The role of the adenosine A3 receptor (A3AR) in experimental colitis is controversial. The A3AR agonist N6-(3-iodobenzyl)adenosine-5'-N-methyluronamide (IB-MECA) has been shown to have a clinical benefit, although studies in A3AR-deficient mice suggest a pro-inflammatory role. However, there are no studies on the effect of 2-Cl-IB-MECA and the molecular mechanism of action of A3AR in murine colitis models in vivo. Is it the same as that observed in vitro? The interaction between 2-CL-IB-MECA and A3AR in a murine colitis model and the signaling pathways associated with this interaction remain unclear. Here we demonstrate a role for the NF-κB signaling pathway and its effect on modifying the activity of proinflammatory factors in A3AR-mediated biological processes. Our results demonstrated that A3AR activation possessed marked effects on experimental colitis through the NF-κB signaling pathway.
Hepatic fibrosis, which results from chronic liver disease, currently lacks effective treatment. MicroRNAs, a group of small noncoding RNA molecules, have been observed to play an essential role in liver diseases, including hepatic fibrosis. In this study, we described the regulation of nuclear factor kappa B (NF-κB) inhibitor alpha (IκBα) and its possible signaling pathway by miR-126 in human hepatic stellate cell (HSC) line LX-2. The 3'-untranslated region (3'-UTR) of IκBα combined with miR-126 was analyzed by using a dual-luciferase reporter assay. Furthermore, the effects of miR-126 on IκBα mRNA and protein and NF-κB protein expression were assessed by real-time quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) and western blot analysis in the human HSC LX-2 cell line transfected with miR-126 mimic or inhibitor. Moreover, to understand the molecular mechanism of miR-126 in promoting liver fibrosis through NF-κB signaling pathway, the NF-κB downstream signaling factors expression such as transforming growth factor (TGF)-β1 and collagen I mRNA were detected by real-time qRT-PCR. We identified that IκBα is a potential target gene of miR-126, by directly targeting its 3'-UTR. Endogenous miR-126 and exogenous miR-126 mimic inhibited IκBα expression. Moreover, overexpression of miR-126 reduced total and the cytoplasm IκBα protein expression and increased total and cytoblast NF-κB protein expression of LX-2. Conversely, knockdown of miR-126 could inhibit NF-κB activation by upregulation of IκBα protein expression. Further, miR-126 promoted TNF-a-induced TGF-β1 and collagen I mRNA expression in LX-2 cells. miR-126 may play an important role in hepatic fibrosis by downregulating the expression of IκBα partly through the NF-κB signaling pathway.
The implementation of the toll free during holidays makes a large number of traffic jams on the expressway. Real-time and accurate holiday traffic flow forecasts can assist the traffic management department to guide the diversion and reduce the expressway’s congestion. However, most of the current prediction methods focus on predicting traffic flow on ordinary working days or weekends. There are fewer studies for festivals and holidays traffic flow prediction, it is challenging to predict holiday traffic flow accurately because of its sudden and irregular characteristics. Therefore, we put forward a data-driven expressway traffic flow prediction model based on holidays. Firstly, Electronic Toll Collection (ETC) gantry data and toll data are preprocessed to realize data integrity and accuracy. Secondly, after Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) processing, the preprocessed traffic flow is sorted into trend terms and random terms, and the spatial-temporal correlation and heterogeneity of each component are captured simultaneously using the Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN) model. Finally, the fluctuating traffic flow of holidays is predicted using Fluctuation Coefficient Method (FCM). Through experiments of real ETC gantry data and toll data in Fujian Province, this method is superior to all baseline methods and has achieved good results. It can provide reference for future public travel choices and further road network operation.
Tunnels on freeways, as one of the critical bottlenecks, frequently cause severe congestion and passenger delay. To solve the tunnel bottleneck problem, most of the existing research can be divided into two types. One is to adopt Variable Speed Limits (VSL) to regulate a predetermined speed for vehicles to get through a bottleneck smoothly. The other is to adopt High-Occupancy Vehicle (HOV) lane management. In HOV lane management strategies, all traffic is divided into HOVs and Low-occupancy Vehicles (LOV). HOVs are vehicles with a driver and one or more passengers. LOVs are vehicles just with a driver. This kind of research can grant priority to HOVs by providing a dedicated HOV lane. However, the existing research cannot both mitigate congestion and maximize passenger-oriented benefits. To address the research gap, this paper leverages Connected and Automated Vehicle (CAV) technologies on intelligent freeways and develops a tunnel bottleneck management strategy with a Dynamic HOV Lane (DHL). The strategy bears the following features: 1) enable tunnel bottleneck management at a microscopic level; 2) maximize passenger-oriented benefits; 3) grant priority to HOVs even when the HOV lane is open to LOVs; 4) allocate right-of-way segments for HOVs and LOVs in real time; 5) perform well in a mixed traffic environment. The proposed strategy is evaluated through comparison against the non-control baseline and a VSL strategy. Sensitivity analysis is conducted under different congestion levels and penetration rates. The results demonstrate that the proposed strategy outperforms in terms of passenger-oriented delay reduction and HOVs'priority level improvement.
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