Lipid reprogramming induced by the NNMT-ABCA1 axis enhanced membrane fluidity to promote endometrial cancer progression.

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.

An Innovative Three-Stage Model for Prenatal Genetic Disorder Detection Based on Region-of-Interest in Fetal Ultrasound.

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.

Case Report: Early diagnosis of lethal multiple pterygium syndrome with micrognathia: Two novel mutations in the CHRND gene.

Lethal multiple pterygium syndrome (LMPS) is a rare disease with genetic and phenotypic heterogeneity and is inherited in an autosomal recessive (AR) pattern. Here, we have presented clinically significant results describing two novel mutations of CHRND gene: NM_000751.2: c.1006C>T p.(Arg336Ter) and NM_000751.2:c.973_975delGTG p.(Val325del), and measurement of the facial angle for determining micrognathia by prenatal diagnosis in the first trimester of pregnancy for a Lethal multiple pterygium syndrome case. In conclusion, this report complements the spectrum of genetic variants and phenotype of Lethal multiple pterygium syndrome and provides reliable recommendation for the counseling of future pregnancies in families with the disease.

The Two-Stage Ensemble Learning Model Based on Aggregated Facial Features in Screening for Fetal Genetic Diseases.

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.