Our study was aimed to explore the clinical implication of chromosome microarray analysis (CMA) in genetically etiological diagnosis of children with congenital heart disease (CHD). A total of 104 children with CHD with or without multiple congenital anomalies (MCA) or intellectual disabilities/developmental delay (ID/DD) but normal karyotype were investigated using Affymetrix CytoScan HD array. Pathogenic copy number variations (PCNVs) were identified in 29 children (27.9%). The detection rates in children with simple CHD and complex CHD were 31.1% (19/61) and 23.2% (10/43), respectively. The detection rates of PCNVs were 17.9% (7/39), 20% (5/25), 63.2% (12/19) and 23.8% (5/21) in isolated CHD, CHD plus MCA, CHD plus ID/DD, CHD plus MCA and ID/DD, respectively. The PCNVs rate of CHD plus ID/DD was significantly higher than that of isolated CHD. Two genomic loci including 15q11.2 deletion and 1q43-q44 deletion were considered as CHD locus. The DVL1, SKI, STIM1, CTNNA3 and PLN were identified as candidate genes associated with CHD phenotypes. CMA can increase the diagnostic rate and improve the etiological diagnosis in children with CHD. We suggest CMA as a first-tier test in children with CHD, especially in children with CHD plus ID/DD.

With the advancement of medicine, more and more researchers have turned their attention to the study of fetal genetic diseases in recent years. However, it is still a challenge to detect genetic diseases in the fetus, especially in an area lacking access to healthcare. The existing research primarily focuses on using teenagers’ or adults’ face information to screen for genetic diseases, but there are no relevant directions on disease detection using fetal facial information. To fill the vacancy, we designed a two-stage ensemble learning model based on sonography, Fgds-EL, to identify genetic diseases with 932 images. Concretely speaking, we use aggregated information of facial regions to detect anomalies, such as the jaw, frontal bone, and nasal bone areas. Our experiments show that our model yields a sensitivity of 0.92 and a specificity of 0.97 in the test set, on par with the senior sonographer, and outperforming other popular deep learning algorithms. Moreover, our model has the potential to be an effective noninvasive screening tool for the early screening of genetic diseases in the fetus.

Elucidating the mechanism for the high metastasis capacity of Endometrial cancer (EC) is crucial to improve treatment outcomes of EC. We have recently reported that nicotinamide N-methyltransferase (NNMT) is overexpressed in EC, especially in EC, and predicts poor survival of chemotherapy patients. Here, we aimed to determine the function and mechanism of NNMT on metastasis of EC. Additionally, analysis of public datasets indicated that NNMT is involved in cholesterol metabolism. In vitro, NNMT overexpression promoted migration and invasion of EC by reducing cholesterol levels in the cytoplasm and cell membrane. Mechanistically, NNMT activated ABCA1 expression, leading to cholesterol efflux and membrane fluidity enhancement, thereby promoting EC's epithelial-mesenchymal transition (EMT). In vivo, the metastasis capacity of EC was weakened by targeting NNMT. Our findings suggest a new molecular mechanism involving NNMT in metastasis, poor survival of EC mediated by PP2A and affecting cholesterol metabolism.

Background: There are a few studies on the chromosomal aberration of Ultrasound soft markers (USMs). The aim of this study was to determine the detection rate of clinically significant chromosomal abnormalities (CSCA) in fetuses with different USMs. Methods: This study included fetuses with USMs who underwent invasive prenatal diagnosis for karyotype and/or chromosomal microarray (CMA) by categorizing into two groups: a single USM (SUSM) and multiple USMs (MUSMs). Results: Of the 358 cases with USMs, CSCA occurred in 3.09% (8/259) and 8.08% (8/99) of the SUSM and MUSM groups, respectively (P < 0.05). Of 16 cases identified with CSCA, theoretically 68.75% (11/16) could be detected by karyotype, while 31.25% (5/16) could be recognized only by CMA. Among CSCA cases, the most frequent USM was an absent or hypoplastic nasal bone (62.5%, 10/16). In cases with negative karyotypes and/or CMA, follow-up results were available in 307 cases, including 292 term deliveries, 6 preterm deliveries, 8 terminations of pregnancy due to USMs, and 1 still birth. Conclusion: MUSMs increased the risk of chromosomal abnormalities. An absent or hypoplastic nasal bone was the most clinically significant marker either alone or in combination with other USMs. Most of SUSM had a good prognosis.

A global survey has revealed that genetic syndromes affect approximately 8% of the population, but most genetic diagnoses are typically made after birth. Facial deformities are commonly associated with chromosomal disorders. Prenatal diagnosis through ultrasound imaging is vital for identifying abnormal fetal facial features. However, this approach faces challenges such as inconsistent diagnostic criteria and limited coverage. To address this gap, we have developed FGDS, a three-stage model that utilizes fetal ultrasound images to detect genetic disorders. Our model was trained on a dataset of 2554 images. Specifically, FGDS employs object detection technology to extract key regions and integrates disease information from each region through ensemble learning. Experimental results demonstrate that FGDS accurately recognizes the anatomical structure of the fetal face, achieving an average precision of 0.988 across all classes. In the internal test set, FGDS achieves a sensitivity of 0.753 and a specificity of 0.889. Moreover, in the external test set, FGDS outperforms mainstream deep learning models with a sensitivity of 0.768 and a specificity of 0.837. This study highlights the potential of our proposed three-stage ensemble learning model for screening fetal genetic disorders. It showcases the model’s ability to enhance detection rates in clinical practice and alleviate the burden on medical professionals.