Background::The goal of the assisted reproductive treatment is to transfer one euploid blastocyst and to help infertile women giving birth one healthy neonate. Some algorithms have been used to assess the ploidy status of embryos derived from couples with normal chromosome, who subjected to preimplantation genetic testing for aneuploidy (PGT-A) treatment. However, it is currently unknown whether artificial intelligence model can be used to assess the euploidy status of blastocyst derived from populations with chromosomal rearrangement.Methods::From February 2020 to May 2021, we collected the whole raw time-lapse videos at multiple focal planes from in vitro cultured embryos, the clinical information of couples, and the comprehensive chromosome screening results of those blastocysts that had received PGT treatment. Initially, we developed a novel deep learning model called the Attentive Multi-Focus Selection Network (AMSNet) to analyze time-lapse videos in real time and predict blastocyst formation. Building upon AMSNet, we integrated additional clinically predictive variables and created a second deep learning model, the Attentive Multi-Focus Video and Clinical Information Fusion Network (AMCFNet), to assess the euploidy status of embryos. The efficacy of the AMCFNet was further tested in embryos with parental chromosomal rearrangements. The receiver operating characteristic curve (ROC) was used to evaluate the superiority of the model. Results::A total of 4112 embryos with complete time-lapse videos were enrolled for the blastocyst formation prediction task, and 1422 qualified blastocysts received PGT-A ( n = 589) or PGT for chromosomal structural rearrangement (PGT-SR, n = 833) were enrolled for the euploidy assessment task in this study. The AMSNet model using seven focal raw time-lapse videos has the best real-time accuracy. The real-time accuracy for AMSNet to predict blastocyst formation reached above 70% on the day 2 of embryo culture, and then increased to 80% on the day 4 of embryo culture. Combing with 4 clinical features of couples, the AUC of AMCFNet with 7 focal points increased to 0.729 in blastocysts derived from couples with chromosomal rearrangement. Conclusion::Integrating seven focal raw time-lapse images of embryos and parental clinical information, AMCFNet model have the capability of assessing euploidy status in blastocysts derived from couples with chromosomal rearrangement.

Objective This study aims to discuss the research hotspot and development trend in the field of polycystic ovary syndrome(PCOS)through bibliometric statistics and visual analysis of long noncoding RNA(lncRNA)related studies.Methods Utilizing the Web of Science core database as the literature data source,we searched for PCOS lncRNA-related literature from 2015 to 2023.CiteSpace software was used to conduct a visual analysis,including the annual distribution,citation trends,countries,institutions,funding sources and key words,as well as co-occurrence and cluster analysis of key words.Results The visual analysis of 108 PCOS lncRNA literature revealed that China was the country with the highest number of publications.The first contributing institution was the Shandong University.The national natural science fund of China gave the biggest funding.The keyword cluster analysis suggested that PCOS lncRNA signal pathway regulation,related receptor activators,and the expression of regulatory factors were the research hotspots in ovary syndrome lncRNA research.Conclusion LncRNA related regulatory factors,bioinformatics analysis,and gene transcription in PCOS are new targetsfor PCOS treatment,providing valuable insights for clinical therapy and new strategies for the development of PCOS-related pharmaceuticals.