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Late rescue therapy with cord-derived mesenchymal stromal cells for established lung injury in experimental bronchopulmonary dysplasia.

脐带间充质基质细胞晚期挽救治疗实验性支气管肺发育不良的肺损伤。

  • 影响因子:3.30
  • DOI:10.1089/scd.2019.0116
  • 作者列表:"O'Reilly M","Möbius MA","Vadivel A","Ionescu L","Fung M","Eaton F","Greer JJ","Thébaud B
  • 发表时间:2020-01-09
Abstract

:Bronchopulmonary dysplasia (BPD), the main complication of extreme prematurity, has life-long consequences for lung health. Mesenchymal stromal cells (MSC) prevent lung injury in experimental BPD in newborn rodents when given in the immediate neonatal period. Whether MSC therapy can restore normal lung growth after established lung injury in adulthood is clinically relevant, but currently unknown. Experimental BPD was achieved by exposing newborn rats to 95% O2 from postnatal days 4-14. Human umbilical cord-derived MSCs were intratracheally administered to rats (1x106cells/kg body weight) as a single dose at 3 or 6 months of age followed by assessment at 5 or 8 months of age, respectively. Lung alveolar structure and vessel density were histologically analyzed. O2-exposed rats exhibited persistent lung injury characterized by arrested alveolar growth with airspace enlargement and a lower vessel density at both 5 and 8 months of age compared to controls. Single-dose MSC treatment at 3 months partially attenuated O2-induced alveolar injury and restored vessel density at 5 months. Treatment with a single dose at 6 months did not attenuate alveolar injury or vessel density at 8 months. However, treatment with multiple MSC doses at 6, 6.5, 7, and 7.5 months significantly attenuated alveolar injury and improved vessel density at 8 months of age. Treatment of the adult BPD lung with MSCs has the potential to improve lung injury if administered in multiple doses or at an early stage of adulthood.

摘要

: 支气管肺发育不良 (BPD) 是极度早产的主要并发症,对肺部健康有终身影响。间充质基质细胞 (MSC) 在新生儿期给药时可防止新生啮齿类动物实验性 BPD 的肺损伤。MSC 治疗是否能在成年期建立肺损伤后恢复正常的肺生长是临床相关的,但目前尚不清楚。实验 BPD 是通过将新生大鼠暴露于出生后 4-14 天的 95% O2 来实现的。人脐带来源的 MSCs 在 3 或 6 月龄时以单次剂量气管内给予大鼠 (1x106cells/kg 体重),然后在 5 或 8 月龄时分别进行评估。对肺泡结构和血管密度进行组织学分析。与对照组相比,O2-暴露的大鼠表现出以肺泡生长停滞、空域扩大和 5 月龄和 8 月龄血管密度较低为特征的持续性肺损伤。3 个月时单剂量 MSC 治疗可部分减轻 O2-诱导的肺泡损伤,并在 5 个月时恢复血管密度。6 个月时单剂量治疗未减轻 8 个月时的肺泡损伤或血管密度。然而,在 6 、 6.5 、 7 和 7.5 个月时给予多次 MSC 治疗可显著减轻肺泡损伤,并在 8 个月时改善血管密度。用 MSCs 治疗成人 BPD 肺,如果多次给药或在成年早期阶段,有可能改善肺损伤。

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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"]

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