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The relationship of airway structural changes to blood and bronchoalveolar lavage biomarkers, and lung function abnormalities in asthma.

气道结构变化与血液和支气管肺泡灌洗生物标志物的关系,以及哮喘的肺功能异常。

  • 影响因子:4.19
  • DOI:10.1111/cea.13501
  • 作者列表:"Kozlik P","Zuk J","Bartyzel S","Zarychta J","Okon K","Zareba L","Bazan JG","Kosalka J","Soja J","Musial J","Bazan-Socha S
  • 发表时间:2020-01-01
Abstract

BACKGROUND:Airway structural changes are important in asthma pathology and require further investigations. OBJECTIVE:We sought to evaluate which computed tomography (CT) indices, bronchial histological traits, or blood and bronchoalveolar lavage (BAL) biomarkers correlate best with lung function abnormalities in asthma. METHODS:In 105 white adult asthmatics (53 with a component of fixed airflow obstruction), we determined airway cross-sectional geometry of two proximal (the right upper lobe apical segmental and the left apicoposterior) and two distal (the right and the left basal posterior) bronchi, quantified the low-attenuation lung area (LAA%), and analysed clusters based on airway CT-metrics. We also performed bronchofiberoscopy with BAL and endobronchial biopsy, assessed blood and BAL biomarkers, including interleukin (IL)-4, IL-5, IL-6, IL-10, IL-12p70, IL-17A, IL-23, interferon (INF)γ and periostin, together with circulating a disintegrin and metalloproteinase domain-containing protein (ADAM)33, and investigated interplays between analysed variables. RESULTS:Patients with fixed airflow limitation were characterized by lower lumen area and increased wall area and wall thickness ratios in distal airways, accompanied by raised LAA%. They had also higher blood neutrophilia, blood and BAL eosinophilia, increased circulating fibrinogen, periostin, and ADAM33. Blood neutrophilia, serum high density lipoproteins, thyroid-stimulating hormone, and shortened activated partial thromboplastin time were determinants of thicker reticular basement membrane (RBM). BAL eosinophilia was the only positive predictor of collagen I accumulation. Surprisingly, we observed a negative correlation between RBM thickening and collagen I deposit. Cluster analysis based on CT-metrics of the right lower lobe basal posterior bronchus revealed three well-separated clusters similar in age, asthma duration, and BMI, but different in RBM thickness, collagen I accumulation, and inflammatory markers. CONCLUSIONS AND CLINICAL RELEVANCE:Airway remodelling traits are mainly related to the Th2 profile, higher circulating ADAM33, and blood neutrophilia. Lung function abnormalities and RBM thickening correlate better with CT-metrics of distal than proximal airways.

摘要

背景: 气道结构改变在哮喘病理中很重要,需要进一步研究。 目的: 我们试图评估哪些计算机断层扫描 (CT) 指标、支气管组织学特征或血液和支气管肺泡灌洗 (BAL) 生物标志物与哮喘肺功能异常相关性最好。 方法: 在 105 例白色成人哮喘患者 (53 例伴有固定气流阻塞) 中,我们确定了两个近端 (右上叶心尖段和左侧心尖段) 的气道横截面几何形状和两个远端 (右侧和左侧基底后部) 支气管,定量低衰减肺面积 (LAA %),并分析了基于气道 CT 指标的聚类。我们还进行了支气管纤维镜检查和支气管内膜活检,评估了血液和支气管肺泡生物标志物,包括白细胞介素 (IL)-4 、 IL-5 、 IL-6 、 IL-10 、 IL-12p70 、 IL-17A 、 IL-23 、干扰素 (INF) γ 和 periostin,连同循环的一种解整合素和含金属蛋白酶结构域的蛋白 (ADAM) 33,并研究了分析变量之间的相互作用。 结果: 固定气流受限患者表现为下管腔面积和远端气道管壁面积及壁厚比增加,并伴有 LAA % 升高。他们也有较高的血液中性粒细胞增多、血液和 BAL 嗜酸性粒细胞增多、循环纤维蛋白原、骨膜蛋白和 adam33 增加。血中性粒细胞增多、血清高密度脂蛋白、促甲状腺激素和活化部分凝血活酶时间缩短是网状基底膜 (RBM) 增厚的决定因素。BAL 嗜酸性粒细胞增多是 I 型胶原蓄积的唯一阳性预测因子。令人惊讶的是,我们观察到 RBM 增厚和胶原 I 沉积之间呈负相关。基于右下叶基底后支气管 CT 指标的聚类分析显示,三个分离良好的聚类在年龄、哮喘持续时间和 BMI 方面相似,但在 RBM 厚度方面不同。胶原 I 蓄积,和炎症标志物。 结论和临床相关性: 气道重塑特征主要与 Th2 谱、较高的循环 ADAM33 和血液中性粒细胞增多有关。肺功能异常和 RBM 增厚与远端气道 CT 指标的相关性优于近端气道。

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影响因子:3.18
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"]

METHODS:Background Dye localization is a useful method for the resection of unidentifiable small pulmonary lesions. This study compares the transbronchial route with augmented fluoroscopic bronchoscopy (AFB) and conventional transthoracic CT-guided methods for preoperative dye localization in thoracoscopic surgery. Methods Between April 2015 and March 2019, a total of 231 patients with small pulmonary lesions who received preoperative dye localization via AFB or percutaneous CT-guided technique were enrolled in the study. A propensity-matched analysis, incorporating preoperative variables, was used to compare localization and surgical outcomes between the two groups. Results After matching, a total of 90 patients in the AFB group ( N  = 30) and CT-guided group ( N  = 60) were selected for analysis. No significant difference was noted in the demographic data between both the groups. Dye localization was successfully performed in 29 patients (96.7%) and 57 patients (95%) with AFB and CT-guided method, respectively. The localization duration (24.1 ± 8.3 vs. 21.4 ± 12.5 min, p  = 0.297) and equivalent dose of radiation exposure (3.1 ± 1.5 vs. 2.5 ± 2.0 mSv, p  = 0.130) were comparable in both the groups. No major procedure-related complications occurred in either group; however, a higher rate of pneumothorax (0 vs. 16.7%, p  = 0.029) and focal intrapulmonary hemorrhage (3.3 vs. 26.7%, p  = 0.008) was noted in the CT-guided group. Conclusion AFB dye marking is an effective alternative for the preoperative localization of small pulmonary lesions, with a lower risk of procedure-related complications than the conventional CT-guided method.

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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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