Search Results (200)

The emergence and global dissemination of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli (ESBL-E. coli) represent a major public health concern. However, the characterization and capacity for horizontal gene transfer (HGT) of ESBL-E. coli in captive black bears remain substantially understudied. In the present study, 19 ESBL-E. coli strains were successfully identified (13.38%, 19/142). A total of 11 sequence types (STs) were identified from 19 ESBL-E. coli strains using MLST. This included eight known types (ST10, ST2690, ST208, ST695, ST4160, ST540, ST3865 and ST2792) and three new STs. Antimicrobial susceptibility testing demonstrated that all 19 ESBL-E. coli exhibited high resistance to KZ (100.00%), CRO (78.95%), and CTX (73.68%). Polymerase chain reaction (PCR) screening for 14 β-lactam antibiotic resistance genes (ARGs) and their variants revealed that bla_CTX-M was the most prevalent, followed by bla_SHV, bla_TEM, and bla_DHA. Furthermore, eight β-lactamase variants were detected, including five bla_CTX-M variants (bla_CTX-M-15, bla_CTX-M-3, bla_CTX-M-14, bla_CTX-M-55, and bla_CTX-M-27) and one variant each of bla_SHV-1, bla_TEM-1, and bla_DHA-14. Conjugation assays revealed that eight ESBL-E. coli strains were capable of conjugative transfer. Five plasmid types (IncFII, IncW, IncFrepB, IncY, and IncHI1) and three mobile genetic elements (MGEs) (IS26, ISEcp1, and trbC) were identified as co-transferred with bla_CTX-M. ESBL-E. coli poses a potential threat to captive black bears and may lead to further transmission. Consequently, the implementation of continuous surveillance and targeted interventions is imperative to prevent the transmission of ESBL-E. coli. Full article

Molecular toxicity prediction plays a crucial role in drug screening and environmental health risk assessment. Traditional toxicity prediction models primarily rely on molecular fingerprints and other structural features, while neglecting the complex biological mechanisms underlying compound toxicity, resulting in limited predictive accuracy, poor interpretability, and reduced generalizability. To address this challenge, this study proposes a novel molecular toxicity prediction framework that integrates knowledge graphs with Graph Neural Networks (GNNs). Specifically, we constructed a heterogeneous toxicological knowledge graph (ToxKG) based on ComptoxAI. ToxKG incorporates data from authoritative databases such as PubChem, Reactome, and ChEMBL, and covers multiple entities and relationships including chemicals, genes, signaling pathways, and bioassays. We then systematically evaluated six representative GNN models (GCN, GAT, R-GCN, HRAN, HGT, and GPS) on the Tox21 dataset. Experimental results demonstrate that heterogeneous graph models enriched with ToxKG information significantly outperform traditional models relying solely on structural features across multiple metrics including AUC, F1-score, ACC, and balanced accuracy (BAC). Notably, the GPS model achieved the highest AUC value (0.956) for key receptor tasks such as NR-AR, highlighting the critical role of biological mechanism information and heterogeneous graph structures in toxicity prediction. This study provides a promising pathway toward the development of interpretable and efficient intelligent models for toxicological risk assessment. Full article

This comprehensive review compiles current knowledge about the connection between plastic waste and the selection and transmission of antibiotic resistance genes (ARGs) in aquatic ecosystems, which can result in ARG contamination of fishery products—a significant source of microplastic (MP) introduction into the food chain. Plastic debris in aquatic environments is covered by a biofilm (the plastisphere) in which antibiotic-resistant bacteria (ARB) are selected and horizontal gene transfer (HGT) of ARGs is facilitated. The types of plastic waste considered in this study for their role in ARG enrichment are mainly microplastics (MPs), and also nanoplastics (NPs) and macroplastics. Studies regarding freshwaters, seawaters, aquaculture farms, and ARG accumulation favored by MPs in aquatic animals were considered. Most studies focused on the identification of the microbiota and its correlation with ARGs in plastic biofilms, while a few evaluated the effect of MPs on ARG selection in aquatic animals. A higher abundance of ARGs in the plastisphere than in the surrounding water or natural solid substrates such as sand, rocks, and wood was repeatedly reported. Studies regarding aquatic animals showed that MPs alone, or in association with antibiotics, favored the increase in ARGs in exposed organisms, with the risk of their introduction into the food chain. Therefore, reducing plastic pollution in water bodies and aquaculture waters could mitigate the ARG threat. Further investigations focused on ARG selection in aquatic animals should be conducted to better assess health risks and increase awareness of this ARG transmission route, enabling the adoption of appropriate countermeasures. Full article

Marine and island power systems usually incorporate various forms of energy supply, which poses challenges to the coordinated control of the system under diverse, irregular, and complex load operation modes. To improve the stability and self-sufficiency of island-isolated microgrids with high penetration of renewable energy, this study proposes a coordinated control strategy for an island microgrid with PV, HGT, and HESS, combining primary power allocation via low-pass filtering with a fuzzy logic-based secondary correction. The fuzzy controller dynamically adjusts power distribution based on the states of charge of the battery and supercapacitor, following a set of predefined rules. A comprehensive system model is developed in Matlab R2023b, integrating PV generation, an electrolyzer, HGT and a battery–supercapacitor HESS. Simulation results across four operational cases demonstrate that the proposed strategy reduces DC bus voltage fluctuations to a maximum of 4.71% (compared to 5.63% without correction), with stability improvements between 0.96% and 1.55%. The HESS avoids overcharging and over-discharging by initiating priority charging at low SOC levels, thereby extending service life. This work provides a scalable control framework for enhancing the resilience of marine and island microgrids with high renewable energy penetration. Full article

Hypersonic glide targets (HGTs) pose significant challenges for radar tracking due to complex maneuver strategies and time-varying statistics of measurement noise. Conventional single-model tracking methods are generally insufficient to fully capture maneuver modes, while existing multiple-model methods face trade-offs between model set completeness and computational efficiency. In addition, existing tracking methods struggle to cope with the non-Gaussian noise during hypersonic flight. To overcome these limitations, a Hierarchical Adaptive Moment Matching (HAMM) multiple-model method is proposed in this paper. Firstly, a comprehensive model set is constructed to cover characteristic maneuver modes. Subsequently, a hierarchical multiple-model framework is developed where: (1) a coarse model set is dynamically adapted by multi-frame posterior probability evolution and Rényi divergence criteria; (2) a fine model set is generated based on the moment matching method. Furthermore, the minimum error entropy cubature Kalman filter (MEECKF) is proposed to suppress the non-Gaussian measurement noise with high stability. Monte Carlo simulations demonstrate that the proposed method achieves improved positioning accuracy and faster convergence. Full article

The global rise of antimicrobial resistance (AMR), exacerbated by the COVID-19 pandemic, has led to a surge in infections caused by multidrug-resistant (MDR) bacteria. A key driver of this phenomenon is co-selection, where exposure to one antimicrobial promotes resistance to others via horizontal gene transfer (HGT) mediated by mobile genetic elements (MGEs). Carbapenem-resistant Enterobacteriaceae, known for their genomic plasticity, are particularly worrisome; yet genomic data from Latin America—especially Ecuador—remain scarce. This study investigated four carbapenem-resistant clinical isolates (two Klebsiella pneumoniae ST1440 and two Serratia marcescens) from tracheal aspirates of three ICU patients during a COVID-19 outbreak at Hospital IESS Quito Sur, Ecuador. Phenotypic profiling and whole-genome sequencing were performed, followed by bioinformatic reconstruction of plasmid content. Nineteen plasmids were identified, carrying 70 resistance-related genes, including antimicrobial resistance genes (ARGs), metal resistance genes (MRGs), integrons, transposons, and insertion sequences. Hierarchical clustering revealed six distinct gene clusters, with several co-localizing ARGs and genes for resistance to disinfectants and heavy metals—suggesting strong co-selective pressure. Conjugative plasmids harboring high-risk elements such as blaKPC-2, qacE, and Tn4401 were found in multiple isolates, indicating potential interspecies dissemination. These findings emphasize the importance of plasmid-mediated resistance during the pandemic and highlight the urgent need to enhance genomic surveillance and infection control, particularly in resource-limited healthcare settings. Full article

Edge intelligence plays an increasingly vital role in ensuring the reliability of distributed microservice-based applications, which are widely used in domains such as e-commerce, industrial IoT, and cloud-edge collaborative platforms. However, anomaly detection in these systems encounters a critical challenge: labeled anomaly data are scarce. This scarcity leads to severe class asymmetry and compromised detection performance, particularly under the resource constraints of edge environments. Recent approaches based on Graph Neural Networks (GNNs)—often integrated with DeepSVDD and regularization techniques—have shown potential, but they rarely address this asymmetry in an adaptive, scenario-specific way. This work proposes Heterogeneous Graph Adaptive Augmentation (HGAA), a framework tailored for edge intelligence scenarios. HGAA dynamically optimizes graph data augmentation by leveraging feedback from online anomaly detection. To enhance detection accuracy while adhering to resource constraints, the framework incorporates a selective bias toward underrepresented anomaly types. It uses knowledge distillation to model dataset-dependent distributions and adaptively adjusts augmentation probabilities, thus avoiding excessive computational overhead in edge environments. Additionally, a dynamic adjustment mechanism evaluates augmentation success rates in real time, refining the selection processes to maintain model robustness. Experiments were conducted on two real-world datasets (TraceLog and FlowGraph) under simulated edge scenarios. Results show that HGAA consistently outperforms competitive baseline methods. Specifically, compared with the best non-adaptive augmentation strategies, HGAA achieves an average improvement of 4.5% in AUC and 4.6% in AP. Even larger gains are observed in challenging cases: for example, when using the HGT model on the TraceLog dataset, AUC improves by 14.6% and AP by 18.1%. Beyond accuracy, HGAA also significantly enhances efficiency: compared with filter-based methods, training time is reduced by up to 71% on TraceLog and 8.6% on FlowGraph, confirming its suitability for resource-constrained edge environments. These results highlight the potential of adaptive, edge-aware augmentation techniques in improving microservice anomaly detection within heterogeneous, resource-limited environments. Full article

Beyond sensing bitter-tasting compounds, bitter taste receptors (TAS2Rs) have been demonstrated to play a functional role in proton secretion as a key mechanism of gastric acid secretion (GAS) and the cellular uptake of the zinc metal ion. Given its chemical similarity and comparable effects in GAS, we focused this work on cadmium and hypothesized that gastric TAS2Rs are involved in (i) cadmium-induced inhibition of proton secretion and (ii) in its cellular uptake. To test this hypothesis, immortalized human parietal HGT-1 cells were exposed to 62.5–1000 µM CdCl₂ for 30 min to elucidate TAS2R-mediated proton secretory activity (PSA) using a fluorescence-based pH cell assay and to quantitate cellular cadmium uptake by ICP-MS. HGT-1 cells exposed to CdCl₂ exhibited a dose-dependent decrease in PSA, accompanied by a corresponding increase in intracellular cadmium concentrations. Following a TAS2R RT-qPCR screening, the functional roles of TAS2R8 and TAS2R10 were clarified using a siRNA knockdown approach, demonstrating that TAS2R8 promotes and TAS2R10 mediates protection against excessive cellular cadmium accumulation. An additional cDNA microarray screening revealed, via gene ontology analysis, a distinct gene association of TAS2R8 and TAS2R10 with several metal ion transporters. These results provide the first evidence for a specific role of individual TAS2Rs beyond taste perception, particularly in metal ion homeostasis and gastric physiology. Full article

Colistin, a polymyxin antibiotic reintroduced as a last-resort therapy against multidrug-resistant Gram-negative bacteria, is increasingly being compromised by the emergence of plasmid-mediated colistin resistance genes (mcr-1 to mcr-10). The human gut microbiota serves as a major reservoir and transmission hub for these resistance determinants, even among individuals without prior colistin exposure. This review explores the mechanisms, dissemination, and clinical implications of mcr-mediated colistin resistance within the gut microbiota, highlighting its role in horizontal gene transfer, colonization, and environmental persistence. A comprehensive synthesis of the recent literature was conducted, focusing on epidemiological studies, molecular mechanisms, neonatal implications and decolonization strategies. The intestinal tract supports the enrichment and exchange of mcr genes among commensal and pathogenic bacteria, especially under antibiotic pressure. Colistin use in agriculture has amplified gut colonization with resistant strains in both animals and humans. Surveillance gaps remain, particularly in neonatal populations, where colonization may occur early and persist silently. Promising interventions, such as fecal microbiota transplantation and phage therapies, are under investigation but lack large-scale clinical validation. The gut microbiome plays a central role in the global spread of colistin resistance. Mitigating this threat requires integrated One Health responses, improved diagnostics for gut colonization, and investment in microbiome-based therapies. A proactive, multisectoral approach is essential to safeguard colistin efficacy and address the expanding threat of mcr-mediated resistance. Full article

Antibiotics are widely used in modern medicine. However, as global antibiotic consumption rises, environmental contamination with antibiotics and antibiotic resistance genes (ARGs) is becoming a serious concern. The impact of antibiotic use on human health is now under scrutiny, particularly regarding the emergence of antibiotic-resistant bacteria (ARB) in the environment. This has heightened interest in technologies for treating ARGs, highlighting the need for effective solutions. This review traces the life cycle of ARB and ARGs driven by human activity, revealing pathways from antibiotic use to human infection. We address the mechanisms enabling resistance in ARB during this process. Beyond intrinsic resistance, the primary cause of ARB resistance is the horizontal gene transfer (HGT) of ARGs. These genes exploit mobile genetic elements (MGEs) to spread via conjugation, transformation, transduction, and outer membrane vesicles (OMVs). Currently, biological wastewater treatment is the primary pollution control method due to its cost-effectiveness. However, these biological processes can promote ARG propagation, significantly amplifying the environmental threat posed by antibiotics. This review also summarizes key mechanisms in the biological treatment of antibiotics and evaluates risks associated with major ARB/ARG removal processes. Our aim is to enhance understanding of ARB risks, their pathways and mechanisms in biotreatment, and potential biomedical applications for pollution control. Full article

Background: Centella asiatica, a medicinally important species that is rich in bioactive compounds, lacks a characterized mitochondrial genome, despite nuclear and chloroplast assemblies. We sequenced and annotated its mitochondrial genome to elucidate its genetic foundations and evolutionary mechanisms. Methods: Assembly using Illumina short-reads and Nanopore long-reads was used to characterize the mitochondrial genome. Analyses included structural characterization, codon usage bias, repetitive sequences, horizontal gene transfer (HGT), collinearity, and phylogeny. The resulting tissue-specific (root, stem, and leaf) long non-coding RNA (lncRNA) profiles identified RNA editing sites. Results: The complete mitochondrial genome (249,777 bp, 45.5% GC) comprises three circular contigs encoding 51 genes (33 protein-coding, 15 tRNA, and 3 rRNA). Comparative genomics revealed synteny with the Apiaceae family of plants and evidence of HGT. Phylogenetic analysis resolved taxonomic relationships within Apiales. We predicted that 547 RNA editing sites would be identified in its protein-coding genes. Tissue profiling identified 725 (root), 711 (stem), and 668 (leaf) editing sites, with >71% concordance to predictions. RNA editing-generated cryptic promoters/terminators occur in mitochondrial core function genes (e.g., ATP synthase, cytochrome c reductase/oxidase, ribosome large subunit, and cytochrome c biogenesis), exhibiting a lower frequency in the leaves compared to the roots and stems. Conclusions: We provide the first complete mitochondrial genome assembly for C. asiatica, delineating its complex structure, tissue-modulated RNA editing, and evolutionary trajectory. This high-quality genomic resource establishes a foundation for molecular evolutionary studies and enhances the genomic toolkit for this pharmacologically significant species. Full article

Antimicrobial resistance (AMR) presents a growing global threat, driven by widespread antibiotic misuse across human and veterinary medicine. Companion animals, particularly dogs and cats, harbor complex natural microbiota—including skin, mucosal, and gastrointestinal communities—that are essential to their health yet also serve as reservoirs of antibiotic resistance genes (ARGs). These ARGs can spread through horizontal gene transfer (HGT), especially during bacterial imbalances such as endogenous infections or surgical interventions, increasing the risk of difficult-to-treat infections. Documented zoonotic and anthroponotic transmissions of resistant strains such as MRSA, MRSP, and ESBL-producing E. coli highlight the bidirectional nature of ARG flow between animals and humans. This underscores the critical importance of the One Health approach, which promotes interdisciplinary collaboration to monitor, understand, and combat AMR across the human–animal-environment interface. Key mechanisms of ARG dissemination, the role of companion animal microbiota, and real-world examples of resistance transfer between species illustrate the complexity and urgency of addressing AMR. Targeted surveillance, rational antibiotic use, and public awareness are essential to preserving antimicrobial efficacy and safeguarding both human and animal populations. Full article

Background/Objectives: Adolescent idiopathic scoliosis (AIS) is a three-dimensional spinal deformity often requiring surgical correction in severe cases. Halo-gravity traction (HGT) is commonly employed preoperatively to enhance spinal flexibility and reduce curve severity. This study aimed to evaluate the effectiveness of HGT in reducing Cobb angles in AIS and to assess how patient age, skeletal maturity (Risser score), and curve type (Lenke classification) influence treatment response. Methods: We conducted a retrospective cohort study of 28 AIS patients with Cobb angles > 65° who underwent preoperative HGT followed by posterior spinal fusion. Traction was applied for a mean of 24.64 days, reaching 40–50% of each patient’s body weight. Radiographic measurements were collected pre-traction, post-traction, and postoperatively. Statistical analyses included paired t-tests, Pearson correlations, Kruskal–Wallis tests, and linear regression. Results: Mean primary Cobb angle was reduced from 82.46° pre-traction to 61.00° post-traction (26.09% reduction) and further to 29.54° postoperatively (64.58% total reduction). Similar reductions were observed in secondary curves. No statistically significant correlations were found between age or Risser score and the magnitude of correction. Lenke type 3 showed the highest traction response, while type 5 had the greatest surgical gain. Curve type and skeletal maturity did not significantly affect final outcomes. Conclusions: Halo-gravity traction is a safe and effective adjunct in the surgical treatment of severe AIS, achieving substantial Cobb angle reduction. The degree of correction was not significantly influenced by age, Risser score, or curve type, supporting the broad applicability of HGT across adolescent patients. Full article

This article addresses the task of building personalized educational recommendations based on a heterogeneous knowledge graph that integrates data from university curricula, job vacancies, and online courses. To solve the problem of course recommendations by their relevance to a user’s competencies, a graph neural network (GNN)-based approach is proposed, specifically utilizing and comparing the Heterogeneous Graph Transformer (HGT) architecture, Graph Sample and Aggregate network (GraphSAGE), and Heterogeneous Graph Attention Network (HAN). Experiments were conducted on a heterogeneous graph comprising various node and relation types. The models were evaluated using regression and ranking metrics. The results demonstrated the superiority of the HGT-based recommendation model as a link regression task, especially in terms of ranking metrics, confirming its suitability for generating accurate and interpretable recommendations in educational systems. The proposed approach can be useful for developing adaptive learning recommendations aligned with users’ career goals. Full article

Proteus mirabilis is a zoonotic pathogen that poses a growing threat to both animal and human health due to rising antimicrobial resistance (AMR). It is widely found in animals, including China’s nationally protected captive giant and red pandas. This study isolated Proteus mirabilis from panda feces to assess AMR and virulence traits, and used whole-genome sequencing (WGS) to evaluate the spread of resistance genes (ARGs) and virulence genes (VAGs). In this study, 37 isolates were obtained, 20 from red pandas and 17 from giant pandas. Multidrug-resistant (MDR) strains were present in both hosts. Giant panda isolates showed the highest resistance to ampicillin and cefazolin (58.8%), while red panda isolates were most resistant to trimethoprim/sulfamethoxazole (65%) and imipenem (55%). Giant panda-derived strains also exhibited stronger biofilm formation and swarming motility. WGS identified 31 ARGs and 73 VAGs, many linked to mobile genetic elements (MGEs) such as plasmids, integrons, and ICEs. In addition, we found frequent co-localization of drug resistance genes/VAGs with MGEs, indicating a high possibility of horizontal gene transfer (HGT). This study provides crucial insights into AMR and virulence risks in P. mirabilis from captive pandas, supporting targeted surveillance and control strategies. Full article