Early Hepatic Gene Expression Profile of Lipid Metabolism of Mice on High Fat Diet after Treatment with Anti‐Obesity Drugs

Obesity is a multifactorial disorder of global scale. The liver plays a vital role in fat metabolism. Disorder of hepatic fat metabolism is associated with obesity and fatty liver disease. This study aimed to detect the effects of anti‐obesity drugs (sulforaphane; SFN and leptin) on hepatic gene expression of fat metabolism in mice that were fed HFD during an early time of DIO. Thirty‐two wild type (WT) ten‐week‐old CD1 male mice were fed high fat diet for four weeks in order to induce diet‐induced obesity (DIO). The mice were treated with a vehicle, or SFN for one week and then each group is treated with leptin or saline for 24 hours. Four groups of treatment were obtained; control group (vehicle + saline), group 2 (vehicle + leptin), group 3 (SFN + saline), and group 4 (SFN + leptin). Body weight and food intake were monitored during the treatment period. Following the treatments, liver tissue was collected, and total RNA was extracted to assess the expression of 84 genes involved in hepatic fat metabolism using RT‐PCR profiler array technique. Leptin treatment upregulated the genes involved in fatty acid beta‐oxidation (Acsbg2, Acsm4) and fatty acyl‐CoA biosynthesis (Acot6, Acsl6), and down‐regulated the fatty acid transport gene (Slc27a2). SFN upregulated acyl‐CoA hydrolase (Acot3) and long chain fatty acid activation for lipids synthesis and beta‐oxidation (Acsl1). Leptin + SFN upregulated fatty acid beta‐oxidation‐related genes (Acad11, Acam) and acyl‐CoA hydrolases (Acot3, Acot7), and downregulated fatty acid elongation gene, Acot2. As a result, treatment with both SFN and leptin has a more profound effect in ameliorating the pathways involved in hepatic lipogenesis and TG accumulation and body weight gain than other types of intervention. We conclude that early intervention of obesity pathogenesis could ameliorate the metabolic changes of fat metabolism in the liver, as observed in mice on HFD in response to SFN anti‐obesity treatment.NPRP Grant No. 9‐351‐3‐07

The small chromatin-binding protein p8 coordinates the association of anti-proliferative and pro-myogenic proteins at the myogenin promoter

Quiescent muscle progenitors called satellite cells persist in adult skeletal muscle and, upon injury to muscle, re-enter the cell cycle and either undergo self-renewal or differentiate to regenerate lost myofibers. Using synchronized cultures of C2C12 myoblasts to model these divergent programs, we show that p8 (also known as Nupr1), a G1-induced gene, negatively regulates the cell cycle and promotes myogenic differentiation. p8 is a small chromatin protein related to the high mobility group (HMG) family of architectural factors and binds to histone acetyltransferase p300 (p300, also known as CBP). We confirm this interaction and show that p300-dependent events (Myc expression, global histone acetylation and post-translational acetylation of the myogenic regulator MyoD) are all affected in p8-knockdown myoblasts, correlating with repression of MyoD target-gene expression and severely defective differentiation. We report two new partners for p8 that support a role in muscle-specific gene regulation: p68 (Ddx5), an RNA helicase reported to bind both p300 and MyoD, and MyoD itself. We show that, similar to MyoD and p300, p8 and p68 are located at the myogenin promoter, and that knockdown of p8 compromises chromatin association of all four proteins. Thus, p8 represents a new node in a chromatin regulatory network that coordinates myogenic differentiation with cell-cycle exit

Fault Prediction Recommender Model for IoT Enabled Sensors Based Workplace

Industry 5.0 benefits from advancements being made in the field of machine learning and the Internet of Things. Different sensors have been installed in a variety of IoT devices present in different industries such as transportation, healthcare, manufacturing, agriculture, etc. The sensors present in these devices should automatically predict errors due to the extensive use of sensors in urban living. To ensure the integrity, precision, security, dependability and fidelity of sensor nodes, it is, therefore, necessary to foresee faults before they occur. Additionally, as more data is being collected by these devices every day, cloud computing becomes more necessary for sustainable urban living. The proposed model emphasizes solution recommendations for faults that occurred in real-life smart devices to mitigate faults at an early stage, which is a key requirement in today’s smart offices. The proposed model monitors the real-time health of IoT devices through an ML algorithm to make devices more efficient and increase the quality of life. Through the use of K-Nearest Neighbor, Decision Tree, Gaussian Naive Bayes and Random Forest approach, the proposed fault prediction recommender model has been evaluated and Random Forest shows the highest accuracy compared to other classifiers. Several performance indicators such as recall, accuracy, F1 score and precision were utilized to examine the performance of the model. The results have demonstrated the effectiveness of ML techniques applied to sensors in predicting faults in smart offices with Random Forest being observed as the best technique with a maximum accuracy of 94.27%. In future, deep learning can also be applied to bigger datasets to provide more accurate results