Detection Signal Research Articles

Near-infrared light induced cathodic photoelectrochemical biosensor for duplex-specific nuclease mediated detection of miRNA166-a

The photoelectrochemical (PEC) biosensor is a new sensing platform which has the merits of low background signal, low cost, easy miniaturization and so on. However, at present, most PEC biosensors utilize visible light or ultraviolet light as the excitation light source. This type of light has high energy and may cause some light damage to the detected object and some photoelectric materials, thus limiting its application scope. Herein, based on NaYF4:Yb,Tm/BiOBr0.8I0.2 a cathode PEC biosensor that can be excited by near-infrared light was constructed to avoid the occurrence of anode false positives and light damage. Through two cycles of the miRNA cycle assisted by Duplex-Specific Nuclease (DSN) and the dissolved oxygen cycle assisted by gold nanoparticles during the detection process, the detection signal was amplified and miRNA166-a was sensitively detected. By using up-conversion materials, the near-infrared light of 980 nm can be converted into ultraviolet light and visible light, thus exciting the photogenerated carriers of BiOBr0.8I0.2. The photocurrent reaction shows a positive correlation with the quantity of gold nanoparticles present on the electrode surface, which are derived from the reporter molecules produced by the hybridization of miRNA166-a and H1. It has a good linear relationship and selectivity towards miRNA166-a, and the detection limit is 0.32 fM. This sensing method provides a cathode PEC biosensor, which can be excited by near-infrared light. The overall light damage of the system is low, and the possibility of false positive of anode PEC biosensor can be avoided, which has certain significance.

Highly sensitive intensity-type polarization chiral sensor with reference light.

Optical rotation is a special phenomenon in which the plane of polarization of polarized light is rotated after passing through a chiral medium. This work first, to our knowledge, presents and optimizes a polarization chiral sensor with a reference light on the basis of a weak measurement light path. The influences of phase shift and polarization angle difference on sensitivity were theoretically optimized and verified experimentally. The introduction of a reference light further reduced the impact of light source fluctuations on the detection signal, improving the signal-to-noise ratio and reducing the optical rotation resolution to 2 × 10-5 rad. This method has the characteristics of low cost, high sensitivity, and real-time rapid detection, making it potentially applicable in the field of chemical analysis, such as chiral molecule detection.

Enhanced Sensitivity and Homogeneity of SERS Signals on Plasmonic Substrate When Coupled to Paper Spray Ionization–Mass Spectrometry

This work reports on the development of an analyte sampling strategy on a plasmonic substrate to amplify the detection capability of a dual analytical system, paper spray ionization–mass spectrometry (PSI-MS) and surface-enhanced Raman spectroscopy (SERS). While simply applying only an analyte solution to the plasmonic paper results in a limited degree of SERS enhancement, the introduction of plasmonic gold nanoparticles (AuNPs) greatly improves the SERS signals without sacrificing PSI-MS sensitivity. It is initially revealed that the concentration of AuNPs and the type of analytes highly influence the SERS signals and their variations due to the “coffee ring effect” flow mechanism induced during sampling and the degree of the interfacial interactions (e.g., van der Waals, electrostatic, covalent) between the plasmonic substrate and analyte. Subsequent PSI treatment at high voltage conditions further impacts the overall SERS responses, where the signal sensitivity and homogeneity significantly increase throughout the entire substrate, suggesting the ready migration of adsorbed analytes regardless of their interfacial attractive forces. The PSI-induced notable SERS enhancements are presumably associated with creating unique conditions for local aggregation of the AuNPs to induce effective plasmonic couplings and hot spots (i.e., electromagnetic effect) and for repositioning analytes in close proximity to a plasmonic surface to increase polarizability (i.e., chemical effect). The optimized sampling and PSI conditions are also applicable to multi-analyte analysis by SERS and MS, with greatly enhanced detection capability and signal uniformity.

Enhancement of microRNA Detection Signal Based on Au-nanoparticle-catalyzed Ag Accumulation Observed by Electrochemical and Surface Plasmon Resonance Analyses

Enhancement of microRNA Detection Signal Based on Au-nanoparticle-catalyzed Ag Accumulation Observed by Electrochemical and Surface Plasmon Resonance Analyses

Temporal attention network for CNNE model of variable-length ECG signals in early arrhythmia detection

<p>Cardiac arrhythmia identification and categorization are crucial for prompt treatment and better patient outcomes. Arrhythmia identification is the main focus of this study's temporal attention network (TAN)-based multiclass categorization of varied-length electrocardiogram (ECG) data. The suggested TAN is designed to handle variable-duration ECG signals, making it ideal for real-time monitoring. The TAN uses a dynamic snippet extraction approach to choose meaningful ECG segments to ensure the model captures essential properties despite the constraints of processing such heterogeneous data. Training and assessment use a large dataset of atrial fibrillation, ventricular, and supraventricular arrhythmias. The TAN outperforms current approaches in multiclass early arrhythmia classification and is very accurate. Concatenating EfficientNet with CNN layer helped overcome different data and variable-length signals. High accuracy: 98% of normal, 97.1% of atrial fibrillation (AF), 98% of other, and 98% of noisy using the proposed CEEC model. Early arrhythmia diagnosis has improved due to the TAN's ability to effectively identify varied-length ECG data and give interpretability. It enables quicker interventions, personalised treatment plans, and improved arrhythmia control, which can greatly benefit patient care.</p>

Interlaboratory Evaluation of Bretziella fagacearum Molecular Detection Assays to Guide the eDNA Monitoring of Oak Wilt Disease

ABSTRACTOak wilt disease, caused by the fungus Bretziella fagacearum, can kill mature red oaks within months of infection, severely affecting biodiversity, landscapes, and industries. The disease, originally only present in the United States, was officially reported for the first time in Canada in June 2023. The aim of this study was to suggest a standardized assay and sample processing method to optimize oak wilt detection both in infection centers and ahead of the disease front. Two previously published molecular assays, a Nested PCR and a TaqMan qPCR, were compared to detect B. fagacearum in a variety of samples in a ring trial across five laboratories. Sample types investigated included eDNA from trapped insect vectors (sorted insects and bulk content from traps), infested and healthy oak wood chips, and B. fagacearum conidia dilutions. Results demonstrated that both Nested and TaqMan assays can be used for molecular confirmation of oak wilt, and results are reproducible across different labs. There is a general agreement between both detection assays when testing true‐positive and true‐negative samples. Both methods demonstrated overall good accuracy. The TaqMan assay was more sensitive and detected lower amounts of DNA target. Both tests were 100% specific to oak wood samples, which was the best sample type to use for detection. In general, samples with high Cts were more prompted to yield false negative Nested results. Detecting oak wilt from bulk insect samples was by far more rapid than sorted sap beetles, but resulted in lower detection signals, especially with the Nested assay. The time‐period when the insect traps were set up also had considerable influence on detection results. We hope this study helps to formulate guidelines in oak wilt detection and biosurveillance management.

Center-concave nanosheets of core-shell WO3@Prussian blue based handheld microchip-devices for ultrasensitive lysine determination

Lysine is one of the essential amino acids for human body, and its imbalance is a major cause to anemia, aging process, leukemia cell proliferation and tumor growth. Therefore, its monitoring is dominative to the prevention the disease progress and guidance to the clinical treatment. However, traditional in-hospital detection methods, such as colorimetry and fluorometric, often suffer the disadvantages of high cost and long time-consuming. These drawbacks show a difficulty in the home-in and dairy monitoring for the lysine regulation in body. In this study, we have proposed an ultrasensitive microchip-based portable device to achieve the onsite and precise determination of lysine within only 10 s. This microchip was functionalized through constructing a center-concave nanosheet of core-shell WO3@Prussian blue (WO3@PB) to remarkably strengthen the generation and transfer of the detection signal. In this special architecture, the core WO3 nanosheet can be exposed at the center region of this nanocomposite to effectively promote the enzymatic oxidation, while the PB shell enables to strongly reduce the H2O2 produced by the enzymatic reaction. Under above synergetic effects, a handheld device was designed to support the plug-and-play microchip, which performed an outstanding accuracy for the lysine detection in blood.

Ultrasonic motion scanning method based on flexible microchannel array

Abstract Immersion ultrasound scanning faces limitations in specific detection scenarios, such as those involving moving workpieces or highly intricate surfaces. This issue is particularly pronounced when workpieces exhibit relative motion, resulting in significant signal decay. To address this challenge, an ultrasonic motion scanning method based on a flexible microchannel array is proposed in this paper. This innovative method utilizes a flexible microchannel array as the coupling medium. Initially, the principle of water column formation within the arrays is examined. Subsequently, various shapes and sizes of arrays are meticulously analyzed. Finally, the behavior of the detection signal of the probe in motion is investigated. It is demonstrated that stable detection results can be achieved even while the ultrasound probe is in motion.

Ultrasonic guided Wave Signal Denoising Method Based on one-dimensional convolution and Full connection Model Fusion Denoising autoencoder

Abstract Ultrasonic guided wave (UGW) detection is widely used in pipeline monitoring but faces challenges from weak flaw echo signals within the detection data, making weak UGW signals difficult to recognize. It is essential to denoise the UGW detection signals to identify weak echo signals. This paper proposes an improved denoising autoencoder (DAE) based on the fusion of one-dimensional convolution neural network (1DCNN) and full connection (FC). The model expands the amount of training data by adding noise in batches and uses 1DCNN to enhance the ability of extracting UGW signal features. The model was validated using Several numerical simulation signals. Numerical simulation results show that the signal-to-noise ratio (SNR) of the UGW signals can be improved from -20 dB to 8 dB; it has a strong improved SNR, and the mean square error is greatly reduced while maintaining the original phase almost unchanged. The improved DAE method has significant advantages in denoising effect, and it can effectively reduce the noise of the UGW detection signal and realize the identification of small defects of the simulation pipeline.

Influence of polyethylene pipe butt fusion welding in ultrasonic non-destructive testing

Abstract Butt fusion welding is a commonly used manufacturing method for polyethylene pipes. To investigate the influence of butt fusion welding on oblique ultrasonic wave in ultrasonic non-destructive testing, a numerical simulation model was established for polyethylene pipe with welding joints. Wave propagation in the polyethylene pipe was analysed, which showed that there exist longitudinal and shear waves propagating and their mode conversion. Detection signals of pipes with different defects and different acoustic attenuations were calculated. The special structure of welding joint was then modelled. Results show that the signal response declines with acoustic attenuation coefficient. Butt welding joints, especially the inner joints, would affect waves propagation. Finally, the welding process of polyethylene pipes was also simulated. The influence of cooling time was discussed, which indicated that insufficient cooling time would cause thermal residual stress and then alter the ultrasonic signal.

SERS and electrochemical dual-mode detection of miRNA-141 by using single Au@Ag nanowire as a new platform.

As biomarkers of cancer, the accurate and sensitive detection of microRNAs is of great significance. Therefore, we proposed a surface-enhanced Raman scattering (SERS)/electrochemical (EC) dual-mode nanosensor for sensitively detecting miRNA-141. The nanosensor uses Au@Ag nanowires as a novel SERS/EC sensing platform, which has the advantages of good biocompatibility, fast response, and high sensitivity. The dual-mode nanosensor can not only effectively overcome the problem of insufficient reliability of single signal, but also realize the amplification and stable output of the detection signal, to ensure the reliability and repeatability of miRNA detection. With this sensing strategy, the target miRNA-141 can be detected over a wide linear range (100 fM to 50 nM) (LOD of 18.4 fM for SERS and 16.0 fM for electrochemical methods). In addition, the process shows good selectivity and can distinguish miRNA-141 from other interfering miRNAs. The actual analysis of human serum samples also proves that our strategy has good reliability, repeatability, and has broad application prospects in the field of analysis and detection.

Research on signal denoising algorithm based on ICEEMDAN eddy current detection

This study addresses the challenges of non-stationarity and significant background noise interference in eddy current detection signals by proposing a noise reduction method based on Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise (ICEEMDAN). The process commences with the signal being decomposed using Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise into a finite number of Intrinsic Mode Functions (IMFs). Each Intrinsic Mode Function is then evaluated for the presence of high-frequency noise using a Power Spectral Density (PSD) analysis. The high-frequency noise present in the Intrinsic Mode Functions is then reduced using Normalized Least Mean Squares (NLMS) before being reconstructed with the remaining Intrinsic Mode Functions. Subsequently, the reconstructed signals are subjected to another round of decomposition using Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise. The Pearson Product Moment Correlation Coefficient (PPMCC) is utilised to calculate the correlation between the Intrinsic Mode Functions within each layer, retaining those with a strong correlation to further attenuate noise. Ultimately, the local maxima judgement method selectively amplifies defect signals by assessing changes in peak and valley degrees, thereby improving the signal-to-noise ratio of the eddy current detection signal. The experimental results demonstrate that, in comparison to the use of only the conventional Improved Complete Ensemble Empirical Mode Decomposition with Adapted Noise and Normalized Least Mean Squares denoising methods, the proposed method increases the Signal-to-Noise Ratio (SNR) by 1.08 dB and 2.31 dB, respectively, and decreases the Mean Square Error (MSE) by 106.9 and 223.9, respectively. The false alarm rate for stainless steel welded tubes with defects is 1.4%, while the false alarm rate for stainless steel welded tubes without defects is 0.4%.

Convolutional neural network framework for EEG-based ADHD diagnosis in children.

Attention-deficit hyperactivity disorder (ADHD) stands as a significant psychiatric and neuro-developmental disorder with global prevalence. The prevalence of ADHD among school children in India is estimated to range from 5% to 8%. However, certain studies have reported higher prevalence rates, reaching as high as 11%. Utilizing electroencephalography (EEG) signals for the early detection and classification of ADHD in children is crucial. In this study, we introduce a CNN architecture characterized by its simplicity, comprising solely two convolutional layers. Our approach involves pre-processing EEG signals through a band-pass filter and segmenting them into 5-s frames. Following this, the frames undergo normalization and canonical correlation analysis. Subsequently, the proposed CNN architecture is employed for training and testing purposes. Our methodology yields remarkable results, with 100% accuracy, sensitivity, and specificity when utilizing the complete 19-channel EEG signals for diagnosing ADHD in children. However, employing the entire set of EEG channels presents challenges related to the computational complexity. Therefore, we investigate the feasibility of using only frontal brain EEG channels for ADHD detection, which yields an accuracy of 99.08%. The proposed method yields high accuracy and is easy to implement, hence, it has the potential for widespread practical deployment to diagnose ADHD.

Magnetic Porous Hydrogel-Enhanced Wearable Patch Sensor for Sweat Zinc Ion Monitoring.

Wearable sensors for sweat trace metal monitoring have the challenges of effective sweat collection and the real-time recording of detection signals. The existing detection technologies are implemented by generating enough sweat through exercise, which makes detecting trace metals in sweat cumbersome. Generally, it takes around 20 min to obtain enough sweat, resulting in dallied and prolonged detection signals that cannot reflect the endogenous fluctuations of the body. To solve these problems, we prepared a multifunctional hydrogel as an electrolyte and combined it with a flexible patch electrode to realize real-time monitoring of sweat Zn2+. Such hydrogel has magnetic and porous properties, and the porous structure of hydrogel enables a fast absorption of sweat, and the magnetic property of the addition of fabricated Fe3O4 NPs not only improves the conductivity but also ensures the adjustable internal structures of the hydrogel. Such a sensing platform for sweat Zn2+ monitoring shows a satisfied linear relationship in the concentration range of 0.16-16 µg/mL via differential pulsed anodic striping voltammetry (DPASV) and successfully detects the sweat Zn2+ of four volunteers during exercise and resting, displaying a promising path for commercial application.

Thylakoid-based green preparation of porous microneedles for antibiotic residues detection in food samples

BackgroundAntibiotic residues in food chain have raised concerns regarding their toxicity and involvement in antimicrobial resistance. However, most existing antibiotic biosensors are primarily applicable to liquid food samples. Given the complex matrix characteristics of foods, there is an urgent need for the development of effective antibiotic detection platforms that exhibit high universality and flexibility. Porous microneedles (PMN) are microdevice structures with needle-like shapes and microscale pores throughout their composition, which facilitate rapid sampling. Consequently, the integration of PMN with biosensors holds significant promise for the detection of antibiotic residues in complex food samples. ResultsIn this study, hydrogel-forming PMN are fabricated by leveraging the oxygen-production capacity of thylakoid to generate bubbles and form porous structures. These PMN are then integrated with a fluorescence aptasensor for the quantification of the antibiotic netilmicin. The aptasensor consists of a netilmicin (NET) aptamer with stem loop and hairpin structure, which facilitated the binding of SYBR Green I to produce a fluorescent signal. In the presence of NET, the complete binding between NET and the aptamer results in a reduction of fluorescence intensity, thereby generating a detectable signal change for the detection of NET. Utilizing capillary action accelerate fluid extraction (2.9 times faster than nonporous microneedles) and a large specific surface area (5.1072 m2/g) conducive to aptasensor adsorb, the PMN achieve efficient capture and quantification of antibiotic with limits of detection and quantitation of 5.99 nM and 19.8 nM, respectively. SignificancePorous microneedles with tunable porosity and desirable mechanical properties are successfully fabricated. The integration of PMN with aptasensor enable the efficient detection of netilmicin in fish, milk and river water samples, demonstrating high recovery rates. The PMN represent potential tools for the convenient and rapid detection of antibiotic residues within complex food matrices, thereby enhancing food safety monitoring.

Fault Detection of Wheelset Bearings through Vibration-Sound Fusion Data Based on Grey Wolf Optimizer and Support Vector Machine

This study aims to detect faults in wheelset bearings by analyzing vibration-sound fusion data, proposing a novel method based on Grey Wolf Optimizer (GWO) and Support Vector Machine (SVM). Wheelset bearings play a vital role in transportation. However, malfunctions in the bearing might result in extensive periods of inactivity and maintenance, disrupting supply chains, increasing operational costs, and causing delays that affect both businesses and consumers. Fast fault identification is crucial for minimizing maintenance expenses. In this paper, we proposed a new integration of GWO for optimizing SVM hyperparameters, specifically tailored for handling sound-vibration signals in fault detection. We have developed a new fault detection method that efficiently processes fusion data and performs rapid analysis and prediction within 0.0027 milliseconds per data segment, achieving a test accuracy of 98.3%. Compared to the SVM and neural network models built in MATLAB, the proposed method demonstrates superior detection performance. Overall, the GWO-SVM-based method proposed in this study shows significant advantages in fault detection of wheelset bearing vibrations, providing an efficient and reliable solution that is expected to reduce maintenance costs and improve the operational efficiency and reliability of equipment.

Two proposed experiments for the Tachyon-Neutrino theory of dark matter

Recent theory of tachyon neutrinos, providing an explanation for Dark Matter, leads to two sets of experiments for testing that theory. One predicts an enhanced signal in detection of the Cosmic Neutrino Background. The other compares models of Dark Matter used to fit data on galaxy rotation curves and also gravitational lensing. This paper starts with a semi-review of the previous theoretical work. One sees new symmetries in comparing the theory of tachyons to that of ordinary particles. In addition, we present a debunking of the false claim that tachyons would produce a causal paradox; and an apparent contradiction within the new theory, related to Dark Energy, is identified and addressed.

Dimeric-molecular beacon based intramolecular strand displacement amplification enables robust analysis of miRNA

Given the critical role of miRNAs in regulating gene expression and their potential as biomarkers for various diseases, accurate and sensitive miRNA detection is essential for early diagnosis and monitoring of conditions such as cancer. In this study, we introduce a dimeric molecular beacon (Di-MB) based isothermal strand displacement amplification (ISDA) system (Di-MB-ISDA) for enhanced miRNA detection. The Di-MB system is composed of two monomeric MBs (Mono-MBs) connected by a double-stranded DNA linker with single-stranded sequences in the middle, facilitating binding with the flexible arms of the Mono-MBs. This design forms a compact, high-density structure, significantly improving biostability against nuclease degradation. In the absence of target miRNA, the Di-MB maintains its stable structure. When target miRNA is present, it binds to the stem-loop regions, causing the hairpin structure to unfold and expose the stem sequences. These sequences serve as templates for the built-in primers, triggering DNA replication through an intramolecular recognition mechanism. This spatial confinement effect accelerates the strand displacement reaction, allowing the target miRNA to initiate additional reaction cycles and amplify the detection signal. The Di-MB-ISDA system addresses key challenges such as poor biostability and limited sensitivity seen in traditional methods. By enhancing biostability and optimizing reaction conditions, this system demonstrates robust performance for miRNA detection with a detection limit of 100 pM. The findings highlight the potential of Di-MB-ISDA for sensitive and accurate miRNA analysis, paving the way for its application in biomedical study and disease diagnosis in complex biological samples.

Integrating IoMT and AI for Proactive Healthcare: Predictive Models and Emotion Detection in Neurodegenerative Diseases

Neurodegenerative diseases, such as Parkinson’s and Alzheimer’s, present considerable challenges in their early detection, monitoring, and management. The paper presents NeuroPredict, a healthcare platform that integrates a series of Internet of Medical Things (IoMT) devices and artificial intelligence (AI) algorithms to address these challenges and proactively improve the lives of patients with or at risk of neurodegenerative diseases. Sensor data and data obtained through standardized and non-standardized forms are used to construct detailed models of monitored patients’ lifestyles and mental and physical health status. The platform offers personalized healthcare management by integrating AI-driven predictive models that detect early symptoms and track disease progression. The paper focuses on the NeuroPredict platform and the integrated emotion detection algorithm based on voice features. The rationale for integrating emotion detection is based on two fundamental observations: (a) there is a strong correlation between physical and mental health, and (b) frequent negative mental states affect quality of life and signal potential future health declines, necessitating timely interventions. Voice was selected as the primary signal for mood detection due to its ease of acquisition without requiring complex or dedicated hardware. Additionally, voice features have proven valuable in further mental health assessments, including the diagnosis of Alzheimer’s and Parkinson’s diseases.

One-tube target amplification-free CRISPR-Cas13 autocatalytic system

Clustered regularly interspaced short palindromic repeats (CRISPR) diagnostics have shown great promise for nucleic acid testing. However, CRISPR/Cas13a-based RNA sensing is hindered by the absence of accurate, fast and sensitive assays. Current Cas-based methods generally involve two separate steps: target-amplification and Cas-detection. This two-step method complicates detection, causes cross-contamination, and is difficult to deploy. Therefore, it is necessary to develop a facile one-step Cas13a-based RNA detection assay. We present a one-tube target amplification-free CRISPR-Cas13a autocatalytic circuit system that combines the Cas13a nuclease, T4 polynucleotide kinase, Bsu DNA polymerase and T7 RNA polymerase with a universally designed combinatorial DNA probe to create a one-pot assay for isothermal RNA detection. In this assay, after target RNA recognition, the cis-cleavage and trans-cleavage activities of Cas13 are activated, which leads to the initiation of a Cas13a autocatalytic reaction, resulting in an exponentially amplifying detection signal, thus enabling the detection of approximately 23.01 (95 % CI:16.95-41.23) copies/µL RNA in 45 min. A facile, easy-to-deploy, and one-tube Cas13a-based autocatalytic reaction system was constructed for rapid and highly sensitive RNA detection for the first time. This Cas13a-based autocatalytic system offers not only a versatile platform for directly detecting RNA targets but also a route toward point-of-care nucleic acid testing.