Transcriptomics analysis and human preimplantation development

Artificial intelligence in reproductive medicine

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.

Acquisition of oocyte competence to develop as an embryo: integrated nuclear and cytoplasmic events

Infertility affects ~7% of couples of reproductive age with little change in incidence in the last two decades. ART, as well as other interventions, have made major strides in correcting this condition. However, and in spite of advancements in the field, the age of the female partner remains a main factor for a successful outcome. A better understanding of the final stages of gamete maturation yielding an egg that can sustain embryo development and a pregnancy to term remains a major area for improvement in the field. This review will summarize the major cellular and molecular events unfolding at the oocyte-to-embryo transition. We will provide an update on the most important processes/pathways currently understood as the basis of developmental competence, including the molecular processes involved in mRNA storage, its recruitment to the translational machinery, and its degradation. We will discuss the hypothesis that the translational programme of maternal mRNAs plays a key role in establishing developmental competence. These regulations are essential to assemble the machinery that is used to establish a totipotent zygote. This hypothesis further supports the view that embryogenesis begins during oogenesis. A better understanding of the events required for developmental competence will guide the development of novel strategies to monitor and improve the success rate of IVF. Using this information, it will be possible to develop new biomarkers that may be used to better predict oocyte quality and in selection of the best egg for IVF.