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Genetic stability of a Newcastle disease virus vectored infectious laryngotracheitis virus vaccine after serial passages in chicken embryos.

新城疫病毒载体传染性喉气管炎病毒疫苗在鸡胚中连续传代后的遗传稳定性。

  • 影响因子:3.18
  • DOI:10.1016/j.vaccine.2019.10.074
  • 作者列表:"Yu Q","Li Y","Dimitrov K","Afonso CL","Spatz S","Zsak L
  • 发表时间:2020-01-22
Abstract

:Previously, we have demonstrated that the recombinant Newcastle disease virus (NDV) expressing the infectious laryngotracheitis virus (ILTV) glycoprotein D (gD) conferred protection against both virulent NDV and ILTV challenges in chickens. In this study, we evaluated the genetic stability of the recombinant vaccine after eight serial passages in embryonated chicken eggs (ECE). The vaccine master seed virus at the original egg-passage level 3 (EP3) was diluted and passaged in three separate repetitions (A, B and C) in ECE eight times (EP4 to EP11). RT-PCR analysis of the vaccine seed and egg-passaged virus stocks showed that there was no detectable insertion/deletion in the ILTV gD insert region. Next-generation sequencing analysis of the EP3 and EP11 virus stocks confirmed their genome integrity and revealed a total of thirteen single-nucleotide polymorphisms (SNPs). However, none of these SNPs were located in the ILTV gD insert or any of the known critical biological determinant positions. Virological and immunofluorescent assays provided additional evidence that the EP11 virus stocks retained their growth kinetics, low pathogenicity, and robust level of gD expression comparable to that of the vaccine master seed virus. This indicated that the SNPs were non-detrimental sporadic mutations. These results demonstrated that the insertion of ILTV gD gene into the NDV LaSota backbone did not significantly affect the genetic stability of the recombinant virus and that the rLS/ILTV-gD virus is a safe and genetically stable vaccine candidate after at least eight serial passages in ECE.

摘要

以前,我们已经证明了表达传染性喉气管炎病毒 (ILTV) 糖蛋白 D (gD) 的重组新城疫病毒 (NDV) 对鸡的强毒 NDV 和 ILTV 挑战都具有保护作用。在本研究中,我们评价了重组疫苗在鸡胚卵 (ECE) 中连续传代 8 次后的遗传稳定性。将原始卵传代 3 级 (EP3) 的疫苗掌握种子病毒稀释并分三次重复 (A 、 B 和 C) 传代在欧洲经委会八次 (EP4 至 EP11)。疫苗种子和蛋传代病毒库的 RT-PCR 分析显示,ILTV gD 插入区没有可检测到的插入/缺失。EP3 和 EP11 病毒库的新一代测序分析证实了它们的基因组完整性,共发现了 13 个单核苷酸多态性 (SNPs)。然而,这些 SNPs 都没有位于 ILTV gD 插入片段或任何已知的关键生物决定簇位置。病毒学和免疫荧光试验提供了额外的证据,表明 EP11 病毒原种保留了其生长动力学、低致病性和与疫苗主种子病毒相当的 gD 表达水平。这表明 SNPs 是非有害的散发性突变。这些结果表明,ILTV-gD 基因插入 NDV LaSota 骨架并不显著影响重组病毒的遗传稳定性,rLS/ILTV-gD 病毒是一种安全的遗传欧洲经委会至少 8 次连续传代后的稳定疫苗候选者。

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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"]

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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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