Search

Mitochondrial dysfunction plays a critical role in metabolic associated fatty liver disease (MAFLD). This study aims to characterize mitochondrial dysfunctions in a human MAFLD Huh7 cell model triggered by free fatty acid (FFA) (palmitate and oleate) overload for 24 hours. We investigate its impact on cellular energy metabolism and identify potential targets for MAFLD treatment. FFA-treated cells displayed an accumulation of lipid droplets and slightly decreased viability but no significant changes in mitochondrial superoxide levels. Bioenergetic analysis showed a shift to more respiration and less glycolytic fermentation. Comprehensive transcriptomics and proteomics analyses identified changes in the expression of genes prominently involved in fatty acid handling and metabolism. The expressions of seven genes were consistently and significantly (p < 0.05) altered (4 upregulated and 3 downregulated genes) in both proteomics and transcriptomics. The FFA-treated Huh7 cell model is an appropriate in vitro model to study fatty acid metabolism and suitable to investigate the role of mitochondria, glycolysis, and multiple metabolic pathways in MAFLD. Our comprehensive analyses form a basis for drug discovery and screening using this model.

This study aims to examine the effect of short-term changes in the concentration of particulate matter of diameter ≤2.5 µm (PM2.5) and ≤10 µm (PM10) on pediatric hospital admissions for pneumonia in Jinan, China. It explores confoundings factors of weather, season, and chemical pollutants. Information on pediatric hospital admissions for pneumonia in 2014 was extracted from the database of Jinan Qilu Hospital. The relative risk of pediatric hospital admissions for pneumonia was assessed using a case-crossover approach, controlling weather variables, day of the week, and seasonality. The single-pollutant model demonstrated that increased risk of pediatric hospital admissions for pneumonia was significantly associated with elevated PM2.5 concentrations the day before hospital admission and elevated PM10 concentrations 2 days before hospital admission. An increment of 10 μg/m3 in PM2.5 and PM10 was correlated with a 6% (95% CI 1.02–-1.10) and 4% (95% CI 1.00–1.08) rise in number of admissions for pne...

Energy efficiency is an essential issue in the applications of wireless sensor networks (WSNs). Clustering is a significant direction to improve energy efficiency through software. In this paper, a prediction-based energy efficient clustering (PBEEC) approach which makes full use of the history information is developed to clustering the network. The inter-cluster communication cost and cluster size are also considered when clustering. Nodes with high residual energy and low energy consumption ratio have more possibility to be cluster heads. The proposed algorithm is simple enough to load on senor nodes which have limited memory and computing ability. The simulation results show,the whole lifetime of PBEEC increases approximately 47% more than that of LEACH on the total runtime of the entire network, as well as 16% more than that of HEED