Fast Detection Method Research Articles

Rapid detection of Ebolavirus using isothermal recombinase-aided amplification.

Ebolavirus disease (EVD) is an often-lethal disease caused by the genus Ebolavirus (EBOV). Although vaccines are being developed and recently used, outbreak control still relies on a combination of various factors, including rapid identification of EVD cases. This allows rapid patient isolation and control measure implementation. Ebolavirus diagnosis is performed in treatment centers or reference laboratories, which usually takes a few hours to days to confirm the outbreak or deliver a clear result. A fast and field-deployable molecular detection method, such as the isothermal amplification recombinase-aided amplification (RAA), could significantly reduce sample-to-result time. In this study, a RT-RAA assay was evaluated for EBOV detection. Various primer and probe combinations were screened; analytical sensitivity and cross-specificity were tested. A total of 40 archived samples from the 2014 to 2016 Ebola outbreak in West Africa were tested with both the reference method real-time RT-PCR and the established RT-RAA assay. The assay could detect down to 22.6 molecular copies per microliter. No other pathogens were detected with the Ebolavirus RT-RAA assay. Testing 40 samples yield clinical sensitivity and specificity of 100% each. This rapid isothermal RT-RAA assay can replace the previous RT-RPA and continue to offer rapid EBOV diagnostics.

Computerized analysis of haptens for the ultrasensitive and specific detection of Pyriftalid

Pyriftalid (Pyr) is one of the most commonly used herbicides and due to its widespread and improper use, it has led to serious pollution of groundwater, soil and other ecosystems, threatening human health. A rapid method to detect Pyr was urgently needed. A high specific monoclonal antibody (mAb) against Pyr with IC50 values of 4.7 ng/mL was obtained by mAb screening technique and method with enhanced matrix effect. The study firstly proposed colloidal gold immunochromatographic test strips (CGIA) for Pyr, which enables rapid qualitative and quantitative determination of a large number of samples anytime and anywhere, so as to effectively monitor Pyr in environment and grain samples. Based on the properties of the desired Pyr antibody, the hapten Pyr-hapten-4 with high structural similarity to Pyr molecule, similar electrostatic potential distribution, and the ability to expose Pyr functional groups was screened out from five different Pyr haptens, which was consistent with mouse antiserum test. The CGIA quickly analyze the Pyr content in positive samples such as water samples, soil samples, paddy samples, brown rice samples within 10 min, the LOD for Pyr by CGIA as low as 1.84 ng/g, the v LOD value as low as 6 ng/g, and the extinction value as low as 25 ng/g. The content of positive samples detected by CGIA was consistent with the quantitative results of LC-MS/MS, the relative accuracy was within the range of 97–103 %. The recovery rate range for Pyr by CGIA was 92.0–99.7 %, and the coefficient of variation was between 1.30–8.56 %. It indicated Pyr-targeted CGIA test strip was an efficient and fast detection method to detect real environment and food samples.

Early Detection and Monitoring of Anastomotic Leaks via Naked Eye-Readable, Non-Electronic Macromolecular Network Sensors.

Anastomotic leakage (AL) is the leaking of non-sterile gastrointestinal contents into a patient's abdominal cavity. AL is one of the most dreaded complications following gastrointestinal surgery, with mortality rates reaching up to 27%. The current diagnostic methods for anastomotic leaks are limited in sensitivity and specificity. Since the timing of detection directly impacts patient outcomes, developing new, fast, and simple methods for early leak detection is crucial. Here, a naked eye-readable, electronic-free macromolecular network drain fluid sensor is introduced for continuous monitoring and early detection of AL at the patient's bedside. The sensor array comprises three different macromolecular network sensing elements, each tailored for selectivity toward the three major digestive enzymes found in the drainage fluid during a developing AL. Upon digestion of the macromolecular network structure by the respective digestive enzymes, the sensor produces an optical shift discernible to the naked eye. The diagnostic efficacy and clinical applicability of these sensors are demonstrated using clinical samples from 32 patients, yielding a Receiver Operating Characteristic Area Under the Curve (ROC AUC) of 1.0. This work has the potential to significantly contribute to improved patient outcomes through continuous monitoring and early, low-cost, and reliable AL detection.

Smart Sensing of Nerve Agents.

The recent international scenario highlights the importance to protect human health and environmental quality from toxic compounds. In this context, organophosphorous (OP) Nerve Agents (NAs) have received particular attention, due to their use in terrorist attacks. Classical instrumental detection techniques are sensitive and selective, but they cannot be used in real field due to the high cost, specialized personnel requested and huge size. For these reasons, the development of practical, easy and fast detection methods (smart methods) is the future of this field. Indeed, starting from initial sensing research, based on optical and/or electrical sensors, today the development and use of smart strategies to detect NAs is the current state of the art. This review summarizes the smart strategies to detect NAs, highlighting some important parameters, such as linearity, limit of detection and selectivity. Furthermore, some critical comments of the future on this field, and in particular, the problems to be solved before a real application of these methods, are provided.

A versatile ratiometric fluorescence nanoprobe for the determination of clonazepam in patients' plasma samples.

Despite the necessity of the study of therapeutic drug monitoring of clonazepam (CLZ), there are only a few fast detection methods available for determining CLZ in biological media. This study aims to develop a cost-effective and ratiometric probe for the quantification of CLZ in plasma samples. Fluorescent polydopamine nanoparticles were produced through a self-polymerization process at a pH of 8.5. Rhodamine B molecules were employed as a fluorescent reference material, emitting stable fluorescence in the visible range. The fabricated probe exhibited a specific detection capability for CLZ. The fluorescence emission of the probe was enhanced in two concentration ranges: from 50 ng/mL to 1.0 μg/mL and from 1.0 to 15.0 μg/mL with a lower limit of quantification of 50 ng/mL, indicating the sensitivity of the probe for detecting CLZ plasma levels. The accuracy of the probe is favorable which could be recommended for CLZ monitoring in the biological media. Furthermore, this probe is highly specific towards CLZ in the presence of various interfering agents which is mainly caused by its ratiometric nature. The developed platform showed high reliability in quantifying CLZ concentrations in patients' plasma samples. Hence, the fabricated probe could be recommended as a reliable method for the routine detection of CLZ in clinical settings.

Central composite design-assisted visual and non-invasive detection of sertraline by sweet lemon waste-derived core-shell AuNPs@CDs

This study reports a fast and visual detection method of antidepressant sertraline (SRT) drug by the core-shell AuNPs@CDs as the nanoprobes. The CDs has been eco-friendly synthesized from sweet lemon wastes to directly reduce Au+ to AuNPs without any external photoirradiation process or additional reductants. Optimizing key variables that impact the sensing process has been done using the central composite design (CCD) approach to simulate the assay condition before the analysis. Adding SRT with different concentrations to the nanoprobes under mildly acidic conditions presents an absorbance peak at 560 nm with purple color tonalities that differ from the behavior of alone nanoprobes (530 nm, pink color). The obtained absorption change is linearly proportional to the increase of SRT concentration from 1 μM to 35 μM with a limit of detection (LOD) value of 100 nM. The color changes with a vivid tonality from pink and purple to violet as the colorful fingerprint patterns are readily traceable by the naked eye, allowing the visual assay of SRT. The greenness of the developed approach is well evaluated by some international indexes including the complimentary green analytical procedure (ComplexGAPI) and also, the analytical greenness (AGREE) indexes. The proposed waste-derived nanoprobes based on the eco-friendly procedure not only conduct quantitative and qualitative non-invasive analysis of SRT by the naked eye but also, may widen for other applications in various fields.

A sandwich FRET biosensor for lysozyme detection based on peptide-functionalized gold nanoparticles and FAM-labeled aptamer

Lysozyme (LYZ) plays a crucial role in the body's immune defense system. Monitoring LYZ levels can provide valuable insights into the diagnosis and severity assessment of various diseases. Traditionally, antibody-based sandwich assays are employed for LYZ detection, but they are often time-consuming and operationally complicated. In this research, a novel sandwich FRET biosensor was developed, which enables rapid detection of LYZ based on peptide-functionalized gold nanoparticles (pAuNPs) and FAM-labeled aptamer (Apt-FAM). Initially, a mixture of Apt-FAM and pAuNPs resulted in partial quenching of the Apt-FAM fluorescence emission through an inner filter effect (IFE), with negligible energy transfer because of the electrostatic repulsion between the negatively charged pAuNPs and Apt-FAM. The introduction of LYZ into the mixture drove the specific binding of Apt-FAM and pAuNPs to LYZ, facilitating the formation of a pAuNPs-LYZ-aptamer sandwich structure. The formation of this complex drew the pAuNPs and Apt-FAM into close enough proximity to enable FRET to occur, which in turn effectively quenched the fluorescence emission of FAM. The decrease in FAM fluorescence intensity was correlated with the increasing concentration of LYZ. Thus, a sandwich FRET biosensor was successfully developed for LYZ detection with a linear detection range of 0–1.75 μM and a detection limit of 85 nM. Additionally, the biosensor allowed visual detection of LYZ in a 96-well microplate, with a rapid response time of just 15 s. This study introduces a innovative sandwich FRET biosensor that combines aptamer and peptide recognition elements, offering a fast and antibody-free method for protein detection.

Fast bus voltage detection method for single-phase power converters with split capacitors based on reduced-order generalized integrals

Fast bus voltage detection method for single-phase power converters with split capacitors based on reduced-order generalized integrals

Mo(IV) Ion-Modulated BSA-Protected Gold Nanocluster Probe for Fluorescence Turn-On Detection of Trimethylamine N-Oxide (TMAO).

Trimethylamine N-oxide (TMAO), a molecule produced by the microbiota, has been associated with human health and illness. Its early discovery in body fluids may affect our understanding of the pathophysiology and treatment of many illnesses. Therefore, our knowledge of the pathophysiology and diagnostics of disorders associated with TMAO might be enhanced by the creation of dependable and fast methods for TMAO detection. Therefore, we developed a fluorescent probe for detecting TMAO utilizing an on-off-on strategy. Bovine serum albumin (BSA)@AuNCs luminescence is effectively quenched by Mo4+ because BSA@AuNCs and Mo4+ have a strong binding relationship. Mo4+ ions can substantially decrease the emission intensity of gold nanoclusters by establishing a BSA@AuNCs-Mo system. Then, the luminescence of BSA@AuNCs was restored due to the interaction between Mo4+ and TMAO. A significant linear relationship was seen between the emission intensity and TMAO concentration within the 0-201 μM range, with a detection limit of 1.532 μM. Additionally, the method can measure TMAO in blood and urine samples.

Vis-NIR spectroscopic discriminant analysis of aflatoxin B1 excessive standard in peanut meal as feedstuff materials

A fast, simple and reagent-free detection method for aflatoxin B1 (AFB1) is of great significance to food safety and human health. Visible and near-infrared (Vis-NIR) spectroscopy was applied to the discriminant analysis of AFB1 excessive standard of peanut meal as feedstuff materials. Two types of excessive standard discriminant models based on spectral quantitative analysis with partial least squares (PLS) and direct pattern recognition with partial least squares-discrimination analysis (PLS-DA) were established, respectively. Multi-parameter optimization of Norris derivative filtering (NDF) was used for spectral preprocessing; the two-stage wavelength screening method based on equidistant combination-wavelength step-by-step phase-out (EC-WSP) was used for wavelength optimization. A rigorous sample experimental design of calibration-prediction-validation was utilized. The calibration and prediction samples were used for modeling and parameter optimization, and the selected model was validated using the independent validation samples. For quantitative analysis-based, the positive, negative and total recognition-accuracy rates in validation (RARV+, RARV-, and RARV) were 84.8 %, 74.6 % and 79.8 %, respectively; but, the relative root mean square error of prediction was as high as 51.0 %. For pattern recognition-based, the RARV+, RARV-, and RARV were 93.3 %, 90.5 % and 91.9 %, respectively. Moreover, the number of wavelengths N was drastically reduced to 17, and the discrete wavelength combination was in NIR overtone frequency region. The results indicated that, the EC-WSP-PLS-DA model achieved significantly better discrimination effect. Thus demonstrated that Vis-NIR spectroscopy has feasibility for the excessive standard discrimination of aflatoxin B1 in feedstuff materials.

Fast Attack Detection for Cyber-Physical Systems Using Dynamic Data Encryption.

To defend the cyber-physical system (CPSs) from cyber-attacks, this work proposes an unified intrusion detection mechanism which is capable to fast hunt various types of attacks. Focusing on securing the data transmission, a novel dynamic data encryption scheme is developed and historical system data is used to dynamically update a secret key involved in the encryption. The core idea of the dynamic data encryption scheme is to establish a dynamic relationship between original data, secret key, ciphertext and its decrypted value, and in particular, this dynamic relationship will be destroyed once an attack occurs, which can be used to detect attacks. Then, based on dynamic data encryption, a unified fast attack detection method is proposed to detect different attacks, including replay, false data injection (FDI), zero-dynamics, and setpoint attacks. Extensive comparison studies are conducted by using the power system and flight control system. It is verified that the proposed method can immediately trigger the alarm as soon as attacks are launched while the conventional χ2 detection could only capture the attacks after the estimation residual goes over the predetermined threshold. Furthermore, the proposed method does not degrade the system performance. Last but not the least, the proposed dynamic encryption scheme turns to normal operation mode as the attacks stop.

Fast loop closure detection using probabilistic integration of pose and appearance similarity

Loop closure detection is a key technique for robots to minimize the accumulated localization and mapping errors after long-time explorations of simultaneous localization and mapping. However, the requirement for efficiency and accuracy performance for mobile robot applications is not well satisfied. In this article, we propose a fast and accurate loop closure detection method by exploiting both pose-based and appearance-based information in a probabilistic manner, inspired by the complementarity between the pose-based and the appearance-based information. Our approach formulates a probability framework combing the pose-based loop closure detection probability and the appearance-based loop closure detection probability. In the proposed framework, the pose-based loop closure detection model is firstly derived from the nonlinear optimization model of odometry. Then the appearance-based loop similarity and the pose-based loop similarity are combined into a joint framework to improve the loop closure detection performance. We implemented our approach using C++ and ROS and thoroughly tested it on the publicly available datasets. The experiments presented in this article suggest that the proposed method can achieve high efficiency and accuracy performance on loop closure detection.

Early-Stage ISC Fault Detection for Ship Lithium Batteries Based on Voltage Variance Analysis

With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale deployment of lithium batteries has also brought a series of safety problems to ship operations, especially the battery internal short circuit (ISC). Battery ISC faults are very hidden and unpredictable at the initial stage and often fail to be detected in time, ultimately leading to overheating, fire or even an explosion of the ship’s power system. Based on this, this paper proposes a fast and accurate method for early-stage ISC fault location and detection of lithium batteries. Initially, voltage variations across the lithium battery packs are quantified using curvilinear Manhattan distances to pinpoint faulty battery units. Subsequently, the localized characteristics of voltage variance among adjacent batteries are leveraged to detect an early-stage ISC fault. Simulation results indicate that the proposed method can quickly and accurately locate the position of 5 Ω, 10 Ω and 15 Ω ISC faulty batteries within the battery pack, as well as detect the abnormal batteries in a timely manner with considerable sensitivity and reliability.

Fast Rail Fastener Screw Detection for Vision-Based Fastener Screw Maintenance Robot Using Deep Learning

Fastener screws are critical components of rail fasteners. For the fastener screw maintenance robot, an image-based fast fastener screw detection method is urgently needed. In this paper, we propose a light-weight model named FSS-YOLO based on YOLOv5n for rail fastener screw detection. The C3Fast module is presented to replace the C3 module in the backbone and neck to reduce Params and FLOPs. Then, the SIoU loss is introduced to enhance the convergence speed and recognition accuracy. Finally, for the enhancement of the screw detail feature fusion, the shuffle attention (SA) is incorporated into the bottom-up process in the neck part. Experiment results concerning CIoU and DIoU for loss, MobileNetv3 and GhostNet for light-weight improvement, simple attention mechanism (SimAM), and squeeze-and-excitation (SE) attention for the attention module, and YOLO series methods for performance comparison are listed, demonstrating that the proposed FSS-YOLO significantly improves the performance, with higher accuracy and lower computation cost. It is demonstrated that the FSS-YOLO is 7.3% faster than the baseline model in FPS, 17.4% and 19.5% lower in Params and FLOPs, respectively, and the P, mAP@50, Recall, and F1 scores are increased by 10.6% and 6.4, 13.4%, and 12.2%, respectively.

Systematic Optimization of Universal Real-Time Hypersensitive Fast Detection Method for HBsAg in Serum Based on SERS.

Hepatitis B virus (HBV) is a major cause of liver cirrhosis and hepatocellular carcinoma, with HBV surface antigen (HBsAg) being a crucial marker in the clinical detection of HBV. Due to the significant harm and ease of transmission associated with HBV, HBsAg testing has become an essential part of preoperative assessments, particularly for emergency surgeries where healthcare professionals face exposure risks. Therefore, a timely and accurate detection method for HBsAg is urgently needed. In this study, a surface-enhanced Raman scattering (SERS) sensor with a sandwich structure was developed for HBsAg detection. Leveraging the ultrasensitive and rapid detection capabilities of SERS, this sensor enables quick detection results, significantly reducing waiting times. By systematically optimizing critical factors in the detection process, such as the composition and concentration of the incubation solution as well as the modification conditions and amount of probe particles, the sensitivity of the SERS immune assay system was improved. Ultimately, the sensor achieved a sensitivity of 0.00576 IU/mL within 12 min, surpassing the clinical requirement of 0.05 IU/mL by an order of magnitude. In clinical serum assay validation, the issue of false positives was effectively addressed by adding a blocker. The final sensor demonstrated 100% specificity and sensitivity at the threshold of 0.05 IU/mL. Therefore, this study not only designed an ultrasensitive SERS sensor for detecting HBsAg in actual clinical serum samples but also provided theoretical support for similar systems, filling the knowledge gap in existing literature.

Control and fault diagnosis for two-interleaved boost converter associated with to PEMFC

This paper presents an enhanced control and fast fault detection method, localisation and isolisation for a two-phase interleaved boost DC/DC converter specifically designed for fuel cell applications. Fuel cells are known for their rapidly changing output voltage and current due to fluctuating load demands. Conventional converters might not be able to respond fast enough to maintain stable operation under such conditions.The two-phase interleaved design offers significant advantages. such as reduced ripple current delivered by dividing the Input current, the converter significantly lowers input and current ripple compared to single-phase designs. Each phase handles a smaller portion (1/N) of the total current, leading to reduced stress on individual components and enhanced reliability and operating margins. Additionally, Interleaving minimizes conduction losses by dividing the current among multiple converter stages. This work presents a model of a two-phase interleaved boost DC/DC converter and implements a PI controller for its output voltage regulation. This control method guarantees good performance even under varying reference voltages and load conditions. The proposed algorithm addresses the challenge of open circuit switch failures by utilizing current slopes for swift fault detection and rapid corrective action. This ensures accurate reference tracking, desired dynamic response, and timely fault detection and localization. The entire system was rigorously tested using MATLAB/Simulink software under various conditions, including reference voltage and load variations, as well as open circuit switch faults. The results showcase the proposed system's superior performance in terms of: Dynamic performance: The system exhibits rapid response to changes in operating conditions. Fast fault detection, localization and isolation.

Regression Method for Rail Fastener Tightness Based on Center-Line Projection Distance Feature and Neural Network

In the railway system, fasteners have the functions of damping, maintaining the track distance, and adjusting the track level. Therefore, routine maintenance and inspection of fasteners are important to ensure the safe operation of track lines. Currently, assessment methods for fastener tightness include manual observation, acoustic wave detection, and image detection. There are limitations such as low accuracy and efficiency, easy interference and misjudgment, and a lack of accurate, stable, and fast detection methods. Aiming at the small deformation characteristics and large elastic change of fasteners from full loosening to full tightening, this study proposes high-precision surface-structured light technology for fastener detection and fastener deformation feature extraction based on the center-line projection distance and a fastener tightness regression method based on neural networks. First, the method uses a 3D camera to obtain a fastener point cloud and then segments the elastic rod area based on the iterative closest point algorithm registration. Principal component analysis is used to calculate the normal vector of the segmented elastic rod surface and extract the point on the centerline of the elastic rod. The point is projected onto the upper surface of the bolt to calculate the projection distance. Subsequently, the mapping relationship between the projection distance sequence and fastener tightness is established, and the influence of each parameter on the fastener tightness prediction is analyzed. Finally, by setting up a fastener detection scene in the track experimental base, collecting data, and completing the algorithm verification, the results showed that the deviation between the fastener tightness regression value obtained after the algorithm processing and the actual measured value RMSE was 0.2196 mm, which significantly improved the effect compared with other tightness detection methods, and realized an effective fastener tightness regression.

System design and clinical validation of intelligent nucleic acid POCT for un SARS-CoV-2 detection within 30 min

Rapid and accurate point-of-care-test (POCT) allows us to substantially accelerate taking clinical decisions and implement strategic planning at the national level of preventative measures in epidemic prevention and control. Here, we report Intelligent Nucleic Acid Test (iNAT) for the RT-qPCR detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA with a limit of detection (LOD) of 22.4 copies/mL. iNAT is a sample-to-answer system consisting of an instrument and a fully enclosed detection chip containing reaction reagents, based on rotary valve liquid control technology, ultrasonic-assisted nucleic acid extraction (NAE) technology, thin-sheet rapid PCR technology, freeze-drying technology, liquid transfer, NAE and RT-qPCR are all completed automatically, quickly and efficiently, and free of cold chain transportation and storage. The user only needs to put the chip with 160 μL sample into the instrument and click to start, the test result will report after 30 min. In a clinical trial between the iNAT and the traditional RT-qPCR kits approved by National Medical Products Administration (NMPA), involving 158 throat swab samples, the concordance rate reached 96.2%, the positive percent agreement (PPA) is 95.8%, and the negative percent agreement (NPA) is 97.3%, the parallel analysis of Ct values of 115 positive samples was a high correlation (R2 = 0.95), 4 of 10 weak positive samples were missed by iNAT while 6 of 10 weak positive samples were missed by reference kits. In a word, iNAT system provide an ultra-high sensitivity and fast POCT detection method for SARS-CoV-2.

Predictive modeling of milk adulteration with urea content using the gray wolf optimization algorithm and long and short-term memory network model

Traditional methods for the determining of urea contaminants in milk, including chromatography, spectrophotometry, and electrochemical processes, have drawbacks such as long testing times and sample destruction. However, hyperspectral imaging technology provides a fast, easy-to-operate, and real-time alternative. In this study, five preprocessing methods are employed, including standard scalar, standard normal variational, first derivative, multivariate scattering correction algorithm, and Savitzky-Golay smoothing. To further enhance the accuracy of predicting urea content in milk, a prediction optimization method based on the gray wolf optimization algorithm for the long short-term memory network was proposed, using a competitive adaptive reweighted sampling algorithm feature wavelength selection algorithm. In the optimized model in the test set experiments, the decision coefficient was improved by 0.56% to 0.9906, and root mean square error was reduced by 7.69% to 0.4996 compared to the long short-term memory network model. This study not only provides a theoretical basis but also presents a fast and nondestructive detection method for accurately predicting urea content in milk.

A Highly Sensitive and Rapid Colloidal Gold Immunoassay for Puerarin Detection.

Radix Puerariae is a traditional Chinese medicinal material with a rich history of use in East and Southeast Asia. Puerarin, a unique component of the Pueraria genus, serves as a quality control marker for herbal medicines like Pueraria lobata and Pueraria thomsonii in China, displaying diverse pharmacological properties. This study developed puerarin colloidal gold immunoassay dipsticks utilizing an anti-puerarin monoclonal antibody, resulting in a fast and sensitive detection method with a limit of 500-1000 ng·mL-1. Evaluation using tap water-extracted P. lobata and P. thomsonii samples showed consistent results compared to LC-MS analysis. Cross-reactivity assessments of puerarin analogs revealed minimal interference, affirming the dipstick's reliability for distinguishing between the two species.