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A novel NFKBIA variant substituting serine 36 of IκBα causes immunodeficiency with warts, bronchiectasis and juvenile rheumatoid arthritis in the absence of ectodermal dysplasia.

一种新的 NFKBIA 变体替代 i κ b α 的丝氨酸 36 可导致免疫缺陷伴疣、支气管扩张和无外胚层发育不良的幼年类风湿关节炎。

  • 影响因子:3.12
  • DOI:10.1016/j.clim.2019.108269
  • 作者列表:"Sogkas G","Adriawan IR","Ringshausen FC","Baumann U","Schröder C","Klemann C","von Hardenberg S","Schmidt G","Bernd A","Jablonka A","Ernst D","Schmidt RE","Atschekzei F
  • 发表时间:2020-01-01
Abstract

:Genetic studies have led to identification of an increasing number of monogenic primary immunodeficiency disorders. Monoallelic pathogenic gain-of-function (GOF) variants in NFKBIA, the gene encoding IκBα, result in an immunodeficiency disorder, typically accompanied by anhidrotic ectodermal dysplasia (EDA). So far, 14 patients with immunodeficiency due to NFKBIA GOF mutations have been reported. In this study we report three patients from the same family with immunodeficiency, presenting with recurrent respiratory tract infections, bronchiectasis and viral skin conditions due to a novel pathogenic NFKBIA variant (c.106 T > G, p.Ser36Ala), which results in reduced IκBα degradation. Immunological investigations revealed inadequate antibody responses against vaccine antigens, despite hypergammaglobulinemia. Interestingly, none of the studied patients displayed features of EDA. Therefore, missense NFKBIA variants substituting serine 36 of IκBα, differ from the rest of pathogenic GOF NFKBIA variants in that they cause combined immunodeficiency, even in the absence of EDA.

摘要

: 遗传学研究导致越来越多的单基因原发性免疫缺陷病的鉴定。编码 i κ b α 的基因 NFKBIA 中的单等位基因致病性功能获得 (GOF) 变异导致免疫缺陷病,通常伴有无汗性外胚层发育不良 (EDA)。迄今为止,已有 14 例因 NFKBIA GOF 突变导致免疫缺陷的患者被报道。在本研究中,我们报告了来自同一家族的 3 例免疫缺陷患者,由于一种新的致病性 NFKBIA 变异 (c.106 tt  > g g, p. ser36Ala),导致 i κ b α 降解减少。免疫学调查显示,尽管存在高丙种球蛋白血症,但针对疫苗抗原的抗体反应不足。有趣的是,没有研究的患者表现出 EDA 的特征。因此,错义 NFKBIA 变异体替代 i κ b α 的丝氨酸 36,与其他致病性 GOF NFKBIA 变异体的不同之处在于,即使在没有 EDA 的情况下,它们也会引起联合免疫缺陷。

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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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