103 research outputs found

    Anesthetic Isoflurane Increases Phosphorylated Tau Levels Mediated by Caspase Activation and AĪ² Generation

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    Anesthetic isoflurane has been shown to promote Alzheimerā€™s disease (AD) neuropathogenesis by inducing caspase activation and accumulation of Ī²-amyloid (AĪ²). Phosphorylation of tau protein is another important feature of AD neuropathogenesis. However, the effects of isoflurane on phosphorylated tau levels remain largely to be determined. We therefore set out to determine whether isoflurane can increase phosphorylated tau levels. 5 to 8 month-old wild-type and AD transgenic mice [B6.Cg-Tg (APPswe, PSEN1dE9)85Dbo/J] were treated with 1.4% isoflurane for two hours. The mice brain tissues were harvested at six, 12 and 24 hours after the anesthesia. For the in vitro studies, primary neurons from wild-type and the AD transgenic mice were exposed to 2% isoflurane for six hours, and were harvested at the end of anesthesia. The harvested brain tissues and neurons were subjected to Western blot analysis by which the levels of phosphorylated tau protein at Serine 262 (Tau-PS262) were determined. Here we show that the isoflurane anesthesia increased Tau-PS262 levels in brain tissues and primary neurons from the wild-type and AD transgenic mice. Moreover, the isoflurane anesthesia may induce a greater increase in Tau-PS262 levels in primary neurons and brain tissues from the AD transgenic mice. Finally, caspase activation inhibitor Z-VAD and AĪ² generation inhibitor L-685,458 attenuated the isoflurane-induced increases in Tau-PS262 levels. In conclusion, clinically relevant isoflurane anesthesia increases phosphorylated tau levels, which may result from the isoflurane-induced caspase activation and AĪ² generation. These findings will promote more studies to determine the effects of anesthetics on tau phosphorylation

    RNA interference-mediated silencing of BACE and APP attenuates the isoflurane-induced caspase activation

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    <p>Abstract</p> <p>Background</p> <p>Ī²-Amyloid protein (AĪ²) has been shown to potentiate the caspase-3 activation induced by the commonly used inhalation anesthetic isoflurane. However, it is unknown whether reduction in AĪ² levels can attenuate the isoflurane-induced caspase-3 activation. We therefore set out to determine the effects of RNA interference-mediated silencing of amyloid precursor protein (APP) and Ī²-site APP-cleaving enzyme (BACE) on the levels of AĪ² and the isoflurane-induced caspase-3 activation.</p> <p>Methods</p> <p>H4 human neuroglioma cells stably transfected to express full-length human APP (H4-APP cells) were treated with small interference RNAs (siRNAs) targeted at silencing BACE and APP for 48 hours. The cells were then treated with 2% isoflurane for six hours. The levels of BACE, APP, and caspase-3 were determined using Western blot analysis. Sandwich Enzyme-linked immunosorbent assay (ELISA) was used to determine the extracellular AĪ² levels in the conditioned cell culture media.</p> <p>Results</p> <p>Here we show for the first time that treatment with BACE and APP siRNAs can decrease levels of BACE, full-length APP, and APP c-terminal fragments. Moreover, the treatment attenuates the AĪ² levels and the isoflurane-induced caspase-3 activation. These results further suggest a potential role of AĪ² in the isoflurane-induced caspase-3 activation such that the reduction in AĪ² levels attenuates the isoflurane-induced caspase-3 activation.</p> <p>Conclusion</p> <p>These findings will lead to more studies which aim at illustrating the underlying mechanism by which isoflurane and other anesthetics may affect Alzheimer's disease neuropathogenesis.</p

    An Oil Spill Spatial Data Model for Qinzhou Bay Based on the KML

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    Qinzhou Bay is an important channel of Chinese southwest goes to sea. The Qinzhou Bay was chosen as the study area in this paper. Using the international advanced model Oilmap, Analysis of oil spill on the initial oil membrane formation factors, exclude some of initial oil film effects are not important factors, Find suitable for oil spill earlier an oil spill force model. Combined with the natural condition of Qinzhou Bay, The transport process of oil membrane in the condition of different wind and current was observed in Qinzhou Bay by means of experiments. This paper analyzes the main factors, such as the shape of oil membrane and migration directions, which leads to the oil spill, and got a suitable model of oil spill to Qinzhou Bay. In order to achieve its visual in the software of geographic information system, the model of oil spill was defined through the KML

    Robust Representation Learning for Unified Online Top-K Recommendation

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    In large-scale industrial e-commerce, the efficiency of an online recommendation system is crucial in delivering highly relevant item/content advertising that caters to diverse business scenarios. However, most existing studies focus solely on item advertising, neglecting the significance of content advertising. This oversight results in inconsistencies within the multi-entity structure and unfair retrieval. Furthermore, the challenge of retrieving top-k advertisements from multi-entity advertisements across different domains adds to the complexity. Recent research proves that user-entity behaviors within different domains exhibit characteristics of differentiation and homogeneity. Therefore, the multi-domain matching models typically rely on the hybrid-experts framework with domain-invariant and domain-specific representations. Unfortunately, most approaches primarily focus on optimizing the combination mode of different experts, failing to address the inherent difficulty in optimizing the expert modules themselves. The existence of redundant information across different domains introduces interference and competition among experts, while the distinct learning objectives of each domain lead to varying optimization challenges among experts. To tackle these issues, we propose robust representation learning for the unified online top-k recommendation. Our approach constructs unified modeling in entity space to ensure data fairness. The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations. Moreover, the proposed method balances conflicting objectives through the homoscedastic uncertainty weights and orthogonality constraints. Various experiments validate the effectiveness and rationality of our proposed method, which has been successfully deployed online to serve real business scenarios.Comment: 14 pages, 6 figures, submitted to ICD
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