Triplet pregnancy with hydatidiform mole: A report of two cases with literature review.

AIM: We present two cases of triplet pregnancy with complete hydatidiform mole (CHM) in contrasting outcomes and discuss the complications of mothers and outcomes of fetuses through a literature review, raising an important issue on the management of this special pregnancy. METHODS: We share our manage experience for two cases of triplet pregnancy with CHM and retrospectively analyze 18 similar pregnancies reported previously with different pregnancy outcomes. RESULTS: In our cases, one case receiving Clomiphene ovulation induction delivered two live fetuses by cesarean section at 30+ weeks without GTN (gestational trophoblastic neoplasia), unfortunately, the other case following ICSI-ET terminated the pregnancy in the setting of complications at 18+ weeks without GTN. No severe complications were detected during pregnancy and no pGTD was developed after delivery in neither of the pregnant. CONCLUSIONS: Co-existing complete hydatidiform mole in multiple pregnancies may become more common owing to the spreading use of ART. The decision for whether continue pregnancy depending on the personalized conditions including the complications of the pregnancy, the outcomes of the fetuses, the gestational age for delivery, and the potential progression of persistent gestational trophoblastic disease (pGTD). Furthermore, close monitor is necessary for the pregnant with triplet pregnancy with CHM who want to continue pregnancy.

Heterogeneous graph framework for predicting the association between lncRNA and disease and case on uterine fibroid.

Long non-coding RNAs (lncRNAs) play crucial regulatory roles in various cellular processes, including gene expression, chromatin remodeling, and protein localization. Dysregulation of lncRNAs has been linked to several diseases, making it essential to understand their functions in disease mechanisms and therapeutic strategies. However, traditional experimental methods for studying lncRNA function are time-consuming, expensive, and offer limited insights. In recent years, computational methods have emerged as valuable tools for predicting lncRNA functions and their associations with diseases. However, many existing methods focus on constructing separate networks for lncRNA and disease similarity, resulting in information loss and insufficient processing capacity for isolated nodes. To address this, we developed 'RGLD' by combining Random Walk with restarting (RWR), Graph Neural Network (GNN), and Graph Attention Networks (GAT) to predict lncRNA-disease associations in a heterogeneous network. RGLD achieved an impressive AUC of 0.88, outperforming other methods. It can also predict novel associations between lncRNAs and diseases. RGLD identified HOTAIR, MEG3, and PVT1 as lncRNAs associated with uterine fibroids. Biological experiments directly or indirectly verified the involvement of these three lncRNAs in uterine fibroids, validating the accuracy of RGLD's predictions. Furthermore, we extensively discussed the functions of the target genes regulated by these lncRNAs in uterine fibroids, providing evidence for their role in the development and progression of the disease.