Detection Of Specific Targets Research Articles

Development and performance of NLISA for C-reactive protein detection based on Prussian blue nanoparticle conjugates.

Prussian blue nanoparticles (PBNPs), also called nanozymes, are very attractive as an alternative to horseradish peroxidase in immunoassay development due to their simple and low-cost synthesis, stability and high catalytic activity. Today, there is a method for highly effective PBNP synthesis based on the reduction of an FeCl3/K3[Fe(CN)6] mixture by hydrogen peroxide. However, there is a lack of research showcasing the use of these highly effective PBNPs for specific target detection in clinical settings, as well as a lack of comprehensive comparisons with conventional methods. To address this gap, we prepared diagnostic reagents based on highly effective PBNPs by modifying them using gelatin and attaching anti-C-reactive protein (CRP) monoclonal antibodies through cross-linking with glutaraldehyde. As a result, a solid-phase colorimetric immunoassay in a sandwich format (nanozyme-linked immunosorbent assay [NLISA]) using highly effective PBNPs as a label for CRP detection has been demonstrated for the first time. The assay demonstrated a detection limit of 21.8pg/mL, along with acceptable selectivity, precision (CV < 25%) and accuracy (the recovery index was within acceptable limits (75-125%) for LLOQ /ULOQ range. The analytical performance of this method is on par with sensitive assays developed in the last 5 years. Notably, the results obtained from NLISA align with those from an immunofluorescence assay conducted by a certified clinical laboratory. Furthermore, this study underscores the technological challenges involved in constructing an analysis that necessitate further exploration.

A universal self-assembled peptide nanoparticle coupled SERS tag in biosensing

Surface-enhanced Raman scattering (SERS) nanotags have been extensively studied for targets detection. However, achieving accurate detection of specific targets often requires the functionalization of SERS nanostructures with various biological components like antibodies, nucleic acid aptamers, or peptides. This process is intricate and involves introducing different functional groups on the protective shell layer, which may result in SERS nanotags aggregation, reduced biorecognition element activity, and chaotic orientation on the surface. In this study, we devised spherical peptide nanoparticles with both recognition and anchoring functions through the self-assembly of TFF and CFF peptides, presenting a versatile strategy for SERS nanotag functionalization. The exposed cysteine residues and T sequences (served as the target-specific recognition sequence) after self-assembly, demonstrated binding to gold SERS nanostructure and recognition of specific targets, respectively. We showcase the high sensitivity and specificity of this method by detecting the liver fibrosis marker LECT2. The method was highly sensitive for the detection of LECT2 with an LOD of 0.0015 ng/mL. The method's adaptability is underscored by the ease of adjusting the sequence within the T portion, suggesting its potential generalization for the efficient detection of various targets.

Exploring an application-oriented land-based hyperspectral target detection framework based on 3D–2D CNN and transfer learning

Target detection based on hyperspectral images refers to the integrated use of spatial information and spectral information to accomplish the task of localization and identification of targets. There are two main methods for hyperspectral target detection: supervised and unsupervised methods. Supervision method refers to the use of spectral differences between the target to be tested and the surrounding background to identify the target when the target spectrum is known. In ideal situations, supervised object detection algorithms perform better than unsupervised algorithms. However, the current supervised object detection algorithms mainly have two problems: firstly, the impact of uncertainty in the ground object spectrum, and secondly, the universality of the algorithm is poor. A hyperspectral target detection framework based on 3D–2D CNN and transfer learning was proposed to solve the problems of traditional supervised methods. This method first extracts multi-scale spectral information and then preprocesses hyperspectral images using multiple spectral similarity measures. This method not only extracts spectral features in advance, but also eliminates the influence of complex environments to a certain extent. The preprocessed feature maps are used as input for 3D–2D CNN to deeply learn the features of the target, and then, the softmax method is used to output and obtain the detection results. The framework draws on the ideas of integrated learning and transfer learning, solves the spectral uncertainty problem with the combined similarity measure and depth feature extraction network, and solves the problem of poor robustness of traditional algorithms by model migration and parameter sharing. The area under the ROC curve of the proposed method has been increased to over 0.99 in experiments on both publicly available remote sensing hyperspectral images and measured land-based hyperspectral images. The availability and stability of the proposed method have been demonstrated through experiments. A feasible approach has been provided for the development and application of specific target detection technology in hyperspectral images under different backgrounds in the future.

Photoactivatable CRISPR/Cas12a Sensors for Biomarkers Imaging and Point-of-Care Diagnostics.

In recent years, the CRISPR/Cas12a-based sensing strategy has shown significant potential for specific target detection due to its rapid and sensitive characteristics. However, the "always active" biosensors are often insufficient to manipulate nucleic acid sensing with high spatiotemporal control. It remains crucial to develop nucleic acid sensing devices that can be activated at the desired time and space by a remotely applied stimulus. Here, we integrated photoactivation with the CRISPR/Cas12a system for DNA and RNA detection, aiming to provide high spatiotemporal control for nucleic acid sensing. By rationally designing the target recognition sequence, this photoactivation CRISPR/Cas12a system could recognize HPV16 and survivin, respectively. We combined the lateral flow assay strip test with the CRISPR/Cas12a system to realize the visualization of nucleic acid cleavage signals, displaying potential instant test application capabilities. Additionally, we also successfully realized the temporary control of its fluorescent sensing activity for survivin by photoactivation in vivo, allowing rapid detection of target nucleic acids and avoiding the risk of contamination from premature leaks during storage. Our strategy suggests that the CRISPR/Cas12a platform can be triggered by photoactivation to sense various targets, expanding the technical toolbox for precise biological and medical analysis. This study represents a significant advancement in nucleic acid sensing and has potential applications in disease diagnosis and treatment.

Aptamers: A Cutting-Edge Approach for Gram-Negative Bacterial Pathogen Identification.

Early and accurate diagnoses of pathogenic microorganisms is essential to correctly identify diseases, treating infections, and tracking disease outbreaks associated with microbial infections, to develop precautionary measures that allow a fast and effective response in epidemics and pandemics, thus improving public health. Aptamers are a class of synthetic nucleic acid molecules with the potential to be used for medical purposes, since they can be directed towards any target molecule. Currently, the use of aptamers has increased because they are a useful tool in the detection of specific targets. We present a brief review of the use of aptamers to detect and identify bacteria or even some toxins with clinical importance. This work describes the advances in the technology of aptamers, with the purpose of providing knowledge to develop new aptamers for diagnoses and treatment of different diseases caused by infectious microorganisms.

Modelling of Interactive Experience and User Satisfaction in Interior Design Based on Virtual Reality Technology

Abstract This paper firstly analyzes the essential features of virtual reality technology, constructs the framework of the interior design system based on virtual reality technology, and designs the interaction content of the system as well as the interaction process architecture. Secondly, the interior design system is technically analyzed, and the system’s moving trajectory relative to the environment is determined by using specific point calculation and camera position estimation algorithms, and specific target detection algorithms extract specific target HOC features. Finally, the platform is tested for performance, and the data is quantitatively analyzed using relevant data collected from users in the system as a research sample. The results show that 43 users are satisfied with being able to visit freely in a continuous space, 45 users are all very satisfied with switching the elements in the scene, and they think that replacing the items in the scene is conducive to participating in the design of the program and selecting the decorations they are satisfied with. It shows that the interior design system based on VR technology can directly interact with virtual information in real time, which improves the user’s interactive experience and makes the design “human-oriented”, starting from human needs and feelings.

Coordination effect enhanced visualization of latent fingerprint with Eu (TTA)3phen-SiO2 microspheres

Latent fingerprint (LFP) powders are crucial in the detection of LFPs in forensic science. However, it is often plagued by poor image resolution and low contrast. Herein, enhanced LFP fluorescence (FL) visualizations are achieved by doping Eu(III) coordination compound Eu(TTA)3phen directly into SiO2 microspheres instead of Eu(III) ions. Using the synthesized Eu(TTA)3phen-SiO2 microspheres, the fine characteristic structure of LFP can be seen and recognized under 365 nm irradiation, up to Level 3. However, the Eu3+-SiO2 microspheres were difficult to recognize the Level 2,3 fingerprint structure. The difference between the ridge and furrow gray values of Eu(TTA)3phen-SiO2 microspheres is 2.1 times that of Eu3+-SiO2 microspheres. The coordination effect increased the asymmetry around Eu(III) ions, resulting in the ultrasensitive 5D0→7F2 transition, thus increasing the FL intensity, and the uniform doping of the Eu(III) coordination compound into SiO2 also reduced the surface FL quenching due to shielding from oxygen. Under this dual effect, the LFP performance of Eu(TTA)3phen-SiO2 microspheres has been significantly improved. We believe that this novel and easy LFP visualization method is a promising routine in specific target detection including criminal investigation, customhouse check-in, and drug control.

Recent Trends in Lateral Flow Immunoassays with Optical Nanoparticles.

Rapid, accurate, and convenient diagnosis is essential for effective disease management. Various detection methods, such as enzyme-linked immunosorbent assay, have been extensively used, with lateral flow immunoassay (LFIA) recently emerging as a major diagnostic tool. Nanoparticles (NPs) with characteristic optical properties are used as probes for LFIA, and researchers have presented various types of optical NPs with modified optical properties. Herein, we review the literature on LFIA with optical NPs for the detection of specific targets in the context of diagnostics.

A Lightweight and Detector-Free 3D Single Object Tracker on Point Clouds

Recent works on 3D single object tracking treat the task as a target-specific 3D detection task, where an off-the-shelf 3D detector is commonly employed for the tracking. However, it is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and incomplete. In this paper, we address this issue by explicitly leveraging temporal motion cues and propose DMT, a Detector-free Motion-prediction-based 3D Tracking network that completely removes the usage of complicated 3D detectors and is lighter, faster, and more accurate than previous trackers. Specifically, the motion prediction module is first introduced to estimate a potential target center of the current frame in a point-cloud-free manner. Then, an explicit voting module is proposed to directly regress the 3D box from the estimated target center. Extensive experiments on KITTI and NuScenes datasets demonstrate that our DMT can still achieve better performance ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 10% improvement over the NuScenes dataset) and a faster tracking speed (i.e., 72 FPS) than state-of-the-art approaches without applying any complicated 3D detectors. Our code is released at https://github.com/jimmy-dq/DMT.

Carbon-based biosensors: Next-generation diagnostic tool for target-specific detection of SARS-CoV-2 (COVID-19)

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) was declared a global pandemic in 2020. Having rapidly spread around the globe, with the emergence of new variants, there is a crucial need to develop diagnostic kits for its rapid detection. Since it validated accuracy and reliability, the reverse transcription polymerase chain reaction (RT-PCR) test has been declared the gold standard for disease detection. However, despite its reliability, the requirement of specialized facilities, reagents, and duration of a PCR run limits its usage for rapid detection. There is thus a continuous increase in the design and development of rapid, point-of-care (PoC), and cost-effective diagnostic kits. In this review, we discuss the potential of carbon-based biosensors for target-specific detection of coronavirus disease 19 (COVID-19) and present an overview of investigation within the timeframe of the last four years (2019–2022), which have developed novel platforms using carbon nanomaterial-based approaches for viral detection. The approaches discussed offer rapid, accurate, and cost-effective strategies for COVID-19 detection for healthcare personnel and research workers.

Evolution of the Probe-Based Loop-Mediated Isothermal Amplification (LAMP) Assays in Pathogen Detection.

Loop-mediated isothermal amplification (LAMP), as the rank one alternative to a polymerase chain reaction (PCR), has been widely applied in point-of-care testing (POCT) due to its rapid, simple, and cost-effective characteristics. However, it is difficult to achieve real-time monitoring and multiplex detection with the traditional LAMP method. In addition, these approaches that use turbidimetry, sequence-independent intercalating dyes, or pH-sensitive indicators to indirectly reflect amplification can result in false-positive results if non-specific amplification occurs. To fulfill the needs of specific target detection and one-pot multiplex detection, a variety of probe-based LAMP assays have been developed. This review focuses on the principles of these assays, summarizes their applications in pathogen detection, and discusses their features and advantages over the traditional LAMP methods.

Feature-Selection-Based Attentional-Deconvolution Detector for German Traffic Sign Detection Benchmark

In this study, we propose a novel traffic sign detection algorithm based on the deeplearning approach. The proposed algorithm, which we termed the feature-selection-based attentionaldeconvolution detector (FSADD), is used along with the “you look only once” (YOLO) v5 structure for feature selection. When applying feature selection inside a detection algorithm, the network divides the extracted feature maps after the convolution layer into similar and non similar feature maps. Generally, the feature maps obtained after the convolution layers are the outputs of filters with random weights. Owing to the randomness of the filter, the network obtains various kinds of feature maps with unnecessary components, which degrades the detection performance. However, grouping feature maps with high similarities can increase the relativeness of each feature map, thereby improving the network detection of specific targets from images. Furthermore, the proposed FSADD model has modified sizes of the receptive fields for improved traffic sign detection performance. Many of the available general detection algorithms are unsuitable for the German traffic sign detection benchmark (GTSDB) because of the small sizes of these signs in the images. Experimental comparisons were performed with respect to the GTSDB to show that the proposed FSADD is comparable to the state-of-the-art while detecting 29 kinds of traffic signs with 73.9% accuracy of classification performances.

Research on transformer internal defect detection based on large scale model

Aiming at the problem that the traditional convolution network can not be used to detect and identify targets in the more complex target detection application scenarios in power grid business, a target detection scheme based on the ability of large model is proposed. The effects of convolution neural network layers, convolution kernel size and feature dimension on the recognition effect are systematically analyzed and compared. The differences between traditional convolution network and large model in data volume, accuracy and model construction efficiency are compared. The experimental results show that for the specific target detection business scenario downstream of power grid, the model constructed by this scheme has the advantages of fast and efficient, high accuracy and less data dependence, and the comprehensive performance is better than the traditional convolution network detection method.

Electrochemical Nanosensors for Sensitization of Sweat Metabolites: From Concept Mapping to Personalized Health Monitoring.

Sweat contains a broad range of important biomarkers, which may be beneficial for acquiring non-invasive biochemical information on human health status. Therefore, highly selective and sensitive electrochemical nanosensors for the non-invasive detection of sweat metabolites have turned into a flourishing contender in the frontier of disease diagnosis. A large surface area, excellent electrocatalytic behavior and conductive properties make nanomaterials promising sensor materials for target-specific detection. Carbon-based nanomaterials (e.g., CNT, carbon quantum dots, and graphene), noble metals (e.g., Au and Pt), and metal oxide nanomaterials (e.g., ZnO, MnO2, and NiO) are widely used for modifying the working electrodes of electrochemical sensors, which may then be further functionalized with requisite enzymes for targeted detection. In the present review, recent developments (2018-2022) of electrochemical nanosensors by both enzymatic as well as non-enzymatic sensors for the effectual detection of sweat metabolites (e.g., glucose, ascorbic acid, lactate, urea/uric acid, ethanol and drug metabolites) have been comprehensively reviewed. Along with this, electrochemical sensing principles, including potentiometry, amperometry, CV, DPV, SWV and EIS have been briefly presented in the present review for a conceptual understanding of the sensing mechanisms. The detection thresholds (in the range of mM-nM), sensitivities, linear dynamic ranges and sensing modalities have also been properly addressed for a systematic understanding of the judicious design of more effective sensors. One step ahead, in the present review, current trends of flexible wearable electrochemical sensors in the form of eyeglasses, tattoos, gloves, patches, headbands, wrist bands, etc., have also been briefly summarized, which are beneficial for on-body in situ measurement of the targeted sweat metabolites. On-body monitoring of sweat metabolites via wireless data transmission has also been addressed. Finally, the gaps in the ongoing research endeavors, unmet challenges, outlooks and future prospects have also been discussed for the development of advanced non-invasive self-health-care-monitoring devices in the near future.

Recent Advances in Sensing Materials Targeting Clinical Volatile Organic Compound (VOC) Biomarkers: A Review.

In general, volatile organic compounds (VOCs) have a high vapor pressure at room temperature (RT). It has been reported that all humans generate unique VOC profiles in their exhaled breath which can be utilized as biomarkers to diagnose disease conditions. The VOCs available in exhaled human breath are the products of metabolic activity in the body and, therefore, any changes in its control level can be utilized to diagnose specific diseases. More than 1000 VOCs have been identified in exhaled human breath along with the respiratory droplets which provide rich information on overall health conditions. This provides great potential as a biomarker for a disease that can be sampled non-invasively from exhaled breath with breath biopsy. However, it is still a great challenge to develop a quick responsive, highly selective, and sensitive VOC-sensing system. The VOC sensors are usually coated with various sensing materials to achieve target-specific detection and real-time monitoring of the VOC molecules in the exhaled breath. These VOC-sensing materials have been the subject of huge interest and extensive research has been done in developing various sensing tools based on electrochemical, chemoresistive, and optical methods. The target-sensitive material with excellent sensing performance and capturing of the VOC molecules can be achieved by optimizing the materials, methods, and its thickness. This review paper extensively provides a detailed literature survey on various non-biological VOC-sensing materials including metal oxides, polymers, composites, and other novel materials. Furthermore, this review provides the associated limitations of each material and a summary table comparing the performance of various sensing materials to give a better insight to the readers.

Reverse transcriptase-free detection of viral RNA using Hemo Klentaq DNA polymerase

COVID-19 pandemic highlighted the demand for the fast and reliable detection of viral RNA. Although various methods for RNA amplification and detection have been proposed, some limitations, including those caused by reverse transcription (RT), need to be overcome. Here, we report on the direct detection of specific RNA by conventional polymerase chain reaction (PCR) requiring no prior RT step. It was found that Hemo KlenTaq (HKTaq), which is posed as DNA-dependent DNA polymerase, possesses reverse transcriptase activity and provides reproducible amplification of RNA targets with an efficiency comparable to common RT-PCR. Using nasopharyngeal swab extracts from COVID-19-positive patients, the high reliability of SARS-CoV-2 detection based on HKTaq was demonstrated. The most accurate detection of specific targets are provided by nearby primers, which allow to determine RNA in solutions affected to multiple freeze-thaw cycles. HKTaq can be used for elaboration of simplified amplification techniques intended for the analysis of any specific RNA and requiring only one DNA polymerase.

Troop camouflage detection based on deep action learning

&lt;p&gt;&lt;span lang="EN-US"&gt;Detecting troop camouflage on the battlefield is crucial to beat or decide in critical situations to survive. This paper proposed a hybrid model based on deep action learning for camouflage recognition and detection. To involve deep action learning in this proposed system, deep learning based on you only look once (YOLOv3) with SquezeeNet and the fourth steps on action learning were engaged. Following the successful formulation of the learning cycle, an instrument examines the environment and performance in action learning with qualitative weightings; specific target detection experiments with view angle, target localization, and the firing point procedure were performed. For each deep action learning cycle, the complete process is divided into planning, acting, observing, and reflecting. If the results do not meet the minimal passing grade after the first cycle, the cycle will be repeated until the system succeeds in the firing point. Furthermore, this study found that deep action learning could enhance intelligence over earlier camouflage detection methods, while maintaining acceptable error rates. As a result, deep action learning could be used in armament systems if the environment is properly identified.&lt;/span&gt;&lt;/p&gt;

Quantitative detection of C-reactive protein in human saliva using an electrochemical lateral flow device

Lateral flow devices (LFDs) allow for low-cost decentralized testing with a short time to result and are therefore an indispensable tool for point-of-care diagnostics. Here, we report on a novel LFD with electrochemical readout for the quantitative detection of C-reactive protein (CRP), a known biomarker for inflammation, in filtered human saliva and demonstrate the possibility of simultaneous multichannel measurements. The detection of CRP is enabled by a sandwich assay with target specific capture antibodies immobilized in a nitrocellulose membrane and target specific detection antibodies conjugated to an enzyme label. The subsequent enzymatic reaction leads to a product, which can be oxidized by an electrochemical sensor placed on top of the LFD strip and produce a concentration dependent and analyte specific electrical current. To optimize the system, single channel sensors were used to investigate different conjugate compositions and loads for their effect on assay performance. That way, limits of detection (LOD) of 3 and 25 ng/mL were achieved in buffer and filtered saliva, respectively. Then, a single sensor with four distinct channels was introduced for the testing of simultaneous multichannel measurements. There, the CRP dedicated channel showed a typical concentration dependency (with LODs of 13 and 55 ng/mL in buffer and filtered saliva, respectively), while the control channel expectedly produced elevated signals across all CRP concentrations. The signals corresponding to the other two channels (zeros) remained low with no indication of crosstalk, showing the ability to measure multiple locations on the LFD simultaneously and thus, paving the way for multiplexing.

Rapid and highly specific detection of communicable pathogens using one-pot loop probe-mediated isothermal amplification (oLAMP)

Rapid nucleic acid isothermal amplification techniques with minimal demand for laboratory instruments are desirable for early screening and mitigation of communicable diseases (e.g., COVID-19). However, most of the current techniques available employ non-specific signal generation strategies which increase the chance of false positives. Others require a two-step amplification-detection process, in which the tube is opened after amplification, prior to the detection step. This promotes cross-contamination that would lead to unreliable results. Here, we developed a one-pot loop probe-mediated isothermal amplification scheme (oLAMP) coupled with a concurrent target-specific detection readout. We have shown that the said scheme is compatible with crude sample preparation methods and is readily integrated with either fluorescence or electrochemical readouts. The entire process would take 30–40 min, and we show that our scheme could detect 0.5 or 1 copy/μL of the SARS-CoV-2 N gene using fluorescence or electrochemical readouts, respectively. Moreover, we demonstrate that the scheme is robust across different biological matrices and other interfering substances. We further show that our method can detect the presence of Salmonella drug resistance gene in crude food samples. Thus, our platform offers a readily scalable and highly specific diagnostic pathway for rapid and decentralized detection of communicable diseases.

Rapid identification of Atlantic salmon (Salmo salar) based on loop-mediated isothermal amplification (LAMP) using self-quenching fluorogenic approach

Loop-mediated isothermal amplification (LAMP) has emerged as an important diagnostic tool for fish authenticity purpose. While the sequence-independent detection methods greatly increased the likelihood of detecting false-positive signals. Instead, target-specific detection of LAMP amplicons can be achieved through the use of target-specific probes or modified primers as biorecognition elements. The present work selected Salmo salar as a case study, and developed a novel LAMP assay coupled with the self-quenching approach for accurate species identification.Specifically, the loop primer (LB-6) was identified as the candidate primer to design the self-quenching element. The fluorophore attached to LB-6 is self-quenched in unbound state. After binding to the dumb bell shaped DNA specifically, the FAM fluorophore is de-quenched, resulting in the fluorescence release. With the novel assay, as little as 5 pg of S. salar DNA could be detected. Moreover, the self-quenching assay embraces a high tolerance to impurities and the positive LAMP results can always be obtained, regardless of the DNA extraction methods and sample treatments. While significant difference with the Ct value according to both the extraction methods and sample treatments, highlighted also an improved LAMP amplification using DNA with higher purity.