Structural modification based on the diclofenac scaffold: Achieving reduced colitis side effects through COX-2/NLRP3 selective inhibition.

COX-2/NLPR3-targeted therapy might be beneficial for the inflammation diseases. To discover novel anti-inflammatory compounds with favorable safety profiles, three new series of non-carboxylic diclofenac analogues bearing various ring systems, such as oxadiazoles 4a-4w, triazoles 6a-6m, and cyclic imides 7a and 7b, were synthesized. The synthesized analogues were evaluated for their inhibitory activity against COX-2 enzyme. Among them, compound 6k exhibited potent selective COX-2 inhibition (IC50 = 1.53 µM; selectivity ((IC50 (COX-1)/IC50(COX-2) = 17.19). Treatment with compound 6k effectively suppressed the NF-κB/NLRP3 signaling pathway, resulting in reduced expression of pro-inflammatory factors. The in vivo ulcerative colitis assay demonstrated that compound 6k significantly ameliorated histological damages and showed strong protection against DSS-induced acute colitis. The collected results indicated that compound 6k displays anti-inflammatory activity through COX-2/NLRP3 inhibition. Therefore, compound 6k represents a promising candidate for further development as a new lead compound with reduced colitis side effects.

Synthesis and anti-inflammatory activity of novel firocoxib analogues with balanced COX inhibition.

The adverse effects caused by nonselective and selective cyclooxygenase-2 (COX-2) inhibitors remain a challenge for current anti-inflammatory medications. A balanced inhibition of COX-1/-2 represents a promising strategy for the development of novel COX-2 inhibitors. In this study, we present the design and synthesis of a novel series of firocoxib analogues incorporating an amide bond to facilitate essential hydrogen bonding with amino residues in COX-2. The synthesized analogs were evaluated for their inhibitory activity against both COX-1 and COX-2 enzymes. Among them, compound 9d demonstrated potent and balanced inhibition. Inhibition of COX enzymes by 9d in lipopolysaccharide (LPS)-stimulated murine RAW264.7 macrophages resulted in the suppression of the NF-κB signaling pathway to reduced expression of pro-inflammatory factors such as inducible nitric oxide synthase (iNOS), COX-2, nitric oxide (NO), and reactive oxygen species (ROS). The remarkable in vitro anti-inflammatory activity exhibited by 9d positions it as a promising candidate for further development as a novel lead compound for inflammation treatment.

Traffic-Data Recovery Using Geometric-Algebra-Based Generative Adversarial Network.

Traffic-data recovery plays an important role in traffic prediction, congestion judgment, road network planning and other fields. Complete and accurate traffic data help to find the laws contained in the data more efficiently and effectively. However, existing methods still have problems to cope with the case when large amounts of traffic data are missed. As a generalization of vector algebra, geometric algebra has more powerful representation and processing capability for high-dimensional data. In this article, we are thus inspired to propose the geometric-algebra-based generative adversarial network to repair the missing traffic data by learning the correlation of multidimensional traffic parameters. The generator of the proposed model consists of a geometric algebra convolution module, an attention module and a deconvolution module. Global and local data mean squared errors are simultaneously applied to form the loss function of the generator. The discriminator is composed of a multichannel convolutional neural network which can continuously optimize the adversarial training process. Real traffic data from two elevated highways are used for experimental verification. Experimental results demonstrate that our method can effectively repair missing traffic data in a robust way and has better performance when compared with the state-of-the-art methods.

Trajectory-based identification of critical instantaneous decision events at mixed-flow signalized intersections.

Mixed-flow intersections are prevailing in many developing countries such as China and India. At mixed-flow intersections, there is no clear lane discipline or regular trajectories within the intersection, especially for the non-motorized traffic. This leads to more interactions and encounters between the motorized traffic and the non-motorized traffic. Hence, critical instantaneous decision events such as abrupt accelerating, decelerating, jerking, swerving, and swinging, may occur more frequently, which result in potential traffic conflicts and crashes. This study presents a methodology to identify critical instantaneous decision events at the mixed-flow signalized intersections, based on the entropy theory and high-resolution vehicle trajectory data. A three-dimensional cube searching algorithm is firstly proposed to extract general traffic events by examining the proximity between trajectories. A novel model incorporating vehicle kinematics and Permutation Entropy is then developed to identify critical events, by quantifying driving volatility based on the time-serial trajectory data. Next, 3, 349 vehicle trajectories and 805 bicycle trajectories with a resolution of 0.12 s collected at a signalized intersection in Shanghai are used to demonstrate the proposed method. Results show that the proposed method is capable of identifying critical instantaneous decision events, and tends to produce a higher identification ratio comparing with the conventional method solely based on kinematic thresholds. A sensitivity analysis is also conducted to investigate the effects of model parameters on the performance of the proposed method. The presented work could be applied for traffic safety assessment, real-time driving alert systems, and early diagnosis of risk-prone road users at mixed-flow intersections.

Empirical Analysis and Modeling of Stop-Line Crossing Time and Speed at Signalized Intersections.

In China, a flashing green (FG) indication of 3 s followed by a yellow (Y) indication of 3 s is commonly applied to end the green phase at signalized intersections. Stop-line crossing behavior of drivers during such a phase transition period significantly influences safety performance of signalized intersections. The objective of this study is thus to empirically analyze and model drivers' stop-line crossing time and speed in response to the specific phase transition period of FG and Y. High-resolution trajectories for 1465 vehicles were collected at three rural high-speed intersections with a speed limit of 80 km/h and two urban intersections with a speed limit of 50 km/h in Shanghai. With the vehicle trajectory data, statistical analyses were performed to look into the general characteristics of stop-line crossing time and speed at the two types of intersections. A multinomial logit model and a multiple linear regression model were then developed to predict the stop-line crossing patterns and speeds respectively. It was found that the percentage of stop-line crossings during the Y interval is remarkably higher and the stop-line crossing time is approximately 0.7 s longer at the urban intersections, as compared with the rural intersections. In addition, approaching speed and distance to the stop-line at the onset of FG as well as area type significantly affect the percentages of stop-line crossings during the FG and Y intervals. Vehicle type and stop-line crossing pattern were found to significantly influence the stop-line crossing speed, in addition to the above factors. The red-light-running seems to occur more frequently at the large intersections with a long cycle length.

Exploring stop-go decision zones at rural high-speed intersections with flashing green signal and insufficient yellow time in China.

The objective of this study is to empirically analyze and model the stop-go decision behavior of drivers at rural high-speed intersections in China, where a flashing green signal of 3s followed by a yellow signal of 3s is commonly applied to end a green phase. 1, 186 high-resolution vehicle trajectories were collected at four typical high-speed intersection approaches in Shanghai and used for the identification of actual stop-go decision zones and the modeling of stop-go decision behavior. Results indicate that the presence of flashing green significantly changed the theoretical decision zones based on the conventional Dilemma Zone theory. The actual stop-go decision zones at the study intersections were thus formulated and identified based on the empirical data. Binary Logistic model and Fuzzy Logic model were then developed to further explore the impacts of flashing green on the stop-go behavior of drivers. It was found that the Fuzzy Logic model could produce comparably good estimation results as compared to the traditional Binary Logistic models. The findings of this study could contribute the development of effective dilemma zone protection strategies, the improvement of stop-go decision model embedded in the microscopic traffic simulation software and the proper design of signal change and clearance intervals at high-speed intersections in China.