Rapid Detection System Research Articles

Deep-Learning Empowered Customized Chiral Metasurface for Calibration-Free Biosensing.

As a 2D metamaterial, metasurfaces offer an unprecedented avenue to facilitate light-matter interactions. The current "one-by-one design" method is hindered by time-consuming, repeated testing within a confined space. However, intelligent design strategies for metasurfaces, limited by data-driven properties, have rarely been explored. To address this gap, a data iterative strategy based on deep learning, coupled with a global optimization network is proposed, to achieve the customized design of chiral metasurfaces. This methodology is applied to precisely identify different chiral molecules in a label-free manner. Fundamentally different from the traditional approach of collecting data purely through simulation, the proposed data generation strategy encompasses the entire design space, which is inaccessible by conventional methods. The dataset quality is significantly improved, with a 21-fold increase in the number of chiral structures exhibiting the desired circular dichroism (CD) response (>0.6). The method's efficacy is validated by a monolayer structure that is easily prepared, demonstrating advanced sensing abilities for enantiomer-specific analysis of bio-samples. These results demonstrate the superior capability of data-driven schemes in photonic design and the potential of chiral metasurface-based platforms for calibration-free biosensing applications. The proposed approach will accelerate the development of complex systems for rapid molecular detection, spectroscopic imaging, and other applications.

Acoustic pipette and biofunctional elastomeric microparticle system for rapid picomolar-level biomolecule detection in whole blood.

Most biosensing techniques require complex processing steps that generate prolonged workflows and introduce potential points of error. Here, we report an acoustic pipette to purify and label biomarkers in 70 minutes. A key aspect of this technology is the use of functional negative acoustic contrast particles (fNACPs), which display biorecognition motifs for the specific capture of biomarkers from whole blood. Because of their large size and compressibility, the fNACPs robustly trap along the pressure antinodes of a standing wave and separate from blood components in under 60 seconds with >99% efficiency. fNACPs are subsequently fluorescently labeled in the pipette and are analyzed by both a custom, portable fluorimeter and flow cytometer. We demonstrate the detection of anti-ovalbumin antibodies from blood at picomolar levels (35 to 60 pM) with integrated controls showing minimal nonspecific adsorption. Overall, this system offers a simple and versatile approach for the rapid, sensitive, and specific capture of biomolecules.

Development of a High-Resolution Melting Method for the Detection of Clarithromycin-Resistant Helicobacter pylori in the Gastric Microbiome.

Background: The issue of Helicobacter pylori (H. pylori) resistance to clarithromycin (CLR) has consistently posed challenges for clinical treatment. Hence, a rapid susceptibility testing (AST) method urgently needs to be developed. Methods: In the present study, 35 isolates of H. pylori were isolated from 203 gastritis patients of the Guangzhou cohort, and the antimicrobial resistance phenotypes were associated with their genomes to analyze the relevant mutations. Based on these mutations, a rapid detection system utilizing high-resolution melting (HRM) curve analysis was designed and verified by the Shenzhen cohort, which consisted of 38 H. pylori strains. Results: Genomic analysis identified the mutation of the 2143 allele from A to G (A2143G) of 23S rRNA as the most relevant mutation with CLR resistance (p < 0.01). In the HRM system, the wild-type H. pylori showed a melting temperature (Tm) of 79.28 ± 0.01 °C, while the mutant type exhibited a Tm of 79.96 ± 0.01 °C. These differences enabled a rapid distinction between two types of H. pylori (p < 0.01). Verification examinations showed that this system could detect target DNA as low as 0.005 ng/μL in samples without being affected by other gastric microorganisms. The method also showed a good performance in the Shenzhen validation cohort, with 81.58% accuracy, and 100% specificity. Conclusions: We have developed an HRM system that can accurately and quickly detect CLR resistance in H. pylori. This method can be directly used for the detection of gastric microbiota samples and provides a new benchmark for the simple detection of H. pylori resistance.

A Versatile Method to Create Antibody/Split‐Enzyme Complexes and Its Application to a Rapid, Homogeneous, and Universal Electrochemical Immunosensing System

AbstractIn this study, a versatile method is developed to create an antibody/split‐enzyme complex for use in electrochemical immunosensors. SpyCatchers are genetically fused at each terminus of flavin adenine dinucleotide‐dependent glucose dehydrogenase (FAD‐GDH) and a protease recognition sequence is inserted into a loop region to prepare a soluble protein and the split GDH. SpyTag‐fused single‐chain variable fragments (scFvs) are employed, which leads to the simple preparation of antibody‐split enzyme complexes (split AECs). The addition of pentameric C‐reactive protein (CRP), as a representative multimeric protein, restored the GDH activity of the split AECs, as the two split AEC fragments formed active GDH when in close proximity. CRP in human serum is electrochemically detected within 5 min without any washing steps with a clinically relevant CRP detection range (0.03–1 mg dL−1). The detection system is expanded to detect hemoglobin, SARS‐CoV‐2 spike protein, and inactivated SARS‐CoV‐2 by exchanging the scFvs. These results show that the convenient preparation method of the split GDH and the split AEC by the insertion of the protease recognition sequence and the use of SpyCatcher/SpyTag can realize a rapid, homogeneous, and universal electrochemical detection system that can be integrated into a commercially available electrochemical glucose sensor.

Rapid and equipment-free identification of papaya mealybug Paracoccus marginatus based on RPA-CRISPR/Cas12a.

Paracoccus marginatus has invaded many countries, spreading rapidly and causing significant economic losses to crops. Accurate detection during the monitoring process is critical to prevent its expansion into new areas, therefore it is necessary to develop efficient and reliable detection methods. Traditional detection methods are time-consuming and instrument-dependent owing to the morphological similarities and small sizes of P. marginatus and other mealybugs, therefore establishing an efficient, rapid, and sensitive method for field detection in resource-limited settings is critical. A sensitive and rapid detection system was developed to detect P. marginatus using recombinase polymerase amplification (RPA) combined with clustered regularly interspaced short palindromic repeats (CRISPR)/Cas12a. The RPA-CRISPR/Cas12a assay distinguished P. marginatus from 10 other mealybugs. The entire process can be completed in approximately an hour, and the identification results can be determined by the naked eye using lateral flow strips or a portable mini-UV torch. A method was developed to extract DNA from P. marginatus within 5 min. This method was combined with the RPA-CRISPR/Cas12a assay to achieve rapid and simple detection. In addition, two portable thermos cups with temperature displays were used to maintain the reagents and assay reactions in the field. This assay represents the first application of portable and easily available items (mini-UV torch and thermos cup) based on the combination of RPA and CRISPR/Cas12a for rapid pest detection. This method is rapid, highly specific, and instrument-flexible, allowing for the early monitoring of P. marginatus in the field. This study provides guidance for the development of suitable management strategies. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Highly Durable Chemoresistive Micropatterned PdAu Hydrogen Sensors: Performance and Mechanism.

Hydrogen (H2) is a promising alternative energy source for Net-zero, but the risk of explosion requires accurate and rapid detection systems. As the use of H2 energy expands, sensors require high performance in a variety of properties. Palladium (Pd) is an attractive material for H2 detection due to its high H2 affinity and catalytic properties. However, poor stability caused by volume changes and reliability due to environmental sensitivity remain obstacles. This study proposes a micropatterned thin film of PdAu with optimized composition (Pd0.62Au0.38) as a chemoresistive sensor to overcome these issues. At room temperature, the sensor has a wide detection range of 0.0002% to 5% and a fast response time of 9.5 s. Significantly, the sensor exhibits excellent durability for repeated operation (>35 h) in 5% H2 and resistance to humidity and carbon monoxide. We also report a negative resistivity change in PdAu, which is opposite to that of Pd. Density functional theory (DFT) calculations were performed to investigate the resistance change. DFT analysis revealed that H2 penetrates specific interstitial sites, causing partial lattice compression. The lattice compression causes a decrease in electrical resistance. This work is expected to contribute to the development of high-performance H2 sensors using Pd-based alloys.

Establishment of cross-priming amplification for point-of-care detection of Mycobacterium tuberculosis and non-tuberculosis mycobacteria

ObjectiveTo simplify sputum sample preparation steps and achieve point-of-care testing (POCT) of Mycobacterium tuberculosis (MTB) and non-tuberculosis mycobacteria (NTM) using Cross-Priming Amplification (CPA) technology on portable devices, overcoming the challenges of existing nucleic acid detection technologies that cannot be widely promoted in grassroots settings in China. MethodsEvaluate the liquefying ability of high-concentration guanidine thiocyanate (GTC) for sputum and the effectiveness of MTB inactivation; establish a rapid detection system for MTB and NTM based on CPA technology using EasyNAT integrated detection tubes, with the left amplification zone specific to MTB CPA amplification and the right amplification zone specific to both MTB and NTM CPA amplification. Suspected pulmonary tuberculosis (PTB) patients or patients diagnosed as suspected NTM pulmonary infections specimens collected from the Second Hospital of Longyan, Fujian Province, from September 2022 to September 2023, acid-fast bacilli (AFB) smear, quantitative real-time PCR (RT-PCR) and CPA-POCT were performed. The kappa coefficients was used to evaluate the consistency between the RT-PCR and CPA-POCT. ResultsThe liquefaction effect of 6M GTC on sputum was equivalent to 4 % NaOH, and no MTB growth was observed in the Lowenstein-Jensen medium of sputum samples treated with 6M GTC incorporating the H37Rv strain. The newly established CPA-POCT method showed good agreement with RT-PCR with a positive compliance rate of 86.27 %, a negative compliance rate of 89.36 %, an overall compliance rate of 87.75 %, and a Kappa coefficients of 0.786 (P < 0.05). Conclusion6M GTC can liquefy sputum and render MTB non-viable, eliminating the need for Mycobacterium nucleic acid testing in BSL-2 laboratories; the newly established CPA method can rapidly and accurately distinguish MTB and NTM in the form of POCT, with simple and fast operation, suitable for promotion and application in grassroots medical institutions and remote rural areas.

Magnetic nanoparticles and quantum dots coupled immuno nano fluorescence assay for visual detection of HPV16-induced cervical cancer cells from cytology/biopsy samples

Biopsy-based histopathology and immunohistochemistry for cervical cancer detection are costly, time-consuming, and require expert personnel for data interpretation. We developed a simple magnetic nanoparticle (MNPs) and quantum dots (QD) coupled immuno nano fluorescence assay (MNPQDCINFA) for visual detection of HPV16-induced cervical cancer cells under UV light from cytology/biopsy samples exploiting host cancer cells expressing viral E7 protein as a biomarker. The E7 domain-specific polyclonal antibodies were generated against the 1–44 amino acid N-terminal (anti-domainN antibody) and 48–98 amino acid C-terminal domain (anti-domainC antibody). These antibodies were bioconjugated with nonfluorescent MNPs (60 % efficiency) and fluorescent QDs (66 % efficiency) to generate capturing (MNPs-anti-domainN antibody) and detecting (QDs-anti-domainC antibody) nano-complex, respectively. Assay conditions, such as concentration of capturing (20 μM) and detecting (50 nM) antibody nano-complexes and incubation duration (30 min), were standardized. The analytical sensitivity using pure HPV16 E7 protein was recorded up to 200 ng with very high specificity to differentiate from other HPV strains E7 proteins. The diagnostic performance characteristics with cytology samples showed 100 % sensitivity and specificity compared to immunofluorescence and biopsy-based histopathology analysis. The present invention can be effectively used for a quick, disposable, rapid cervical cancer cell detection system as an alternate test for immunofluorescence and histopathology.

Construction of on-site DNA pre-treatment device and rapid electrochemical biosensor set for Escherichia coli detection in milk

Restaurants and cafeterias are constantly exposed to bacterial contamination. In such environments, it is highly needed the rapid bacteria detection system with simple pre-treatment device. To solve this problem, this study presented the pre-treatment device and Rapid electrochemical (REC) biosensor set for detecting Escherichia coli (E. coli) gene in milk sample. The pre-treatment device is consisted of tetraethyl orthosilicate (TEOS)-coated bead that operated with sonication method, and it can effectively remove the E. coli residues and extract E. coli DNA well. By analyzing the sequences of various foodborne pathogens, we selected the highly variable region sequences of E. coli DNA to use as the bioprobe for REC biosensor. The selected thiol-modified bioprobe was immobilized onto the fabricated biosensor electrode. For detecting the extracted E. coli gene fragment rapidly, the alternating current electrothermal flow (ACEF) technique was introduced to REC-biosensor. ACEF technique minimize the target-bioprobe binding time to 10 mins. Using the extracted E. coli DNA fragments with the pre-treatment device, the proposed REC-biosensor shows the limit of detection (LOD) in milk samples was 9.235 × 10-5 ng/μL, and the target detection accuracy was 92.5 %, with a maximal error rate up to 6.730 %. Moreover, the biosensor demonstrated high selectivity in electrochemical performance evaluation (Matrix effect = 7.425 %). The developed pre-treatment device provides simple and quick preprocessing time (within 20 min) of E. coli, and REC biosensor provides the rapid detection of E. coli (within 10 min). In conclusion, the proposed pre-treatment device and biosensor set can be easily applied to other types of bacteria, virus or germs in the near future.

A novel peptide pair-based rapid fluorescent diagnostic system for malaria Plasmodium falciparum detection

Advanced diagnostic materials, such as aptamers, are required due to the scarcity of efficient diagnostic antibodies and the low sensitivity of rapid diagnostic kits at detecting the malaria parasite, Plasmodium falciparum. MethodsTwo peptides M2.9 [(KPTAEQTESPELQSAPEN) and M2.17 (KILFNVYSPLGCTCECWV)] were designed using simple epitope prediction tools and modified against the merozoite surface antigen 2 of P. falciparum (Pf.MSP2) by 3-dimensional modeling based on binding affinity. Based on five prediction tools for hydropathy, M2.17 was selected as an appropriate capture peptide. A peptide-based fluorescence-linked immunosorbent assay (FLISA) and a peptide pair-based fluorescent immunochromatographic test strip (FICT) were developed to detect P. falciparum 3D7 (drug-sensitive) and P. falciparum K1 (multi drugs-resistant) strains. ResultsBioinformatic analysis of two peptides demonstrated the potential binding affinity with the merozoite surface protein 2 of P. falciparum (Pf.MSP2) with a positive hydropathy value. The limit of detection (LOD) of FLISA was 10 parasites/μL and of a peptide pair-linked rapid FICT system was 5 and 200 parasites/μL for P. falciparum 3D7 and K1, respectively. Compared to commercial rapid detection systems (RDTs), a peptide pair-linked FICT system exhibited a 20-fold greater efficiency in detecting P. falciparum 3D7 and specifically discriminated another protozoan spp. ConclusionA peptide pair-linked rapid diagnostic strip could be an alternative to conventional RDTs for monitoring wild-type and drug-resistant malaria parasites.

Rapid quantitative detection system constructed via photonic PCR based on the photothermal effect of NH2-MWCNTs/TiO2

In this work, the photothermal effect of aminomodified MWCNTs/TiO2/SiO2/PDMS nanocomposites was utilized as a photothermal substrate to replace the metal heating block of traditional qPCR combining of the advantages of high photothermal effect of composite materials and low sample volume of static microfluidic PCR. Meanwhile, Multiple PCR rapid thermal cycles under 808 nm laser irradiation was accomplished through a non-contact energy conversion method and the use of chamber-immobilized PCR chips as specific reaction vessels. Notably, the chip chamber architecture requires merely about 8 μL of reagents compared to traditional PCR tubes. While reducing the amount of PCR reagents used, the higher specific surface area results in a larger contact surface (SSA) between the heat source and the solution. Thereafter, the CCD image sensor is then used as a fluorescence detector to monitor the amplification of the target DNA during the PCR reaction, thus enabling simultaneous fluorescence detection. In addition, the threshold cycle (Ct) value of qPCR and standard PCR curves for SARS-CoV-2 virus have been analysed in depth. Importantly, this study has important implications for the development of low-cost materials, miniaturized and portable Point-of-Care Testing (POCT) chips/systems.

Development and application of a novel recombinase polymerase amplification-Pyrococcus furiosus argonaute system for rapid detection of goose parvovirus

Rapid and accurate detection of goose parvovirus (GPV) is crucial for controlling outbreaks and mitigating their economic impact on the poultry industry. This study introduces recombinase polymerase amplification combined with the Pyrococcus furiosus argonaute (RPA-PfAgo) system, a novel diagnostic platform designed to address the limitations of traditional GPV detection methods. Capitalizing on the rapid DNA amplification of RPA and stringent nucleic acid cleavage by the PfAgo protein, the RPA-PfAgo system offers high specificity and sensitivity in detecting GPV. Our optimization efforts included primer and probe configurations, reaction parameters, and guided DNA selection, culminating in a detection threshold of 102 GPV DNA copies per microlitre. The specificity of the proposed method was rigorously validated against a spectrum of avian pathogens. Clinical application to lung tissues from GPV-infected geese yielded a detection concordance of 100%, surpassing that of qPCR and PCR in both rapidity and operational simplicity. The RPA-PfAgo system has emerged as a revolutionary diagnostic modality for managing this disease, as it is a promising rapid, economical, and onsite GPV detection method amenable to integration into broad-scale disease surveillance frameworks. Future explorations will extend the applicability of this method to diverse avian diseases and assess its field utility across various epidemiological landscapes.

Application of MOF Nanoenzyme in Rapid Detection of Food Allergens System

Food allergies are a common immune response that can lead to serious health problems. Rapid and accurate detection of allergens in food is crucial for protecting people’s health and quality of life. In this study, β-lactoglobulin in milk serves as a model allergen, and nucleic acid aptamer-loaded MN patches are utilized as rapid capture elements. Moreover, metal organic frameworks loaded with complementary aptamer sequences are employed as signal amplification elements. The combination of these two elements establishes a rapid visualization detection system for allergens, employing specific microneedle array patches-MOF nanoenzymes. The results show that the combination of TMB+H2O2 +MOF-RC has the highest peroxidase like activity of 1.732. Compared to TMB+H2O2 +MOF, it has increased by 0.547. Meanwhile, the MN-MOF detection method has excellent specificity for the detection of β-Lg. This specific microneedle array patches and MOF nanoenzymes-based allergen rapid detection system demonstrates practical application benefits, providing a viable solution for swiftly detecting food allergens and safeguarding people’s health and quality of life.

A DAMP-Based Assay for Rapid and Affordable Diagnosis of Bacterial Meningitis Agents: Haemophilus influenzae, Neisseria meningitidis, and Streptococcus pneumoniae.

The rapid and accurate diagnosis of meningitis is critical for preventing severe complications and fatalities. This study addresses the need for accessible diagnostics in the absence of specialized equipment by developing a novel diagnostic assay. The assay utilizes dual-priming isothermal amplification (DAMP) with unique internal primers to significantly reduce non-specificity. For fluorescence detection, the dye was selected among Brilliant Green, Thioflavin T, and dsGreen. Brilliant Green is preferred for this assay due to its availability, high fluorescence level, and optimal sample-to-background (S/B) ratio. The assay was developed for the detection of the primary causative agents of meningitis (Haemophilus influenzae, Neisseria meningitidis, and Streptococcus pneumoniae), and tested on clinical samples. The developed method demonstrated high specificity, no false positives, sensitivity comparable to that of loop-mediated isothermal amplification (LAMP), and a high S/B ratio. This versatile assay can be utilized as a standalone test or an integrated assay into point-of-care systems for rapid and reliable pathogen detection.

Dynamic STEM-EELS for single-atom and defect measurement during electron beam transformations.

This study introduces the integration of dynamic computer vision-enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)-based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS2, uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli.

Skin-on-a-Chip Coupled with a Rapid Inflammation Detection System for Cosmetic Ingredient Screening

Skin-on-a-Chip Coupled with a Rapid Inflammation Detection System for Cosmetic Ingredient Screening

Advancing common bean (Phaseolus vulgaris L.) disease detection with YOLO driven deep learning to enhance agricultural AI

Common beans (CB), a vital source for high protein content, plays a crucial role in ensuring both nutrition and economic stability in diverse communities, particularly in Africa and Latin America. However, CB cultivation poses a significant threat to diseases that can drastically reduce yield and quality. Detecting these diseases solely based on visual symptoms is challenging, due to the variability across different pathogens and similar symptoms caused by distinct pathogens, further complicating the detection process. Traditional methods relying solely on farmers’ ability to detect diseases is inadequate, and while engaging expert pathologists and advanced laboratories is necessary, it can also be resource intensive. To address this challenge, we present a AI-driven system for rapid and cost-effective CB disease detection, leveraging state-of-the-art deep learning and object detection technologies. We utilized an extensive image dataset collected from disease hotspots in Africa and Colombia, focusing on five major diseases: Angular Leaf Spot (ALS), Common Bacterial Blight (CBB), Common Bean Mosaic Virus (CBMV), Bean Rust, and Anthracnose, covering both leaf and pod samples in real-field settings. However, pod images are only available for Angular Leaf Spot disease. The study employed data augmentation techniques and annotation at both whole and micro levels for comprehensive analysis. To train the model, we utilized three advanced YOLO architectures: YOLOv7, YOLOv8, and YOLO-NAS. Particularly for whole leaf annotations, the YOLO-NAS model achieves the highest mAP value of up to 97.9% and a recall of 98.8%, indicating superior detection accuracy. In contrast, for whole pod disease detection, YOLOv7 and YOLOv8 outperformed YOLO-NAS, with mAP values exceeding 95% and 93% recall. However, micro annotation consistently yields lower performance than whole annotation across all disease classes and plant parts, as examined by all YOLO models, highlighting an unexpected discrepancy in detection accuracy. Furthermore, we successfully deployed YOLO-NAS annotation models into an Android app, validating their effectiveness on unseen data from disease hotspots with high classification accuracy (90%). This accomplishment showcases the integration of deep learning into our production pipeline, a process known as DLOps. This innovative approach significantly reduces diagnosis time, enabling farmers to take prompt management interventions. The potential benefits extend beyond rapid diagnosis serving as an early warning system to enhance common bean productivity and quality.

Rapid quantitative and qualitative analysis of talcum powder in wheat flour by microwave detection combined with multivariate analysis

Rapid and accurate monitoring of talcum powder in wheat flour is of great practical importance for maintaining market order, public health and food safety. In this study, the potential of a home-made microwave detection system for rapid quantitative and qualitative detection of talcum powder adulteration in wheat flour at 2.5–11.5 GHz was investigated. Useful microwave spectral information is selected using three feature selection strategies, the bootstrapping soft shrinkage (BOSS), the multiple feature spaces ensemble with least absolute shrinkage and selection operator (MEF-LASSO), and the competitive adaptive reweighted sampling (CARS). The eXtreme Gradient Boosting (XGBoost) classification and regression models are constructed and evaluated, and three nature-inspired optimization algorithms (NIOA) are used for parameter optimization: the Osprey Optimization Algorithm (OOA), the Crested Porcupine Optimizer (CPO), and the Sparrow Search Algorithm (SSA). The experimental results show that both feature selection strategies and NIOA algorithms can effectively improve the model performance. The SSA-XGBoost classification model has the highest prediction accuracy, and its prediction accuracy and F1-score can reach 100 % and 1, respectively. The MEF-LASSO-SSA-XGBoost regression model has strong robustness. The root mean square error (RMSE) was 1.76 %, the prediction coefficient of determination (R2) was 0.98, and the ratio of performance to deviation (RPD) was 8.66. The results of this study showed that the combination of microwave technology and machine learning model with multivariate analysis method can realize the rapid and accurate determination of qualitative and quantitative talcum powder in wheat flour.

Explainable Spark-based PSO Clustering for Intrusion Detection

Given the exponential growth of available data in large networks, the existence of rapid, transparent and explainable intrusion detection systems has become of high necessity to effectively discover attacks in such huge networks. To deal with this challenge, we propose a novel explainable intrusion detection system based on Spark, Particle Swarm Optimization (PSO) clustering and eXplainable Artificial Intelligence (XAI) techniques. Spark is used as a parallel processing model for the effective processing of large-scale data, PSO is integrated for improving the quality of the intrusion detection system by avoiding sensitive initialization and premature convergence of the clustering algorithm and finally, XAI techniques are used to enhance interpretability and explainability of intrusion recommendations by providing both micro and macro explanations of detected intrusions. Experiments are conducted on several large collections of real datasets to show the effectiveness of the proposed intrusion detection system in terms of explainability, scalability and accuracy. The proposed system has shown high transparency in assisting security experts and decision-makers to understand and interpret attack behavior.

A Rapid PCR-Based Diagnostic Method for Skin Infection with Mycobacterium marinum.

The increasing incidence of chronic skin infections caused by Mycobacterium marinum, coupled with the time-consuming and low detection rates nature of traditional culture and histological-based diagnostic methods, underscores the need for an expedited approach. The study aims to develop a rapid and efficient method for detecting M. marinum with PCR technology. We designed four pairs of primers based on DNA sequences from GeneBank and prior studies, we utilized both PCR and Real-time PCR to identify M. marinum. Specificity and sensitivity assessments were conducted in vitro by DNAs extracted from M. marinum and other bacterial or fungal cultures. Further validation was performed through the implementation of a mouse skin infection model to optimize and confirm the efficacy of the detection method in both fresh and paraffin-embedded skin tissues. The same PCR testing system was further confirmed with paraffin-embedded skin tissues samples from patients as well. The results of the study indicate promising outcomes for the four-pair primers system. It demonstrated 100% sensitivity in detecting M. marinum from purified cultures, including typical strains and nine clinical isolates, while achieving a specificity of 100%. This specificity was evidenced by the absence of PCR products from 12 bacterial species, 12 fungi species, and six other non-tuberculous mycobacterium (NTM) species. In the animal model, the PCR assay exhibited high detection efficacy for both infected fresh tissues and paraffin-embedded tissues, with a slight superiority observed in fresh tissues. However, the PCR assay exhibited high detection efficacy for clinical paraffin-embedded tissues. These findings collectively underscore the robust detection capabilities of our four-pair primers in both in vitro and in vivo settings. A sensitive and highly specific rapid detection system has been successfully developed that can be used to detect M. marinum in both infected fresh tissues and paraffin-embedded tissues.