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Dexamethasone alleviate allergic airway inflammation in mice by inhibiting the activation of NLRP3 inflammasome.

地塞米松通过抑制 NLRP3 炎症小体的激活减轻小鼠过敏性气道炎症。

  • 影响因子:3.32
  • DOI:10.1016/j.intimp.2019.106017
  • 作者列表:"Guan M","Ma H","Fan X","Chen X","Miao M","Wu H
  • 发表时间:2020-01-01
Abstract

:Dexamethasone (DEX) is the mainstay treatment for asthma, which is a common chronic airway inflammation disease. However, the mechanism of DEX resolute symptoms of asthma is not completely clear. Here, we aimed to analyze the effect of DEX on airway inflammation in OVA-induced mice and whether this effect is related to the inhibition of the activation of NLRP3 inflammasome. Female (C57BL/6) mice were used to establish the allergic airway inflammation model by inhalation OVA. The number of inflammatory cells in the bronchi alveolar lavage fluid (BALF) was counted by Swiss-Giemsa staining, and the contents of IL-1β, IL-18, IL-5 and IL-17 were detected by ELISA. The degree of inflammatory cells infiltration and mucous cells proliferation in lung tissue were separately observed by H&E and PAS staining. The proteins expression of NLRP3, pro-caspase-1, caspase-1, IL-1β, IL-6 and IL-17 in lung tissue were detected by Western blotting. We found that DEX significantly inhibited OVA-induced inflammatory cells infiltration, airway mucus secretion and goblet cell proliferation in mice. The total and classified numbers of inflammatory cells and the levels of IL-1β, IL-18, IL-5 and IL-17 in the BALF of the experimental group were significantly lower than those of the model group after DEX treatment. DEX also significantly inhibited the activity of NLRP3 inflammasome and reduced the protein contents of Pro-Caspase-1, Caspase-1, Capase-1/Pro-Caspase-1, IL-1β, IL-6 and IL-17 in lung tissues. Our study suggested that DEX alleviates allergic airway inflammation by inhibiting the activity of NLRP3 inflammasome and the levels of IL-1β and IL-18.

摘要

: 地塞米松 (DEX) 是治疗哮喘的主要方法,哮喘是一种常见的慢性气道炎症疾病。然而,DEX 缓解哮喘症状的机制尚不完全清楚。在此,我们旨在分析 DEX 对 OVA 诱导的小鼠气道炎症的影响,以及这种影响是否与抑制 NLRP3 炎症小体的激活有关。采用雌性 (C57BL/6) 小鼠吸入 OVA 建立过敏性气道炎症模型。采用 Swiss-Giemsa 染色法计数支气管肺泡灌洗液 (BALF) 中炎性细胞数量,ELISA 法检测 il-1 β 、 IL-18 、 IL-5 、 IL-17 含量。H & E 和 PAS 染色分别观察肺组织炎症细胞浸润程度和黏液细胞增殖情况。Western blotting 检测肺组织 NLRP3 、 pro-caspase-1 、 caspase-1 、 il-1 β 、 IL-6 、 IL-17 蛋白表达。我们发现 DEX 显著抑制 OVA 诱导的小鼠炎症细胞浸润、气道黏液分泌和杯状细胞增殖。DEX 治疗后,实验组 BALF 中炎症细胞总数、分类数量及 il-1 β 、 IL-18 、 IL-5 、 IL-17 水平均明显低于模型组。DEX 还明显抑制 NLRP3 炎性体的活性,降低肺组织中 Pro-Caspase-1 、 Caspase-1 、 Capase-1/Pro-Caspase-1 、 il-1 β 、 IL-6 、 IL-17 的蛋白含量。我们的研究提示 DEX 通过抑制 NLRP3 炎性体的活性和 il-1 β 、 IL-18 水平减轻过敏性气道炎症。

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发表时间:2020-01-13
来源期刊:Surgical Endoscopy
DOI:10.1007/s00464-019-07334-4
作者列表:["Yang, Shun-Mao","Chen, Yi-Chang","Ko, Wei-Chun","Huang, Hsin-Chieh","Yu, Kai-Lun","Ko, Huan-Jang","Huang, Pei-Ming","Chang, Yeun-Chung"]

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影响因子:3.31
发表时间:2020-01-02
DOI:10.1007/s10916-019-1481-4
作者列表:["Matava, Clyde","Pankiv, Evelina","Raisbeck, Sam","Caldeira, Monica","Alam, Fahad"]

METHODS:Background The use of artificial intelligence, including machine learning, is increasing in medicine. Use of machine learning is rising in the prediction of patient outcomes. Machine learning may also be able to enhance and augment anesthesia clinical procedures such as airway management. In this study, we sought to develop a machine learning algorithm that could classify vocal cords and tracheal airway anatomy real-time during video laryngoscopy or bronchoscopy as well as compare the performance of three novel convolutional networks for detecting vocal cords and tracheal rings. Methods Following institutional approval, a clinical dataset of 775 video laryngoscopy and bronchoscopy videos was used. The dataset was divided into two categories for use for training and testing. We used three convolutional neural networks (CNNs): ResNet, Inception and MobileNet. Backpropagation and a mean squared error loss function were used to assess accuracy as well as minimize bias and variance. Following training, we assessed transferability using the generalization error of the CNN, sensitivity and specificity, average confidence error, outliers, overall confidence percentage, and frames per second for live video feeds. After the training was complete, 22 models using 0 to 25,000 steps were generated and compared. Results The overall confidence of classification for the vocal cords and tracheal rings for ResNet, Inception and MobileNet CNNs were as follows: 0.84, 0.78, and 0.64 for vocal cords, respectively, and 0.69, 0.72, 0.54 for tracheal rings, respectively. Transfer learning following additional training resulted in improved accuracy of ResNet and Inception for identifying the vocal cords (with a confidence of 0.96 and 0.93 respectively). The two best performing CNNs, ResNet and Inception, achieved a specificity of 0.985 and 0.971, respectively, and a sensitivity of 0.865 and 0.892, respectively. Inception was able to process the live video feeds at 10 FPS while ResNet processed at 5 FPS. Both were able to pass a feasibility test of identifying vocal cords and tracheal rings in a video feed. Conclusions We report the development and evaluation of a CNN that can identify and classify airway anatomy in real time. This neural network demonstrates high performance. The availability of artificial intelligence may improve airway management and bronchoscopy by helping to identify key anatomy real time. Thus, potentially improving performance and outcomes during these procedures. Further, this technology may theoretically be extended to the settings of airway pathology or airway management in the hands of experienced providers. The researchers in this study are exploring the performance of this neural network in clinical trials.

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影响因子:3.84
发表时间:2020-01-01
来源期刊:Chest
DOI:10.1016/j.chest.2019.06.018
作者列表:["Dhooria S","Chaudhary S","Ram B","Sehgal IS","Muthu V","Prasad KT","Aggarwal AN","Agarwal R"]

METHODS:BACKGROUND:The optimal mode of delivering topical anesthesia during flexible bronchoscopy remains unknown. This article compares the efficacy and safety of nebulized lignocaine, lignocaine oropharyngeal spray, or their combination. METHODS:Consecutive subjects were randomized 1:1:1 to receive nebulized lignocaine (2.5 mL of 4% solution, group A), oropharyngeal spray (10 actuations of 10% lignocaine, group B), or nebulization (2.5 mL, 4% lignocaine) and two actuations of 10% lignocaine spray (group C). The primary outcome was the subject-rated severity of cough according to a visual analog scale. The secondary outcomes included bronchoscopist-rated severity of cough and overall procedural satisfaction on a visual analog scale, total lignocaine dose, subject's willingness to undergo a repeat procedure, adverse reactions to lignocaine, and others. RESULTS:A total of 1,050 subjects (median age, 51 years; 64.8% men) were included. The median (interquartile range) score for subject-rated cough severity was significantly lower in group B compared to group C or group A (4 [1-10] vs 11 [4-24] vs 13 [5-30], respectively; P < .001). The bronchoscopist-rated severity of cough was also the least (P < .001), and the overall satisfaction was highest in group B (P < .001). The cumulative lignocaine dose administered was the least in group B (P < .001). A significantly higher proportion of subjects (P < .001) were willing to undergo a repeat bronchoscopy in group B (73.7%) than in groups A (49.1%) and C (59.4%). No lignocaine-related adverse events were observed. CONCLUSIONS:Ten actuations of 10% lignocaine oropharyngeal spray were superior to nebulized lignocaine or their combination for topical anesthesia during diagnostic flexible bronchoscopy. TRIAL REGISTRY:ClinicalTrials.gov; No.: NCT03109392; URL: www.clinicaltrials.gov.

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