Xuelian Du^1,#, Hao Shi^2,#, Haiyan Liu¹, Linghua Zhou¹, Anqun Xie¹, Jufang Guo^1,*

Oncologie, Vol.24, No.4, pp. 769-787, 2022, DOI:10.32604/oncologie.2022.026248

Abstract Background: Gestational diabetes mellitus (GDM) affects the health of numerous women around the world. A recent study has shown that GDM is associated with an increased incidence of cancer. In this study, we aimed to explore the possible shared mechanisms and potential common therapeutic targets between GDM and cancer. Methods: The limma package was used to identify differentially expressed genes (DEGs) in GDM. The Cytoscape plugin cytoHubba was used to screen hub genes. The CIBERSORT algorithm was used to explore the correlation between hub genes and immunity. Cox regression analysis was used to assess the relationship between protein tyrosine kinase… More >

YUEHUA SHI^1,#, QIUYING YAN^2,#, QIN LI³, WEI QIAN¹, DONGYAN QIAO¹, DONGDONG SUN², HONG YU^1,*

BIOCELL, Vol.47, No.1, pp. 165-173, 2023, DOI:10.32604/biocell.2022.023043

Abstract The placenta plays an important role in nutrient transport to maintain the growth and development of the embryo. Gestational diabetes mellitus (GDM), the most common complication during pregnancy, highly affects placental function in late gestation. Advanced glycation end-products (AGEs), a complex and heterogeneous group of compounds engaged by the receptor for AGEs (RAGE), are closely associated with diabetes-related complications. In this study, AGEs induced a decrease in the expression of tight junction (TJ) proteins in BeWo cells and increased the paracellular permeability of trophoblast cells by regulating RAGE/NF-κB. Sprague-Dawley (SD) rats injected with 100 mg/kg AGEs-rat serum albumin (RSA) via… More >

YANQIU LI^#,*, CHENJUN HAO^#, WEIYI CHEN, QINGJU MENG

BIOCELL, Vol.46, No.6, pp. 1565-1573, 2022, DOI:10.32604/biocell.2022.018347

Abstract This work aimed to clarify the interaction between the fetus and pregnant patients with gestational diabetes mellitus (GDM), the lipid metabolomics analysis of the fetal umbilical cord blood of GDM patients and normal pregnant women were performed to screen out the specific lipid metabolites for pathogenesis of GDM. From 2019–2020, 21 patients with GDM and 22 normal pregnant women were enrolled in Hexian Memorial Hospital, Panyu District, Guangzhou. The general information such as weight, height, age, body mass index (BMI) before pregnancy were analyzed. Non-targeted metabonomic detection and analysis were performed in umbilical cord plasma using LC-MS method. The age,… More >

A. Sumathi^*, S. Meganathan

Computer Systems Science and Engineering, Vol.40, No.1, pp. 313-325, 2022, DOI:10.32604/csse.2022.017484

Abstract Gestational Diabetes Mellitus (GDM) is an illness that represents a certain degree of glucose intolerance with onset or first recognition during pregnancy. In the past few decades, numerous investigations were conducted upon early identification of GDM. Machine Learning (ML) methods are found to be efficient prediction techniques with significant advantage over statistical models. In this view, the current research paper presents an ensemble of ML-based GDM prediction and classification models. The presented model involves three steps such as preprocessing, classification, and ensemble voting process. At first, the input medical data is preprocessed in four levels namely, format conversion, class labeling,… More >

A. Sumathi^1,*, S. Meganathan¹, B. Vijila Ravisankar²

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3109-3126, 2021, DOI:10.32604/cmc.2021.017612

Abstract Gestational Diabetes Mellitus (GDM) is one of the commonly occurring diseases among women during pregnancy. Oral Glucose Tolerance Test (OGTT) is followed universally in the diagnosis of GDM diagnosis at early pregnancy which is costly and ineffective. So, there is a need to design an effective and automated GDM diagnosis and classification model. The recent developments in the field of Deep Learning (DL) are useful in diagnosing different diseases. In this view, the current research article presents a new outlier detection with deep-stacked Autoencoder (OD-DSAE) model for GDM diagnosis and classification. The goal of the proposed OD-DSAE model is to… More >