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MChip, a low density microarray, differentiates among seasonal human H1N1, North American swine H1N1, and the 2009 pandemic H1N1

机译:低密度微芯片MChip可区分季节性人类H1N1,北美猪H1N1和2009年大流行H1N1

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Please cite this paper as: Heil et al. (2010) MChip, a low density microarray, differentiates among seasonal human H1N1, North American swine H1N1, and the 2009 pandemic H1N1. Influenza and Other Respiratory Viruses 4(6), 411–416.Background The MChip uses data from the hybridization of amplified viral RNA to 15 distinct oligonucleotides that target the influenza A matrix (M) gene segment. An artificial neural network (ANN) automates the interpretation of subtle differences in fluorescence intensity patterns from the microarray. The complete process from clinical specimen to identification including amplification of viral RNA can be completed in 8 hours for under US$10.Objectives The work presented here represents an effort to expand and test the capabilities of the MChip to differentiate influenza A/H1N1 of various species origin.Methods The MChip ANN was trained to recognize fluorescence image patterns of a variety of known influenza A viruses, including examples of human H1N1, human H3N2, swine H1N1, 2009 pandemic influenza A H1N1, and a wide variety of avian, equine, canine, and swine influenza viruses. Robustness of the MChip ANN was evaluated using 296 blinded isolates.Results Training of the ANN was expanded by the addition of 71 well-characterized influenza A isolates and yielded relatively high accuracy (little misclassification) in distinguishing unique H1N1 strains: nine human A/H1N1 (88·9% correct), 35 human A/H3N2 (97·1% correct), 31 North American swine A/H1N1 (80·6% correct), 14 2009 pandemic A/H1N1 (87·7% correct), and 23 negative samples (91·3% correct). Genetic diversity among the swine H1N1 isolates may have contributed to the lower success rate for these viruses.Conclusions The current study demonstrates the MChip has the capability to differentiate the genetic variations among influenza viruses with appropriate ANN training. Further selective enrichment of the ANN will improve its ability to rapidly and reliably characterize influenza viruses of unknown origin.
机译:请将此论文引用为:Heil等。 (2010)MChip,一种低密度微阵列,可在季节性人类H1N1,北美猪H1N1和2009大流行H1N1之间进行区分。流感和其他呼吸道病毒4(6),411–416。背景MChip使用扩增的病毒RNA与针对A型流感基质(M)基因片段的15种不同寡核苷酸杂交获得的数据。人工神经网络(ANN)可自动解释微阵列中荧光强度模式的细微差异。从临床标本到鉴定,包括病毒RNA扩增的完整过程,只需不到10美元即可在不到8小时的时间内完成。目的本文提出的工作是努力扩展和测试MChip区分各种A型/ H1N1型流感病毒的能力。方法对MChip ANN进行训练以识别各种已知的A型流感病毒的荧光图像模式,包括人类H1N1,人类H3N2,猪H1N1、2009大流行性甲型H1N1和各种禽类,马类,犬和​​猪流感病毒。使用296种盲盲分离株评估了MChip ANN的鲁棒性。结果通过添加71种特征明确的甲型流感分离株扩大了ANN的训练,并且在区分独特的H1N1菌株方面产生了相对较高的准确性(极少分类错误):9种人A / H1N1 (正确率88·9%),35人A / H3N2(正确率97·1%),31头北美猪A / H1N1(正确率80·6%),2009年大流行A / H1N1大流行(正确率87·7%),和23个阴性样本(正确率为91·3%)。猪H1N1分离株之间的遗传多样性可能导致这些病毒的成功率降低。结论本研究表明,通过适当的ANN训练,MChip具有区分流感病毒遗传变异的能力。 ANN的进一步选择性富集将提高其快速可靠地鉴定未知来源流感病毒的能力。

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