Search Results (65)

The overlapping circulation of influenza (Flu), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; SC2), and respiratory syncytial virus (RSV) continues to challenge clinical laboratories, particularly in settings with limited automation and fragmented healthcare coverage. This study expanded the CDC Flu-SC2 assay by incorporating a laboratory-developed test (LDT) for RSV A/B detection into a fully automated quadruplex RT-qPCR (LDRA) on the Panther Fusion^® Open Access™ system. The design, based on more than 8000 RSV genomic sequences targeting the conserved M gene, achieved optimal amplification efficiencies (97–105%) and full multiplex compatibility. Analytical assessment established limits of detection between 9.6 and 37.8 copies per reaction, absence of cross-reactivity with 30 respiratory pathogens, and inclusivity for 32 viral variants. Commutability and diagnostic performance among the LDRA, CE IVD-marked Allplex™ SARS-CoV-2/FluA/FluB/RSV, and US IVD-marked Panther Fusion^® SARS-CoV-2/Flu A/B/RSV Assays were evaluated using 405 nasopharyngeal UTM-preserved swabs. The LDRA demonstrated excellent concordance (overall agreement ≥ 98%, κ > 0.95), strong diagnostic accuracy, and reliable detection of mixed infections. This quadruplex provides a fully automated, rapid, and accurate solution for the simultaneous detection of influenza A, influenza B, SARS-CoV-2, and RSV viruses, enhancing molecular diagnostic capacity and supporting equitable, timely clinical decision-making in middle-income healthcare systems such as that of the Dominican Republic. Full article

Urban flood disasters have become one of the most significant natural hazards under the dual pressures of rapid urbanization and intensified climate change. With the increasing interconnection among urban subsystems, these disasters often evolve into urban flood disaster chains, characterized by cascading failures across infrastructure, environment, and society. Current research hotspots mainly focus on three key aspects: the formation mechanisms, identification methods, and risk assessment approaches of urban flood disaster chains. In terms of formation mechanisms, most studies qualitatively describe the triggering and transmission processes of cascading events, revealing how interactions among hazard-inducing factors, disaster-formative environments, and disaster receptor generate chain reactions. Identification methods are categorized into four paradigms: qualitative identification based on experiential reasoning, semantic identification driven by data, structural identification through model inference, and behavioral identification using simulation modeling. Risk assessment approaches include historical disaster analysis, indicator-based evaluation models, uncertainty models, numerical simulation models, and intelligent algorithm models that integrate machine learning with physical simulations. The review finds that, due to the scarcity and heterogeneity of disaster chain event data, existing studies lack a unified quantitative framework to represent the mechanisms of urban flood disaster chains, as well as dynamic identification and assessment methods that can adapt to their evolutionary processes. Future research should focus on developing integrated mathematical paradigms, enhancing multisource data fusion and causal reasoning, and constructing hybrid models to support real-time risk assessment for urban flooding disaster chains. Full article

Flagellin is the main structural protein of the bacterial flagellum, responsible for the movement of flagellated bacteria. Flagellin activates Toll-like receptor 5, inducing both innate and adaptive immune reactions, which highlights its potential as a vaccine adjuvant, particularly efficient in case of administration via mucosal routes. Genetic fusion of an antigen to flagellin has been shown to enhance the immune responses against the antigen. The molecular architecture of flagellin provides versatile and robust adjuvant functionality, facilitating the development of diverse vaccination strategies against multiple diseases as recombinant protein-based vaccines demonstrate substantial advantages over conventional live-attenuated and inactivated vaccines in both developmental efficiency and safety profiles. We present a comprehensive overview of vaccine design strategies employing genetic fusion of antigens to flagellin for protection against various infectious diseases. The proven effectiveness of flagellin-based delivery has enabled several vaccine candidates to enter clinical trials. Full article

The development of advanced energy materials is critical for the safety and efficiency of next-generation nuclear energy systems. Aluminum alloys present a compelling option due to their excellent neutronic properties, notably a low thermal neutron absorption cross-section. However, their historically poor high-temperature performance has limited their use in commercial power reactors. This makes them prime candidates for specialized, lower-temperature but high-radiation environments, such as research reactors, spent fuel storage systems, and spallation neutron sources. In these applications, mitigating radiation damage—particularly swelling and embrittlement from helium produced during irradiation—remains a paramount challenge. Grain Boundary Engineering (GBE) is a potent strategy to mitigate radiation damage by increasing the fraction of low-energy Coincident Site Lattice (CSL) boundaries. These interfaces act as effective sinks for radiation-induced point defects (vacancies and self-interstitials), suppressing their accumulation and subsequent clustering into damaging dislocation loops and voids. By controlling the defect population, GBE can substantially reduce macroscopic effects like volumetric swelling and embrittlement, enhancing material performance in harsh radiation environments. In this article we evaluate the efficacy of GBE in an AlSi10Mg alloy, a candidate material for nuclear applications. Samples were prepared via KOBO extrusion, with a subset undergoing subsequent annealing to produce varied initial grain sizes and grain boundary character distributions. This allows for a direct comparison of how these microstructural features influence the material’s response to helium ion irradiation, which simulates damage from fission and fusion reactions. The resulting post-irradiation defect structures and their interaction with the engineered grain boundary network were characterized using a combination of Transmission Electron Microscopy (TEM) and High-Resolution Transmission Electron Microscopy (HRTEM), providing crucial insights for designing next-generation, radiation-tolerant energy materials. Full article

Background/Objectives: Synovial sarcoma (SS) is an aggressive soft-tissue tumor characterized by the chromosomal translocation t(X;18) (p11.2;q11.2), most commonly involving the fusion of the SYT gene on chromosome 18 with the SSX1 or SSX2 genes on chromosome X. This study aims to explore the clinicopathological and molecular characteristics of synovial sarcoma in a cohort of Moroccan patients. Methods: We analyzed 48 cases of synovial sarcoma using formalin-fixed, paraffin-embedded (FFPE) tissue samples. Histological grading was performed according to the FNCLCC system. Immunohistochemical staining was employed to detect cytokeratin (CK) and epithelial membrane antigen (EMA). Molecular analysis included fluorescence in situ hybridization (FISH) to identify SS18 gene rearrangements and reverse transcription–polymerase chain reaction (RT-PCR) to detect SYT-SSX fusion transcripts. Results: Among the cohort, 56% of cases showed SS18 gene rearrangements via FISH, while RT-PCR confirmed the presence of SS18-SSX1 and SS18-SSX2 transcripts in 60% and 32% of cases, respectively. The remainder was classified as undifferentiated sarcoma. Notably, no significant associations were observed between SYT-SSX fusion type and clinicopathological features. Conclusions: These findings underscore the importance of integrating molecular techniques for precise diagnosis in synovial sarcoma. The results align with global patterns, emphasizing the necessity for molecular testing to enhance diagnostic accuracy and informing potential therapeutic advancements. Full article

Individual cow identification is a prerequisite for automatically monitoring behavior patterns, health status, and growth data of each cow, and can provide the assistance in selecting excellent cow individuals for breeding. Despite high recognition accuracy, traditional implantable electronic devices such as RFID (i.e., Radio Frequency Identification) can cause some degree of harm or stress reactions to cows. Image-based methods are widely used due to their non-invasive advantages, but these methods have poor adaptability to different environments and target size, and low detection accuracy in complex scenes. To solve these issues, this study designs a Dy_Conv (i.e., dynamic convolution) module and innovatively constructs a Dynamic_Bottleneck module based on the Dy_Conv and S2Attention (Sparse-shift Attention) mechanism. On this basis, we replaces the first and fourth bottleneck layers of Resnet50 with the Dynamic_Bottleneck to achieve accurate extraction of local features and global information of cows. Furthermore, the QAConv (i.e., query adaptive convolution) module is introduced into the front end of the backbone network, and can adjust the parameters and sizes of convolution kernels to adapt to the scale changes in cow targets and input images. At the same time, NAM (i.e., normalization-based attention module) attention is embedded into the backend of the network to achieve the feature fusion in the channels and spatial dimensions, which contributes to better distinguish visually similar individual cows. The experiments are conducted on the public datasets collected from different cowsheds. The experimental results showed that the Rank-1, Rank-5, and mAP metrics reached 96.8%, 98.9%, and 95.3%, respectively. Therefore, the proposed model can effectively capture and integrate multi-scale features of cow body appearance, enhancing the accuracy of individual cow identification in complex scenes. Full article

The classification of cancer based on gene expression profiles is a central challenge in precision oncology due to the high dimensionality and low sample size inherent in microarray datasets. Effective gene selection is crucial for improving classification accuracy while minimizing computational overhead and model complexity. This study introduces Genetic-Embedded Nuclear Reaction Optimization (GNR), a novel hybrid metaheuristic that enhances the conventional Nuclear Reaction Optimization (NRO) algorithm by embedding a genetic uniform crossover mechanism into its fusion phase. The proposed algorithm leverages a two-stage process: an initial F-score filtering step to reduce dimensionality, followed by GNR-driven optimization to identify compact, informative gene subsets. Evaluations were conducted on six widely used microarray cancer datasets, with Support Vector Machines (SVM) employed as classifiers and performance assessed via Leave-One-Out Cross-Validation (LOOCV). Results show that GNR consistently outperforms the original NRO and several benchmark hybrid algorithms, achieving 100% classification accuracy with significantly smaller gene subsets across all datasets. These findings confirm the efficacy of the genetic-embedded fusion strategy in enhancing local exploitation while preserving the global search capabilities of NRO, thereby offering a robust and interpretable approach for gene selection in cancer classification. Full article

Mycoplasma pneumoniae is a significant causative agent of atypical pneumonia in both children and adults. Timely and accurate diagnosis is crucial for appropriate patient management. Conventional methods for detecting M. pneumoniae, such as culture and serology, exhibit several limitations regarding sensitivity, specificity, and turnaround time. In contrast, real-time PCR is considered the most reliable, rapid, and sensitive technique for the diagnosis of M. pneumoniae infection. In this study, we adapted and validated an in-house real-time PCR assay for use on the fully automated Panther Fusion^® System. The validation process included two artificial samples, five external quality controls, and sixty-two patient samples. We evaluated the performance in terms of precision, sensitivity, linearity, and analytical sensitivity, comparing it to the original in-house assay. The Panther Fusion^® System demonstrated a broad dynamic range (16–1.6 × 10⁷ copies/reaction), a robust correlation (94%) with the in-house assay, and comparable sensitivity (46 copies/mL vs. 25 copies/mL). The concordance between the in-house real-time PCR and the Panther Fusion^® System was 100% for both clinical samples and external quality controls. The adaptation of the test to the Panther Fusion^® System enabled the inclusion of M. pneumoniae among the pathogens monitored for respiratory infection surveillance. Throughout 2024, we analyzed 2567 samples, with a peak positivity rate of 38% observed in August. These findings underscore the significance of employing the M. pneumoniae diagnostic assay on the Panther Fusion^® System which proves valuable for the detection of M. pneumoniae infections. This platform offers the advantages of increased automation and greater throughput potential compared to other platforms, enhancing the efficiency of respiratory pathogen detection in clinical settings. Full article

The Spodoptera litura, a Lepidopteran pest known for its high fecundity, undergoes a complete metamorphosis, including a distinctive process during which the male testes fuse from two separate organs into a single entity, significantly enhancing its fertility. To elucidate the molecular mechanisms underlying this testicular fusion, this study employed an integrated multi-omics approach to investigate concurrent changes at the transcriptomic and proteomic levels. We identified a series of synchronized alterations on the peritestic larval membrane, including heme binding, peptidase activity, hydrolase activity, metal ion transport, redox reactions, and chitin metabolism, all of which are substantially enriched at specific temporal points during testicular fusion. Nine genes/proteins co-expressed at the mRNA and protein levels were selected for targeted quantitative proteomics (PRM) and quantitative PCR (qPCR) validation, leading to the identification of five genes potentially involved in the testicular fusion process: Sl3030, ARCP, PSLRE, Obstructor-E, and Osris9B. Notably, the gene Sl3030, once knocked out, not only disrupted the normal fusion process but also resulted in reduced testis size, thickened peritestic membranes, and abnormal sperm development. Transcriptomic sequencing of the Sl3030 knockout mutant revealed its primary influence on the fusion process by affecting the assembly of the microtubule system and cytoskeleton. This research, for the first time, provides a multi-omics perspective on the response of key signaling pathways and molecular changes during the testicular fusion of S. litura and validates the role of the previously uncharacterized gene Sl3030 in this process, offering valuable insights into the complex mechanisms of testicular fusion in this species. Full article

Background: Maillard reaction products (MRPs) are known for their antioxidant properties; however, their effects on muscle cells remain unclear. This study aims to elucidate the effects of MRPs on muscle hypertrophy and atrophy in C2C12 myotubes. Methods: MRPs were prepared by heating L-lysine and D-glucose, and their antioxidant activity was assessed using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay. Subsequently, mouse C2C12 myoblasts were cultured with MRPs until myotubes formed, and their diameters were measured to assess hypertrophic and atrophic changes. Akt phosphorylation was evaluated by Western blotting, and gene expression levels were analyzed via quantitative PCR. Results: The prepared MRPs exhibited high antioxidant activity in the DPPH radical scavenging assay. MRP treatment significantly increased the average myotube diameter by approximately 40% and enlarged the largest myotube diameter by up to 80%, potentially mediated by enhanced Akt phosphorylation. Under dexamethasone-induced atrophy, MRPs modestly attenuated the reduction in myotube diameter by approximately 20%, although the effect was not statistically significant, and did not significantly alter the fusion index either. Quantitative PCR analysis revealed that MRP treatment significantly reduced the mRNA expression of Nfe2l2, a key regulator of antioxidant response, whereas it had no notable effects on the expression of genes related to myoblast proliferation (Myod1), differentiation (Myog), hypertrophy (Igf1), atrophy (Foxo1 and Trim63), and oxidative stress (Cat, Gclc, and Nqo1). Conclusions: Our findings suggested that MRPs possess antioxidant activity and promote myotube hypertrophy via Akt signaling. This study highlighted the potential of MRPs as functional ingredients for promoting muscle health, though further in vivo studies are required to validate their physiological relevance. Full article

Driver factors are the main cause of road traffic accidents. For the research of automotive active safety, an identification method for road hypnosis of a driver of a car with dynamic human–vehicle heterogeneous data fusion calculation is proposed. Road hypnosis is an unconscious driving state formed by the combination of external environmental factors and the psychological state of the car driver. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task. The safety of humans and cars is greatly affected. Therefore, the study of the identification of drivers’ road hypnosis is of great significance. Vehicle and virtual driving experiments are designed and carried out to collect human and vehicle data. Eye movement data and EEG data of human data are collected with eye movement sensors and EEG sensors. Vehicle speed and acceleration data are collected by a mobile phone with AutoNavi navigation, which serves as an onboard sensor. In order to screen the characteristics of human and vehicles related to the road hypnosis state, the characteristic parameters of the road hypnosis in the preprocessed data are selected by the method of independent sample T-test, the hidden Markov model (HMM) is constructed, and the identification of the road hypnosis of the Ridge Regression model is combined. In order to evaluate the identification performance of the model, six evaluation indicators are used and compared with multiple regression models. The results show that the hidden Markov-Ridge Regression model is the most superior in the identification accuracy and effect of the road hypnosis state. A new technical scheme reference for the development of intelligent driving assistance systems is provided by the proposed comprehensive road hypnosis state identification model based on human–vehicle data can provide, which can effectively improve the life recognition ability of automobile intelligent cockpits, enhance the active safety performance of automobiles, and further improve traffic safety. Full article

Background/Objectives: Cancer classification using microarray datasets presents a significant challenge due to their extremely high dimensionality. This complexity necessitates advanced optimization methods for effective gene selection. Methods: This study introduces and evaluates the Nuclear Reaction Optimization (NRO)—drawing inspiration from nuclear fission and fusion—for identifying informative gene subsets in six benchmark cancer microarray datasets. Employed as a standalone approach without prior dimensionality reduction, NRO was assessed using both Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN). Leave-One-Out Cross-Validation (LOOCV) was used to rigorously evaluate classification accuracy and the relevance of the selected genes. Results: Experimental results show that NRO achieved high classification accuracy, particularly when used with SVM. In select datasets, it outperformed several state-of-the-art optimization algorithms. However, due to the absence of additional dimensionality reduction techniques, the number of selected genes remains relatively high. Comparative analysis with Harris Hawks Optimization (HHO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Firefly Algorithm (FFA) shows that while NRO delivers competitive performance, it does not consistently outperform all methods across datasets. Conclusions: The study concludes that NRO is a promising gene selection approach, particularly effective in certain datasets, and suggests that future work should explore hybrid models and feature reduction techniques to further enhance its accuracy and efficiency. Full article

Artificial intelligence (AI)-assisted prediction of adverse drug reactions (ADRs) has significant potential for improving drug safety and reducing financial costs. Early studies often relied on limited dimensions such as the molecular structure of drugs or interactions with biomolecules. In contrast, integrating these characteristics provides valuable insights into ADR predictions from multiple perspectives, enhancing the comprehensiveness and accuracy of the prediction models. In addition, previous studies have focused on whether a specific adverse drug reaction occurs with a particular drug, ignoring the fact that multiple adverse drug reactions may occur concurrently with a single drug. To address these, we developed a predictor that identifies ADRs early in drug discovery, using a deep learning model designed to fuse multiple drug characteristics. Our approach employed four modules to extract one- and two-dimensional sequence structure information of drug molecules, drug–protein interaction data, and drug similarity. A fusion model integrated these characteristics to predict the precise probability of ADRs. The receiver operating characteristic–area under curve (ROC-AUC), area under precision–recall curve (AUPR), and F1 scores on the benchmark dataset are 0.7002, 0.6619, and 0.6330, respectively. The AUPR is significantly improved compared to the conventional multi-label classifier (from 64.02% to 66.19%). In addition, we compared the results with the state-of-the-art methods on LIU’s dataset and the AUPR increased from 34.65% to 68.82%, which shows that our model outperforms them in terms of accuracy and robustness. Ablation experiments further validated the effectiveness of the individual modules. This model accurately predicted the probability of various ADR classes by integrating comprehensive information, thereby offering significant value in enhancing monitoring measures for new drug development and clinical use. Full article

Background: Next-generation vaccines against coronavirus disease 2019 (COVID-19) focus on inducing a long-lasting immune response against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its emerging variants. To achieve this, antigens other than spike proteins have been proposed, and different platforms have been evaluated. Nucleic acid-based vaccines are fundamental for this process. Preclinical data have shown that the SARS-CoV-2 nucleocapsid protein induces a protective cellular immune response, and when combined with the spike protein, the resulting humoral and cellular immune responses are effective against some SARS-CoV-2 variants. Methods: We designed a DNA vaccine against the spike and nucleocapsid proteins of SARS-CoV-2 to generate fusion proteins based on the Delta and Omicron B.5 strains. The most immunogenic regions of the spike and nucleocapsid proteins of the Delta and Omicron B strains were selected using bioinformatics. The nucleotide sequences were cloned into pcDNA3.1, and named pcDNA3.1/D-S1, pcDNA3.1/D-S1N, and pcDNA3.1/O-SN. The immunogenicity of the generated fusion proteins was evaluated by analyzing the humoral and cellular responses elicited after the immunization of BALB/c mice. Results: DNA immunization induced antibody production, neutralization activity, and IFN-γ production. The inclusion of the nucleocapsid regions in the plasmid greatly enhanced the immune response. Moreover, cross-reactions with the variants of interest were confirmed. Conclusions: Plasmids-encoding fusion proteins combining the most immunogenic regions of the spike and nucleocapsid proteins present a promising strategy for designing new and effective vaccines against SARS-CoV-2. Full article

Enriched lithium isotopes (⁶Li and ⁷Li) are essential in the nuclear energy industry, where ⁶Li is bombarded with neutrons to produce tritium for fusion reactions, while ⁷Li is used as a core coolant and pH regulator. Separation of ⁶Li and ⁷Li by electromigration is a promising method for producing enriched lithium isotopes that fulfill industrial needs. In this work, based on a previously proposed biphasic system electromigration routine, a three-stage system of ‘LiCl aqueous solution (anolyte)|B12C4-[EMIm][NTf₂] organic solution|NH₄Cl aqueous solution (catholyte)’ was constructed and the rules of lithium isotope separation and lithium-ion migration investigated. It was shown that the isotope enrichment effect of the catholyte was greatly affected by the experimental conditions, while that of the organic solution was less affected. As the B12C4 concentration increased, enhancement of ⁷Li enrichment in the catholyte and ⁶Li enrichment in the organic solution was observed, and α(C/O) and α(O/A) reached 0.975 and 1.018 at B12C4 of 0.5 mol/L. With the increase in current, migration time, and LiCl concentration, the isotope that was enriched in the catholyte trended from ⁷Li to ⁶Li (about 6 mA, 12 h or LiCl of 5 mol/L). Taking lithium-ion transport efficiency and lithium isotope separation effect into consideration together, a current of at least 6 mA, duration of at least 12 h, LiCl concentration of at least 1 mol/L and B12C4 concentration of 0.2 mol/L are suggested for the electromigration process. The work provides an important reference for system construction and experimental design of a biphasic electromigration separation method, which is expected to be an industrial alternative because of its environmental protection and high efficiency. Full article