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Polycystic ovary syndrome (PCOS) is a common reproductive disorder that has many characteristic features including hyperandrogenemia, insulin resistance and obesity, which may have significant implications for pregnancy outcomes and long-term health of women. Daughters born to PCOS mothers constitute a high-risk group for metabolic and reproductive derangements, but no report has described potential growth and metabolic risk factors for such female offspring. Hence, we used a mouse model of dehydroepiandrosterone (DHEA)-induced PCOS to study the mechanisms underlying the pathology of PCOS by investigating the growth, developmental characteristics, metabolic indexes and expression profiles of key genes of offspring born to the models. We found that the average litter size was significantly smaller in the DHEA group, and female offspring had sustained higher body weight, increased body fat and triglyceride content in serum and liver; they also exhibited decreased energy expenditure, oxygen consumption and impaired glucose tolerance. Genes related to glucolipid metabolism such as Pparγ, Acot1/2, Fgf21, Pdk4 and Inhbb were upregulated in the liver of the offspring in DHEA group compared with those in controls, whereas Cyp17a1 expression was significantly decreased. However, the expression of these genes was not detected in male offspring. Our results show that female offspring in DHEA group exhibit perturbed growth and glucolipid metabolism that were not observed in male offspring.

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.