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Wei Shen, Lan Li, Zhaodai Bai, Qingjie Pan, Mingxiao Ding and Hongkui Deng

Little is known about the mechanisms underlying primordial follicular formation and the acquisition of competence to resume meiosis by growing oocytes. It is therefore important to establish an in vitro experimental model that allows one to study such mechanisms. Mouse follicular development has been studied in vitro over the past several years; however, no evidence has been presented showing that mature oocytes can be obtained from mouse fetal germ cells prior to the formation of primordial follicles. In this study, a method has been established to obtain mature oocytes from the mouse fetal germ cells at 16.5 days postcoitum (dpc). From the initiation of primordial follicular formation to the growth of early secondary follicles, ovarian tissues from 16.5 dpc fetal mice were cultured in vitro for 14 days. Subsequently, 678 intact secondary follicles were isolated from 182 mouse fetal ovaries and cultured for 12 days. A total of 141 oocytes inside antral follicles were matured in vitro, and 102 oocytes underwent germinal vesicle breakdown. We found that 97 oocytes were fertilized and 15 embryos were able to form morula–blastocysts. We also analyzed various genomic imprinting markers and showed that the erasure of genomic imprinting markers in the parental generation was also imposed on the oocytes that developed from fetal germ cells. Our results demonstrate that mouse fetal germ cells are able to form primordial follicles with ovarian cells, and that oocytes within the growing follicles are able to mature normally in vitro.

Open access

Renjie Wang, Wei Pan, Lei Jin, Yuehan Li, Yudi Geng, Chun Gao, Gang Chen, Hui Wang, Ding Ma and Shujie Liao

Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine learning techniques have been used to improve the performance of assisted reproductive technology (ART). Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. In this review, we discuss the basic aspects of AI and machine learning, and we address the applications, potential limitations and challenges of AI. We also highlight the prospects and future directions in the context of reproductive medicine.