Identification Results Research Articles

Assessing Artificial Intelligence in Oral Cancer Diagnosis: A Systematic Review

Background: With the use of machine learning algorithms, artificial intelligence (AI) has become a viable diagnostic and treatment tool for oral cancer. AI can assess a variety of information, including histopathology slides and intraoral pictures. Aim: The purpose of this systematic review is to evaluate the efficacy and accuracy of AI technology in the detection and diagnosis of oral cancer between 2020 and 2024. Methodology: With an emphasis on AI applications in oral cancer diagnostics, a thorough search approach was used to find pertinent publications published between 2020 and 2024. Using particular keywords associated with AI, oral cancer, and diagnostic imaging, databases such as PubMed, Scopus, and Web of Science were searched. Among the selection criteria were actual English-language research papers that assessed the effectiveness of AI models in diagnosing oral cancer. Three impartial reviewers extracted data, evaluated quality, and compiled the findings using a narrative synthesis technique. Results: Twelve papers that demonstrated a range of AI applications in the diagnosis of oral cancer satisfied the inclusion criteria. This study showed encouraging results in lesion identification and prognostic prediction using machine learning and deep learning algorithms to evaluate oral pictures and histopathology slides. The results demonstrated how AI-driven technologies might enhance diagnostic precision and enable early intervention in cases of oral cancer. Conclusion: Unprecedented prospects to transform oral cancer diagnosis and detection are provided by artificial intelligence. More resilient AI systems in oral oncology can be achieved by joint research and innovation efforts, even in the face of constraints like data set variability and regulatory concerns.

MSMTRIU-Net: Deep Learning-Based Method for Identifying Rice Cultivation Areas Using Multi-Source and Multi-Temporal Remote Sensing Images

Identifying rice cultivation areas in a timely and accurate manner holds great significance in comprehending the overall distribution pattern of rice and formulating agricultural policies. The remote sensing observation technique provides a convenient means to monitor the distribution of rice cultivation areas on a large scale. Single-source or single-temporal remote sensing images are often used in many studies, which makes the information of rice in different types of images and different growth stages hard to be utilized, leading to unsatisfactory identification results. This paper presents a rice cultivation area identification method based on a deep learning model using multi-source and multi-temporal remote sensing images. Specifically, a U-Net based model is employed to identify the rice planting areas using both the Landsat-8 optical dataset and Sentinel-1 Polarimetric Synthetic Aperture Radar (PolSAR) dataset; to take full into account of the spectral reflectance traits and polarimetric scattering traits of rice in different periods, multiple image features from multi-temporal Landsat-8 and Sentinel-1 images are fed into the network to train the model. The experimental results on China’s Sanjiang Plain demonstrate the high classification precisions of the proposed Multi-Source and Multi-Temporal Rice Identification U-Net (MSMTRIU-NET) and that inputting more information from multi-source and multi-temporal images into the network can indeed improve the classification performance; further, the classification map exhibits greater continuity, and the demarcations between rice cultivation regions and surrounding environments reflect reality more accurately.

A Deep Learning Approach to Identify Potential Sites for Pocket Park Installation in Nanjing, China

ABSTRACT Pocket parks are considered valuable tools for supporting green space provision in high-density environments. As the factors affecting pocket parks vary in fine-grained granularity, analyzing them efficiently on a large scale using traditional methods is challenging. Therefore, considering Nanjing as the study site, a potential identification framework was established, which includes categories of space, vitality, facility, and pleasurability for pocket park installation (PPI) based on visual information from street view images (SVIs). Then, the TrueSkill algorithm was employed to construct four parallel-task training sets according to the four categories and a convolutional neural network (CNN) and active learning (AL) approaches were applied to establish four parallel-task models of deep learning (DL). A two-phase grading score method was designed to integrate the identification results of the four DL models into a comprehensive potential score to determine priority sites for PPI. Lastly, the attributes of the potential sites and corresponding planning strategies in different scenarios were analyzed. The results showed that this modelling approach can integrally assess multiple factors on PPI within an enormous scope, overcoming traditional methods’ spatial and dimensional constraint in high-density environments. Our method and results can inform future pocket park planning and design in Nanjing and similar cities worldwide.

A Method for Classifying Wood-Boring Insects for Pest Control Based on Deep Learning Using Boring Vibration Signals with Environment Noise

Wood-boring pests are difficult to monitor due to their concealed lifestyle. To effectively control these wood-boring pests, it is first necessary to efficiently and accurately detect their presence and identify their species, which requires addressing the limitations of traditional monitoring methods. This paper proposes a deep learning-based model called BorerNet, which incorporates an attention mechanism to accurately identify wood-boring pests using the limited vibration signals generated by feeding larvae. Acoustic sensors can be used to collect boring vibration signals from the larvae of the emerald ash borer (EAB), Agrilus planipennis Fairmaire, 1888 (Coleoptera: Buprestidae), and the small carpenter moth (SCM), Streltzoviella insularis Staudinger, 1892 (Lepidoptera: Cossidae). After preprocessing steps such as clipping and segmentation, Mel-frequency cepstral coefficients (MFCCs) are extracted as inputs for the BorerNet model, with noisy signals from real environments used as the test set. BorerNet learns from the input features and outputs identification results. The research findings demonstrate that BorerNet achieves an identification accuracy of 96.67% and exhibits strong robustness and generalization capabilities. Compared to traditional methods, this approach offers significant advantages in terms of automation, recognition efficiency, and cost-effectiveness. It enables the early detection and treatment of pest infestations and allows for the development of targeted control strategies for different pests. This introduces innovative technology into the field of tree health monitoring, enhancing the ability to detect wood-boring pests early and making a substantial contribution to forestry-related research and practical applications.

Identification and Mapping of Eucalyptus Plantations in Remote Sensing Data Using CCDC Algorithm and Random Forest

Eucalyptus plantations are one of the primary artificial forests in southern China, experiencing rapid expansion in recent years due to their significant socio-economic benefits. This expansion has raised concerns about the ecological environment, necessitating accurate mapping of eucalyptus plantations. In this study, the phenological characteristics of eucalyptus plantations were utilized as the primary classification basis. Long-term time series Landsat and Sentinel-2 data from 2000 to 2022 were rigorously preprocessed pixel by pixel using the Google Earth Engine (GEE) platform to obtain high-quality observation data. The Continuous Change Detection and Classification (CCDC) algorithm was employed to fit the multi-year observation data with harmonic curves, utilizing parameters such as normalized intercept, slope, phase, and amplitude of the fitted curves to characterize the phenological features of vegetation. A total of 127 phenological indices were generated using the Normalized Burn Ratio (NBR), Normalized Difference Fractional Index (NDFI), and six spectral bands, with the top 20 contributing indices selected as input variables for the random forest algorithm to obtain preliminary classification results. Subsequently, eucalyptus plantation rotation features and the Simple Non-Iterative Clustering (SNIC) superpixel segmentation algorithm were employed to filter the results, enhancing the accuracy of the identification results. The producer’s accuracy, user’s accuracy, and overall accuracy of the eucalyptus plantation map for the year 2020 were found to be 96.67%, 89.23%, and 95.83%, respectively, with a total area accuracy of 94.39%. Accurate mapping of eucalyptus plantations provides essential information and evidence for ecological environment protection and the formulation of carbon-neutral strategies.

Modal identification of wind turbine tower based on optimal fractional order statistical moments

AbstractIn vibration testing of civil engineering structures, the first two vibration modes are crucial in representing the global dynamic behavior of the structure measured. In the present study, a comprehensive method is proposed to identify the first two vibration modes of wind turbine towers, which is based on the analysis of fractional order statistical moments (FSM). This study offers novel contributions in two key aspects: (1) theoretical derivations of the relationship between FSM and vibration mode; and (2) successful use of 32/7‐order displacement statistical moment as the optimal FSM to identify wind turbine tower modes, by combining with noise resistance analysis, sensitivity analysis, and stability analysis, respectively. Using the proposed method, the FSM was first used to identify the modal vibration of wind turbine towers. By obtaining the response of the structure on the same vertical line, FSM was then calculated to estimate the corresponding structural modal vibration. Considering other influencing factors in the field test, the modal identification results of this index under different excitation forms and noise conditions were analyzed based on numerical simulation and verified with field wind tower test data. The results of the evaluation show that the proposed statistical moments of can accurately identify the first two vibration modes of wind turbine towers. This presents a new robust method for modal vibration identification, that is, simple and effective in its implementation.

An improved generalized flexibility matrix-based damage identification technique for derrick steel structures

An improved generalized flexibility matrix-based method using matrix partitioning technique for structure damage identification is proposed. As an effective damage identification method, the generalized flexibility matrix requires fewer vibration frequencies and corresponding modal shapes compared with the traditional flexibility matrix method. By introducing a new partitioning technique of the matrices involved in the constructive process of the governing equation, the improved method simplifies the process of calculation with a dramatic reduction in computational cost while maintaining accuracy of identification results. In the case of incomplete measured modal data, an improved matrix partitioning technique based on the model expansion method is applied and the damaged indicators can be identified using partial measured modal data. A K-type derrick steel structure in service is considered as a numerical example for inspection the validity and feasibility of the proposed method. Numerical results show better accuracy and efficiency of this method for various damage scenarios, and can be used for damage diagnosis or health monitoring of other large structures.

The safety and comfort of football stadiums in Indonesia: an analysis based on spectator perspectives

This research aims to analyze the aspects of safety and comfort in Indonesian football stadiums. This analytical survey research was conducted in three stadiums in Indonesia that have FIFA standards and are always used in national or international matches to analyze and describe the factors of security and comfort variables. There were five stages in this study: the first stage was observation, the second stage was collecting evidence of scientific articles, the third stage was instrument validation with focus group discussion (FGD), the fourth stage was data collection, and the fifth stage was research reporting with scientific articles. Then, there were six dimensions of the survey category, namely Stadium Construction, Physical Security, Social Security, Communication and Coordination, Recommendations and Feedback, and Clarity to the Spectators. The initial identification results were 3,090 spectators, 2,408 men and 682 women. They were aged 14 – 47 years, averaging 23.66±7.2 years. The number of visits to the stadium in one year was two to six times. The spectators’ experience in visiting different stadiums was two to seven stadiums. The following result, the Kruskal-Wallis test, showed the Asymp. The sig value was 0.319>0.05, meaning there was no difference in answers based on the spectators visiting the three stadiums. The publication of this study can provide valid and clear information insights to stadium managers to pay more attention and strive to provide the best service to football spectators. Because indirectly, supporters who watch the match will provide greater moral support to the football team.

Intelligent aerodynamic identification method based on wavelet transform and deep learning

Abstract Wind tunnel force testing is particularly crucial for the aerodynamic characteristics of hypersonic vehicles, and the intelligent and high-precision aerodynamic identification technology serves as the key supporting technology. Due to the short effective test time of the pulse combustion wind tunnel, the inertial force vibration generated by the force measurement system is difficult to be attenuated completely at the start of the wind tunnel. At the same time, there are many interference signals in the output signal during the test, which makes it difficult to achieve high-precision aerodynamic load identification. Therefore, in order to eliminate the influence of interference signals on aerodynamic identification accuracy and obtain accurate aerodynamic loads, this paper introduces an intelligent aerodynamic identification method based on wavelet transform and deep learning. Firstly, meyer wavelet is selected as the wavelet basis, and signal processing is performed on the output signal through wavelet transform to eliminate interference signals and improve signal quality. Then, through RNN-GRU network model to identify the load. The reliability of the method is verified by the rod balance test platform. The results show that the method can effectively reduce the influence of interference signal, and the relative error between the identified load and the real load is about 2%. At the same time, the identification results of the method are compared with those of the mean value method, which further verifies the accuracy of the proposed method.

Characterization of Odorants in Clustered Mountain Mint Pycnanthemum muticum.

The clustered mountain mint, Pycnanthemum muticum, is a pleasant-smelling, native North American plant. Despite its wide geographical presence across the United States and Canada, little is known about the specific odorants responsible for its pleasing aroma. Herein, 42 odorants were identified in the plant through solvent-assisted flavor evaporation (SAFE), gas chromatography-olfactometry (GC-O), and gas chromatography-mass spectrometry (GC-MS). Subsequent analysis involved determining flavor dilution (FD) factors using aroma extract dilution analysis (AEDA), quantitating 14 odorants through stable isotope dilution assays (SIDA), and calculating their odor activity values (OAVs). Several noteworthy odorants with OAV ≥ 1 included pulegone (mint, medicinal; OAV 276), 1-octen-3-one (mushroom; OAV 149), menthofuran (mint, petrol; OAV 139), nonanal (citrus; OAV 21), γ-nonalactone (coconut; OAV 13), 1,8-cineole (eucalyptus; OAV 12), mintlactone (mint, coconut; OAV 12), menthone (mint, fresh; OAV 7.3), α-pinene (pine; OAV 4), and piperitenone (mint; OAV 1.9). The study also determined the stereochemistry of various chiral odorants. An aroma simulation model was developed to validate the identification and quantitative results; upon evaluation using olfactory profile analysis, no significant differences were found between the aroma model and an aqueous infusion of P. muticum (P > 0.05). These findings lay the foundation for future investigations into the diversity of P. muticum selections and can provide valuable insights for studies on plant hybridization for food and flavor applications.

The incident and Molecular Identification of the Cryptosporidium Parvum in Cultivated Carp Fish (Cyprinus carpio) in Baghdad

The current study aimed to investigate the occurrence of Cryptosporidium species by traditional and molecular methods in freshwater fish in Baghdad city. A convenient number of stomach and intestinal continent samples were collected from three types of freshwater fish from different areas of Baghdad city from November, 2023 to April, 2024. All samples were examined phenotypically using a modified Ziehl-Neelsen stain and genotypically (nested PCR technique) based on a partial sequence of 18SrRNA genes with sequencing and phylogenetic tree analysis. The overall infection rate of fish with C. parvum was 5.5% (10/180). The common carp (Cyprinus carpio) was the only fish type infected, with a rate of 16.66% (10 60). January and February had the highest infection rate, 3.33%. The molecular identification and sequencing confirmed the infections with C. parvum. Molecular speciation of the parasite confirmed microscopical primary identification results. This study suggests that fish became a new source for transmission to humans and livestock.

Detection of underground natural gas pipeline micro-leakage based on UAV hyperspectral remote sensing and GIS

ABSTRACT Detection of underground natural gas pipeline micro-leakage based on unmanned aerial vehicle (UAV) hyperspectral remote sensing and GIS. Hyperspectral images can indirectly detect underground natural gas pipeline micro-leakage through spectral and spatial variation characteristics of surface vegetation. However, most of existing studies were based on ground-mounted platforms, which could only perform small-range single-point detection and might occur misidentifications. UAV hyperspectral remote sensing can allow wide-range detection of surface vegetation. Moreover, underground pipeline distribution GIS data can provide prior knowledge about leakage points to exclude misidentifications. Therefore, a natural gas pipeline micro-leakage experiment was set up. UAV hyperspectral images of grasslands and pipeline distribution vector data were obtained, which led to a proposed new wide-range multi-points detection methodology of underground natural gas micro-leakage. Firstly, the vegetation identification index (WVI) R 470 + R 674 / R 555 + R 750 was designed based on WOA–VMD (whale optimization algorithm – variational modal decomposition) to segment and extract the vegetation stress zones. Then, UAV- and GIS-based natural gas micro-leakage vegetation stress identification model was constructed to obtain the leak points of natural gas micro-leakage. Finally, the recognition ability was evaluated by comparing with three stress vegetation identification indices proposed in previous studies. The result showed that there was no missing or false detection in identification results; the identification and positioning effect was better than other indices, which could meet the practical application requirements.

Lie-theory-based dynamic model identification of serial robots considering nonlinear friction and optimal excitation trajectory

Abstract Accurate dynamic model is essential for the model-based control of robotic systems. However, on the one hand, the nonlinearity of the friction is seldom treated in robot dynamics. On the other hand, few of the previous studies reasonably balance the calculation time-consuming and the quality for the excitation trajectory optimization. To address these challenges, this article gives a Lie-theory-based dynamic modeling scheme of multi-degree-of-freedom (DoF) serial robots involving nonlinear friction and excitation trajectory optimization. First, we introduce two coefficients to describe the Stribeck characteristics of Coulomb and static friction and consider the dependency of friction on load torque, so as to propose an improved Stribeck friction model. Whereafter, the improved friction model is simplified in a no-load scenario, a novel nonlinear dynamic model is linearized to capture the features of viscous friction across the entire velocity range. Additionally, a new optimization algorithm of excitation trajectories is presented considering the benefits of three different optimization criteria to design the optimal excitation trajectory. On the basis of the above, we retrieve a feasible dynamic parameter set of serial robots through the hybrid least square algorithm. Finally, our research is supported by simulation and experimental analyses of different combinations on the seven-DoF Franka Emika robot. The results show that the proposed friction has better accuracy performance, and the modified optimization algorithm can reduce the overall time required for the optimization process while maintaining the quality of the identification results.

Met4DX: A Unified and Versatile Data Processing Tool for Multidimensional Untargeted Metabolomics Data.

Liquid chromatography-mass spectrometry (LC-MS) is a powerful tool in untargeted metabolomics, enabling the high-sensitivity and high-specificity characterization of metabolites. The integration of ion mobility (IM) with LC-MS, known as LC-IM-MS, enhances the analytical depth, facilitating more comprehensive metabolite profiling. However, the complexity of data generated by these technologies presents significant challenges in data processing. Addressing these challenges, we developed Met4DX, a unified and versatile software tool for processing both 3D and 4D untargeted metabolomics data. Met4DX incorporates a new MS1-oriented peak detection approach coupled with our bottom-up assembly algorithm, enabling highly sensitive and comprehensive peak detection in untargeted metabolomics data. Additionally, Met4DX employs a uniform quantification strategy to enhance the precision of peak integration across different samples. The software provides a user-friendly interface that simplifies data processing with default parameter sets, consolidating peak detection, alignment, quantification, and other procedures into a single streamlined workflow. Together, Met4DX offers a comprehensive solution for multidimensional metabolomics data processing, transforming raw data from diverse MS instruments into a final feature table containing quantification and identification results. We postulate Met4DX facilitates metabolite discovery in biological samples by deciphering the complex untargeted metabolomics data. Met4DX is freely available on the Internet (https://met4dx.zhulab.cn/).

YOLOv5s-Based Image Identification of Stripe Rust and Leaf Rust on Wheat at Different Growth Stages.

Stripe rust caused by Puccinia striiformis f. sp. tritici and leaf rust caused by Puccinia triticina, are two devastating diseases on wheat, which seriously affect the production safety of wheat. Timely detection and identification of the two diseases are essential for taking effective disease management measures to reduce wheat yield losses. To realize the accurate identification of wheat stripe rust and wheat leaf rust during the different growth stages, in this study, the image-based identification of wheat stripe rust and wheat leaf rust during different growth stages was investigated based on deep learning using image processing technology. Based on the YOLOv5s model, we built identification models of wheat stripe rust and wheat leaf rust during the seedling stage, stem elongation stage, booting stage, inflorescence emergence stage, anthesis stage, milk development stage, and all the growth stages. The models were tested on the different testing sets in the different individual growth stages and in all the growth stages. The results showed that the models performed differently in disease image identification. The model based on the disease images acquired during an individual growth stage was not suitable for the identification of the disease images acquired during the other individual growth stages, except for the model based on the disease images acquired during the milk development stage, which had acceptable identification performance on the testing sets in the anthesis stage and the milk development stage. In addition, the results demonstrated that wheat growth stages had a great influence on the image identification of the two diseases. The model built based on the disease images acquired in all the growth stages produced acceptable identification results. Mean F1 Score values between 64.06% and 79.98% and mean average precision (mAP) values between 66.55% and 82.80% were achieved on each testing set composed of the disease images acquired during an individual growth stage and on the testing set composed of the disease images acquired during all the growth stages. This study provides a basis for the image-based identification of wheat stripe rust and wheat leaf rust during the different growth stages, and it provides a reference for the accurate identification of other plant diseases.

PROCEDURE FOR IDENTIFYING ARTILLERY MEANS OF DESTRUCTION

For evidence in the International Criminal Court of the crimes of the Russian Federation for strikes on civilian infrastructure, substantial evidence is required. Indeed, indisputable evidence includes fragments of missiles and munitions found at the sites of impact. The article presents the procedure for identifying enemy artillery means of destruction, which consists of eight stages. The procedure for identifying artillery means of destruction from fragments found at the scene should be comprehensive, including the determination of the type of munition, identification of the probable area of enemy firing positions from which the strike was conducted, and should include the following stages: inspection of the crater to collect informative fragments; determination of the type of munition; identification of the type of artillery system from which the fire was conducted; determination of the direction from which the artillery shell was fired; identification of the probable area of enemy firing positions. The use of 3D models for reconstructing the shell casing has been proposed, which will enable the identification of enemy types of munitions much faster and without the need for laboratory analysis. Based on the results of the munition type identification, an expert report is compiled, which may also include the names of individuals involved in the crime. Step-by-step execution of the stages in the procedure for identifying artillery shell explosions, starting with working at the crater and ending with comparative measurements, including the use of 3D models, will optimize and facilitate the work of experts at the scene and speed up the process of providing information to investigators. Additionally, applying the stages of the procedure will enable the creation of a database of identified fragments for further study and comparison during the experts' work. Thus, the proposed procedure for identifying fragments of enemy artillery munitions collected from the scene will significantly ease the work of military personnel and forensic experts, facilitate cooperation between forensic laboratories and law enforcement agencies at both national and international levels, and ultimately contribute to the successful investigation of war crimes.

Clutch Torque Optimization for Heavy-Duty Vehicles Starting Based on Intelligent Identification of Driver’s Starting Intention, Vehicle Mass, and Road Grade

<div>For heavy-duty vehicles equipped with automated mechanical transmission (AMT), the control of automatic clutch torque is crucial during the start-up process. However, the difficulty of controlling clutch torque is exacerbated by differences in driver’s starting intentions, changes in vehicle mass, and road gradient. Therefore, this article proposes the clutch starting torque optimization strategy based on intelligent recognition of driver’s starting intention, vehicle mass, and road gradient. First, an intelligent recognition strategy is proposed based on the combination of data-driven and onboard transmission control unit (TCU) algorithms, which improves the accuracy of recognizing the driver’s intention to start as well as the vehicle mass and road gradient. Based on the vehicle’s historical state data information, the predictive model is trained offline using a long–short-term memory (LSTM) network to obtain predicted parameter identification results, which are then used to calibrate the computed values of the onboard TCU algorithm. Second, the clutch torque optimization strategy is designed based on the driver’s starting intention, while considering the effects of road gradient and vehicle mass on the clutch starting resistance torque. The weight coefficients of the objective performance function are adjusted according to the driver’s starting intention, and the Pontryagin’s minimum principle (PMP) is used to solve the clutch target torque. Finally, offline data training and real-vehicle testing are performed. The results show that the optimization strategy can effectively reduce the friction work and the degree of impact during the starting process, minimize the clutch slipping time, and improve the smoothness of vehicle starting and driving comfort.</div>

GP-Plotter: Flexible Spectral Visualization for Proteomics Data with Emphasis on Glycoproteomics Analysis.

Identification evaluation and result dissemination are essential components in mass spectrometry-based proteomics analysis. The visualization of fragment ions in mass spectrum provides strong evidence for peptide identification and modification localization. Here, we present an easy-to-use tool, named GP-Plotter, for ion annotation of tandem mass spectra and corresponding image output. Identification result files of common searching tools in the community and user-customized files are supported as input of GP-Plotter. Multiple display modes and parameter customization can be achieved in GP-Plotter to present annotated spectra of interest. Different image formats, especially vector graphic formats, are available for image generation which is favorable for data publication. Notably, GP-Plotter is also well-suited for the visualization and evaluation of glycopeptide spectrum assignments with comprehensive annotation of glycan fragment ions. With a user-friendly graphical interface, GP-Plotter is expected to be a universal visualization tool for the community. GP-Plotter has been implemented in the latest version of Glyco-Decipher (v1.0.4) and the standalone GP-Plotter software is also freely available at https://github.com/DICP-1809.

Evaluation of the analytical performance of four different manufacturer's reagent red blood cells in antibody detection and identification.

The detection/identification of clinically significant antibodies to red cell antigens form the foundation for safe transfusion practices. This study aimed to evaluate the diagnostic performance of commercially available 0.8% reagent red blood cells (RRBCs) in Australia. 166 patient-derived plasma samples with a positive indirect antiglobulin test (IAT) were tested using column agglutination technology (CAT) with Immulab, Bio-Rad, Grifols and QuidelOrtho screening and identification RRBCs with the respective manufacturer's proprietary CAT system. False-negative antibody screening and identification results were obtained with Bio-Rad (3/61), Grifols (14/68) and Quidel-Ortho (3/59) RRBCs when tested with the respective manufacturer's proprietary CAT system. Zero false-negative results were observed with Immulab RRBCs when tested with samples across all platforms. The sensitivity of the RRBCs used in this study were calculated to be 95.83% (95%CI 88.30-99.13%) for Bio-Rad RRBCs, 82.50% (95%CI 72.38-90.09%) for Grifols RRBCs and 95.65% (95%CI 87.82-99.09%) for QuidelOrtho RRBCs. The sensitivity of Immulab RRBCs were stratified based on performance in the 3 CAT platforms: Bio-Rad CAT (100%, 95%CI 95.01-100%), Grifols CAT (100%, 95%CI 95.49-100%) and QuidelOrtho CAT (100%, 95%CI 94.79-100%). RRBCs used in antibody detection and identification vary in diagnostic performance and should therefore be carefully considered before being implemented in routine patient testing.

Comparative analysis of the results of live anthrax vaccine identification by immunofluorescence and immunochromatography

INTRODUCTION. The quality evaluation of live anthrax vaccines will benefit from the implementation of alternative testing methods that are capable of specific identification of the Bacillus anthracis spore antigen using the appropriate diagnostic products that are authorised in the Russian Federation for the detection of B. anthracis spores.AIM. This study aimed to investigate the applicability of immunofluorescence analysis to the identification of live anthrax vaccines and compare this method with immunochromatography.MATERIALS AND METHODS. The study used commercial batches of a live anthrax vaccine and Russian diagnostic products, including diagnostic dry adsorbed fluorescent anti-anthrax spore immunoglobulins and an immunochromatographic assay (ICA) reagent kit for rapid detection and identification of B. anthracis spores (ICA system for B. anthracis). For smears and ICA system reactions, the authors prepared working solutions of bacterial suspensions at spore concentrations typical of the B. anthracis STI-1 vaccine strain. The spore concentrations were achieved using the pharmacopoeial reference standard (RS) for the opacity of bacterial suspensions of 10 international units (IU). Identification reactions involved the registration of immune complex formation.RESULTS. Immunofluorescence tests of the live anthrax vaccine demonstrated bright greenish-yellow envelope fluorescence with an intensity score of 3+ to 4+ for smears stained with diagnostic immunoglobulins at 1:32 and 1:64 dilutions. Immunochromatographic tests of the live anthrax vaccine detected the spore antigen at vaccine concentrations of 109 and 108 spores/mL, with the test strips showing two distinct dark-pink lines indicative of immunoprecipitation. According to the results obtained using the selected methods, the tested microbial culture was confirmed as B. anthracis.CONCLUSIONS. Immunochromatography and immunofluorescence tests with appropriate diagnostic preparations are convenient and reliable tools for the species-specific detection of B. anthracis STI-1 spores in the live anthrax vaccine. The results obtained in the anthrax vaccine identification tests provide a basis for recommending the above methods as supplementary alternatives to Ziehl–Neelsen bacteriological staining, which is currently prescribed by the State Pharmacopoeia of the Russian Federation.