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  1. 1
    Academic Journal

    Source: FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology; Vol 18, No 3 (2025); 365-375 ; ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология; Vol 18, No 3 (2025); 365-375 ; 2070-4933 ; 2070-4909

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    Relation: https://www.pharmacoeconomics.ru/jour/article/view/1256/635; Schaumburg F., Berli C. Challenges and proposed solutions for optical reading on point-of-need testing systems. Front Sensors. 2023; 4. https://doi.org/10.3389/fsens.2023.1327240.; Visalini S., Kanagavalli R. A comprehensive survey of pneumonia diagnosis: image processing and deep learning advancements. In: 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). https://doi.org/10.1109/ICIMIA60377.2023.10426403.; Prabha S., Gupta S., Pandey S.P. Deep learning for medical image segmentation using convolutional neural networks. In: 2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC). https://doi.org/10.1109/ICOCWC60930.2024.10470841.; Das M., Sambodhi P.P., Khare A., Naik S.A. Challenges of medical text and image processing. In: 2022 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC). https://doi.org/10.1109/ASSIC55218.2022.10088402.; Choudhury S., Gowri R., Babu Sena P., Dinh-Thuan D. (Eds) Intelligent Communication, Control and Devices Proceedings of ICICCD 2020: Proceedings of ICICCD 2020. https://doi.org/10.1007/978-981-16-1510-8.; Ламоткин А.И., Корабельников Д.И., Ламоткин И.А. и др. Искусственный интеллект в здравоохранении и медицине: история ключевых событий, его значимость для врачей, уровень развития в разных странах. ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2024; 17 (2): 243–50. https://doi.org/10.17749/2070-4909/farmakoekonomika.2024.254.; Ламоткин А.И., Корабельников Д.И., Ламоткин И.А. и др. Точность предварительной диагностики злокачественных меланоцитарных опухолей кожи с помощью программы искусственного интеллекта Melanoma Check. Медицинский вестник Главного военного клинического госпиталя им. Н.Н. Бурденко. 2025; 1: 42–51. https://doi.org/10.53652/2782-1730-2025-6-1-42-51.; Zhou Z., Jin Y., Ye H., et al. Classification, detection, and segmentation performance of image-based AI in intracranial aneurysm: a systematic review. BMC Med Imaging. 2024; 24 (1): 164. https://doi.org/10.1186/s12880-024-01347-9.; Корабельников Д.И., Ламоткин А.И. Эффективность применения искусственного интеллекта в клинической медицине. ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2025; 18 (1): 114–24. https://doi.org/10.17749/2070-4909/farmakoekonomika.2025.287.; Alzubaidi L., Zhang J., Humaidi A.J., et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021; 8 (1): 53. https://doi.org/10.1186/s40537-021-00444-8.; LeCun Y., Bengio Y., Hinton G. Deep learning. Nature. 2015; 521: 436–44. https://doi.org/10.1038/nature14539.; Ламоткин А.И., Корабельников Д.И., Ламоткин И.А. Предварительная дифференциальная диагностика доброкачественных и злокачественных опухолей из эпидермальной ткани кожи с применением программы искусственного интеллекта «Derma Onko Check». Современные проблемы здравоохранения и медицинской статистики. 2025; 2: 223–42. https://doi.org/10.24412/2312-2935-2025-2-223-242.; Ламоткин А.И., Корабельников Д.И., Олисова О.Ю., Ламоткин И.А. Эффективность предварительной дифференциальной диагностики доброкачественных и злокачественных новообразований кожи с помощью программы искусственного интеллекта Derma Onko Check. ФАРМАКОЭКОНОМИКА. Современная фармакоэкономика и фармакоэпидемиология. 2025; 18 (2): 261–70. https://doi.org/10.17749/2070-4909/farmakoekonomika.2025.294.; Milletari F., Ahmadi S.A., Kroll C., et al. Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Computer Vision Image Underst. 2017; 164: 92–102. https://doi.org/10.48550/arXiv.1601.07014.; Yamada M., Saito Y., Imaoka H., et al. Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy. Sci Rep. 2019; 9 (1): 14465. https://doi.org/10.1038/s41598-019-50567-5.; Yadav D., Rathor S. Bone fracture detection and classification using deep learning approach. In: 2020 International Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC). https://doi.org/10.1109/PARC49193.2020.236611.; Rahman T., Chowdhury M.E., Khandakar A., et al. Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray. Appl Sci. 2020; 10 (9): 3233. https://doi.org/10.3390/app10093233.; Hamamoto R., Suvarna K., Yamada M., et al. Application of artificial intelligence technology in oncology: towards the establishment of precision medicine. Cancers. 2020; 12 (12): 3532. https://doi.org/10.3390/cancers12123532.; Asada K., Kobayashi K., Joutard S., et al. Uncovering prognosisrelated genes and pathways by multi-omics analysis in lung cancer. Biomolecules. 2020; 10: 524. https://doi.org/10.3390/biom10040524.; Kobayashi K., Bolatkan A., Shiina S., Hamamoto R. Fully-connected neural networks with reduced parameterization for predicting histological types of lung cancer from somatic mutations. Biomolecules. 2020; 10 (9): 1249. https://doi.org/10.3390/biom10091249.; Takahashi S., Asada K., Takasawa K., et al. Predicting deep learning based multi-omics parallel integration survival subtypes in lung cancer using reverse phase protein array data. Biomolecules. 2020; 10 (10): 1460. https://doi.org/10.3390/biom10101460.; Takahashi S., Sakaguchi Y., Kouno N., et al. Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review. J Med Syst. 2024; 48 (1): 84. https://doi.org/10.1007/s10916-024-02105-8.; Selvaraju R.R., Cogswell M., Das A., et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 Proceedings of the IEEE international conference on computer vision. https://doi.org/10.48550/arXiv.1610.02391.; Takahashi S., Takahashi M., Kinoshita M., et al. Fine-tuning approach for segmentation of gliomas in brain magnetic resonance images with a machine learning method to normalize image differences among facilities. Cancers. 2021; 13: 1415. https://doi.org/10.3390/cancers13061415.; Nam H., Lee H., Park J., et al. Reducing domain gap by reducing style bias. In: 2021 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.48550/arXiv.1910.11645.; Yan W., Wang Y., Gu S., et al. The domain shift problem of medical image segmentation and vendor-adaptation by Unet-GAN. In: Medical Image Computing and Computer Assisted Intervention– MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II. https://doi.org/10.48550/arXiv.1910.13681.; Barzekar H., Patel Y., Tong L., Yu Z. MultiNet with transformers: a model for cancer diagnosis using images. arXiv:230109007. https://doi.org/10.48550/arXiv.2301.09007.; Vaswani A., Shazeer N., Parmar N., et al. Attention is all you need. In: Advances in Neural Information Processing Systems 30 (NIPS 2017). https://doi.org/10.48550/arXiv.1706.03762.; Dosovitskiy A., Beyer L., Kolesnikov A., et al. An image is worth 16×16 words: transformers for image recognition at scale. arXiv:201011929. https://doi.org/10.48550/arXiv.2010.11929.; Liu Y., Wu Y.H., Sun G., et al. Vision transformers with hierarchical attention. arXiv:210603180. https://doi.org/10.48550/arXiv.2106.03180.; Han K., Wang Y., Chen H., et al. A survey on vision transformer. arXiv:2012.12556. https://doi.org/10.48550/arXiv.2012.12556.; Hatamizadeh A., Yin H., Heinrich G., et al. In: 2023 Global context vision transformers. arXiv:2206.09959. https://doi.org/10.48550/arXiv.2206.09959.; He K., Gan C., Li Z., et al. Transformers in medical image analysis. Intel Med. 2023; 3 (1): 59–78. https://doi.org/10.1016/j.imed.2022.07.002.; Stassin S., Corduant V., Mahmoudi S.A., Siebert X. Explainability and evaluation of vision transformers: an in-depth experimental study. Electronics. 2023; 13 (1): 175. https://doi.org/10.3390/electronics13010175.; Chetoui M., Akhloufi M.A. Explainable vision transformers and radiomics for COVID-19 detection in chest X-rays. J Clin Med. 2022; 11 (11): 3013. https://doi.org/10.3390/jcm11113013.; Dipto S.M., Reza M.T., Rahman M.N.J., et al. An XAI integrated identification system of white blood cell type using variants of vision transformer. In: Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). https://doi.org/10.1007/978-3-031-35308-6_26.; Cao Y.H., Yu H., Wu J. Training vision transformers with only 2040 images. arXiv:2201.10728. https://doi.org/10.48550/arXiv.2201.10728.; Lee S.H., Lee S., Song B.C. Vision transformer for small-size datasets. arXiv:211213492. https://doi.org/10.48550/arXiv.2112.13492.; Liu Y., Sangineto E., Bi W., et al. Efficient training of visual transformers with small datasets. arXiv:2106.03746. https://doi.org/10.48550/arXiv.2106.03746.; Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybernetics. 1980; 36 (4): 193–202. https://doi.org/10.1007/BF00344251.; LeCun Y., Bottou L., Bengio Y., Haffner P. Gradient-based learning applied to document recognition. Proceedings IEEE. 1998; 86 (11): 2278–324. https://doi.org/10.1109/5.726791.; Hamamoto R., Komatsu M., Takasawa K., et al. Epigenetics analysis and integrated analysis of multiomics data, including epigenetic data, using artificial intelligence in the era of precision medicine. Biomolecules. 2020; 10 (1): 62. https://doi.org/10.3390/biom10010062.; Himel G.M.S., Islam M.M., Al-Aff K.A., et al. Skin cancer segmentation and classification using vision transformer for automatic analysis in dermatoscopy-based noninvasive digital system. Int J Biomed Imaging. 2024; 2024: 3022192. https://doi.org/10.1155/2024/3022192.; https://www.pharmacoeconomics.ru/jour/article/view/1256

  2. 2
    Academic Journal

    Source: Obstetrics, Gynecology and Reproduction; Vol 19, No 1 (2025); 59-67 ; Акушерство, Гинекология и Репродукция; Vol 19, No 1 (2025); 59-67 ; 2500-3194 ; 2313-7347

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    Relation: https://www.gynecology.su/jour/article/view/2282/1293; Адамян Л.В., Попов А.А., Козаченко А.В. Беременность и доброкачественные опухоли яичников. Акушерство и гинекология: новости, мнения, обучение. 2015;1(4):58–62.; Cathcart A.M., Nezhat F.R., Emerson J. et al. Adnexal masses during pregnancy: diagnosis, treatment, and prognosis. Am J Obstet Gynecol. 2023;228(6):601–12. https://doi.org/10.1016/j.ajog.2022.11.1291.; Oprescu N., Ionescu C., Dragan I. et al. Adnexal masses in pregnancy: perinatal impact. Rom J Morphol Embryol. 2018;59(1):153–8.; Vara J., Manzour N., Chacón E. et al. Ovarian Adnexal Reporting Data System (O-RADS) for classifying adnexal masses: a systematic review and meta-analysis. Cancers. 2022;14(13):3151. https://doi.org/10.3390/cancers14133151.; Мартынов C.A. Хирургическая тактика при лечении беременных с опухолевидными образованиями и опухолями яичников: Автореф. дис… докт. мед. наук. М., 2015. 35 с.; Recent advances in diagnosis and management of ovarian cancer – First Edition. Eds. S.E. Jensen, S.A. Farghaly. USA: Springer, 2014. 20 p.; Peccatori F.A., Azim H.A, Orecchia R. et al. Cancer, pregnancy and fertility: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2013;24 Suppl 6:vi160–70. https://doi.org/10.1093/annonc/mdt199.; Pearl J.P, Price R.R., Tonkin A.E. et al. SAGES guidelines for the use of laparoscopy during pregnancy. Surg Endosc. 2017;31(10):3767–82. https://doi.org/10.1007/s00464-017-5637-3.; Rocha R.M., Barcelos I.D.E.S. Practical recommendations for the management of benign adnexal masses. Rev Bras Ginecol Obstet. 2020;42(9):569–76. https://doi.org/10.1055/s-0040-1714049.; Тихонова В.В., Саушкин А.С. Обзор возможностей применения Рамановской спектроскопии в процессно-аналитической технологии (РАТ). Вопросы биологической, медицинской и фармацевтической химии. 2020;(10):35–9. https://doi.org/10.29296/25877313-2020-10-05.; Александров М.Т., Зуев В.М., Кукушкин В.В. и др. Исследование спектральных характеристик органов малого таза у женщин и их клиническое значение. Онкогинекология. 2013;(3):61–7.; Приказ Минздрава России от 20.10.2020 N 1130н «Об утверждении Порядка оказания медицинской помощи по профилю "акушерство и гинекология"». М.: Министерство здравоохранения Российской Федерации, 2020. 829 с. Режим доступа: https://helper.gosuslugi.ru/netcat_files/8/9/prikaz_20102020_1130_akusherstvo.pdf. [Дата обращения: 25.03.2024].; Bonifacio A., Cervo S., Sergo V. Label-free surface-enhanced Raman spectroscopy of biofluids: fundamental aspects and diagnostic applications. Anal Bioanal Chem. 2015;407(27):8265–77. https://doi.org/10.1007/s00216-015-8697-z.; Зуев В.М., Лысцев Д.В., Артемьев Д.Н. и др. Оценка результатов поверхностно-усиленной рамановской спектроскопии у женщин с доброкачественными и злокачественными заболеваниями эндометрия. Архив акушерства и гинекологии имени В.Ф. Снегирева. 2023;10(4):299–310. https://doi.org/10.17816/2313-8726-2023-10-4-299-310.; Боев В.М., Борщук Е.Л., Екимов А.К., Бегун Д.Н. Руководство по обеспечению решения медико-биологических задач с применением программы Statistica 10. Оренбург: ОАО «ИПК «Южный Урал», 2014. 208 c.; Rubina S., Krishna C.M. Raman spectroscopy in cervical cancers: an update. J Cancer Res Ther. 2015;11(1):10–7. https://doi.org/10.4103/0973-1482.154065.; Parlatan U., Inanc M.T., Ozgor B. Yu. et al. Raman spectroscopy as a non-invasive diagnostic technique for endometriosis. Sci Rep. 2019;9(1):19795. https://doi.org/10.1038/s41598-019-56308-y.; Barnas E., Skret-Magierlo J., Skret A. et al. Simultaneous FTIR and Raman spectroscopy in endometrial atypical hyperplasia and cancer. Int J Mol Sci. 2020;21(14):4828. https://doi.org/10.3390/ijms21144828.; Feng S., Wang W., Tai I.T. et al. Label-free surface-enhanced Raman spectroscopy for detection of colorectal cancer and precursor lesions using blood plasma. Biomed Opt Express. 2015;6(9):3494–502. https://doi.org/10.1364/BOE.6.003494.; Song H., Peng J.-S., Dong-Sheng Y. et al. Serum metabolic profiling of human gastric cancer based on gas chromatography/mass spectrometry. Braz J Med Biol Res. 2012;45(1):78–85. https://doi.org/10.1590/S0100-879X2011007500158.; Atkins C.G., Buckley K., Blades M.W., Turner R.F.B. Raman spectroscopy of blood and blood components. Appl Spectrosc. 2017;71(5):767–93. https://doi.org/10.1177/0003702816686593.; Кендэл М. Ранговые корреляции. М.: Статистика, 1975. 214 c.; Lin D., Feng S., Pan J. et al. Colorectal cancer detection by gold nanoparticle based surface-enhanced Raman spectroscopy of blood serum and statistical analysis. OptЕxpress. 2011;19(14):13565–77. https://doi.org/10.1364/OE.19.013565.; Krishna C.M., Sockalingum G.D., Bhat R.A. et al. FTIR and Raman microspectroscopy of normal, benign, and malignant formalin-fixed ovarian tissues. Anal Bioanal Chem. 2007;387(5):1649–56. https://doi.org/10.1007/s00216-006-0827-1.; Paraskevaidi M., Ashton K.M., Stringfellow H.F. et al. Raman spectroscopic techniques to detect ovarian cancer biomarkers in blood plasma. Talanta. 2018;189:281–8. https://doi.org/10.1016/j.talanta.2018.06.084.; https://www.gynecology.su/jour/article/view/2282

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  9. 9
    Academic Journal

    Authors: Рожков, B.

    Source: Clinical anatomy and operative surgery; Vol. 5 No. 3 (2006); 16-19
    Клиническая анатомия и оперативная хирургия; Том 5 № 3 (2006); 16-19
    Клінічна анатомія та оперативна хірургія; Том 5 № 3 (2006); 16-19

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  10. 10
    Academic Journal

    Source: Russian Journal of Pediatric Hematology and Oncology; Том 8, № 1 (2021); 85-92 ; Российский журнал детской гематологии и онкологии (РЖДГиО); Том 8, № 1 (2021); 85-92 ; 2413-5496 ; 2311-1267

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    Relation: https://journal.nodgo.org/jour/article/view/693/636; Riccardi V.M. Neurofibromatosis: clinical heterogeneity. Curr Probl Cancer 1982;7(2):1–34. doi:10.1016/s0147-0272(82)80016-0.; Evans D.G., Howard E., Giblin C., Clancy T., Spencer H., Huson S.M., Lalloo F. Birth incidence and prevalence of tumor-prone syndromes: estimates from a UK family genetic register service. Am J Med Genet A 2010;152A(2):327–32. doi:10.1002/ajmg.a.33139.; Lammert M., Friedman J.M., Kluwe L., Mautner V.F. Prevalence of neurofi bromatosis 1 in German children at elementary school enrollment. Arch Dermatol 2005;141(1):71–4. doi:10.1001/archderm.141.1.71.; Kallionpää R.A., Uusitalo E., Leppävirta J., Pöyhönen M., Peltonen S., Peltonen J. Prevalence of neurofibromatosis type 1 in the Finnish population. Genet Med 2018;20(9):1082–6. doi:10.1038/gim.2017.215.; NIH. National Institutes of Health Consensus Development Conference Statement: neurofibromatosis. Bethesda, Md, USA, July 13–15, 1987. Neurofibromatosis 1988;1:172–8. PMID: 3152465.; Wallace M.R., Marchuk D.A., Andersen L.B., Letcher R., Odeh H.M., Saulino A.M., Fountain J.W., Brereton A., Nicholson J., Mitchell A.L. Type 1 neurofibromatosis gene: identification of a large transcript disrupted in three NF1 patients. Science 1990;249(4965):181–6. doi:10.1126/science.2134734.; Brems H., Chmara M., Sahbatou M., Denayer E., Taniguchi K., Kato R., Somers R., Messiaen L., De Schepper S., Fryns J.P., Cools J., Marynen P., Thomas G., Yoshimura A., Legius E. Germline loss-of-function mutations in SPRED1 cause a neurofi bromatosis 1-like phenotype. Nat Genet 2007;39(9):1120–6. doi:10.1038/ng2113.; Daston M.M., Scrable H., Nordlund M., Sturbaum A.K., Nissen L.M., Ratner N. The protein product of the neurofibromatosis type 1 gene is expressed at highest abundance in neurons, Schwann cells, and oligodendrocytes. Neuron 1992;8(3):415–28. doi:10.1016/0896-6273(92)90270-n.; Ferner R.E. Neurofi bromatosis 1 and neurofibromatosis 2: a twenty first century perspective. Lancet Neurol 2007;6(4):340–51. doi:10.1016/S1474-4422(07)70075-3.; Brems H., Beert E., de Ravel T., Legius E. Mechanisms in the pathogenesis of malignant tumours in neurofibromatosis type 1. Lancet Oncol 2009;10(5):508–15. doi:10.1016/S1470-2045(09)70033-6.; Uusitalo E., Rantanen M., Kallionpää R.A., Pöyhönen M., Leppävirta J., Ylä-Outinen H., Riccardi V.M., Pukkala E., Pitkäniemi J., Peltonen S., Peltonen J. Distinctive Cancer Associations in Patients With Neurofi bromatosis Type 1. J Clin Oncol 2016;34(17):1978–86. doi:10.1200/JCO.2015.65.3576.; Chikkannaiah P., Boovalli M.M., Nathiyal V., Venkataramappa S. Morphological spectrum of peripheral nerve sheath tumours: An insight into World Health Organization 2013 classification. J Neurosci Rural Pract 2016;7(3):346–54. doi:10.4103/0976-3147.182768.; Evans D.G., Baser M.E., McGaughran J., Sharif S., Howard E., Moran A. Malignant peripheral nerve sheath tumors in neurofibromatosis 1. J Med Genet 2002;39(5):311–4. doi:10.1136/jmg.39.5.311.; Farid M., Demicco E.G., Garcia R., Ahn L., Merola P.R., Cioffi A., Maki R.G. Malignant peripheral nerve sheath tumors. Oncologist 2014;19(2):193–201. doi:10.1634/theoncologist.2013-0328.; Kim A., Stewart D.R., Reilly K.M., Viskochil D., Miettinen M.M., Widemann B.C. Malignant Peripheral Nerve Sheath Tumours State of the Science: Leveraging Clinical and Biological Insights into Effective Therapies. Sarcoma 2017;2017:7429697. doi:10.1155/2017/7429697.; Seminog O.O., Goldacre M.J. Risk of benign tumors of nervous system, and of malignant neoplasms, in people with neurofibromatosis: population-based record-linkage study. Br J Cancer 2013;108(1):193–8. doi:10.1038/bjc.2012.535.; Ferner R.E., Huson S.M., Thomas N., Moss C., Willshaw H., Evans D.G., Upadhyaya M., Towers R., Gleeson M., Steiger C., Kirby A. 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Differentiation of peripheral nerve sheath tumors in patients with neurofibromatosis type 1 using diffusion-weighted magnetic resonance imaging. Neuro Oncol 2019;18;21(4):508–16. doi:10.1093/neuonc/noy199.; Hwang L., Okoye C.C., Patel R.B., Sahgal A., Foote M., Redmond K.J., Hofstetter C., Saigal R., Mossa-Basha M., Yuh W., Mayr N.A., Chao S.T., Chang E.L., Lo S.S. Stereotactic body radiotherapy for benign spinal tumors: Meningiomas, schwannomas, and neurofibromas. J Radiosurg SBRT 2019;6(3):167–77. PMID: 31998537.; Miao R., Wang H., Jacobson A., Lietz A.P., Choy E., Raskin K.A., Schwab J.H., Deshpande V., Nielsen G.P., DeLaney T.F., Cote G.M., Hornicek F.J., Chen Y.E. Radiation-induced and neurofibromatosisassociated malignant peripheral nerve sheath tumors (MPNST) have worse outcomes than sporadic MPNST. 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    Source: Збірник наукових праць Асоціації акушерів-гінекологів України; № 2(48) (2021); 5-12
    Scientific digest of association of obstetricians and gynecologists of Ukraine; No. 2(48) (2021); 5-12
    СБОРНИК НАУЧНЫХ ТРУДОВ Ассоциации акушеров-гинекологов Украины; № 2(48) (2021); 5-12

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    Source: Actual Problems of Pediatrics, Obstetrics and Gynecology; No. 1 (2020); 62-67 ; Актуальные вопросы педиатрии, акушерства и гинекологии; № 1 (2020); 62-67 ; Актуальні питання педіатрії, акушерства та гінекології; № 1 (2020); 62-67 ; 2415-301X ; 2411-4944 ; 10.11603/24116-4944.2020.1

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    Relation: Малоінвазивна хірургія у відновленні репродуктивної функції жінок / В. Б. Мартиненко, А. М. Громова, Л. А. Нестеренко [та ін.] // Вісник проблем біології і медицини. – 2019. – Вип. 2, т. 2 (151). – С. 196–199.; https://repository.pdmu.edu.ua/handle/123456789/14883

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