Fetal development is a critical phase in prenatal care, demanding the timely identification of anomalies in ultrasound images to safeguard the well-being of both the unborn child and the mother. Medical imaging has played a pivotal role in detecting fetal abnormalities and malformations. However, despite significant advances in ultrasound technology, the accurate identification of irregularities in prenatal images continues to pose considerable challenges, often necessitating substantial time and expertise from medical professionals. In this review, we go through recent developments in machine learning (ML) methods applied to fetal ultrasound images. Specifically, we focus on a range of ML algorithms employed in the context of fetal ultrasound, encompassing tasks such as image classification, object recognition, and segmentation. We highlight how these innovative approaches can enhance ultrasound-based fetal anomaly detection and provide insights for future research and clinical implementations. Furthermore, we emphasize the need for further research in this domain where future investigations can contribute to more effective ultrasound-based fetal anomaly detection.

Lysosomal storage disorders (LSDs) are a group of rare genetic conditions characterized by the impaired function of enzymes responsible for lipid digestion. Among these LSDs, Tay-Sachs disease (TSD) and Niemann-Pick type C (NPC) may share a common gene expression profile. In this study, we conducted a bioinformatics analysis to explore the gene expression profile overlap between TSD and NPC. Analyses were performed on RNA-seq datasets for both TSD and NPC from the Gene Expression Omnibus (GEO) database. Datasets were subjected to differential gene expression analysis utilizing the DESeq2 package in the R programming language. A total of 147 differentially expressed genes (DEG) were found to be shared between the TSD and NPC datasets. Enrichment analysis was then performed on the DEGs. We found that the common DEGs are predominantly associated with processes such as cell adhesion mediated by integrin, cell-substrate adhesion, and urogenital system development. Furthermore, construction of protein-protein interaction (PPI) networks using the Cytoscape software led to the identification of four hub genes: APOE, CD44, SNCA, and ITGB5. Those hub genes not only can unravel the pathogenesis of related neurologic diseases with common impaired pathways, but also may pave the way towards targeted gene therapy of LSDs.In addition, they serve as the potential biomarkers for related neurodegenerative diseases warranting further investigations.