Under hypoxic conditions, MSCs affect the expression and methylation level of survival-related genes in ALL independent of apoptosis pathways in vitro.

Mesenchymal stem cells (MSCs) are one of the most prominent cells in the bone marrow. MSCs can affect acute lymphocytic leukemia (ALL) cells under hypoxic conditions. With this aim, we used MOLT-4 cells as simulators of ALL cells cocultured with bone marrow mesenchymal stem cells (BMMSCs) under hypoxic conditions in vitro. Then, mRNA and protein expression of the MAT2A, PDK1, and HK2 genes were evaluated by real-time PCR and Western blot which was also followed by apoptosis measurement by a flow-cytometric method. Next, the methylation status of the target genes was investigated by MS-qPCR. Additionally, candidate gene expressions were examined after treatment with rapamycin using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. We found that the mRNA expression of the candidate genes was augmented under the hypoxic condition in which MAT2A was upregulated in cocultured cells compared to MOLT-4, while HK2 and PDK1 were downregulated. Moreover, we found an association between gene expression and promoter methylation levels of target genes. Besides, expressions of the candidate genes were decreased, while their methylation levels were promoted following treatment with rapamycin. Our results suggest an important role for the BMMSC in regulating the methylation of genes involved in cell survival in hypoxia conditions; however, we found no evidence to prove the MSCs' effect on directing malignant lymphoblastic cells to apoptosis.

Cloning and Embryo Splitting in Mammalians: Brief History, Methods, and Achievements.

Embryo splitting is one of the newest developed methods in reproductive biotechnology. In this method, after splitting embryos in 2-, 4-, and even 8-cell stages, every single blastomere can be developed separately, but the embryos are genetically identical. Embryo splitting, as an approach in reproductive cloning, is extensively employed in reproductive medicine studies, such as investigating human diseases, treating sterility, embryo donation, and gene therapy. In the present study, cloning in mammalians and cloning approaches are briefly reviewed. In addition, embryo splitting and the methods commonly used in embryo splitting and recent achievements in this field, as well as the applications of embryo splitting into livestock species, primate animals, and humans, are outlined. Finally, a perspective of embryo splitting is provided as the conclusion.

Numerical investigation of nanofluid flow using CFD and fuzzy-based particle swarm optimization.

This paper is focused on the application and performance of artificial intelligence in the numerical modeling of nanofluid flows. Suspension of metallic nanoparticles in the fluids has shown potential in heat transfer enhancement of the based fluids. There are many numerical studies for the investigation of thermal and hydrodynamic characteristics of nanofluids. However, the optimization of the computational fluid dynamics (CFD) modeling by an artificial intelligence (AI) algorithm is not considered in any study. The CFD is a powerful technique from an accuracy point of view. However, it could be time and cost-consuming, especially in large-scale and complicated problems. It is expected that the machine learning technique of the AI algorithms could improve such CFD drawbacks by patterning the CFD data. Once the AI finds the CFD pattern intelligently, there is no need for CFD calculations. The particle swarm optimization-based fuzzy inference system (PSOFIS) is considered in this study to predict the velocity profile of Al2O3/water turbulent flow in a heated pipe. One of the challenging problems in CFD modeling is the lost data for a specific boundary condition. For example, the CFD data are available for wall heat fluxes of 75, 85, 105, and 125 w/m2, but there is no data for the wall heat flux of 95 w/m2. So, the PSOFIS learns the available CFD data, and it predicts the velocity profile for where the data is not available (i.e., wall heat flux of 95 w/m2). The intelligence of PSOFIS is checked by the coefficient of determination (R2 pattern) for different values of accept ratio (AR) and inertia weight damping ratio (IWDR). The best intelligence is obtained for the AR and IWDR of 0.7 and 0.99, respectively. At this condition, the velocity profile predicted by both CFD and PSOFIS is compatible. As the performance of the PSOFIS, for learning time of 268 s, the prediction of the CFD data lost was negligible (~ 1 s). In contrast, the CFD calculation takes around 600 s for each simulation.