Detection Techniques Research Articles

Identification and Analysis of Small Nucleolar RNAs by Real-Time Quantitative PCR.

One of the greatest scientific achievements of the twenty-first century is the completion of The Human Genome Project (HGP). Thereafter, we came to know that the human genome codes nearly 2% for making proteins and thus named as coding genes, suggesting the rest of the genome as noncoding or junk. However, research in the past two decades has shown and established that noncoding RNAs are major contributors of regulating and modulating the various function of cells as well as tissues. Noncoding RNAs can be classified as basis of their sizes in two categories, long noncoding RNAs (>200nt) and small noncoding RNAs (<200nt). Small nucleolar RNAs (snoRNAs) are part of the small noncoding RNA family and primarily reside inside the nucleus of eukaryotes. Sno RNAs can be divided into two major categories based on their distinguished structure and function; these are C/D box and HACA box snoRNAs. They participate in the posttranscriptional modifications on ribosomal RNAs (r-RNAs), transfer RNAs (t-RNAs), messenger RNAs (m-RNAs), and small nuclear RNAs (snRNAs). Sno RNAs act as guide RNAs to modify other noncoding RNAs by pseudouridylation or 2'O ribomethylation. We discussed in this protocol about one of the widely used techniques for detection and analysis of snoRNAs, i.e., real-time quantitative PCR (RT-qPCR).

Novel methods for the detection of glutathione by surface-enhanced Raman scattering: A perspective review.

Detection of biomolecules, Glutathione (GSH) in particular, is important because it helps assess antioxidant capacity, cellular protection, detoxification processes, and potential disease associations. Monitoring glutathione levels can provide valuable information about overall health and well-being. Many medical disorders have been connected to glutathione levels. Higher glutathione levels have been seen in several cancer cell types, which may increase their resistance to radiation and chemotherapy. Glutathione levels can be measured through various methods, such as colorimetric assays and fluorescent probes. However, surface-enhanced Raman scattering (SERS) has been known as an efficient and selective technique for biomolecule detection. Here in this perspective review, we have reported two distinctive methods based on SERS technique in detection of GSH; heat-induced method and reversed reporting agent method. Several variables that can impact the detection scheme were elaborated in the "heat-induced method," including pretreatment, nanoparticle reduction time, the process temperature, the pH of the colloidal solution, the concentration of citrate buffer, and the concentration of participating nanoparticles. To choose the best reporting agent for a reverse reporting scheme using SERS approaches, several reporting agents were examined in the second method. In order to grasp the situation at hand, biomolecule detection-specifically, GSH detection schemes-was briefly discussed. SERS spectroscopy and its associated terminology were then covered followed by the perspective and outlook of GSH detection at the end. To meet the demands of real-time applications in everyday life and to enhance SERS methods for biomolecule detection-in particular, GSH detection-such a thorough investigation is unavoidable.

Cortisol quantification in human plasma and urine by liquid chromatography coupled to mass spectrometry: Validation, analysis and application in a reference population and patients with adrenal incidentalomas.

Cortisol is a glucocorticoid hormone, which is involved in cardiovascular, metabolic, inflammatory and behavioral processes in the human body. Immunoassays, used for routine analysis of analytes, usually lead to erroneous quantification due to the low specificity of these methods. In this study, we developed LC-MS/MS methods with surrogating matrices for evaluating cortisol in both urine and plasma samples and validated them according to Brazilian Health Regulatory Agency guidelines. Urine samples were prepared with an enzymatic hydrolysis stage, followed by a solid-phase extraction (C18 cartridges) using dichloromethane:methanol 9:1 as elution solvent. Plasma samples were prepared by protein precipitation with acetonitrile, using 50μL of sample. HPLC was performed using a C8 column under 300mL min-1 flow gradient conditions with water and methanol, both containing 5mM ammonium formate and 0.1% formic acid. Mass spectrometer with electrospray ionization in positive mode and selected reaction monitoring as detection technique were employed. Calibration curves were linear over a concentration range of 1-200ng mL-1 for urine (r2=0.9950) and 0.5-300ng mL-1 for plasma (r2=0.9970). The methods were selective, showed suitable precision, accuracy, and sensibility (limit of quantification=0.85ng mL-1 for urine and 0.15ng mL-1 for plasma). Validated methods were successful applied to 22 real samples and a cohort of patients [n=63 urines and n=79 plasmas (from the Clementino Fraga Filho University Hospital, Rio de Janeiro, Brazil)].

From light to insight: Functional near-infrared spectroscopy for unravelling cognitive impairment during task performance

Cognitive impairment refers to the impairment of higher brain functions such as perception, thinking or memory that affects the individual's ability to perform daily or social activities. Studies have found that changes in neuronal activity during tasks in patients with cognitive impairment are closely related to changes in cerebral cortical hemodynamics. Functional near-infrared spectroscopy is an indirect method to measure neural activity based on changes in blood oxygen concentration in the cerebral cortex. Due to its strong anti-motion interference, high compatibility, and almost no restriction on participants and environment, it has shown great potential in the research field of cognitive impairment. Recognizing these benefits, this comprehensive review systematically elucidates the rationale, historical development, advantages and disadvantages of functional near-infrared spectroscopy, and also discusses the applications of combining functional near-infrared spectroscopy with other detection techniques. Additionally, this review summarized how functional near-infrared spectroscopy can be applied to cognitive impairment caused by different diseases, ultimately aiding the study of neural mechanisms of cognitive activities, which is crucial for the diagnosis, differentiation and treatment of cognitive impairment.

Discovery and solution for microplastics: New risk carriers in food.

Microplastics (MPs), as a kind of plastic particles with an equal volume size of less than 5mm, similar to PM2.5 in the air, are causing severe contamination issues in food. Along with the food chain accumulation, they have been confirmed to appear in daily foods and cause serious health risks to the organisms. However, there were no unifying national and local policies on separating, extracting, and detecting MPs in food, which is an essential and imperative early-warning strategy. This review carefully and comprehensively summarized the validated contaminated food, physical and chemical characteristics, extraction methods, traditional and rapid detection techniques, as well as degradation methods of MPs. We thoroughly analyzed the differences among these traditional strategies, and innovatively generalized the existing rapid detection techniques for MPs. Finally, the shortcomings of existing research were discussed, and the possibility of novel rapid and intelligent detection techniques for MPs in food was proposed.

Advanced Persistent Threats (APTs) Analysing: Current Detection Techniques and Emerging Countermeasures

Advanced persistent threats (APTs) are a significant problem for organisational cyber security because of their sophistication and advanced attacks. This research article analyses the state of the art in current APT detection techniques and cutting-edge countermeasures. Different detection approaches, such as signature-based, behaviour-based, and AI-driven methods, are studied. The study points out the drawbacks of traditional signature-based detection and showcases the rising significance of behavioural analysis and machine learning for spotting sophisticated patterns of APT. Federated learning, blockchain technology for secure communication, deception techniques, and advanced forms of APT defence are explored. The article also assesses what tools and frameworks exist for APT detection, with an open-source versus proprietary comparison. We then discuss key APT detection and mitigation challenges, including detecting stealthy and polymorphic threats, data privacy problems, and resource constraints in real time. Future directions for the research are proposed, centred on explainable AI, collaborative defence mechanisms, and more effective detection in resource-constrained environments. This further advances the body of current work focused on developing more robust and adaptive security procedures against the tricky sophistication of cyber threats, proving beneficial to security experts and researchers attempting to strengthen organisational resiliency to APTs.

Application of bioelectrical impedance detection techniques: Cells and tissues.

Pathological conditions in organisms often arise from various cellular or tissue abnormalities, including dysregulation of cell numbers, infections, aberrant differentiation, and tissue pathologies such as lung tumors and skin tumors. Thus, developing methods for analyzing and identifying these biological abnormalities presents a significant challenge. While traditional bioanalytical methods such as flow cytometry and magnetic resonance imaging are well-established, they suffer from inefficiencies, high costs, complexity, and potential hazards. To address these challenges, bioelectrical impedance detection technology, which leverages the electrical properties of biological cells and tissues to extract relevant biomedical information, has garnered considerable attention in the field of biological detection due to its affordability, convenience, non-invasiveness, and label-free nature. This article first provides a brief introduction to the principles of bioelectrical impedance and related detection techniques, as well as the equivalent circuit models and numerical simulation models developed at the cellular and tissue levels. Next, this article delves into the applications of bioelectrical impedance technology at the cellular level, including recent advancements in cell counting, classification, concentration detection, differentiation, and infection, thereby enriching previous literature reviews from a multicellular perspective. In addition, this article highlights the applications of bioelectrical impedance technology in relevant tissues including muscle, skin, lungs, and so on. Finally, the article explores the future opportunities and challenges of bioelectrical impedance detection and analysis technology, focusing on interdisciplinary research areas and data-driven intelligent analysis, offering researchers broader research directions and perspectives.

Development and application of hydrogels in pathogenic bacteria detection in foods.

Hydrogels are 3D networks of water-swollen hydrophilic polymers. It possesses unique properties (e.g., carrying biorecognition elements and creating a micro-environment) that make it highly suitable for bacteria detection (e.g., expedited and effective bacteria detection) and mitigation of bacterial contamination in specific environments (e.g., food systems). This study first introduces the materials used to create hydrogels for bacteria detection and the mechanisms for detection. We also summarize different hydrogel-based detection methods that rely on external stimuli and biorecognition elements, such as enzymes, temperature, pH, antibodies, and oligonucleotides. Subsequently, a range of widely utilized bacterial detection technologies were discussed where recently hydrogels are being used. These modifications allow for precise, real-time diagnostics across varied food matrices, responding effectively to industry needs for sensitivity, scalability, and portability. After highlighting the utilization of hydrogels and their role in these detection techniques, we outline limitations and advancements in the methods for the detection of foodborne pathogenic bacteria, especially the potential application of hydrogels in the food industry.

Recent developments in isothermal amplification technology for rapid detection of SARS-CoV-2.

Coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally, posing a significant threat to human health. Rapid and accurate detection of infectious disease pathogens is of crucial practical significance for early screening, timely intervention, and outbreak prevention. However, conventional diagnostic methods are increasingly unable to meet clinical demands. Recently developed isothermal analysis methods offer mild reaction conditions and reduce dependence on specialized instruments. These convenient, fast, and reliable methods show great promise for diagnosing infectious pathogens, especially for on-site detection in areas without laboratories or with limited resources. Among them, loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA), which integrate various widely used detection techniques, stand out as rapidly advancing and relatively mature isothermal nucleic acid amplification technologies. This review outlines several representative isothermal amplification technologies and associated detection methods. We summarize the latest advancements in LAMP and RPA technologies for the rapid detection of SARS-CoV-2 and discuss the future prospects of isothermal amplification in diversified testing.

Visualization methods for loop mediated isothermal amplification (LAMP) assays.

Recent advances in nucleic acid (NA) detection techniques have significantly enhanced the diagnosis of diseases caused by a range of pathogens. These NA-based methods that target specific gene sequences for identification offer high specificity. Despite the effectiveness of polymerase chain reaction (PCR), its requirement for sophisticated laboratory settings and expensive equipment restricts its accessibility, particularly in resource-limited settings. As an alternative, isothermal nucleic acid amplification methods are highly sought after due to their rapid, sensitive, and specific detection ability. Among these, loop mediated isothermal amplification (LAMP) stands out due to its simplicity, reliability, and cost-effectiveness. LAMP operates without the need for varied temperature cycles, employing a simple heating block to maintain a constant temperature, thus facilitating onsite rapid testing. In LAMP, the detection step is critical as it shows the outcome of the assay. In order to make the LAMP technique user-friendly and applicable for large scale testing, it is critical to have visual detection where the results can be observed with the naked eye. This review focuses on recent developments of LAMP visualization techniques, including the more common fluorescence, turbidity, and gel electrophoresis methods, as well as innovations in colorimetric techniques applying novel transduction methods such as nanoparticles and digital tools. Additionally, various practical applications of LAMP are discussed.

Tensor ratio small subdomain filtering technique for edge detection

Tensor ratio small subdomain filtering technique for edge detection

A review of uranium (U) elemental detection methods.

With the increasing demand for energy, nuclear energy has been developing rapidly. The quantitative detection and qualitative identification of uranium (U) are of great significance for the comprehensive and efficient use of U resources and the control of nuclear and radioactive substances. In this study, the detection of U is divided into liquid sample detection, solid sample detection, gas sample detection, and industrial detection from the perspectives of the sample state and detection environment. For liquid samples, the advantages and disadvantages of various detection methods are summarized. The application of different detection methods for different samples is also analyzed. For solid samples, the application of laser-induced breakdown spectroscopy and X-ray fluorescence in powder compaction and U-containing solid samples is presented. The problems of low instrumental resolution and poor accuracy of quantitative analyses are discussed, and methods to solve the above problems are proposed. For gas samples, UF6 can be analyzed for U isotopes using laser-induced breakdown spectroscopy. U abundance (235U/(235U + 238U)) is obtained quickly, directly and without the need for sample preparation. Finally, the application of remote detection and in situ detection techniques in industrial detection of U is also discussed in this study. In the future, rapid, high-precision and portable U detection methods can be developed by coupling multiple detection methods to meet the needs of different scenarios.

Efficacy of ultrasonography and mammography in detecting features of breast cancer

ABSTRACT Introduction: Breast cancer (BC) is considered one of the most commonly diagnosed cancers. Early detection is critical for effective management. This study aims to assess the utility of ultrasonography (US) and mammography (MG) in detecting BC features. Methods: This retrospective cross-sectional study involved the electronic records of 263 female patients diagnosed with BC. The mean age was 45.71 ± 12.25 years (17–90 years). A cross-tabulation test was performed to correlate the presence of each malignant feature (Yes/No) on both US and MG and the final ultrasonography diagnosis (benign/malignant). The compatibility between the presence of each feature on both imaging techniques was measured by the percentage of agreement in reporting the feature that was reported as Kappa. The sensitivity and specificity for each feature were calculated, and the receiver operating characteristic curve was used to measure the area under the curve for each feature on both modalities. Results: The strong compatibility between the two techniques was 87.1%, 94.29%, 66.92%, 79.85%, 77.56%, 77.18, and 79.84% for irregular shape, uncircumscribed, spiculated margins, tissue distortion, nipple retraction, skin thickening, and the presence of lymphadenopathy, respectively (P &lt; 0.001). Boxplots show that the sensitivity of the US ranged from 37% to 95%, and the specificity ranged from 27% to 91%. However, MG’s sensitivity ranged from 44% to 93%, and the specificity ranged from 36% to 73%. Conclusion: US and MG images show similar morphological changes, enhancing diagnostic accuracy in breast lesions. US characterizes echogenicity, provides real-time imaging, and uses color and pulsed Doppler techniques for vascularity and lymphadenopathy detection, while MG is better for identifying different calcification types.

Automating egg damage detection for improved quality control in the food industry using deep learning.

The detection and classification of damage to eggs within the egg industry are of paramount importance for the production of healthy eggs. This study focuses on the automatic identification of cracks and surface damage in chicken eggs using deep learning algorithms. The goal is to enhance egg quality control in the food industry by accurately identifying eggs with physical damage, such as cracks, fractures, or other surface defects, which could compromise their quality. A total of 794 egg images were used in the study, comprising two different classes: damaged and not damaged (intact) eggs. Four different deep learning models based on convolutional neural networks were employed: GoogLeNet, Visual Geometry Group (VGG)-19, MobileNet-v2, and residual network (ResNet)-50. GoogLeNet achieved a classification accuracy of 98.73%, VGG-19 achieved 97.45%, MobileNet-v2 achieved 97.47%, and ResNet-50 achieved 96.84%. According to the results, the GoogLeNet model performed the damage detection with the highest accuracy rate (98.73%). This study encompasses artificial intelligence and deep learning techniques for the automatic detection of egg damage. The early detection of egg damage and accurate interventions highlights the significant importance of using these technologies in the food industry. This approach provides producers with the ability to detect damaged eggs more quickly and accurately, thereby minimizing product losses through timely intervention. Additionally, the use of these technologies offers a more efficient means of classifying and identifying damaged eggs compared to traditional methods.

Whole-cell aptamer-based techniques for rapid bacterial detection: Alternatives to traditional methods.

Controlling the spread of bacterial infectious diseases is a major public health issue, particularly in view of the pandemic of bacterial resistance to antibiotics. In this context, the detection and identification of pathogenic bacteria is a prerequisite for the implementation of control measures. Current reference methods are mainly based on culture methods, which generate a delay in obtaining a result and requires equipment. Consequently, focusing on the detection of the whole bacterium represents a very attractive alternative, since no culture is required. Several techniques have already been deployed to identify whole-cell bacteria. In recent decades, growing interest in nucleic acid aptamers has emerged as a viable alternative to antibodies as recognition elements, offering preferable stability, cost-efficiency, good specificity and affinity. This review explores current alternative methods for the detection of whole-cell bacteria, with particular emphasis on aptamer-based assays. These assays have shown promising results in various transduction mechanisms, including optical, electrochemical, and mechanical approaches, enhancing their versatility in different diagnostic platforms. The integration of aptamers in these detection methods offers rapid, sensitive, versatile and portable solutions for pathogen identification, positioning them as valuable tools in the fight against bacterial infections.

Speech-Based Techniques for Emotion Detection in Natural Arabic Audio Files

Emotion detection is one of the greatest challenges of Natural Language Processing (NLP). Often referred to as emotion recognition, it is the process of identifying a person’s various feelings or emotions such as: happiness, sadness, or anger. Emotions are a strong feeling regarding a human's situation or relation with others. They are the mental states that affect human behavior and interactions. In this paper, we propose an approach for emotion detection in audio files, focusing on a natural Arabic audio dataset and applying several Machine Learning (ML) classifiers: Sequential Minimal Optimization (SMO), Random Forest (RF), K-Nearest Neighbours (KNN), and Simple Logistic (SL). The classification experiments were conducted using sixteen acoustic feature sets. Many acoustic features were explored including Mel Frequency Cepstral Coefficient (MFCC), Mel spectrogram, spectral contrast, Zero Crossing Rate (ZCR), and Intensity. The experimental results show that SMO and SL classifiers achieved the highest overall accuracy 83.82% when using combinations of all acoustic features (MFCC, Mel spectrogram, Spectral contrast, ZCR and intensity). Additionally, The RF and KNN classifiers yielded Competitive results, with accuracies of 81.71% and 77.34%, respectively. These results suggest that combining multiple acoustic features significantly enhances the performance of emotion detection models, especially for complex emotions in natural Arabic audio datasets

Structure-switchable branched inhibitors regulate the activity of CRISPR-Cas12a for nucleic acid diagnostics

In current years, the CRISPR (clustered regularly interspaced short palindromic repeats) based strategies have emerged as the most promising molecular tool in the field of gene editing, intracellular imaging, transcriptional regulation and biosensing. However, the recent CRISPR-based diagnostic technologies still require the incorporation of other amplification strategies (such as polymerase chain reaction) to improve the cis/trans cleavage activity of Cas12a, which complicates the detection workflow and lack of a uniform compatible system to respond to the target in one pot. To better fully-functioning CRISPR/Cas12a, we reported a novel technique for straightforward nucleic acid detection by incorporating enzyme-responsive steric hindrance-based branched inhibitors with CRISPR/AsCas12a methodology. The construction-transferable branched inhibitors coupled with a specific overhang flap induce spatial steric effects and result in the loss of the binding ability of Cas12a, which inhibits the activity of Cas12a. Target as the input signal would trigger the site-directed APE1 enzyme incision of the inhibitors, thus transforming the conformation of the inhibitors into split activators to illumine the CRISPR/AsCas12a catalyst system. At the same time, we found that APE1 could drive the enzymatic positive feedback circuit and exhibited considerably high amplification efficiency to enhance the detection ability of nucleic acids. Besides, our method provides universal platforms and can be realized in real-time and one-pot detection of HIV-1 DNA by replacing the inhibitors and crRNA with different target recognition sequences. Overall, due to the high programmability of the nucleic acid network, this work proposed a feasible way to use the steric hindrance-based inhibitors as a switchable element, decorating the CRISPR/Cas12a-based strategy equipment for molecular diagnostics. Besides, this strategy could offer a simple tool for detecting trace nucleic acid, which opens avenues for future clinical application.

Theoretical and Experimental Investigation of the Use of a Roving Mass with Rotary Inertia for Crack Detection in Beam-Like Structures

PurposeNatural frequency is a global dynamic parameter that normally contains limited spatial information on a crack in a structure. However, recently a theoretical concept that a roving mass with rotary inertia causes sudden frequency shifts when located near the crack location has been proposed as a method of locating cracks, although experimental verification is still lacking. This study fills this gap in knowledge, by investigating the measurability of the frequency shift as a roving body passes a crack.MethodsNatural frequencies were measured through impact hammer tests and compared with frequencies calculated using the dynamic stiffness method and finite element method.ResultsThe results show that the natural frequency shift could be clearly measured for a beam featuring a medium sized crack. For smaller cracks, while numerical results show that the current method would still enable the identification of their locations as frequency shifts are in the measurable range, the number of locations at which the measurement needs to be taken can be very large.ConclusionsThis study provides insights into the practical feasibility of the roving mass technique for crack detection.

A simple SERS sensor based on antibody-modified Fe3O4@Au MNPs for the detection of CA19-9 in CRC patients.

This paper presents functionalized magnetic nanoparticles (Fe3O4@Au MNPs) combined with the surface-enhanced Raman spectroscopy (SERS) technique for sensitive detection of colorectal cancer (CRC) protein biomarker carbohydrate antigen 19-9 (CA19-9). Fe3O4@Au MNPs were constructed by the PEI-mediated seed growth method. Then, the signal molecule 5-5'-dithiobis (succinimidyl-2-nitrobenzoic acid) was used as a bridging agent to link CA19-9 antibody, and the SERS sensor was prepared. Using this sensor, detection of CA19-9 can be realized with only a one step sampling reaction. Fe3O4@Au MNPs combine the advantages of magnetic materials and noble metal nanoparticles, effectively amplifying the signal by creating numerous "hot spots" within the Au particle gaps and enhancing magnetic enrichment. Consequently, this approach lowers the detection limit (LOD) and enhances detection sensitivity. The ratio of characteristic peak intensities I1392/I1069 was selected for calculation, and a linear equation was constructed with a LOD as low as 0.27 U mL-1 by quantitative detections of the standard antigen. Finally, the sensor was used to analyze the clinical serum samples from CRC patients and healthy individuals, and the detection results were consistent with the actual results. This method exhibits notable advantages, including simplicity, high sensitivity, specificity, stability and reproducibility. It is expected to provide a new promising analytical method for clinical CA19-9 immunoassay.

Brain Tumor Detection: Integrating Machine Learning and Deep Learning for Robust Brain Tumor Classification

Accurate detection and classification of brain tumors are essential for timely diagnosis and effective treatment planning. This study presents an integrated framework leveraging both machine learning (ML) and deep learning (DL) models for brain tumor detection and classification using MRI images. Two publicly available datasets are utilized: one for binary classification (tumor vs. no tumor) and another for multiclass classification (glioma, meningioma, and pituitary tumors). Comprehensive preprocessing steps, including resizing, feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and feature selection via Chi-square testing, were employed to optimize the dataset for modeling. Machine learning models such as Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and AdaBoost were compared with deep learning architectures like Convolutional Neural Networks (CNNs) and the pre-trained VGG16 model. Hyperparameter optimization techniques, including grid search and the Adam optimizer, were used to enhance model performance. The models were evaluated using metrics such as accuracy, precision, recall, F1-score, Mean Squared Error (MSE), and Mean Absolute Error (MAE). Results indicate that the VGG16 model consistently outperformed other approaches, achieving high validation accuracy. This study highlights the potential of integrating ML and DL techniques for accurate and efficient brain tumor detection and classification, offering valuable tools for medical diagnostics.