Patients with intellectual disabilities have the reproductive rights. The clinical manifestations of intellectual disabilities are diverse and the causes are complex. When faced with the application of assisted reproductive technology (ART) for pregnancy in patients with intellectual disabilities, reproductive physicians still face many controversies and challenges on how to protect the reproductive rights and offspring rights of patients with intellectual disabilities while complying with Chinese laws, regulations, and ethical principles. This article starts with a case of a male and female patient with mild intellectual disabilities applying for assisted pregnancy by ART. Combining different causes of intellectual disabilities and corresponding reproductive strategies, it elaborates and analyzes the ethical conflicts and principle basis of the patient's application for ART, in order to help clinical physicians better balance the contradiction among patients' needs, offspring rights and social risks, and promote the further improvement of relevant ethical guidelines and laws and regulations in China.

Patients with intellectual disabilities have the reproductive rights. The clinical manifestations of intellectual disabilities are diverse and the causes are complex. When faced with the application of assisted reproductive technology (ART) for pregnancy in patients with intellectual disabilities, reproductive physicians still face many controversies and challenges on how to protect the reproductive rights and offspring rights of patients with intellectual disabilities while complying with Chinese laws, regulations, and ethical principles. This article starts with a case of a male and female patient with mild intellectual disabilities applying for assisted pregnancy by ART. Combining different causes of intellectual disabilities and corresponding reproductive strategies, it elaborates and analyzes the ethical conflicts and principle basis of the patient's application for ART, in order to help clinical physicians better balance the contradiction among patients' needs, offspring rights and social risks, and promote the further improvement of relevant ethical guidelines and laws and regulations in China.

Mature gametes include sperms originating from the male and oocytes originating from the female. The sperm and oocyte combine to form a fertilized oocyte and develop into a mature individual. At present, the research on obtaining gametes in vitro mainly focuses on obtaining mature gametes by inducing differentiation of embryonic stem cells (ESCs) or induced pluripotent stem cells (iPSCs) in vitro. In vitro, under specific conditions, pluripotent stem cells (PSCs) can differentiate into three germ layers tissues which include primordial germ cells. Therefore, by inducing differentiation and meiosis in vitro of PSCs, researchers can obtain mature gametes and offspring. Focusing on the production process of mature gametes, this paper summarized the research progress and difficulties to be overcome in the production of mature gametes in vitro.

Mature gametes include sperms originating from the male and oocytes originating from the female. The sperm and oocyte combine to form a fertilized oocyte and develop into a mature individual. At present, the research on obtaining gametes in vitro mainly focuses on obtaining mature gametes by inducing differentiation of embryonic stem cells (ESCs) or induced pluripotent stem cells (iPSCs) in vitro. In vitro, under specific conditions, pluripotent stem cells (PSCs) can differentiate into three germ layers tissues which include primordial germ cells. Therefore, by inducing differentiation and meiosis in vitro of PSCs, researchers can obtain mature gametes and offspring. Focusing on the production process of mature gametes, this paper summarized the research progress and difficulties to be overcome in the production of mature gametes in vitro.

Artificial intelligence is the new generation of productivity. Currently, there have been significant breakthroughs in the application of artificial intelligence in the field of medicine. Artificial intelligence models have been applied to support clinical decision. In the field of assisted reproductive medicine, artificial intelligence models have been established for predicting pregnant outcomes, automating ultrasound monitoring, monitoring ovulation induction processes, and automating semen analysis and embryo selection. This article reviews the research progress of artificial intelligence in reproductive clinicals, discusses future issues related to quality control and management of artificial intelligence in reproductive clinical practice, and provides recommendations for the further widespread application of artificial intelligence in assisted reproductive clinical activities.

Objective:To follow-up the previous phase Ⅲ clinical trial of recombinant human follicle-stimulating hormone (Follitrope ?), and to evaluate the cumulative pregnancy rate, the cumulative live birth rate, and the neonatal outcomes of subjects. Methods:The phase Ⅲ clinical study of Follitrope ? in China (CTR20150341/CTR20150341, May 15, 2015—June 27, 2016) was followed up until December 31, 2022. Patients were divided into Follitrope ? group and Gonal-F ? group. According to the age, patients were divided into three subgroups: 20-30 years old subgroup, 31-35 years old subgroup and 36-39 years old subgroup. Cumulative pregnancy rate, cumulative live birth rate, number of embryos transferred per cycle, live births per embryo transfer cycle, live births per oocyte retrieved, and neonatal characteristics were analyzed. Results:A total of 446 patients were included in the analysis, of which 336 (75.3%) were in the Follitrope ? group and 110 (24.7%) in the Gonal-F ? group, with a follow-up period of 6.5 years. There were no statistically significant differences between the Follitrope ? group and the Gonal-F ? group in terms of cumulative pregnancy rate and cumulative live birth rate (all P>0.05). Similar cumulative pregnancy rates and cumulative live birth rates were observed between the two groups within each age subgroup (all P>0.05). In the 36-39 years old subgroup, the Follitrope ? group showed a trend towards higher cumulative pregnancy rate [60.0% (12/20)] and cumulative live birth rate [55.0% (11/20)] compared with the Gonal-F ? group [28.6% (2/7), 14.3% (1/7)], however, none of the differences were statistically significant (all P>0.05). Twin pregnancy rates, live births per embryo, live birth per oocyte, newborn gender, birth weight, and birth defect rates were similar between the Follitrope ? group and the Gonal-F ? group without statistically significant differences (all P>0.05). Conclusion:The safety and effectiveness of Follitrope ? in controlled ovarian hyperstimulation are similar to those of Gonal-F ?. Compared with Gonal-F ?, there is a trend toward higher cumulative pregnancy rates and cumulative live birth rates in elderly patients with Follitrope ?, although there is no statistical difference.

Artificial intelligence is the new generation of productivity. Currently, there have been significant breakthroughs in the application of artificial intelligence in the field of medicine. Artificial intelligence models have been applied to support clinical decision. In the field of assisted reproductive medicine, artificial intelligence models have been established for predicting pregnant outcomes, automating ultrasound monitoring, monitoring ovulation induction processes, and automating semen analysis and embryo selection. This article reviews the research progress of artificial intelligence in reproductive clinicals, discusses future issues related to quality control and management of artificial intelligence in reproductive clinical practice, and provides recommendations for the further widespread application of artificial intelligence in assisted reproductive clinical activities.

Objective:To follow-up the previous phase Ⅲ clinical trial of recombinant human follicle-stimulating hormone (Follitrope ?), and to evaluate the cumulative pregnancy rate, the cumulative live birth rate, and the neonatal outcomes of subjects. Methods:The phase Ⅲ clinical study of Follitrope ? in China (CTR20150341/CTR20150341, May 15, 2015—June 27, 2016) was followed up until December 31, 2022. Patients were divided into Follitrope ? group and Gonal-F ? group. According to the age, patients were divided into three subgroups: 20-30 years old subgroup, 31-35 years old subgroup and 36-39 years old subgroup. Cumulative pregnancy rate, cumulative live birth rate, number of embryos transferred per cycle, live births per embryo transfer cycle, live births per oocyte retrieved, and neonatal characteristics were analyzed. Results:A total of 446 patients were included in the analysis, of which 336 (75.3%) were in the Follitrope ? group and 110 (24.7%) in the Gonal-F ? group, with a follow-up period of 6.5 years. There were no statistically significant differences between the Follitrope ? group and the Gonal-F ? group in terms of cumulative pregnancy rate and cumulative live birth rate (all P>0.05). Similar cumulative pregnancy rates and cumulative live birth rates were observed between the two groups within each age subgroup (all P>0.05). In the 36-39 years old subgroup, the Follitrope ? group showed a trend towards higher cumulative pregnancy rate [60.0% (12/20)] and cumulative live birth rate [55.0% (11/20)] compared with the Gonal-F ? group [28.6% (2/7), 14.3% (1/7)], however, none of the differences were statistically significant (all P>0.05). Twin pregnancy rates, live births per embryo, live birth per oocyte, newborn gender, birth weight, and birth defect rates were similar between the Follitrope ? group and the Gonal-F ? group without statistically significant differences (all P>0.05). Conclusion:The safety and effectiveness of Follitrope ? in controlled ovarian hyperstimulation are similar to those of Gonal-F ?. Compared with Gonal-F ?, there is a trend toward higher cumulative pregnancy rates and cumulative live birth rates in elderly patients with Follitrope ?, although there is no statistical difference.

After implantation, complex and highly specialized molecular events render functionally distinct organ formation, whereas how the epigenome shapes organ-specific development remains to be fully elucidated. Here, nano-hmC-Seal, RNA bisulfite sequencing (RNA-BisSeq), and RNA sequencing (RNA-Seq) were performed, and the first multilayer landscapes of DNA 5-hydroxymethylcytosine (5hmC) and RNA 5-methylcytosine (m⁵C) epigenomes were obtained in the heart, kidney, liver, and lung of the human foetuses at 13-28 weeks with 123 samples in total. We identified 70,091 and 503 organ- and stage-specific differentially hydroxymethylated regions (DhMRs) and m⁵C-modified mRNAs, respectively. The key transcription factors (TFs), T-box transcription factor 20 (TBX20), paired box 8 (PAX8), krueppel-like factor 1 (KLF1), transcription factor 21 (TCF21), and CCAAT enhancer binding protein beta (CEBPB), specifically contribute to the formation of distinct organs at different stages. Additionally, 5hmC-enriched Alu elements may participate in the regulation of expression of TF-targeted genes. Our integrated studies reveal a putative essential link between DNA modification and RNA methylation, and illustrate the epigenetic maps during human foetal organogenesis, which provide a foundation for for an in-depth understanding of the epigenetic mechanisms underlying early development and birth defects.
Humans ; DNA Methylation ; RNA ; Epigenesis, Genetic ; DNA/genetics* ; Basic Helix-Loop-Helix Transcription Factors/genetics*

Objective:To explore whether the chromosomal polymorphism variation is the new indication of preimplantation genetic testing for aneuploidies (PGT-A).Methods:Clinical data of the patients who received PGT-A frozen-thawed embryo transfer and in vitro fertilization-embryo transfer (IVF-ET) fresh embryo transfer from the Center for Reproductive Medicine of the First Affiliated Hospital of Zhengzhou University from January 1, 2012 to December 31, 2021 were analyzed in a retrospective cohort study. According to the karyotype of the couples, PGT-A cycles were divided into female, male and couple chromosome polymorphism variation groups, and couples with normal chromosomes at the same time were included in control group. IVF cycles were divided into female and male chromosome polymorphism variation groups, and the couples with normal chromosomes at the same time were included in control group. The laboratory results and pregnancy outcomes were compared among the groups. Results:In PGT-A cycles, there were no significant differences in chromosome aneuploidy rate, clinical pregnancy rate after frozen-thawed embryo transfer, early abortion rate and live birth rate among the four groups (all P>0.05). In IVF fresh embryo transfer cycles, there were no significant differences in clinical pregnancy rate, early abortion rate and live birth rate among the three groups (all P>0.05). Among the early abortion patients after IVF cycles, the abnormal rates of copy number variation (CNV) in the abortion tissues of the female chromosome polymorphism variation group, the male chromosome polymorphism variation group and the normal control group were 26.67% (4/15), 57.89% (11/19) and 64.59% (363/562), respectively, with a statistical difference ( P=0.010). Conclusion:Chromosomal polymorphism of couples does not affect the aneuploidy rate of embryos, and does not affect the pregnancy outcomes of PGT-A frozen-thawed embryo transfer cycle and IVF fresh embryo transfer cycle, and does not increase the chromosome abnormality rate of abortion tissues. At present, there is no clear evidence to support that chromosomal polymorphisms as a new indication of PGT-A.