Rapid and low-cost, and disposable electrical sensor using an extended gate field-effect transistor for cardiac troponin I detection.

Field effect transistor (FET) biosensor is based on metal oxide field effect transistor that is gated by changes in the surface charges induced the reaction of biomolecules. In most cases of FET biosensor, FET biosensor is not being reused after the reaction; therefore, it is an important concept of investigate the biosensor with simplicity, cheap and reusability. However, the conventional cardiac troponin I (cTnI) sensing technique is inadequate owing to its low sensitivity and high operational time and cost. In this study, we developed a rapid and low-cost, and disposable electrical sensor using an extended gate field-effect transistor (EGFET) to detect cTnI, as a key biomarker for myocardiac infarction. We first investigated pH sensing characteristics according to the pH level, which provided a logarithmically linear sensitivity in the pH sensing buffer solution of approximately 57.9 mV/pH. Subsequently, we prepared a cTnI sample and monitored the reaction between cTnI and cTnI antibodies through the changes in the drain current and transfer curves. Our results showed that the EGFET biosensor could successfully detect the cTnI levels as well as the pH with low-cost and rapid detection.

Origami paper-based sample preconcentration using sequentially driven ion concentration polarization.

Ion concentration polarization (ICP) is one of the preconcentration techniques which can acquire a high preconcentration factor. Still, the main hurdles of ICP are its instability and low efficiency under physiological conditions with high ionic strength and abundant biomolecules. Here, we suggested a sequentially driven ICP process, which enhanced the electrokinetic force required for preconcentration, enabling enrichment of highly ionic raw samples without increasing the electric field. We acquired a 13-fold preconcentration factor (PF) in human serum using a paper-based origami structure consisting of multiple layers for three-dimensional sequential ICP (3D seq-ICP). Moreover, we demonstrated a paper-based enzyme-linked immunosorbent assay (ELISA) by 3D seq-ICP using tau protein, showing a 6-fold increase in ELISA signals.

Correction to: Spectral analysis framework for compressed sensing ultrasound signals.

In the original publication of the article, the Acknowledgements section was published incorrectly. The correct Acknowledgements section is given in this Correction.

Enhanced paper-based ELISA for simultaneous EVs/exosome isolation and detection using streptavidin agarose-based immobilization.

EVs/exosomes are considered as the next generation of biomarkers, including for liquid biopsies. Consequently, the quantification of EVs/exosomes is crucial for facilitating EV/exosome research and applications. Paper-based enzyme-linked immunosorbent assay (p-ELISA) is a portable diagnostic system with low cost that is simple and easy to use; however, it shows low sensitivity and linearity. In this study, we develop p-ELISA for targeting EVs/exosomes by using streptavidin agarose resin-based immobilization (SARBI). This method reduces assay preparation times, provides strong binding, and retains good sensitivity and linearity. The time required for the total assay, including preparation steps and surface immobilization, was shortened to ∼2 h. We evaluated SARBI p-ELISA systems with/without CD63 capture Ab and then with fetal bovine serum (FBS) and EVs/exosome-depleted fetal bovine serum (dFBS). The results provide evidence supporting the selective capture ability of SARBI p-ELISA. We obtain semiquantitative p-ELISA results using an exosome standard (ES) and human serum (HS), with R2 values of 0.95 and 0.92, respectively.

Highly selective reduced graphene oxide (rGO) sensor based on a peptide aptamer receptor for detecting explosives.

An essential requirement for bio/chemical sensors and electronic nose systems is the ability to detect the intended target at room temperature with high selectivity. We report a reduced graphene oxide (rGO)-based gas sensor functionalized with a peptide receptor to detect dinitrotoluene (DNT), which is a byproduct of trinitrotoluene (TNT). We fabricated the multi-arrayed rGO sensor using spin coating and a standard microfabrication technique. Subsequently, the rGO was subjected to photolithography and an etching process, after which we prepared the DNT-specific binding peptide (DNT-bp, sequence: His-Pro-Asn-Phe-Se r-Lys-Tyr-IleLeu-HisGln-Arg-Cys) and DNT non-specific binding peptide (DNT-nbp, sequence: Thr-Ser-Met-Leu-Leu-Met-Ser-Pro-Lys-His-Gln-Ala-Cys). These two peptides were prepared to function as highly specific and highly non-specific (for the control experiment) peptide receptors, respectively. By detecting the differential signals between the DNT-bp and DNT-nbp functionalized rGO sensor, we demonstrated the ability of 2,4-dinitrotoluene (DNT) targets to bind to DNT-specific binding peptide surfaces, showing good sensitivity and selectivity. The advantage of using the differential signal is that it eliminates unwanted electrical noise and/or environmental effects. We achieved sensitivity of 27 ± 2 × 10-6 per part per billion (ppb) for the slope of resistance change versus DNT gas concentration of 80, 160, 240, 320, and 480 ppm, respectively. By sequentially flowing DNT vapor (320 ppb), acetone (100 ppm), toluene (1 ppm), and ethanol (100 ppm) onto the rGO sensors, the change in the signal of rGO in the presence of DNT gas is 6400 × 10-6 per ppb whereas the signals from the other gases show no changes, representing highly selective performance. Using this platform, we were also able to regenerate the surface by simply purging with N2.

Spectral analysis framework for compressed sensing ultrasound signals.

PURPOSE: Compressed sensing (CS) is the theory of the recovery of signals that are sampled below the Nyquist sampling rate. We propose a spectral analysis framework for CS data that does not require full reconstruction for extracting frequency characteristics of signals by an appropriate basis matrix. METHODS: The coefficients of a basis matrix already contain the spectral information for CS data, and the proposed framework directly utilizes them without completely restoring original data. We apply three basis matrices, i.e., DCT, DFT, and DWT, for sampling and reconstructing processes, subsequently estimating the attenuation coefficients to validate the proposed method. The estimation accuracy and precision, as well as the execution time, are compared using the reference phantom method (RPM). RESULTS: The experiment results show the effective extraction of spectral information from CS signals by the proposed framework, and the DCT basis matrix provides the most accurate results while minimizing estimation variances. The execution time is also reduced compared with that of the traditional approach, which completely reconstructs the original data. CONCLUSION: The proposed method provides accurate spectral analysis without full reconstruction. Since it effectively utilizes the data storage and reduces the processing time, it could be applied to small and portable ultrasound systems using the CS technique.

Optimization of the autocorrelation weighting function for the time-domain calculation of spectral centroids.

Spectral centroid from the backscattered ultrasound provides important information about the attenuation properties of soft tissues and Doppler effects of blood flows. Because the spectral centroid is originally determined from the power spectrum of backscattered ultrasound signals in the frequency domain, it is natural to calculate it after converting time-domain signals into spectral domain signals, using the fast Fourier transform (FFT). Recent research, however, derived the time-domain equations for calculating the spectral centroid using a Parseval's theorem, to avoid the calculation of the Fourier transform. The work only presented the final result, which showed that the computational time of the proposed time-domain method was 4.4 times faster than that of the original FFT-based method, whereas the average estimation error was negligible. In this paper, we present the optimal design of the autocorrelation weighting function, which is used for the timedomain spectral centroid estimation process, to reduce the computational time significantly. We also carry out a comprehensive analysis of the computational complexities of the FFTbased and time-domain methods with respect to the length of ultrasound signal segments. The simulation results using numerical phantoms show that, with the optimized autocorrelation weighting function, we only need approximately 3% of the full set of data points. In addition to that, because the proposed optimization technique requires a fixed number of data points to calculate the spectral centroid, the execution time is constant as the length of the data segment increases, whereas the execution time of the conventional FFT-based method is increased. Analysis of the computational complexities between the proposed method and the conventional FFT-based method presents O(N) and O(Nlog2N), respectively.