L., Ladomersky E., Lauing K.L., Wu M., Genet M., Gritsina G., Győrffy B., Brastianos P.K., Binder D.C., Sosman J.A., Giles F.J., James C.D., Horbinski C., Stupp R., Wainwright D.A. Infiltrating T Cells Increase IDO1 Expression in Glioblastoma and Contribute to Decreased Patient Survival. Clin Cancer Res. 2017;23(21):6650–60. doi:10.1158/1078-0432.CCR-17-0120.; https://journal.nodgo.org/jour/article/view/959
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14Academic Journal
Συγγραφείς: Bahşiev, A.G., Zamorzaeva-Orleanscaia, I.A.
Πηγή: Buletinul Academiei de Ştiinţe a Moldovei. Ştiinţele vieţii 347 (3) 41-47
Θεματικοί όροι: выделение ДНК, plante ruderale, izolarea ADN, nested-PCR, molecular diagnosis, Сорняки, diagnosticul molecular, 'Candidatus Phytoplasma solani', Weeds, молекулярная диагностика, нестед-ПЦР, DNA extraction
Περιγραφή αρχείου: application/pdf
Σύνδεσμος πρόσβασης: https://ibn.idsi.md/vizualizare_articol/176472
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15Academic Journal
Συγγραφείς: A. V. Dil, V. D. Nazarov, D. V. Sidorenko, S. V. Lapin, V. L. Emanuel, А. В. Диль, В. Д. Назаров, Д. В. Сидоренко, С. В. Лапин, В. Л. Эмануэль
Πηγή: Neuromuscular Diseases; Том 12, № 3 (2022); 36-44 ; Нервно-мышечные болезни; Том 12, № 3 (2022); 36-44 ; 2413-0443 ; 2222-8721 ; 10.17650/2222-8721-2022-12-3
Θεματικοί όροι: генная терапия, SMN1, SMN2, molecular diagnostics, pathogenetic therapy, gene therapy, молекулярная диагностика, патогенетическая терапия
Περιγραφή αρχείου: application/pdf
Relation: https://nmb.abvpress.ru/jour/article/view/498/327; Tisdale S., Pellizzoni L. Disease mechanisms and therapeutic approaches in spinal muscular atrophy. J Neurosci 2015;35(23):8691–700. DOI:10.1523/jneurosci.0417-15.2015; Lally C., Jones C., Farwell W. et al. Indirect estimation of the prevalence of spinal muscular atrophy type I, II, and III in the United States. Orphanet J Rare Dis 2017;12(1). DOI:10.1186/s13023-017-0724-z; Butchbach M.E.R. Genomic variability in the survival motor neuron genes (SMN1 and SMN2): Implications for spinal muscular atrophy phenotype and therapeutics development. Int J Mol Sci 2021;22(15):7896–917. DOI:10.3390/ijms22157896; Ruhno C., McGovern V.L., Avenarius M.R. et al. Complete sequencing of the SMN2 gene in SMA patients detects SMN gene deletion junctions and variants in SMN2 that modify the SMA phenotype. Hum Genet 2019;138(3):241–56. DOI:10.1007/s00439-019-01983-0; Seo J., Singh N.N., Ottesen E.W. et al. A novel human-specific splice isoform alters the critical C-terminus of Survival Motor Neuron protein. Sci Rep 2016;6(1). DOI:10.1038/srep30778; Lefebvre S., Bürglen L., Reboullet S. et al. Identification and characterization of a spinal muscular atrophy-determining gene. Cell 1995;80(1):155–65. DOI:10.1016/0092-8674(95)90460-3; Wirth B., Karakaya M., Kye M.J. et al. Twenty-five years of spinal muscular atrophy research: from phenotype to genotype to therapy, and what comes next. Ann Rev Genom Hum Genet 2020;21(1). DOI:10.1146/annurev-genom-102319-103602; Gambardella A., Mazzei R., Toscano A. et al. Spinal muscular atrophy due to an isolated deletion of exon 8 of the telomeric survival motor neuron gene. Ann Neurol 1998;44(5):836–9. DOI:10.1002/ana.410440522; Ottesen E.W., Seo J., Singh N.N. et al. A multilayered control of the human survival motor neuron gene expression by Alu elements. Front Microbiol 2017;8:2252. DOI:10.3389/fmicb.2017.02252; Jedličková I., Přistoupilová A., Nosková L. et al. Spinal muscular atrophy caused by a novel Alu‐mediated deletion of exons 2a‐5 in SMN1 undetectable with routine genetic testing. Mol Genet Genomic Med 2020. DOI:10.1002/mgg3.1238; Singh R.N., Singh N.N. Mechanism of splicing regulation of spinal muscular atrophy genes. Adv Neurobiol 2018;20:31–61. DOI:10.1007/978-3-319-89689-2_2; Mercer J.M. Unequal crossing over. Ref Mod Life Sci 2017. DOI:10.1016/b978-0-12-809633-8.07324-6; Stabley D.L., Holbrook J., Scavina M. et al. Detection of SMN1 to SMN2 gene conversion events and partial SMN1 gene deletions using array digital PCR. Neurogenetics 2021;22(1):53–64. DOI:10.1007/s10048-020-00630-5; Hahnen E., Schönling J., Rudnik-Schöneborn S. et al. Hybrid survival motor neuron genes in patients with autosomal recessive spinal muscular atrophy: new insights into molecular mechanisms responsible for the disease. Am J Hum Genet 1996;59(5):1057–65.; Ogino S., Gao S., Leonard D.G. et al. Inverse correlation between SMN1 and SMN2 copy numbers: evidence for gene conversion from SMN2 to SMN1. Eur J Hum Genet 2003;11(3):275–7. DOI:10.1038/sj.ejhg.5200957; Qu Y., Bai J., Cao Y. et al. Mutation spectrum of the survival of motor neuron 1 and functional analysis of variants in Chinese spinal muscular atrophy. J Molec Diagnostics 2016;18(5):741–52. DOI:10.1016/j.jmoldx.2016.05.004; Kubo Y., Nishio H., Saito K. A new method for SMN1 and hybrid SMN gene analysis in spinal muscular atrophy using long-range PCR followed by sequencing. J Hum Genet 2015;60:233–9. DOI:10.1038/jhg.2015.16; Wadman R.I., Jansen M.D., Stam M. et al. Intragenic and structural variation in the SMN locus and clinical variability in spinal muscular atrophy. Brain Communications 2020;2(2):1–13. DOI:10.1093/braincomms/fcaa075; Niba E.T.E., Nishio H., Wijaya Y.O.S. et al. Clinical phenotypes of spinal muscular atrophy patients with hybrid SMN gene. Brain Develop 2020. DOI:10.1016/j.braindev.2020.09.005; Fang P., Li L., Zeng J. et al. Molecular characterization and copy number of SMN1, SMN2 and NAIP in Chinese patients with spinal muscular atrophy and unrelated healthy controls. BMC Musculoskel Disord 2015;16(1). DOI:10.1186/s12891-015-0457-x; Wang X.B., Cui N.H., Gao J.J. et al. SMN1 duplications contribute to sporadic amyotrophic lateral sclerosis susceptibility: Evidence from a meta-analysis. Jl Neurol Sci 2014;340(1–2):63–8. DOI:10.1016/j.jns.2014.02.026; Darras B.T. More can be less: SMN1 gene duplications are associated with sporadic ALS. Neurology 2012;78(11):770, 771. DOI:10.1212/wnl.0b013e318249f754; https://nmb.abvpress.ru/jour/article/view/498
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16Academic Journal
Συγγραφείς: T. A. Fomina, M. G. Kuleshova, M. Yu. Minaev, E. A. Konorov, Т. А. Фомина, М. Г. Кулешова, М. Ю. Минаев, Е. А. Коноров
Συνεισφορές: Статья подготовлена в рамках выполнения исследований по государственному заданию № FGUS‑2019–0001 Федерального научного центра пищевых систем им. В. М. Горбатова Российской академии наук
Πηγή: Food systems; Vol 5, No 2 (2022); 80-93 ; Пищевые системы; Vol 5, No 2 (2022); 80-93 ; 2618-7272 ; 2618-9771 ; 10.21323/2618-9771-2022-5-2
Θεματικοί όροι: ПЦР, fish identification, DNA, molecular diagnostics, next generation sequencing (NGS), PCR, идентификация рыб, ДНК, молекулярная диагностика, секвенирование NGS
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Reference Series in Phytochemistry, 2063–2117. https://doi.org/10.1007/978–3–319–78030–6_69; Agnew, D. J., Pearce, J., Pramod, G., Peatman T., Watson R., Beddington, J. R. et al. (2009). Estimating the worldwide extent of illegal fishing. PLoS ONE, 4(2), Article e4570. https://doi.org/10.1371/journal.pone.0004570; Galimberti, A., De Mattia, F., Losa, A., Bruni, I., Federici, S., Casiraghi, M. et al. (2013). DNA barcoding as a new tool for food traceability. Food Research International, 50(1), 55–63. https://doi.org/10.1016/j.foodres.2012.09.036; Rasmussen Hellberg, R. S., Morrissey, M. T. (2011). Advances in DNA based techniques for the detection of seafood species substitution on the commercial market. Journal of Laboratory Automation, 16(4), 308– 321. https://doi.org/10.1016/j.jala.2010.07.004; Bybee, S.M., Bracken-Grissom, H., Haynes, B.D., Hermansen R. A., Byers R. L., Clement M. J. et al. (2011). Targeted amplicon sequencing (TAS): A scalable next-gen approach to multilocus, multitaxa phylogenetics. Genome Biology and Evolution, 3(1), 1312–1323. https://doi.org/10.1093/gbe/evr106; Rusk, N. (2010). Torrents of sequence. Nature Methods, 8(1), 44–44. https://doi.org/10.1038/nmeth.f.330; Purushothaman, S., Toumazou, C., Ou, C.-P. (2006). Protons and single nucleotide polymorphism detection: A simple use for the Ion Sensitive Field Effect Transistor. Sensors and Actuators B: Chemical, 114(2), 964– 968. https://doi.org/10.1016/j.snb.2005.06.069; МР 4.2.0019–11 Методические рекомендации. 4.2. Методы контроля. Биологические факторы. Идентификация сырьевого состава мясной продукции. Электронный ресурс https://www.rospotrebnadzor.ru/upload/iblock/e6f/mr4.2.0019_11.pdf Дата обращения 15.03.2022.; Murray, M. G., Thompson, W. F. (1980). Rapid isolation of higher weight DNA. Nuclear Acids Research, 8(19), 4321–4325. https://doi.org/10.1093/nar/8.19.4321; Boom, R., Sol, C. J. 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Электронный ресурс https://rskrf.ru/tips/spetsproekty/v-kakikh-konservakh-sayru-zamenyayut-bolee-deshevoy-ryboy/ Дата обращения 01.02.2022.; Торговые сети продают дешевую восточную скумбрию под видом дорогой атлантической. Электронный ресурс https://www.spbkontrol.ru/ekspertizy2020/773-torgovye-seti-prodayut-deshevuyuvostochnuyu-skumbriyu-pod-vidom-dorogoj-atlanticheskoj Дата обращения 14.03.2022.; Giusti, A., Armani, A., Sotelo, C. G. (2017). Advances in the analysis of complex food matrices: Species identification in surimi-based products using Next Generation Sequencing technologies. Plos ONE, 12(10), Article e0185586. https://doi.org/10.1371/journal.pone.0185586; Park, J.Y., Lee, S.Y., An, C.M., Kang J.-H., Kim J.-H., Chai J. C. et al. (2012). Comparative study between Next Generation Sequencing Technique and identification of microarray for Species Identification within blended food products. Biochip Journal, 6(4), 354–361. https://doi.org/10.1007/s13206–012–6407-x; Bertolini, F., Ghionda, M.C., D’Alessandro, E., Geraci C., Chiofalo, V., Fontanesi, L. (2015). A next generation semiconductor based sequencing approach for the identification of meat species in DNA mixtures. PLoS One, 10(4), Article 0121701. https://doi.org/10.1371/journal.pone.0121701; Tillmar, A.O., Dell’Amico, B., Welander, J., Holmlund, G. (2013). A universal method for species identification of mammals utilizing next generation sequencing for the analysis of DNA mixtures. PLoS One, 8, Article e83761. https://doi.org/10.1371/journal.pone.0083761; https://www.fsjour.com/jour/article/view/154
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17Academic Journal
Συγγραφείς: Zamorzaeva-Orleanscaia, I.A., Заморзаева, И.А., Bahşiev, A.G., Бахшиев, А.Г.
Πηγή: Агрофизический институт: 90 лет на службе земледелия и растениеводства
Θεματικοί όροι: столбур, Сельскохозяйственные культуры, насекомые-переносчики, молекулярная диагностика, открытый грунт, stolbur, agricultural crops, insect vectors, molecular diagnosis, open ground
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Relation: info:eu-repo/grantAgreement/EC/FP7/17203/EU/Conservarea ex-situ de lungă durată a resurselor genetice vegetale în banca de gene cu utilizarea metodelor biologiei moleculare în testarea stării de sănătate a dermoplasmei vegetale/20.80009.5107.11; https://ibn.idsi.md/vizualizare_articol/167171
Διαθεσιμότητα: https://ibn.idsi.md/vizualizare_articol/167171
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18Academic Journal
Συγγραφείς: Shedko E.D., Goloveshkina E.N., Akimkin V.G.
Πηγή: Vestnik dermatologii i venerologii; Vol 97, No 3 (2021); 14-23 ; Вестник дерматологии и венерологии; Vol 97, No 3 (2021); 14-23 ; 2313-6294 ; 0042-4609 ; 10.25208/vdv.973
Θεματικοί όροι: mycoplasma genitalium, antibiotic resistance, 23S rRNA, QRDR, molecular diagnostics, антибиотикорезистентность, молекулярная диагностика
Περιγραφή αρχείου: application/pdf
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19Academic Journal
Συγγραφείς: A. P. Seryakov, R. M. Akhmaev, A. A. Guryanova, A. A. Prokofieva, А. П. Серяков, Р. М. Ахмаев, А. А. Гурьянова, А. А. Прокофьева
Πηγή: Pharmacogenetics and Pharmacogenomics; № 1 (2021); 33-37 ; Фармакогенетика и фармакогеномика; № 1 (2021); 33-37 ; 2686-8849 ; 2588-0527
Θεματικοί όροι: молекулярная диагностика, recurrent malignant tumors, molecular profiling, transcriptomics, RNA sequencing, Oncobox, experimental therapy, molecular diagnostics, рецидивирующие злокачественные опухоли, молекулярное профилирование, транскриптомика, РНК-секвенирование, Онкобокс, экспериментальная терапия
Περιγραφή αρχείου: application/pdf
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20Academic Journal
Συγγραφείς: S. E. Titov, G. A Katanyan, T. L. Poloz, L. G. Izmaylova, О. А. Zentsova, L. G. Dryaeva, V. V. Anishchenko, С. Е. Титов, Г. А. Катанян, Т. Л. Полоз, Л. Г. Измайлова, О. А. Зенцова, Л. Г. Дряева, В. В. Анищенко
Πηγή: Head and Neck Tumors (HNT); Том 10, № 4 (2020); 50-59 ; Опухоли головы и шеи; Том 10, № 4 (2020); 50-59 ; 2411-4634 ; 2222-1468 ; 10.17650/2222-1468-2020-0-4
Θεματικοί όροι: молекулярная диагностика, metastases, lymph nodes, microRNA, molecular diagnostics, метастазы, лимфатические узлы, микроРНК
Περιγραφή αρχείου: application/pdf
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