Search Results (61)

Background: Helcococcus kunzii, a facultative anaerobe and Gram-positive coccus, has been documented as a cunning pathogen, mainly in immunocompromised individuals, as evidenced by recent clinical and microbiological reports. It has been associated with a variety of polymicrobial infections, comprising diabetic foot ulcers, prosthetic joint infections, osteomyelitis, endocarditis, and bloodstream infections. Despite its emerging clinical relevance, no licensed vaccine or targeted immunotherapy currently exists for H. kunzii, and its rising resistance to conventional antibiotics presents a growing public health concern. Objectives: In this study, we employed an integrated subtractive proteomics and immunoinformatics pipeline to design a multi-epitope subunit vaccine (MEV) candidate against H. kunzii. Initially, pan-proteome analysis identified non-redundant, essential, non-homologous, and virulent proteins suitable for therapeutic targeting. Methods/Results: From these, two highly conserved and surface-accessible proteins, cell division protein FtsZ and peptidoglycan glycosyltransferase FtsW, were selected as promising vaccine targets. Comprehensive epitope prediction identified nine cytotoxic T-lymphocyte (CTL), five helper T-lymphocyte (HTL), and two linear B-cell (LBL) epitopes, which were rationally assembled into a 397-amino-acid-long chimeric construct. The construct was designed using appropriate linkers and adjuvanted with the cholera toxin B (CTB) subunit (NCBI accession: AND74811.1) to enhance immunogenicity. Molecular docking and dynamics simulations revealed persistent and high-affinity ties amongst the MEV and essential immune receptors, indicating a durable ability to elicit an immune reaction. In silico immune dynamic simulations predicted vigorous B- and T-cell-mediated immune responses. Codon optimization and computer-aided cloning into the E. coli K12 host employing the pET-28a(+) vector suggested high translational efficiency and suitability for bacterial expression. Conclusions: Overall, this computationally designed MEV demonstrates favorable immunological and physicochemical properties, and presents a durable candidate for subsequent in vitro and in vivo validation against H. kunzii-associated infections. Full article

Prosthetic joint infections (PJIs) pose significant challenges, often requiring multiple surgeries that lead to soft tissue loss, dead space, and fibrosis. Wound breakdown increases the risk of polymicrobial infection and treatment failure. The vertical rectus abdominis myocutaneous (VRAM) flap is a proven method for complex wound coverage, but its role in managing hip PJI is underexplored. This study evaluates outcomes of VRAM flap reconstruction in polymicrobial hip PJI. We retrospectively reviewed five patients who underwent VRAM flap reconstruction for polymicrobial hip PJI between December 2020 and December 2023. Primary outcomes included flap survival, infection control, and wound healing. Secondary outcomes were implant retention, postoperative complications, and functional status. At a mean follow-up of 28 months, four patients achieved wound healing and remained infection-free, while one had persistent sinus drainage but retained the implant. Flap survival was 100%, with no necrosis or failure. No major complications requiring reoperation occurred. Two patients developed deep collections, managed with ultrasound-guided drainage (Clavien-Dindo IIIa). Minor complications included donor-site dehiscence (three), flap dehiscence (one), edge necrosis (two), and hernias (two), all managed non-surgically (Clavien-Dindo I/II). All patients retained implants and remained ambulatory. VRAM flap reconstruction is a reliable option for managing complex polymicrobial hip PJI. Flap survival was excellent, and most patients achieved infection control. However, persistent infection and the need for suppressive antibiotics highlight the ongoing challenges in these cases. Full article

The construction of knowledge graphs in cyber threat intelligence (CTI) critically relies on automated entity–relation extraction. However, sequence tagging-based methods for joint entity–relation extraction are affected by the order-dependency problem. As a result, overlapping relations are handled ineffectively. To address this limitation, a parallel, ensemble-prediction–based model is proposed for joint entity–relation extraction in CTI. The joint extraction task is reformulated as an ensemble prediction problem. A joint network that combines Bidirectional Encoder Representations from Transformers (BERT) with a Bidirectional Gated Recurrent Unit (BiGRU) is constructed to capture deep contextual features in sentences. An ensemble prediction module and a triad representation of entity–relation facts are designed for joint extraction. A non-autoregressive decoder is employed to generate relation triad sets in parallel, thereby avoiding unnecessary sequential constraints during decoding. In the threat intelligence domain, labeled data are scarce and manual annotation is costly. To mitigate these constraints, the SecCti dataset is constructed by leveraging ChatGPT’s small-sample learning capability for labeling and augmentation. This approach reduces annotation costs effectively. Experimental results show a 4.6% absolute F1 improvement over the baseline on joint entity–relation extraction for threat intelligence concerning Advanced Persistent Threats (APTs) and cybercrime activities. Full article

Obesity is a growing global health concern with widespread impacts on physical, psychological, and social well-being. Clinically, it is a major driver of type 2 diabetes (T2D), cardiovascular disease (CVD), non-alcoholic fatty liver disease (NAFLD), and cancer, reducing life expectancy by 5–20 years and imposing a staggering economic burden of USD 2 trillion annually (2.8% of global GDP). Despite its significant health and socioeconomic impact, earlier obesity medications, such as fenfluramine, sibutramine, and orlistat, fell short of expectations due to limited effectiveness, serious side effects including valvular heart disease and gastrointestinal issues, and high rates of treatment discontinuation. The advent of glucagon-like peptide-1 (GLP-1) receptor agonists (e.g., semaglutide, tirzepatide) has revolutionized obesity management. These agents demonstrate unprecedented efficacy, achieving 15–25% mean weight loss in clinical trials, alongside reducing major adverse cardiovascular events by 20% and T2D incidence by 72%. Emerging therapies, including oral GLP-1 agonists and triple-receptor agonists (e.g., retatrutide), promise enhanced tolerability and muscle preservation, potentially bridging the efficacy gap with bariatric surgery. However, challenges persist. High costs, supply shortages, and unequal access pose significant barriers to the widespread implementation of obesity treatment, particularly in low-resource settings. Gastrointestinal side effects and long-term safety concerns require close monitoring, while weight regain after medication discontinuation emphasizes the need for ongoing adherence and lifestyle support. This review highlights the transformative potential of incretin-based therapies while advocating for policy reforms to address cost barriers, equitable access, and preventive strategies. Future research must prioritize long-term cardiovascular outcome trials and mitigate emerging risks, such as sarcopenia and joint degeneration. A multidisciplinary approach combining pharmacotherapy, behavioral interventions, and systemic policy changes is critical to curbing the obesity epidemic and its downstream consequences. Full article

Early biomarkers are needed to predict the long-term persistence of rheumatical symptoms in patients infected with Chikungunya virus (CHIKV). This nested case-control study aimed to assess immunological factors during the early phases of CHIKV infection to predict the risk of post-CHIK chronic rheumatism (pCHIK-CR) in adult patients of two prospective cohorts. We evaluated 46 febrile patients (median age: 33.5 years; IQR: 19 years; women: 50.0%) with CHIKV infection confirmed during the 2014–2015 outbreak in Santander, Colombia. The participants were classified by a rheumatologist as either cases (pCHIK-CR) or controls (WoRM, without rheumatical manifestations). We quantified serum levels of IL-4, IL-6, IL-8/CXCL-8, IL-27, CCL-2, CXCL-9, CXCL-10, and IgG using Luminex and ELISA assays during the acute and subacute phases of infection. Then, we evaluated the association of these immune factors with the case-control status using piecewise logistic regression adjusted for age and sex. There were non-linear associations between IL-8/CXCL-8, CXCL-9, and CXCL-10 with pCHIK-CR. Increases in the levels of IL-8/CXCL-8 (<35.7 pg/mL), CXCL-9 (≥6000 pg/mL), and CXCL-10 (≥36,800 pg/mL) were significantly associated with a reduced risk of pCHIK-CR (adjusted ORs: 0.85, 0.96, and 0.94, respectively). These results suggest that increases in IL-8/CXCL-8, CXCL-9, and CXCL-10 levels, measured in the early stages of CHIKV infection, may predict a chronic disease risk. This suggests the possibility that an early and strong immune response could contribute to enhancing CHIKV control and potentially reduce the risk of persistent joint symptoms. Given their expression patterns and timing, these three immune factors may be considered promising biomarker candidates for assessing the risk of chronic rheumatologic disease. These findings should be considered as exploratory and validated in additional cohort studies. Full article

As Advanced Persistent Threats (APTs) continue to evolve, constructing a dynamic cybersecurity knowledge graph requires precise extraction of entity–relationship triples from unstructured threat intelligence. Existing approaches, however, face significant challenges in modeling low-frequency threat associations, extracting multi-relational entities, and resolving overlapping entity scenarios. To overcome these limitations, we propose the Symmetry-Aware Prototype Contrastive Learning (SAPCL) framework for joint entity and relation extraction. By explicitly modeling syntactic symmetry in attack-chain dependency structures and its interaction with asymmetric adversarial semantics, SAPCL integrates dependency relation types with contextual features using a type-enhanced Graph Attention Network. This symmetry–asymmetry fusion facilitates a more effective extraction of multi-relational triples. Furthermore, we introduce a triple prototype contrastive learning mechanism that enhances the robustness of low-frequency relations through hierarchical semantic alignment and adaptive prototype updates. A non-autoregressive decoding architecture is also employed to globally generate multi-relational triples while mitigating semantic ambiguities. SAPCL was evaluated on three publicly available CTI datasets: HACKER, ACTI, and LADDER. It achieved F1-scores of 56.63%, 60.21%, and 53.65%, respectively. Notably, SAPCL demonstrated a substantial improvement of 14.5 percentage points on the HACKER dataset, validating its effectiveness in real-world cyber threat extraction scenarios. By synergizing syntactic–semantic multi-feature fusion with symmetry-driven dynamic representation learning, SAPCL establishes a symmetry–asymmetry adaptive paradigm for cybersecurity knowledge graph construction, thus enhancing APT attack tracing, threat hunting, and proactive cyber defense. Full article

Oriented ship detection in Synthetic Aperture Radar (SAR) images has broad applications in maritime surveillance and other fields. While deep learning advancements have significantly improved ship detection performance, persistent challenges remain for existing methods. These include the inherent misalignment between regression and classification tasks and the boundary discontinuity problem in oriented object detection. These issues hinder efficient and accurate ship detection in complex scenarios. To address these challenges, we propose TIAR-SAR, a novel oriented SAR ship detector featuring a task interaction head and composite angle regression. First, we propose a task interaction detection head (Tihead) capable of predicting both oriented bounding boxes (OBBs) and horizontal bounding boxes (HBBs) simultaneously. Within the Tihead, a “decompose-then-interact” structure is designed. This structure not only mitigates feature misalignment but also promotes feature interaction between regression and classification tasks, thereby enhancing prediction consistency. Second, we propose a joint angle refinement mechanism (JARM). The JARM addresses the non-differentiability problem of the traditional rotated Intersection over Union (IoU) loss through the design of a composite angle regression loss (CARL) function, which strategically combines direct and indirect angle regression methods. A boundary angle correction mechanism (BACM) is then designed to enhance angle estimation accuracy. During inference, BACM dynamically replaces an object’s OBB prediction with its corresponding HBB if the OBB exhibits excessive angle deviation when the angle of the object is near the predefined boundary. Finally, the performance and applicability of the proposed methods are evaluated through extensive experiments on multiple public datasets, including SRSDD, HRSID, and DOTAv1. Experimental results derived from the use of the SRSDD dataset demonstrate that the mAP50 of the proposed method reaches 63.91%, an improvement of 4.17% compared with baseline methods. The detector achieves 17.42 FPS on 1024 × 1024 images using an RTX 2080 Ti GPU, with a model size of only 21.92 MB. Comparative experiments with other state-of-the-art methods on the HRSID dataset demonstrate the proposed method’s superior detection performance in complex nearshore scenarios. Furthermore, when further tested on the DOTAv1 dataset, the mAP50 can reach 79.1%. Full article

Under the global climate governance framework, the Paris Agreement and the China–U.S. Glasgow Joint Declaration established a non-negotiable target of limiting 21st-century temperature rise to 1.5 °C. To date, over 130 nations have pledged carbon neutrality by mid-century, with agricultural activities contributing 25% of global greenhouse gas (GHG) emissions. The spatiotemporal dynamics of these emissions critically determine the operational efficacy of carbon peaking and neutrality strategies. While China’s Nationally Determined Contributions (NDCs) commit to achieving carbon peaking by 2030, a policy gap persists regarding differentiated implementation pathways at the county level. Addressing this challenge, this study selects the Three Gorges Reservoir (TGRA)—a region characterized by monocultural cropping systems and intensive fertilizer dependency—as a representative case. Guided by IPCC emission accounting protocols, we systematically evaluate spatiotemporal distribution patterns of agricultural CH₄ and N₂O emissions across 18 county-level units from 2006 to 2020. The investigation advances through two sequential phases: Mechanistic drivers analysis: employing the STIRPAT model, we quantify bidirectional effects (positive/negative) of critical determinants—including agricultural mechanization intensity and grain productivity—on CH₄/N₂O emission fluxes. Pathway scenario prediction: We construct three developmental scenarios (low-carbon transition, business-as-usual, and high-resource dependency) integrated with regional planning parameters. This framework enables the identification of optimal peaking chronologies for each county and proposes gradient peaking strategies through spatial zoning, thereby resolving fragmented carbon governance in agrarian counties. Methodologically, we establish a multi-scenario simulation architecture incorporating socioeconomic growth thresholds and agroecological constraints. The derived decision-support system provides empirically grounded solutions for aligning subnational climate actions with global mitigation targets. Full article

Background and Objectives: Central sensitization (CS) has been identified as a significant factor influencing persistent pain and dissatisfaction following total knee arthroplasty (TKA). However, its effect on unicompartmental knee arthroplasty (UKA) remains largely unexplored. Unlike TKA, UKA preserves most native knee structures, with less bone cut, leading to different postoperative pain mechanisms. Nevertheless, the revision rate for unexplained pain following UKA is higher than after TKA. This study investigates the influence of preoperative CS on pain and dissatisfaction after UKA. Materials and Methods: This retrospective cohort study included 121 patients who underwent primary UKA for medial compartment osteoarthritis of the knee. Patients were screened for CS preoperatively using the Central Sensitization Inventory (CSI) and categorized into a CS group (CSI ≥ 40; n = 26) and a non-CS group (CSI < 40; n = 95). Clinical outcomes, including the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Forgotten Joint Score (FJS), and patient satisfaction, were assessed at the 2-year postoperative follow-up visit. A multivariate regression analysis was used to determine the risk factors for postoperative dissatisfaction. Results: The CS group reported significantly worse postoperative WOMAC pain, function, and total scores than the non-CS group (all p < 0.05). FJS was also significantly worse in the CS group than in the non-CS group (64.4 vs. 72.7, respectively, p = 0.005). Patient satisfaction was significantly lower in the CS group than in the non-CS group (65.4% vs. 95.8%, respectively, p < 0.001). The multivariate logistic regression analysis demonstrated that patients with a CSI score ≥ 40 had an 11.349-fold increased likelihood of dissatisfaction after UKA (95% CI: 2.315–55.626, p = 0.003). Conclusions: This study underscores the importance of recognizing CS as a critical determinant of postoperative pain and functional recovery following UKA. Patients with high CSI scores experience greater pain, increased joint awareness, and overall poorer satisfaction despite technically successful surgeries. Full article

Sarcoidosis is a rare multisystem inflammatory disease characterized by non-necrotizing granulomas, typically affecting the lungs, lymph nodes, skin, and bones. Due to its extreme clinical heterogeneity, diagnosis remains challenging. Within the skeletal system, the thoracic spine, ankles, and knees are the most commonly involved joints. We report a rare case of non-articular osseous sarcoidosis with progressive pulmonary lesions and persistently normal inflammatory biomarkers (ACE, CRP, ESR, IL-2, and TNF-α) that required differentiation from metastatic bone tumors and tuberculosis. Prior to presentation at our hospital, the patient did not respond to six months of anti-tuberculosis treatment and one month of systemic glucocorticoid therapy in three other hospitals. Based on lung and bone biopsies, she was finally diagnosed as having active sarcoidosis in our hospital. Despite 3 months of prednisone, pulmonary consolidation and bone lesions persisted until methotrexate was added. This case highlights the preference of combined glucocorticoid and methotrexate therapy for sarcoidosis with atypical osseous involvement and normal biomarkers, underscoring the urgent need for novel diagnostic tools to mitigate misdiagnosis. Full article

Human biomonitoring (HBM) is a critical scientific tool for assessing human exposure by quantifying chemicals and their metabolites in biological specimens such as blood and urine. This approach provides a comprehensive and accurate evaluation of internal exposures from diverse sources and exposure routes. In Ireland, establishing a national HBM programme requires a systematic chemical prioritisation process that aligns global frameworks with local public perceptions. This study integrates insights from international initiatives such as the European Joint Programme Human Biomonitoring for Europe (HBM4EU) and the Partnership for the Assessment of Risks from Chemicals (PARC)—along with HBM programmes from EU countries (Germany, France, Belgium, Norway, Slovenia, Czech Republic, and Sweden) and non-EU countries (US, Canada, South Korea, China, and New Zealand). In addition, a national survey was conducted to capture the perceptions of people in Ireland regarding chemicals of concern to develop a comprehensive priority list of chemicals and biomarkers. The broader chemical groups identified include heavy metals (lead, cadmium, mercury, arsenic, and chromium VI), plasticisers (phthalates), bisphenols, pesticides, flame retardants, PFASs (per- and polyfluoroalkyl substances), PAHs (polycyclic aromatic hydrocarbons), POPs (persistent organic compounds), VOCs (volatile organic compounds), and UV (ultraviolet) filters. This integrated, participatory approach provides a roadmap for a robust, adaptable chemical list that supports evidence-based policy decisions in HBM in Ireland and enhances public health outcomes. Full article

Engaging with social robots has been recognized as potentially beneficial for children facing social communication challenges, especially in developing skills such as joint attention. This systematic review examines the effectiveness of robot-mediated learning in enhancing children’s cognitive abilities, emotional resilience, and self-confidence, based on an analysis of research conducted between 2018 and 2025. While social robots show promise in delivering personalized and engaging educational experiences, challenges such as emotional dependence, accessibility, and ethical concerns persist. The review concludes that further research, particularly longitudinal studies and cross-cultural comparisons, is necessary to fully understand the long-term impacts of robot-mediated learning on children’s cognitive and social development. It explores the application of robotic technology in relation to children’s educational progress across different age groups. The review includes studies of various types, such as experimental, non-experimental, and research articles, covering diverse research fields, robots, contexts of use, target audiences, and outcomes. The findings highlight the growing potential of social robots to support children’s cognitive development by providing interactive and stimulating learning experiences. This review offers valuable insights for researchers and designers aiming to improve robotic interventions and tailor them to meet diverse developmental needs and educational settings. Full article

Osteoarthritis (OA) is a chronic joint disease marked by synovial inflammation, cartilage degradation, and persistent pain. Although Netrin-4 (NTN4) has been implicated in pain modulation in rheumatoid arthritis (RA), its role in OA pain remains less understood. Previous research has documented that NTN4 promotes axonal growth in rodent-derived neurons; however, its effects on human sensory neurons are yet to be fully explored. NTN4 also plays a multifactorial role in various non-neuronal cells, such as endothelial cells, tumor cells, and stromal cells. Nevertheless, its specific impact on synovial fibroblasts, which are key components of the synovium and have been linked to OA pain, is still unclear. This study examined the correlation between NTN4 expression levels and pain severity in OA, specifically investigating its effects on human iPSC-derived sensory neurons (iPSC-SNs) and synovial fibroblasts from OA patients. Our findings indicate a positive correlation between synovial NTN4 expression and pain severity. Recombinant human Netrin-4 (rh-NTN4) was also shown to enhance neurite outgrowth in human iPSC-SNs, suggesting a potential role in neuronal sensitization. Additionally, rh-NTN4 stimulated the production of pro-inflammatory cytokines (IL-6, IL-8) and chemokines (CXCL1, CXCL6, CXCL8) in synovium-derived fibroblastic cells, implicating it in synovial inflammation. Collectively, these results suggest that NTN4 may contribute to KOA pathology by promoting synovial inflammation and potentially sensitizing sensory neurons, thereby influencing the mechanisms of underlying pain. Full article

Background: Femoroacetabular Impingement Syndrome (FAIS) is a musculoskeletal disorder characterized by hip pain, reduced range of motion (ROM), and functional impairment, particularly in young and physically active individuals. While surgery is generally not performed in individuals under 18 due to skeletal immaturity, it remains a standard treatment option for adults presenting with persistent symptoms and functional limitations. However, the overall effectiveness of physiotherapy-based interventions remains unclear. This review aimed to evaluate the effectiveness of conservative rehabilitation strategies for FAIS, assessing their impact on pain management, functional improvement, and quality-of-life outcomes, rather than directly comparing them to surgical interventions. Methods: This scoping review was conducted following the Joanna Briggs Institute (JBI) framework and PRISMA-ScR guidelines. A systematic literature search was performed in PubMed, Cochrane CENTRAL, Scopus, PEDro, and Web of Science. Studies were included if they examined conservative rehabilitation for FAIS, assessing outcomes such as pain reduction, functional improvement, range of motion (ROM), muscle strength, and quality of life. Data were extracted and synthesized narratively. Results: Both conservative rehabilitation and surgical intervention resulted in significant improvements in pain, function, and quality of life. Exercise-based physiotherapy, particularly programs incorporating core stability, progressive strengthening, and neuromuscular training, demonstrated positive outcomes. Surgery provided faster pain relief, ROM improvements, and earlier functional gains, particularly in activities requiring hip flexion. Given the variability in outcome measures, including pain, function, and quality of life, the interpretation of results must consider differences in treatment protocols across studies. Conclusions: Conservative rehabilitation should be considered a first-line treatment for Femoroacetabular Impingement Syndrome (FAIS), as it provides significant improvements in pain relief, function, and quality of life while mitigating the risks associated with surgery. Exercise-based physiotherapy, particularly programs incorporating core stabilization, progressive strengthening, and neuromuscular training, has demonstrated positive clinical outcomes. Although surgery may offer faster symptom relief and greater short-term functional gains, long-term differences between surgical and conservative management appear minimal in selected patient populations. Structured physiotherapy interventions should be prioritized before surgical consideration, except in cases where symptoms persist despite adequate rehabilitation. Future research should aim to establish standardized rehabilitation protocols, define optimal intervention parameters, and identify patient subgroups most likely to benefit from conservative management. Additionally, longitudinal studies with larger sample sizes are needed to clarify the long-term effects of non-surgical treatments on joint health and functional outcomes. Full article

Osteoarthritis (OA) is a chronic and debilitating joint disease characterized by progressive cartilage degeneration for which no definitive cure exists. Conventional management approaches often rely on fragmented and poorly coordinated pharmacological and non-pharmacological interventions that are inconsistently applied throughout the disease course. Persistent controversies regarding the clinical efficacy of chondroprotective agents, frequently highlighted by pharmacovigilance agencies, underscore the need for a structured evidence-based approach. Emerging evidence suggests that synchronizing pharmacotherapy and exercise regimens with circadian biology may optimize therapeutic outcomes by addressing early pathological processes, including low-grade inflammation, oxidative stress, and matrix degradation. Recognizing the influence of the chondrocyte clock on these processes, this study proposes a ‘prototype’ for a novel framework that leverages the circadian rhythm-aligned administration of traditional chondroprotective agents along with tailored, accessible exercise protocols to mitigate cartilage breakdown and support joint function. In addition, this model-based framework emphasizes the interdependence between cartilage chronobiology and time-of-day-dependent responses to exercise, where strategically timed joint activity enhances nutrient and waste exchange, mitigates mitochondrial dysfunction, supports cellular metabolism, and promotes tissue maintenance, whereas nighttime rest promotes cartilage rehydration and repair. This time-sensitive, comprehensive approach aims to slow OA progression, reduce structural damage, and delay invasive procedures, particularly in weight-bearing joints such as the knee and hip. However, significant challenges remain, including inter-individual variability in circadian rhythms, a lack of reliable biomarkers for pharmacotherapeutic monitoring, and limited clinical evidence supporting chronoexercise protocols. Future large-scale, longitudinal trials are critical to evaluate the efficacy and scalability of this rational integrative strategy, paving the way for a new era in OA management. Full article