The effect of gestational diabetes on identification of key genes and pathways in human umbilical vein endothelial cell by integrated bioinformatics analysis

Homeobox genes in the human placenta: Twists and turns on the path to find novel targets

Fetal growth restriction (FGR) is a clinically important human pregnancy disorder that is thought to originate early in pregnancy and while its aetiology is not well understood, the disorder is associated with placental insufficiency. Currently treatment for FGR is limited by increased surveillance using ultrasound monitoring and premature delivery, or corticosteroid medication in the third trimester to prolong pregnancy. There is a pressing need for novel strategies to detect and treat FGR at its early stage. Homeobox genes are well established as master regulators of early embryonic development and increasing evidence suggests they are also important in regulating early placental development. Most important is that specific homeobox genes are abnormally expressed in human FGR. This review focusses on identifying the molecular pathways controlled by homeobox genes in the normal and FGR-affected placenta. This information will begin to address the knowledge gap in the molecular aetiology of FGR and lay the foundation for identifying potential diagnostic and therapeutic targets.

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.

Extracellular Vesicles—New Players in Cell-To-Cell Communication in Gestational Diabetes Mellitus

Research in extracellular vesicles (EVs) has contributed to a better understanding of physiological and pathophysiological conditions. Biologically active cargo, such as miRNAs and proteins, is critical in many different biological processes. In this context, pregnancy is one of the most complex physiological states, which needs a highly regulated system to ensure the correct nourishment and development of the baby. However, pre-existent maternal conditions and habits can modify the EV-cargo and dysregulate the system leading to pregnancy complications, with gestational diabetes mellitus (GDM) being one of the most reported and influential. Calcification and aging of muscle cells, protein modification in vascular control or variations in the levels of specific miRNAs are some of the changes observed or led by EV populations as adaptation to GDM. Interestingly, insulin sensitivity and glucose tolerance changes are not fully understood to date. Nevertheless, the increasing evidence generated has opened new possibilities in the biomarker discovery field but also in the understanding of cellular mechanisms modified and involved in GDM. This brief review aims to discuss some of the findings in GDM and models used for that purpose and their potential roles in the metabolic alterations during pregnancy, with a focus on insulin sensitivity and glucose tolerance.