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Airway smooth muscle adapting in dynamic conditions is refractory to the bronchodilator effect of a deep inspiration.

动态条件下的气道平滑肌适应对深部吸气的支气管扩张剂效应是无效的。

  • 影响因子:3.82
  • DOI:10.1152/ajplung.00270.2019
  • 作者列表:"Gazzola M","Khadangi F","Clisson M","Beaudoin J","Clavel MA","Bossé Y
  • 发表时间:2020-01-08
Abstract

:Airway smooth muscle (ASM) is continuously strained during breathing at tidal volume. Whether this tidal strain influences the magnitude of the bronchodilator response to a deep inspiration (DI) is not clearly defined. The present in vitro study examines the effect of tidal strain on the bronchodilator effect of DIs. ASM strips from sheep tracheas were mounted in organ baths and then subjected to stretches (30% strain) simulating DIs at varying time intervals. In between simulated DIs, the strips were either held at a fixed length (isometric) or oscillated continuously by 6% (length oscillations) to simulate tidal strain. The contractile state of the strips was also controlled by adding either methacholine or isoproterenol to activate or relax ASM, respectively. Although the time-dependent gain in force caused by methacholine was attenuated by length oscillations, part of the acquired force in the oscillating condition was preserved post-simulated DIs, which was not the case in the isometric condition. Consequently, the bronchodilator effect of simulated DIs (i.e., the decline in force post- versus pre-simulated DIs) was attenuated in oscillating versus isometric conditions. These findings suggest that an ASM operating in a dynamic environment acquired adaptations that make it refractory to the decline in contractility inflicted by a larger strain simulating a DI.

摘要

: 潮气量呼吸时气道平滑肌 (ASM) 持续紧张。这种潮汐应变是否影响支气管扩张剂对深部吸气 (DI) 反应的大小尚未明确定义。目前的体外研究考察了潮气应变对 DIs 支气管扩张剂作用的影响。将绵羊气管的 ASM 条带安装在器官浴液中,然后在不同的时间间隔进行拉伸 (30% 应变) 模拟 DIs。在模拟 DIs 之间,条带要么保持固定长度 (等距),要么连续振荡 6% (长度振荡),以模拟潮汐应变。也通过分别加入乙酰甲胆碱或异丙肾上腺素激活或松弛 ASM 来控制条带的收缩状态。虽然乙酰甲胆碱引起的力的时间依赖性增益被长度振荡衰减,但在振荡条件下获得的力的一部分被保留了后模拟 DIs, 在等距条件下情况并非如此。因此,在振荡与等长条件下,模拟 DIs 的支气管扩张剂效应 (即与模拟 DIs 前相比,力的下降) 减弱。这些发现表明,在动态环境中操作的 ASM 获得了适应性,使其对模拟 DI 的较大应变造成的收缩性下降具有抵抗性。

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发表时间:2020-01-13
来源期刊:Surgical Endoscopy
DOI:10.1007/s00464-019-07334-4
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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"]

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