DNA tetrahedron-mediated immune-sandwich assay for rapid and sensitive detection of PSA through a microfluidic electrochemical detection system

AbstractProstate-specific antigen (PSA) is the most widely used biomarker for the early diagnosis of prostate cancer. Existing methods for PSA detection are burdened with some limitations and require improvement. Herein, we developed a novel microfluidic–electrochemical (μFEC) detection system for PSA detection. First, we constructed an electrochemical biosensor based on screen-printed electrodes (SPEs) with modification of gold nanoflowers (Au NFs) and DNA tetrahedron structural probes (TSPs), which showed great detection performance. Second, we fabricated microfluidic chips by DNA TSP-Au NF-modified SPEs and a PDMS layer with designed dense meandering microchannels. Finally, the μFEC detection system was achieved based on microfluidic chips integrated with the liquid automatic conveying unit and electrochemical detection platform. The μFEC system we developed acquired great detection performance for PSA detection in PBS solution. For PSA assays in spiked serum samples of the μFEC system, we obtained a linear dynamic range of 1–100 ng/mL with a limit of detection of 0.2 ng/mL and a total reaction time <25 min. Real serum samples of prostate cancer patients presented a strong correlation between the “gold-standard” chemiluminescence assays and the μFEC system. In terms of operation procedure, cost, and reaction time, our method was superior to the current methods for PSA detection and shows great potential for practical clinical application in the future.

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A Rapid, Direct and Validated HPLC- Fluorescence Method for the Quantification of Abiraterone and Abiraterone Acetate in Urine and Serum Samples from Patients with Castration- Resistant Prostate Cancer

Current Pharmaceutical Analysis ◽

10.2174/1573412913666170213152002 ◽

2018 ◽

Vol 14 (3) ◽

pp. 233-238 ◽

Cited By ~ 1

Author(s):

Juana Rodriguez ◽

Gregorio Castaneda ◽

Isabel Lizcano ◽

Jose C. Villa

Keyword(s):

Prostate Cancer ◽

Abiraterone Acetate ◽

Fluorescence Method ◽

Castration Resistant Prostate Cancer ◽

Serum Samples ◽

Castration Resistant ◽

Urine And Serum

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Adaptive Detection of Direct-Sequence Spread-Spectrum Signals Based on Knowledge-Enhanced Compressive Measurements and Artificial Neural Networks

Sensors ◽

10.3390/s21072538 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2538

Author(s):

Shuang Zhang ◽

Feng Liu ◽

Yuang Huang ◽

Xuedong Meng

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Spread Spectrum ◽

High Efficiency ◽

Detection System ◽

Direct Sequence Spread Spectrum ◽

Detection Performance ◽

Detection Methods ◽

Direct Sequence ◽

Artificial Neural

The direct-sequence spread-spectrum (DSSS) technique has been widely used in wireless secure communications. In this technique, the baseband signal is spread over a wider bandwidth using pseudo-random sequences to avoid interference or interception. In this paper, the authors propose methods to adaptively detect the DSSS signals based on knowledge-enhanced compressive measurements and artificial neural networks. Compared with the conventional non-compressive detection system, the compressive detection framework can achieve a reasonable balance between detection performance and sampling hardware cost. In contrast to the existing compressive sampling techniques, the proposed methods are shown to enable adaptive measurement kernel design with high efficiency. Through the theoretical analysis and the simulation results, the proposed adaptive compressive detection methods are also demonstrated to provide significantly enhanced detection performance efficiently, compared to their counterpart with the conventional random measurement kernels.

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Development of Sensitive Immunoassays for Free and Total Human Glandular Kallikrein 2

Clinical Chemistry ◽

10.1373/clinchem.2004.035253 ◽

2004 ◽

Vol 50 (9) ◽

pp. 1607-1617 ◽

Cited By ~ 36

Author(s):

Ville Väisänen ◽

Susann Eriksson ◽

Kaisa K Ivaska ◽

Hans Lilja ◽

Martti Nurmi ◽

...

Keyword(s):

Prostate Cancer ◽

Detection Limit ◽

Solid Phase ◽

Specific Antigen ◽

Control Group ◽

Serum Samples ◽

Human Kallikrein 2 ◽

Kallikrein 2 ◽

Capture Antibody ◽

Total Psa

Abstract Background: Free and total human kallikrein 2 (hK2) might improve the discrimination between prostate cancer and benign prostatic hyperplasia. Concentrations of hK2 are 100-fold lower than concentrations of prostate-specific antigen (PSA); therefore, an hK2 assay must have a low detection limit and good specificity. Methods: PSA- and hK2-specific monoclonal antibodies were used in solid-phase, two-site immunofluorometric assays to detect free and total hK2. The total hK2 assay used PSA-specific antibodies to block nonspecific signal. The capture antibody of the free hK2 assay did not cross-react with PSA. To determine the hK2 concentrations in the male bloodstream, total hK2 was measured in a control group consisting of 426 noncharacterized serum samples. Free and total hK2 were measured in plasma from 103 patients with confirmed prostate cancer. Results: All 426 males in the control group had a total hK2 concentration above the detection limit of 0.0008 μg/L. The median total hK2 concentration was 0.022 μg/L (range, 0.0015–0.37 μg/L). hK2 concentrations were 0.1–58% of total PSA (median, 3.6%). hK2 concentrations were similar in men 41–50 and 51–60 years of age. The ratio of hK2 to PSA steadily decreased from 5–30% at PSA <1 μg/L to 1–2% at higher PSA concentrations. In 103 patients with prostate cancer, the median hK2 concentration in plasma was 0.079 μg/L (range, 0.0015–16.2 μg/L). The median free hK2 concentration was 0.070 (range, 0.005–12.2) μg/L. The proportion of free to total hK2 varied from 17% to 131% (mean, 85%). Conclusions: The wide variation in the free-to-total hK2 ratio suggests that hK2 in blood plasma is not consistently in the free, noncomplexed form in patients with prostate cancer. The new assay is sufficiently sensitive to be used to study the diagnostic accuracies of free and total hK2 for prostate cancer.

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Quantification of a Proteotypic Peptide from Protein C Inhibitor by Liquid Chromatography–Free SISCAPA-MALDI Mass Spectrometry: Application to Identification of Recurrence of Prostate Cancer

Clinical Chemistry ◽

10.1373/clinchem.2012.199786 ◽

2013 ◽

Vol 59 (10) ◽

pp. 1514-1522 ◽

Cited By ~ 40

Author(s):

Morteza Razavi ◽

Lisa DS Johnson ◽

Julian J Lum ◽

Gary Kruppa ◽

N Leigh Anderson ◽

...

Keyword(s):

Prostate Cancer ◽

Liquid Chromatography ◽

High Throughput ◽

Protein C ◽

Specific Antigen ◽

Serum Concentrations ◽

Serum Samples ◽

Biomarker Validation ◽

Protein C Inhibitor ◽

Proteotypic Peptide

BACKGROUND Biomarker validation remains one of the most challenging constraints to the development of new diagnostic assays. To facilitate biomarker validation, we previously developed a chromatography-free stable isotope standards and capture by antipeptide antibodies (SISCAPA)-MALDI assay allowing rapid, high-throughput quantification of protein analytes in large sample sets. Here we applied this assay to the measurement of a surrogate proteotypic peptide from protein C inhibitor (PCI) in sera from patients with prostate cancer. METHODS A 2-plex SISCAPA-MALDI assay for quantification of proteotypic peptides from PCI and soluble transferrin receptor (sTfR) was used to measure these peptides in 159 trypsin-digested sera collected from 51 patients with prostate cancer. These patients had been treated with radiation with or without neoadjuvant androgen deprivation. RESULTS Patients who experienced biochemical recurrence of prostate cancer showed decreased serum concentrations of the PCI peptide analyte within 18 months of treatment. The PCI peptide concentrations remained increased in the sera of patients who did not experience cancer recurrence. Prostate-specific antigen concentrations had no predictive value during the same time period. CONCLUSIONS The high-throughput, liquid chromatography–free SISCAPA-MALDI assay is capable of rapid quantification of proteotypic PCI and sTfR peptide analytes in complex serum samples. Decreased serum concentrations of the PCI peptide were found to be related to recurrence of prostate cancer in patients treated with radiation with or without hormone therapy. However, a larger cohort of patients will be required for unequivocal validation of the PCI peptide as a biomarker for clinical use.

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Voltammetric Behavior ofp-Nitrophenol and Its Trace Determination in Human Urine by Liquid Chromatography with a Dual Reductive Mode Electrochemical Detection System

Electroanalysis ◽

10.1002/elan.200703989 ◽

2007 ◽

Vol 19 (21) ◽

pp. 2176-2184 ◽

Cited By ~ 41

Author(s):

Kevin C. Honeychurch ◽

John P. Hart

Keyword(s):

Liquid Chromatography ◽

Electrochemical Detection ◽

Human Urine ◽

Detection System ◽

Trace Determination ◽

Voltammetric Behavior

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Cost-Sensitive Approach to Improve the HTTP Traffic Detection Performance on Imbalanced Data

Security and Communication Networks ◽

10.1155/2021/6674325 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Wenmin Li ◽

Sanqi Sun ◽

Shuo Zhang ◽

Hua Zhang ◽

Yijie Shi

Keyword(s):

Feature Extraction ◽

Cost Function ◽

Network Traffic ◽

Detection System ◽

Imbalanced Data ◽

Detection Performance ◽

Learning Technology ◽

The Cost ◽

Decision Boundaries ◽

Traffic Detection

Aim. The purpose of this study is how to better detect attack traffic in imbalance datasets. The deep learning technology has played an important role in detecting malicious network traffic in recent years. However, it suffers serious imbalance distribution of data if the traffic model skews towards the modeling in the benign direction, because only a small portion of traffic is malicious, while most network traffic is benign. That is the reason why the authors wrote this manuscript. Methods. We propose a cost-sensitive approach to improve the HTTP traffic detection performance with imbalanced data and also present a character-level abstract feature extraction approach that can provide features with clear decision boundaries in addition. Finally, we design a spark-based HTTP traffic detection system based on these two approaches. Results. The methods proposed in this paper work well in imbalanced datasets. Compared to other methods, the experiment results indicate that our system has F1-score in a high precision. Conclusion. For imbalanced HTTP traffic detection, we confirmed that the method of feature extraction and the cost function is very effective. In the future, we may focus on how to use the cost function to further improve detection performance.

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