Efficient Detection Method Research Articles

Circulating Extracellular Vesicles as Promising Biomarkers for Precession Diagnostics: A Perspective on Lung Cancer.

Extracellular vesicles (EVs) have emerged as promising biomarkers in liquid biopsy, owing to their ubiquitous presence in bodily fluids and their ability to carry disease-related cargo. Recognizing their significance in disease diagnosis and treatment, substantial efforts have been dedicated to developing efficient methods for EV isolation, detection, and analysis. EVs, heterogeneous membrane-encapsulated vesicles secreted by all cells, contain bioactive substances capable of modulating recipient cell biology upon internalization, including proteins, lipids, DNA, and various RNAs. Their prevalence across bodily fluids has positioned them as pivotal mediators in physiological and pathological processes, notably in cancer, where they hold potential as straightforward tumor biomarkers. This review offers a comprehensive examination of advanced nanotechnology-based techniques for detecting lung cancer through EV analysis. It begins by providing a brief overview of exosomes and their role in lung cancer progression. Furthermore, this review explores the evolving landscape of EV isolation and cargo analysis, highlighting the importance of characterizing specific biomolecular signatures within EVs for improved diagnostic accuracy in lung cancer patients. Innovative strategies for enhancing the sensitivity and specificity of EV isolation and detection, including the integration of microfluidic platforms and multiplexed biosensing technologies are summarized. The discussion then extends to key challenges associated with EV-based liquid biopsies, such as the standardization of isolation and detection protocols and the establishment of robust analytical platforms for clinical translation. This review highlights the transformative impact of EV-based liquid biopsy in lung cancer diagnosis, heralding a new era of personalized medicine and improved patient care.

A robust and efficient change point detection method for high-dimensional linear models

In the context of linear models, a key problem of interest is to estimate the regression coefficient. Nevertheless, in certain instances, the vector of unknown coefficient parameters in a linear regression model differs from one segment to another. In this paper, when the dimension of covariates is high, a new method is proposed to examine a linear model in which the regression coefficient of two subpopulations may be different. To achieve robustness and efficiency, we introduce modal linear regression as a means of estimating the unknown coefficient parameters. Furthermore, our proposed method is capable of selecting variables and checking change points. Under certain mild assumptions, the limiting behavior of our proposed method can be established. Additionally, an estimation algorithm based on kick-one-off and SCAD approach is developed to implement in practice. For illustration, simulation studies and a real data are considered to assess the performance of our proposed method.

Analysis of Trace Heavy Metal in Solution Using Liquid Cathode Glow Discharge Spectroscopy

Heavy metal pollution, particularly from cadmium (Cd) and copper (Cu), poses significant environmental and health risks. To address the need for efficient, portable, and sensitive detection methods, this study introduces an improved atmospheric pressure glow discharge atomic emission spectrometry (APGD-AES) technique for quantifying Cd and Cu in water samples. The APGD-AES method offers key advantages, including low energy consumption (<33 W), high excitation energy, and compact design. The system was optimized for a discharge voltage of 550 V (Cd) and 570 V (Cu), a flow rate of 3.6 mL/min, and a solution pH of 1.0. Under these conditions, detection limits reached 16 µg/L for Cd and 1.3 µg/L for Cu. APGD-AES was tested on real water samples, including sewage and tap water, demonstrating compliance with national safety standards and comparable performance to graphite furnace atomic absorption spectrometry (GFAAS). This technique shows promise for real-time, on-site monitoring of trace heavy metals due to its portability, precision, and cost-efficiency.

A Systematic Literature Review on Automatic Sexism Detection in Social Media

Sexist content has become increasingly prevalent on social media platforms, underscoring the critical need for the development of efficient Automatic Sexism Detection methods. Previous literature reviews have not encompassed the new advancements in Automatic Sexism Detection observed over the past three years. Hence, the present study conducted a Systematic Literature Review (SLR) that examined 48 primary studies published between 2014 and 17th Sept. 2024, retrieved from six bibliographic databases. This paper aims to present a comprehensive literature review on Automatic Sexism Detection, encompassing the datasets, preprocessing techniques, feature extraction methods, text representations, classification approaches, and evaluation models employed in Automatic Sexism Detection research. The paper includes a discussion of the findings, limitations, and future research directions of the chosen articles. Additionally, it provides an overview of the conclusions drawn from the conducted research. The performed analysis reveals a lack of corpus beyond the English and Spanish language encountered in datasets, with most of the latter being annotated for either misogyny or non-misogyny. Common preprocessing techniques analyzed in the current study include lowercase conversion, text removal, tokenization, stemming, and rewriting. Discrete representations, such as TF-IDF, N-grams, and BoW, are frequently utilized, while distributed representations, like Bert and GloVe, are prominent. Bert is the predominant classification model utilized while combining lexical features can enhance the results in the majority of the discussed scenarios. Accuracy (A) and F1 score (F1) are the most widely deployed evaluation metrics in this field.

Construction and application of an electrochemical sensor based on MIL-101-NH2(Fe)-derived Fe-C porous materials doped with reduced graphene oxide for baicalin detection

Baicalin (Bn), a natural flavonoid compound, possesses high pharmacological effects. Here, an electrochemical sensor has been proposed based on an MIL-101-NH2(Fe)-derived Fe porous carbon composite (Fe-C) doped with reduced graphene oxide (rGO) for detecting Bn, designated as Fe-C/rGO. Fabricated through a straightforward physical doping process, Fe-C/rGO exhibits remarkable electrochemical activity. This superiority stems primarily from two key factors. Firstly, the porous structure of Fe-C efficiently concentrates Bn, facilitating its capture and recognition. Secondly, graphene oxide, serving as a substrate for anchoring metal–organic frameworks (MOFs), boosts the electrochemical performance of the composite due to its distinctive two-dimensional structure, abundant active sites, and unparalleled stability. The synergistic interaction between Fe-C and rGO maximizes their respective advantages, leading to a significant enhancement in the sensitivity and selectivity of the electrochemical sensor. Boasting a low detection limit of 7.5 nM and a broad detection range spanning from 30 to 180 nM, this sensor holds immense potential in clinical applications and drug monitoring, offering a reliable and efficient method for Bn detection.

YOLO-MRS: An efficient deep learning-based maritime object detection method for unmanned surface vehicles

Deep learning-based object detection for an unmanned surface vehicle (USV) is an important way of visual perception. However, current methods perform poorly when performing complex maritime object detection tasks. It also lacks available datasets of complex maritime objects for visual perception system of USVs. In order to solve these problems, we propose an improved maritime object detection method, called YOLO-MRS, based on lightweight YOLOv8 model in this paper. Specifically, we introduce a multi-scale cross-axis attention (MCA) mechanism into the backbone network of the model to establish long-distance dependencies between pixels to capture global feature information. In addition, we introduce Simplified Spatial Pyramid Pooling-Fast (SimSPPF) to the backbone to enhance prediction accuracy. Also, considering computational efficiency, we replace the ordinary convolutional layers in the backbone network and neck network with refocused convolutional (RefConv) layers to reduce model parameters. Especially, we construct a maritime object detection dataset, termed MODD-13, which contains over 9000 precisely annotated images. The proposed MODD-13 sufficiently considers the characteristics of object categories (13 types), image diversity, sample independence, and background confusion, and can be used as a benchmark dataset for maritime object detection. The final experimental results show that compared with the representative YOLO series models, YOLO-MRS improves the average mAP50 accuracy by 1.8%–7% and mAP50-95 by 1.1%–11.5%, and effectively balances detection accuracy and computational efficiency, thereby effectively achieving fast and accurate detection of maritime objects.

Development of an advanced acetaldehyde detection solution based on yeast and bacterial surface display technology

Acetaldehyde, a carcinogen widely present in various beverages and the natural environment, necessitates convenient and efficient detection methods. In this work, two different host strains were used to develop a sensitive, convenient, and efficient whole-cell optical biosensor for acetaldehyde detection. Acetaldehyde dehydrogenase (AldH) was displayed on the cell surface of Saccharomyces cerevisiae and E. coli using flocculin protein and the N-terminal ice nucleation protein (INP) protein, respectively. The successful construction of yeast and bacteria surface display platforms was confirmed by laser scanning confocal microscopy. Then, the optimal AldH-display system for yeast and bacteria was confirmed. The optimum reaction conditions were determined by changing testing temperatures and pH values. The differences between the two display systems were compared. The highest whole-cell activities of yeast and bacteria under optimal conditions was 3.68 ± 0.07U/mL/OD600 for BY-S6G and 6.95 ± 0.04U/mL/OD600 for E-32-IrA. The strains with the best performance were chosen for the detection of acetaldehyde in wine and other beverage samples and showed substrate specificity and accuracy, in which the recovery rate ranged between 94.4% and 110.1%. The results demonstrated that the AldH surface display strains could be used as an optical biosensor to detect acetaldehyde in beverages and red wine.

Delamination defects in composite hydrogen storage cylinders: CT scanning and shearography measurement

Carbon fiber-reinforced composite hydrogen storage cylinder is a key component used in hydrogen fuel cell electric vehicles. However, some micro defects such as voids and delamination are inevitable during the manufacturing process. An efficient detection method for manufacturing defects is still lacking at present. In this work, industrial computerized tomography (CT) scanning was carried out and a large number of micro delamination with scattered sizes and random locations were found in the filament winding layer. Shearography technique based on digital speckle pattern interferometry (DSPI) was used to measure the surface deformation of the cylinders. It was found that the "butterfly-shaped” interference fringes representing the anomalous responses from defects can be significantly observed at the pressure difference of 0.62%–0.69% working pressure. Also, the crack was found originated from the delamination defect with the most significant “butterfly-shaped” fringes, which leads to a large area of interlaminar destruction during the hydraulic bursting test.

A non-enzymatic electrochemical sensor based on zinc oxide/reduced graphene oxide (ZnO/rGO) nanocomposite for effective detection of urea

Monitoring urea levels is essential for detecting water and soil pollution and diagnosing urea cycle disorder-related diseases. The present study addresses the need for an efficient, accurate, and cost-effective detection method by developing an electrochemical sensor using ZnO/rGO nanocomposites. The sensor’s performance is evaluated through cyclic voltammetry, differential pulse voltammetry, and amperometry, with different weight ratios of ZnO and rGO (3:1, 1:1, and 1:3). The results show that the ZnO/rGO (1:1) nanocomposite modified electrode exhibits remarkable sensitivity (850.15 μA-1 mM−1 cm−2), a low limit of detection (0.07 μM), excellent selectivity, and long-term stability for urea detection. Developed sensor leverages the advantages of electrochemical sensing, including simplicity, rapid response, high sensitivity, and good selectivity, making it a promising solution for environmental monitoring and medical diagnostics.

Signal amplification strategy electrochemiluminescence based on porous graphite-phase carbon nitride: A novel ECL sensor for ultrasensitive detection of tigecycline

Tigecycline (TGC) is a third-generation tetracycline antibiotic known for its broad-spectrum antibacterial properties. However, the misuse of tigecycline can lead to significant issues, including environmental concerns and the escalation of drug resistance. Consequently, it is essential to develop an efficient and sensitive detection method for tigecycline. In this study, a novel solid-state electrochemical luminescence sensor was firstly developed for highly sensitive detection of TGC. The sensor incorporated porous graphite-phase carbon nitride (PCN) as a luminescent reagent, triethanolamine (TEA) as a co-reagent, Fe3O4 and ZIF-8@Ag NPs as luminescence accelerators, resulting in a robust and stable luminescence signal. Detailed characterization of PCN, comparison of ECL efficiency between PCN and g-C3N4, and optimization of experimental conditions were conducted to showcase the superior ECL performance of PCN. The sensor exhibited a linear relationship between the change of ECL signal and the logarithm of tigecycline concentration in the range of 1.0 × 10−14 to 1.0 × 10−7 mol/L, with a low detection limit of 3.33 × 10−15 mol/L (S/N = 3). Furthermore, the sensor demonstrated excellent stability, selectivity, and reproducibility, making it suitable for detecting TGC in milk samples.

Lightweight Machine Learning-Based DOS Attack Detection in Wireless Sensor Networks Using Decision Tree and Information Gain

Abstract—Wireless Sensor Networks (WSNs) are rapidly expanding across various domains due to their unique characteristics and performance capabilities. However, these networks are highly vulnerable to a range of security threats, particularly Denial-of-Service (DoS) attacks, which are among the most common in WSNs. This paper explores the vulnerabilities of WSNs, focusing on DoS threats, and reviews current techniques for their detection. It introduces a lightweight machine learning-based approach using a decision tree (DT) algorithm with the Information Gain (IG) feature selection method for efficient DoS detection. Tested on an enhanced WSN-DS dataset, the proposed method demonstrated high accuracy and minimal processing time compared to other classifiers, such as XGBoost, and RF. This efficiency makes the proposed method well-suited for real- time DoS attack detection in resource-constrained WSNs. Keywords—Machine Learning,Decision Tree (DT),Information Gain (IG),DoS attack detection

Risk assessment of migrants released from multilayer packaging materials: Direct immersion-solid-phase microextraction coupled to gas chromatography-mass spectrometry

Milk is commonly contained in plastic multilayer pouches or cartons. Substances in these food packaging materials, such as additives, processing aids, degradation products, and residues may migrate into the milk, thereby causing contamination and human health risks. The migration of substances from six plastic multilayer pouches and five multilayer cartons that are commonly used as milk packaging materials into a model food (50 % ethanol) was studied. A direct immersion -solid phase microextraction-gas chromatography mass spectrometry (DI-SPME-GC-MS) method was optimized for the identification and quantification of the migrants. The optimal conditions were determined to be 60 min extraction time, 75 °C extraction temperature, and 1000 rpm stirring rate. Using this method, 36 compounds were found to have migrated into the milk, including several additives (such as bis(2-ethylhexyl) phthalate, acetyl tributyl citrate and erucamide), several degradation products (such as 2,4-di-tert-butylphenol and 7,9-di-tert-butyl-1-oxaspiro(4,5)deca-6,9-diene-2,8-dione), an initiator residue, and some solvents. A risk assessment was then conducted, and it was found that the exposure of certain substances from six plastic multilayer pouches into 50 % ethanol might pose potential human health risk. Our study showed that DI-SPME-GC-MS is a fast and efficient migration detection method, and our findings provide an important reference for the risk assessment of the potential migration of toxic substances from multilayer packaging materials into milk.

A fluorescence probe for monitoring toxic hypochlorous acid in biosystems and environmental waters with a broad pH adaptation

Hypochlorous acid (HOCl) is commonly utilized in various daily applications. As a toxic reactive oxygen species (ROS) that generated from the industrial and pharmaceutical aspects, it plays a critical role in the mass cycle of environmental system and various biological processes. Understanding the complicated roles of HOCl in environment and biosystems requires the development of precise and efficient detection methods. Thus, in this work, a novel fluorescent probe, MQ-ClO, has been designed to detect hypochlorous acid by utilizing hypochlorous acid-triggered oxidative intramolecular cyclization. This probe exhibits rapid and sensitive response, with a detection limit as low as 35 nM. More importantly, the probe is capable of functioning under highly acidic or basic conditions, exhibiting a wide pH range adaptability from pH 2–11. Furthermore, MQ-ClO has been effectively used to detect HOCl in real water samples. Besides, the low toxicity of MQ-ClO enables its practical application in monitoring endogenous/exogenous HOCl levels in living cells as well as zebrafish.

A low illumination target detection method based on a dynamic gradient gain allocation strategy

Current target detection methods perform well under normal lighting conditions; however, they encounter challenges in effectively extracting features, leading to false detections and missed detections in low illumination environments. To address these issues, this study introduces an efficient target detection method for low illumination, named DimNet. This method optimizes the model through enhancements in multi-scale feature fusion, feature extraction, detection head, and loss function. Firstly, efficient multi-scale feature fusion is performed by using a new neck structure in the original model so that it can fully exchange high-level semantic information and low-level spatial information. Secondly, by designing a new feature aggregation module, it can simultaneously fuse channel and spatial information as well as local and global information to improve the representation of the network. Subsequently, to achieve more accurate target recognition, a new detection head is designed by replacing the original convolutional layer and utilizing the reparameterization technique, which enhances recognition performance in complex scenes. Additionally, the size of the improved detection head is reduced by adopting a parameter-sharing approach, thereby balancing detection accuracy with computational efficiency. Finally, to solve the fuzzy boundary problem caused by the target boundary being similar to the surrounding background due to insufficient illumination under low illumination conditions, a new loss function is designed in this paper, which pays more attention to the center of the target and weakly considers the aspect ratio of the target prediction frame, and at the same time, the new loss function employs a dynamic gradient gain assignment strategy to reduce the effect of the low-quality anchor frames and to improve the target localization Accuracy. The experimental results show that DimNet achieves a mAP50 of 75.60% on the ExDark dataset, which is an improvement of 3.77% over the baseline model and 2.25% over the state-of-the-art (SOTA) model. DimNet outperforms the previous and current SOTA methods in terms of detection accuracy and other aspects of performance, which is a clear advantage.

Single-cell Raman spectroscopy for rapid detection of bacteria in ballast water and UV254 treatment evaluation

The increasing global trade has facilitated the transfer of ship ballast water, which has emerged as a primary pathway for alien species invasion into marine ecosystems, posing significant threats to marine biodiversity. Addressing the technical challenges in rapid microorganism detection and treatment efficiency assessment, this study developed a confocal Raman microscopic imaging (CRMI) system integrated with a metal-insulator-metal (MIM) broadband surface-enhanced Raman scattering (SERS) chip, enabling efficient acquisition of single-cell Raman spectroscopy (SCRS). By incorporating machine learning algorithms, the system achieved precise identification of up to 10 bacterial types in ballast water, exhibiting remarkable performance metrics with average accuracy, sensitivity, specificity, and precision above 95.5 %, 95.5 %, 99.5 %, and 95.5 %, respectively. To evaluate the efficacy of ultraviolet (UV) treatment, a Raman spectroscopy-based approach combined with heavy water labeling was introduced to characterize the changes in bacterial single-cell metabolic activity under UV254 irradiation. Experimental results demonstrated that a 10-min UV254 exposure at an effective intensity of 2 mW/cm2 was sufficient to achieve complete bacterial sterilization for the specific ballast water used in our experiment. This study not only established an efficient and accurate method for rapid detection of mixed bacteria but also provided a novel perspective for assessing UV treatment effects. It holds significance and practical value for optimizing ship ballast water management strategies and safeguarding the safety of marine ecosystems.

The Application of Machine Learning in Doping Detection.

Detecting doping agents in sports poses a significant challenge due to the continuous emergence of new prohibited substances and methods. Traditional detection methods primarily rely on targeted analysis, which is often labor-intensive and is susceptible to errors. In response, machine learning offers a transformative approach to enhancing doping screening and detection. With its powerful data analysis capabilities, machine learning enables the rapid identification of patterns and features in complex compound data, increasing both the efficiency and the accuracy of detection. Moreover, when integrated with nontargeted metabolomics, machine learning can predict unknown metabolites, aiding the discovery of long-lasting biomarkers of doping. It also excels in classifying novel compounds, thereby reducing false-negative rates. As instrumental analysis and machine learning technologies continue to advance, the development of rapid, scalable, and highly efficient doping detection methods becomes increasingly feasible, supporting the pursuit of fairness and integrity in sports competitions.

Scd-yolo: a novel object detection method for efficient road crack detection

Scd-yolo: a novel object detection method for efficient road crack detection

Detection of Prenatal Cardiac Disease using Computer Vision and Artificial Intelligence

Prenatal cardiac anomalies, commonly referred to as congenital heart defects (CHDs), comprise a spectrum of pathologies that adversely affect cardiac function. There is a correlation between the numerous risks of cardiovascular diseases and the pressing requirement for precise, reliable, and efficient methods of early detection. The contemporary epoch of voluminous data presents a plethora of novel prospects for clinicians to utilize artificial intelligence in order to identify and enhance treatment for pediatric patients and those afflicted with congenital heart disease. Machine learning, a prevalent technique in the field of artificial intelligence, has been utilized to forecast various outcomes in obstetrics. The application of artificial intelligence in real-time electronic health recording and predictive modelling has demonstrated promising outcomes in the domain of fetal monitoring. The present research provides an in-depth review of recent advancements and challenges in the application of artificial intelligence techniques, such as deep learning and computer vision, for the detection of congenital heart disease.

Development of a Dual-Epitope Nanobody-Based Immunosensor with MXenes@CNTs@AuNPs for Ultrasensitive Detection of Rotavirus.

Immunoassays have become essential tools for detecting infectious viruses. However, traditional monoclonal antibody-dependent immunoassays are costly, fragile, and unstable, especially in complex media. To overcome these challenges, we have developed cost-effective, robust, and high-affinity nanobodies as alternatives to monoclonal antibodies for rapid detection applications. We engineered dual-epitope nanobody (NB) pairs and incorporated them into a sandwich immunosensor design to detect transmitted rotaviruses in rectal swabs and wastewater samples. To further enhance sensitivity, we synthesized an advanced two-dimensional material, MXenes@CNTs@AuNPs, which offers an extensive specific surface area that supports the enrichment and immobilization of NBs. This integration with catalase-modified magnetic probes facilitates signal generation. Subsequently, our sensor achieved a detection limit of 0.0207 pg/mL for the rotavirus VP6 antigen, significantly outperforming commercial antigen kits with a sensitivity enhancement of 3.77 × 105-fold. The exceptional sensor performance extended to specificity, repeatability, stability, and accuracy across various sample types, establishing it as a promising tool for rotavirus detection. This research outlines a viable strategy for creating a robust and ultrasensitive analytical nanoprobe, thereby addressing the critical need for efficient and reliable viral detection methods in various environments.

Fluorescence sensor array for highly sensitive pattern recognition of biothiols in food based on tricolor upconversion luminescence metal-organic frameworks

Fluorescence nanomaterial sensors have exhibited excellent application potential in biothiols analyses. The fluorescence sensor arrays constructed from upconversion luminescence metal-organic frameworks nanocomposites (LMOFs) can provide impressive discrimination and exquisite fingerprinting capabilities for extremely similar analytes. Herein, an upconversion fluorescence sensor array based on LMOFs featuring UiO-type metal-organic frameworks-functionalized lanthanide-doped upconversion nanoparticles was proposed, wherein Cu2+ can make the fluorescence quenching of LMOFs and preferentially bind biothiols to recover fluorescence in different degrees forming unique fingerprinting. The fluorescence sensor array displayed an excellent pattern recognition for five biothiols (glutathione, homocysteine, N-acetylcysteine, and L/D-cysteine) even at 50 µM by linear discriminant analysis, and the discernment for the enantiomers of L/D-cysteine, as well as the accurate identification (90.0% accuracy) of biothiols in food samples (tea beverage and white wine). Such fluorescence sensor array might provide a simple and efficient detection method for biothiols.