Intelligent Quantification of Picomolar Protein Concentration in Serum by Functionalized Nanopores

Das, Naren; Ray, R.K.; Ray, Shounak; RoyChaudhuri, C.

doi:10.1109/jsen.2018.2872853

Cited by 10 publications

(7 citation statements)

References 39 publications

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“…While the compositions of the layers and the deposition techniques vary, LBL coatings are most commonly used to manipulate nanopore size, 164 tailor surface chemistry, [165][166][167][168][169] or allow for the incorporation of other molecules for specific detection of certain analytes. [161][162][163]166,167,170 In order to adjust the diameter and surface charge density of a nanopore, Lepoitevin et al deposited alternating layers of PLL and poly(styrene sulfonate) onto track-etched, conicallyshaped nanopores in PET. 167 This approach modified the pore's ICR characteristics, while the addition of PLL grafted with poly(ethyleneglycol) (N-hydroxysuccinimide 5-pentanoate) ether 2-(biotinylamino) ethane (NHS-mPEG-biotin) made it possible to attach or recognize biotin-binding proteins.…”

Section: Other Coating Methods By Physisorptionmentioning

confidence: 99%

Surface coatings for solid-state nanopores

2019

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Section: Other Coating Methods By Physisorptionmentioning

confidence: 99%

Surface coatings for solid-state nanopores

2019

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“… 57 Moreover, a probabilistic fuzzy algorithm is adopted to quantify the concentration range of analytes through a comparison between the Gaussian distribution of the blockage amplitude and the calibration values. 58 The fuzzy property endows the algorithm with flexibility, which can tolerate the data variation from the experimental conditions to some extent. Details of translocation waveform are considered by invoking more sophisticated physical models 55 , 59 to distinguish proteins based on their fingerprint feature of blockage of current distribution.…”

Section: Non-ml-based Signal Processing For Nanopore Sensingmentioning

confidence: 99%

“…For example, the Mahalanobis distance metrics are adopted to assess the similarity of certain spikes with labeled clusters in the feature space so that five different amino acids can be identified . Moreover, a probabilistic fuzzy algorithm is adopted to quantify the concentration range of analytes through a comparison between the Gaussian distribution of the blockage amplitude and the calibration values . The fuzzy property endows the algorithm with flexibility, which can tolerate the data variation from the experimental conditions to some extent.…”

Section: Non-ml-based Signal Processing For Nanopore Sensingmentioning

confidence: 99%

A Guide to Signal Processing Algorithms for Nanopore Sensors

2021

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Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.

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“…The signal was processed using the Monte Carlo simulation. The resulting biosensor was suitable to detect the thrombin in human serum for a concentration down to 50 pM with a response time of 10 min [248]. Yi-Lun Ying et al developed a functionalized nanopipette for the detection of α-fetal protein (AFP).…”

Section: Protein Assemblymentioning

confidence: 99%