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首页> 外文期刊>IEEE transactions on nanotechnology >Noise Spectroscopy Analysis for Estimation of a Specific Biomolecule in a Complex Mixture Using Solid-State Nanopore
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Noise Spectroscopy Analysis for Estimation of a Specific Biomolecule in a Complex Mixture Using Solid-State Nanopore

机译:使用固态纳米孔估计复杂混合物中特定生物分子的噪声光谱分析

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This paper presents a noise spectroscopy analysis of the current traces recorded in a functionalized silicon oxide nanopore in presence of specific antigen (Hep-B), nonspecific antigen (BSA), and their complex mixture for the first time. It is observed that though the on and off dwell times can differentiate nonspecific antigen from specific antigen in pure buffer, an approximate quantification of the specific antigen with low-dissociation constant of the receptor-ligand pair, becomes almost impossible in complex mixture. This has been ascribed to the significant overlap in the current blockade sensitivity values between the different concentration ranges of the specific antigen. On the contrary, a noise spectroscopy analysis shows a Lorentzian spectrum in presence of specific antigen with a distinct shift in the roll-off frequency, such that upto 1-nM BSA concentration; it has been possible to estimate the concentration of the specific antigen even for 1-pM Hep-B. However, for BSA concentration greater than 1 nM, the roll-off frequency for a particular concentration of specific antigen starts deviating from its value in pure buffer and overlaps with other concentration range. This problem has been addressed by processing the fractional change in current blockade and roll-off frequency by a partial least square-discriminant analysis based multivariate statistical model. It has been observed that the learning model yields 91.5% correct classification with the solutions, and has been able to predict the concentration of Hep-B quite closely even for a low value of 1 pM in presence of 100 nM concentration of BSA.
机译:本文首次对存在特定抗原(Hep-B),非特异性抗原(BSA)及其复杂混合物的功能化氧化硅纳米孔中记录的电流痕迹进行了噪声光谱分析。观察到,尽管接通和断开的停留时间可以在纯缓冲液中区分非特异性抗原和特异性抗原,但是在复杂的混合物中,几乎不可能定量具有受体-配体对的低解离常数的特异性抗原。这被归因于特定抗原的不同浓度范围之间当前的阻断敏感性值的显着重叠。相反,噪声光谱分析显示存在特定抗原的洛伦兹谱,其滚降频率有明显的变化,使得浓度高达1-nM BSA。即使对于1-pM Hep-B,也有可能估计特异性抗原的浓度。但是,对于BSA浓度大于1 nM的情况,特定浓度的特定抗原的滚降频率开始偏离其在纯缓冲液中的值,并与其他浓度范围重叠。通过基于局部最小二乘判别分析的多元统计模型处理当前封锁和滚降频率的部分变化,可以解决此问题。已经观察到,该学习模型使用该解决方案可获得91.5%的正确分类,并且即使在存在100 nM浓度的BSA的情况下,即使对于1 pM的低值,也能够非常紧密地预测Hep-B的浓度。

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