Prostate cancer poses a public health threat to hundreds of people around the world. p62 has been identified as a tumor suppressor, however, the mechanism by which p62 promotes prostate cancer remains poorly understood. The present study aimed to investigate whether p62 promotes proliferation, apoptosis resistance and invasion of prostate cancer cells via the Kelch-like ECH-associated protein 1/nuclear factor erytheroid-derived 2-like 2/antioxidant response element (Keap1/Nrf2/ARE) axis. Immunohistochemical staining and immunoblotting were performed to determine the protein levels. Rates of proliferation, invasion and apoptosis of prostate cancer cells were assessed using an RTCA system and flow cytometric assays. Levels of reactive oxygen species (ROS) were assessed using Cell ROX Orange reagent and mRNA levels of Nrf2 target genes were detected by qRT-PCR. It was revealed that p62 increased the levels and activities of Nrf2 by suppressing Keap1-mediated proteasomal degradation in prostate cancer cells and tissues, and high levels of p62 promoted growth of prostate cancer through the Keap1/Nrf2/ARE system. Silencing of Nrf2 in DU145 cells overexpressing p62 led to decreases in the rate of cell proliferation and invasion and an increase in the rate of cell apoptosis. p62 activated the Nrf2 pathway, promoted the transcription of Nrf2-mediated target genes and suppressed ROS in prostate cancer. Therefore, p62 promoted the development of prostate cancer by activating the Keap1/Nrf2/ARE pathway and decreasing p62 may provide a new strategy to ameliorate tumor aggressiveness and suppress tumorigenesis to improve clinical outcomes.

Novel antibacterial agents are urgently needed to address the infections caused by multi-drug resistant bacteria. Urinary tract infections are common infectious diseases in clinical. Most of these infections are caused by drug-resistant uropathogenic Escherichia coli. PPK1 is an essential kinase for bacterial motility, biofilm formation, quorum sensing, and virulence factors in the expression of uropathogenic E. coli. In the present study, two small molecules potentially targeting PPK1 were discovered through virtual screening and biological assays. The in vitro and in vivo results suggested that the interaction of these compounds with PPK1 can disrupt biofilm formation of uropathogenic E. coli and reduce invasive ability and resistance to oxidative stress of this strain. Moreover, the compounds exhibit good antibacterial bacterial activity in the mice with urinary tract infection. Taken together, our findings could provide a new chemotype for the development of antibacterials targeting PPK1.

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Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing studies mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this article, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real world deployment, and 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which show our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS.

Recently, there have been many advances in autonomous driving society, attracting a lot of attention from academia and industry. However, existing works mainly focus on cars, extra development is still required for self-driving truck algorithms and models. In this paper, we introduce an intelligent self-driving truck system. Our presented system consists of three main components, 1) a realistic traffic simulation module for generating realistic traffic flow in testing scenarios, 2) a high-fidelity truck model which is designed and evaluated for mimicking real truck response in real world deployment, 3) an intelligent planning module with learning-based decision making algorithm and multi-mode trajectory planner, taking into account the truck's constraints, road slope changes, and the surrounding traffic flow. We provide quantitative evaluations for each component individually to demonstrate the fidelity and performance of each part. We also deploy our proposed system on a real truck and conduct real world experiments which shows our system's capacity of mitigating sim-to-real gap. Our code is available at https://github.com/InceptioResearch/IITS