Phthalate drives splenic inflammatory response via activating HSP60/TLR4/NLRP3 signaling axis-dependent pyroptosis.

As the most produced phthalate, di-(2-ethylhexyl) phthalate (DEHP) is a widely environmental pollutant primarily used as a plasticizer, which cause the harmful effects on human health. However, the impact of DEHP on spleen and its underlying mechanisms are still unclear. Pyroptosis is a novel form of cell death induced by activating NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasomes and implicated in pathogenesis of numerous inflammatory diseases. The current study aimed to explore the impact of DEHP on immune inflammatory response in mouse spleen. In this study, the male ICR mice were treated with DEHP (200 mg/kg) for 28 days. Here, DEHP exposure caused abnormal pathohistological and ultrastructural changes, accompanied by inflammatory cells infiltration in mouse spleen. DEHP exposure arouse heat shock response that involves increase of heat shock proteins 60 (HSP60) expression. DEHP also elevated the expressions of toll-like receptor 4 (TLR4) and myeloid differentiation protein 88 (MyD88) proteins, as well as the activation of NF-κB pathway. Moreover, DEHP promoted NLRP3 inflammasome activation and triggered NLRP3 inflammasome-induced pyroptosis. Mechanistically, DEHP drives splenic inflammatory response via activating HSP60/TLR4/NLRP3 signaling axis-dependent pyroptosis. Our findings reveal that targeting HSP60-mediated TLR4/NLRP3 signaling axis may be a promising strategy for inflammatory diseases treatment.

New methods based on a genetic algorithm back propagation (GABP) neural network and general regression neural network (GRNN) for predicting the occurrence of trihalomethanes in tap water.

Monitoring trihalomethanes (THMs) levels in water supply systems is of great significance in ensuring drinking water safety. However, THMs detection is a time-consuming task. Developing predictive THMs models using parameters that are easier to obtain is an alternative. To date, there is still no application of optimization algorithms and general regression neural networks in predicting disinfection by-products levels. This study was to explore the feasibility of back propagation neural network (BPNN), genetic algorithm back propagation (GABP) neural network and general regression neural network (GRNN) for predicting THMs occurrence in real water supply systems. The results showed that the BPNN models' prediction ability was limited (test rp = 0.571-0.857, N25 = 61.5 %-91.5 %). Optimized by the genetic algorithm (GA), GABP models were generated and exhibited better prediction performance (test rp = 0.573 and 0.696-0.863, N25 = 68.2 %-93.6 %). However, GABP models took a lot of time and their prediction performance was unstable. A GRNN was then used to generate simpler neural network models, and the resulting prediction performance was excellent (total trihalomethanes and bromodichloromethane: test rp = 0.657-0.824, N25 = 81.8 %-100 %). In general, GRNN was the best at predicting THMs concentrations among the three models. However, it is worth noting that the prediction accuracy of bromodichloromethane (BDCM) was not high, which may be due to the absence of key parameters affecting BDCM formation. Accurate predictions of THMs by GRNN with these nine water parameters made THMs monitoring in real water supply systems possible and practical.

Designing of anisotropic gradient surfaces for directional liquid transport: Fundamentals, construction, and applications.

Many biological surfaces are capable of transporting liquids in a directional manner without energy consumption. Inspired by nature, constructing asymmetric gradient surfaces to achieve desired droplet transport, such as a liquid diode, brings an incredibly valuable and promising area of research with a wide range of applications. Enabled by advances in nanotechnology and manufacturing techniques, biomimetics has emerged as a promising avenue for engineering various types of anisotropic material system. Over the past few decades, this approach has yielded significant progress in both fundamental understanding and practical applications. Theoretical studies revealed that the heterogeneous composition and topography mainly govern the wetting mechanisms and dynamics behavior of droplets, including the interdisciplinary aspects of materials, chemistry, and physics. In this review, we provide a concise overview of various biological surfaces that exhibit anisotropic droplet transport. We discussed the theoretical foundations and mechanisms of droplet motion on designed surfaces and reviewed recent research advances in droplet directional transport on designed plane surfaces and Janus membranes. Such liquid-diode materials yield diverse promising applications, involving droplet collection, liquid separation and delivery, functional textiles, and biomedical applications. We also discuss the recent challenges and ongoing approaches to enhance the functionality and application performance of anisotropic materials.