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Öğe Basics of artificial intelligence for assisted reproductive technologies(IGI Global, 2024) Gökhan, Aylin; Kilic, Kubilay Dogan; Çavuşoğlu, Türker; Uyanikgil, YiğitIn the field of assisted reproductive technologies (ART), each cycle brings high cost and long-term clinical and laboratory studies. In order to eliminate the negative effects of this process on families, the necessity of standardized ART protocols that can be applied to each individual with low cost and fast results is essential. Although artificial intelligence has the potential to respond strongly to this need, the integration of artificial intelligence into ART is slower compared to other branches of medicine. Increasing understanding of artificial intelligence by researchers will accelerate this integration. In order to understand and be able to use artificial intelligence, this chapter will first discuss the conceptual confusion in artificial neural networks, deep learning, machine learning, and artificial intelligence. Finally, gaps will be filled with artificial intelligence-related application areas and examples in ART. © 2024, IGI Global. All rights reserved.Öğe Circulating mir-200c and mir-33a may be used as biomarkers for predicting high fructose corn syrup-induced fatty liver and vitamin D supplementation-related liver changes(Scientific And Technological Research Council Turkey, 2022) Tanoğlu, Alpaslan; Çağıltay, Eylem; Tanoğlu, Esra Güzel; Gökhan, Aylin; Şirin, Cansın; Çavusoğlu, Türker; Yeşilbaş, SonerBackground/aim: Nonalcoholic fatty liver is one of the most common forms of liver disease and role of microRNAs (miRNAs) on this illness is currently unclear. It was aimed to evaluate the predictive role of circulating miR-33a and mir-200c on high fructose corn syrup (HFCS)-induced fatty liver and vitamin D-3 supplementation-related hepatic changes. Materials and methods: Twenty-four rats were randomized into three groups: sham (n = 8), experimental fatty liver group (n = 8), and fatty liver + vitamin D-3 supplementation group (n = 8). In the treatment group, 10 mu g/kg/week of vitamin D-3 was given by orogastric tube weekly for 4 weeks in addition to a high fructose diet. Serum AST, ALT, TNF-alpha, and MDA levels were tested. Liver tissue samples were examined using hematoxylin/eosin, periodic acid-Schif (PAS) and Masson's Trichrome staining. Circulating miR-33a and mir-200c were quantified by qRT-PCR method. Moreover, in silico analyses were accomplished. Results: In the vitamin D-3 group, results of biochemical parameters were significantly different than those of the fatty liver group (p < 0.001). Moreover, significant differences in serum levels of circulating miR-33a and mir-200c were identified among all groups (p < 0.05). Finally, more favorable histopathological changes were noticed in the vitamin D-3 supplementation group. The expressions of Ki-67 were also considerably reduced in the vitamin D-3 group. According to KEGG pathway analysis, mir-33a and mir-200c were found to play a common role in the Hippo signaling pathway, lysine degradation, and protein processing. Conclusion: Our current experimental fatty liver study showed that, in a specified dose, vitamin D-3 supplementation could alleviate adverse undesirable hepatic effects of HFCS via miR-33a and mir-200c.Öğe Classification of Bovine Cumulus-Oocyte Complexes with Convolutional Neural Networks(2023) Çavuşoğlu, Türker; Gökhan, Aylin; Şirin, Cansın; Tomruk, Canberk; Kılıç, Kubilay Doğan; Ölmez, Emre; Er, OrhanAim: Determining oocyte quality is crucial for successful fertilization and embryonic development, and there is a serious correlation between live birth rates and oocyte quality. Parameters such as the regular/irregular formation of the cumulus cell layer around the oocyte, the number of cumulus cell layers and the homogeneity of the appearance of the ooplasm are used to determine the quality of the oocytes to be used in in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) methods. Material and Methods: In this study, classification processes have been carried out using convolutional neural networks (CNN), a deep learning method, on the images of the cumulus-oocyte complex selected based on the theoretical knowledge and professional experience of embryologists. A convolutional neural network with a depth of 4 is used. In each depth level, one convolution, one ReLU and one max-pooling layer are included. The designed network architecture is trained using the Adam optimization algorithm. The cumulus-oocyte complexes (n=400) used in the study were obtained by using the oocyte aspiration method from the ovaries of the bovine slaughtered at the slaughterhouse. Results: The CNN-based classification model developed in this study showed promising results in classifying three-class image data in terms of cumulus-oocyte complex classification. The classification model achieved high accuracy, precision, and sensitivity values on the test dataset. Conclusion: Continuous research and optimization of the model can further improve its performance and benefit the field of cumulus-oocyte complexes classification and oocyte quality assessment.Öğe The transformative role of artificial intelligence in advancing bovine reproductive biology(IGI Global, 2024) Kilic, Kubilay Dogan; Gökhan, Aylin; Çavuşoğlu, TürkerThe integration of deep learning technologies into bovine reproductive biology heralds a significant paradigm shift that improves our approach to cattle breeding and reproductive health management. This chapter examines the versatile applications of deep learning, including image analysis, genomic information, and behavioral predictions, to advance the understanding and optimization of cattle reproduction. Adoption of these technologies facilitates a more detailed understanding of the genetic and physiological determinants of fertility and disease, contributing to the development of targeted breeding programs and improved herd health strategies. Despite the promise of deep learning to revolutionize greater efficiency and sustainability in livestock production, challenges around data privacy, security, and model interpretability remain. These issues require a concerted effort to develop ethical frameworks and transparent algorithms to ensure the responsible deployment of deep learning tools. This review highlights the transformative potential of deep learning in bovine reproductive biology and advocates for continued interdisciplinary collaboration to address the complexities of applying advanced computational techniques in agriculture. From this perspective, the future of livestock production is envisioned as a place where technological innovations and animal welfare converge, marking a new era in precision agriculture. © 2024, IGI Global. All rights reserved.
