Ketamine is an injectable anesthetic and recreational drug of abuse commonly used worldwide. Many experimental studies have shown that ketamine can impair cognitive function and induce psychotic states. Neuroinflammation has been suggested to play an important role in neurodegeneration. Meanwhile, ketamine has been shown to modulate the levels of inflammatory cytokines. We hypothesized that the effects of ketamine on the central nervous system are associated with inflammatory cytokines. Therefore, we set out to establish acute and chronic ketamine administration models in C57BL/6 mice, to evaluate spatial recognition memory and emotional response, to analyze the changes in the levels of the inflammatory cytokines interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α) in the mouse hippocampus, employing behavioral tests, Western blot, quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) and immunohistochemistry. Our results showed that ketamine at the dose of 60 mg/kg induced spatial recognition memory deficit and reduced anxiety-like behaviors in mice after chronic administration. Moreover, we found that ketamine increased the hippocampal levels of IL-6 and IL-1β after single, multiple and long-term administration in a dose-dependent manner. However, the expression level of TNF-α differed in the mouse hippocampus under different conditions. Single administration of ketamine increased the level of TNF-α, whereas multiple and long-term administration decreased it significantly. We considered that TNF-α expression could be controlled by a bi-directional regulatory pathway, which was associated with the dose and duration of ketamine administration. Our results suggest that the alterations in the levels of inflammatory cytokines IL-6, IL-1β, and TNF-α may be involved in the neurotoxicity of ketamine.

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Background: Ulcerative colitis (UC) is an idiopathic, chronic inflammatory disease of the colonic mucosa. Herb-partitioned moxibustion (HPM) treatment has been demonstrated to be effective in the treatment of UC. However, there is still a lack of high-quality evidence to support the effectiveness and safety of HPM on patients with UC. This study will aim to systematically explore the efficacy of HPM for the treatment of UC. Methods: We will search the electronic databases of Embase, MEDLINE, PubMed, Cochrane Library Central Register of Controlled Trials, China national knowledge infrastructure database (CNKI), Wan fang database, Chongqing VIP information, and SinoMed from their inception to June 2020. Randomized controlled trials (RCTs) of HPM for the treatment of UC will be included. RevMan 5.3 software (The Nordic Cochrane Center, The Cochrane Collaboration, Copenhagen, Denmark) will be applied for statistical analysis. Results: The results of this study will be published in a peer-reviewed journal. Conclusion: The conclusion of our systematic review will provide more appropriate evidence-based decisions to assist clinicians during the decision-making process when dealing with UC.

Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has been widely applied in many real-world applications. The primary goal of GAD is to capture anomalous nodes from graph datasets, which evidently deviate from the majority of nodes. Recent methods have paid attention to various scales of contrastive strategies for GAD, i.e., node-subgraph and node-node contrasts. However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance. In this paper, we fulfill the above idea in the proposed multi-view multi-scale contrastive learning framework with subgraph-subgraph contrast for the first practice. To be specific, we regard the original input graph as the first view and generate the second view by graph augmentation with edge modifications. With the guidance of maximizing the similarity of the subgraph pairs, the proposed subgraph-subgraph contrast contributes to more robust subgraph embeddings despite of the structure variation. Moreover, the introduced subgraph-subgraph contrast cooperates well with the widely-adopted node-subgraph and node-node contrastive counterparts for mutual GAD performance promotions. Besides, we also conduct sufficient experiments to investigate the impact of different graph augmentation approaches on detection performance. The comprehensive experimental results well demonstrate the superiority of our method compared with the state-of-the-art approaches and the effectiveness of the multi-view subgraph pair contrastive strategy for the GAD task. The source code is released at https://github.com/FelixDJC/GRADATE.