23 research outputs found

    Lipid mediators in innate immunity against tuberculosis: opposing roles of PGE2 and LXA4 in the induction of macrophage death

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    Virulent Mycobacterium tuberculosis (Mtb) induces a maladaptive cytolytic death modality, necrosis, which is advantageous for the pathogen. We report that necrosis of macrophages infected with the virulent Mtb strains H37Rv and Erdmann depends on predominant LXA4 production that is part of the antiinflammatory and inflammation-resolving action induced by Mtb. Infection of macrophages with the avirulent H37Ra triggers production of high levels of the prostanoid PGE2, which promotes protection against mitochondrial inner membrane perturbation and necrosis. In contrast to H37Ra infection, PGE2 production is significantly reduced in H37Rv-infected macrophages. PGE2 acts by engaging the PGE2 receptor EP2, which induces cyclic AMP production and protein kinase A activation. To verify a role for PGE2 in control of bacterial growth, we show that infection of prostaglandin E synthase (PGES)βˆ’/βˆ’ macrophages in vitro with H37Rv resulted in significantly higher bacterial burden compared with wild-type macrophages. More importantly, PGESβˆ’/βˆ’ mice harbor significantly higher Mtb lung burden 5 wk after low-dose aerosol infection with virulent Mtb. These in vitro and in vivo data indicate that PGE2 plays a critical role in inhibition of Mtb replication

    Bank earnings management and corporate governance

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    This study investigates the association of earnings management with audit committee characteristics

    A Mechanism of Virulence: Virulent Mycobacterium tuberculosis

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    Cytosolic Phospholipase A 2

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    AI-enhanced cloud-edge-terminal collaborative network : survey, applications, and future directions

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    The cloud-edge-terminal collaborative network (CETCN) is considered as a novel paradigm for emerging applications owing to its huge potential in providing low-latency and ultra-reliable computing services. However, achieving such benefits is very challenging due to the heterogeneous computing power of terminal devices and the complex environment faced by the CETCN. In particular, the high-dimensional and dynamic environment states cause difficulties for the CETCN to make efficient decisions in terms of task offloading, collaborative caching and mobility management. To this end, artificial intelligence (AI), especially deep reinforcement learning (DRL) has been proven effective in solving sequential decision-making problems in various domains, and offers a promising solution for the above-mentioned issues due to several reasons. Firstly, accurate modelling of the CETCN, which is difficult to obtain for real-world applications, is not required for the DRL-based method. Secondly, DRL can effectively respond to high-dimensional and dynamic tasks through iterative interactions with the environment. Thirdly, due to the complexity of tasks and the differences in resource supply among different vendors, collaboration is required between different vendors to complete tasks. The multi-agent DRL (MADRL) methods are very effective in solving collaborative tasks, where the collaborative tasks can be jointly completed by cloud, edge and terminal devices which provided by different vendors. This survey provides a comprehensive overview regarding the applications of DRL and MADRL in the context of CETCN. The first part of this survey provides a depth overview of the key concepts of the CETCN and the mathematical underpinnings of both DRL and MADRL. Then, we highlight the applications of RL algorithms in solving various challenges within CETCN, such as task offloading, resource allocation, caching and mobility management. In addition, we extend discussion to explore how DRL and MADRL are making inroads into emerging CETCN scenarios like intelligent transportation system (ITS), the industrial Internet of Things (IIoT), smart health and digital agriculture. Furthermore, security considerations related to the application of DRL within CETCN are addressed, along with an overview of existing standards that pertain to edge intelligence. Finally, we list several lessons learned in this evolving field and outline future research opportunities and challenges that are critical for the development of the CETCN. We hope this survey will attract more researchers to investigate scalable and decentralized AI algorithms for the design of CETCN

    Development of dual-drug-loaded stealth nanocarriers for targeted and synergistic anti-lung cancer efficacy

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    Combination chemotherapy is widely exploited for suppressing drug resistance and achieving synergistic anticancer efficacy in the clinic. In this paper, the nanostructured targeting methotrexate (MTX) plus pemetrexed (PMX) chitosan nanoparticles (CNPs) were developed by modifying methoxy polye (thylene glycol) (mPEG), in which PEGylation CNPs was used as stealth nanocarriers (PCNPs) and MTX was employed as a targeting ligand and chemotherapeutic agent as well. Studies were undertaken on human lung adenocarcinoma epithelial (A549) and Lewis lung carcinoma (LLC) cell lines, revealing the anti-tumor efficacy of nanoparticle drug delivery system. The co-delivery nanoparticles (MTX-PMX-PCNPs) had well-dispersed with sustained release behavior. Cell counting kit-8 (CCK8) has been used to measure A549 cell viability and the research showed that MTX-PMX-PCNPs were much more effective than free drugs when it came to the inhibition of growth and proliferation. Cell cycle assay by flow cytometry manifested that the MTX-PMX-PCNPs exhibited stronger intracellular taken up ability than free drugs at the same concentration. In vivo anticancer effect results indicated that MTX-PMX-PCNPs exhibited a significantly prolong blood circulation, more tumoral location accumulation, and resulted in a robust synergistic anticancer efficacy in lung cancer in mice. The results clearly demonstrated that such unique synergistic anticancer efficacy of co-delivery of MTX and PMX via stealth nanocarriers, providing a prospective strategy for lung cancer treatment
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