Search Results (253)

Paediatric osteoarticular infections (OAIs) encompass a heterogeneous group of musculoskeletal infections associated with acute septic complications, prolonged morbidity and potentially long-term sequelae. Over the past two decades, advances in microbiological diagnostics—particularly nucleic acid amplification assays—have refined the aetiological understanding of OAIs and started a new therapeutic debate regarding the most appropriate routes of antibiotic administration. Clinicians now evaluate which children can be treated safely using oral antibiotics from the outset (oral-first), which require an initial intravenous (IV) phase before a step-down to oral therapy, and which will need IV therapy all along their care pathway. Treatment debates are particularly relevant in contexts involving constrained healthcare resources and limited hospital bed availability. This narrative review summarises the essential prerequisites for prescribing oral antibiotic therapy for paediatric OAIs and proposes a pharmacokinetic/pharmacodynamic (PK/PD) framework for guiding clinical decision-making. Key considerations include: pathogen identification and resistance profiling; contemporary bacteriological epidemiology; the comparative effectiveness of IV versus oral therapy; the availability of active oral antibiotics and their penetration into bone and joint compartments; achieving adequate systemic exposure and hitting PK/PD targets after oral administration; and the clinical limitations of oral antibiotic therapy, including patient selection criteria. We argue that oral-first and early-switch strategies are best framed as structured selection processes that integrate clinical severity and source control, pathogen/minimal inhibitory concentration constraints, the feasibility of attaining PK/PD targets orally and the reliability of follow-up. No single strategy should be seen as a universal default strategy. Full article

Schistosomiasis japonicum transmission in Indonesia has declined substantially over recent decades, placing it in the last miles of elimination in the Western Pacific Region. As programmes transition from control to interruption of transmission, surveillance systems must be capable of detecting residual transmission. This study synthesised routine epidemiological data from 2015 to 2025 to assess Indonesia’s readiness for elimination and to identify key surveillance gaps in near-elimination settings. Descriptive quantitative analysis was conducted using national surveillance data from two endemic districts in Central Sulawesi, complemented by programme reports on mass drug administration, human diagnosis, animal reservoir surveillance, and snail surveys. Results showed that while prevalence in humans has remained low and responsive to mass drug administration, transmission persists through infected animal reservoirs and intermediate snail hosts. Surveillance performance is constrained by limited diagnostic capacity, inconsistent snail survey coverage, fragmented paper-based reporting systems, and weak integration across human, animal, and environmental sectors. These findings indicated that low prevalence in humans alone is insufficient to demonstrate interruption of transmission, particularly in zoonotic schistosomiasis. In conclusion, Indonesia’s experience highlights the need to strengthen near-elimination surveillance through sensitive diagnostics, integrated One Health approaches, and digitally enabled data systems to sustain elimination and support future verification of schistosomiasis transmission interruption. Full article

The Earth system model ICON (ICOsahedral Nonhydrostatic general circulation) is a flexible framework that can be configured and tuned for various applications such as weather forecasting, simulations of aerosols and trace gases, and climate modelling. The numerical weather prediction component ICON is used in limited area mode (ICON-LAM) in Romania to obtain realistic weather simulations that support operational forecasting activities. The sensitivity of ICON-LAM is preliminarily evaluated for the geographical area of Romania. Numerical simulations using two parameterization schemes for radiation processes, two convection settings and different values for the laminar resistance of heat transfer from the surface to the air are evaluated against a control run employed for operational forecasts at the National Meteorological Administration. The validation is performed focusing on the precipitation field and surface continuous parameters. All configurations were integrated for a short period in summer when forecasted precipitation was strongly overestimated. Further on, selected configurations were evaluated for winter cases. The experiment with the shallow convection only, the ecRad radiation parameterization, and the laminar heat value 10 emerged as the best fit for Romania. This configuration (considered optimal) was evaluated alongside the operational control run for August 2022. Overall results indicate the selected optimal configuration generally outperforms the control run both with regard to precipitation and in forecasting surface parameters. This experiment has been adapted and implemented in operational workflow. Full article

Enteropathogenic Escherichia coli (EPEC) disrupts intestinal barrier integrity, induces inflammation, and alters gut microbial balance, leading to diarrhea and growth impairment. Probiotics are considered promising alternatives to antibiotics for managing enteric infections, yet the functional properties and underlying mechanisms of feline-derived strains remain unclear. This study evaluated the protective effects of Ligilactobacillus (L.) agilis ZY25 and L. salivarius ZY35, isolated from healthy cats, against EPEC-induced intestinal injury in C57BL/6 mice, with a focus on barrier function, immune modulation, and microbial homeostasis. In this 21-day experiment, 48 mice were assigned to six groups (n = 8/group): control, EPEC model (MOD), chlortetracycline treatment (CTC), probiotic treatment (PRO-T; post-infection only), probiotic pre-treatment (PRO-P; pre-infection only), and continuous probiotic supplementation (PRO; pre- and post-infection). EPEC challenge (0.2 mL; 1 × 10⁹ CFU/mL) was performed daily during experimental days 8–14. EPEC challenge resulted in weight loss (p < 0.05), increased (p < 0.05) diarrhea incidence, elevated (p < 0.05) serum D-lactate, diamine oxidase, and lipopolysaccharide levels, impaired intestinal morphology, immune imbalance, and microbial dysbiosis. Probiotic administration alleviated these alterations, as evidenced by restored intestinal morphology, reduced serum markers of barrier permeability (D-lactate, DAO, LPS), enhanced systemic immunoglobulins (IgA, IgG, IgM), a balanced cytokine profile (increased IL-4, IL-10; decreased TNF-α, IL-6, IL-1β, IFN-γ, CRP), and modulation of the gut microbiota (enrichment of beneficial taxa such as Lachnospiraceae_NK4A136_group and suppression of pro-inflammatory Desulfovibrio). The continuous supplementation regimen (PRO) produced the most consistent improvements among the three intervention strategies tested. These findings suggest that feline-derived probiotics mitigate EPEC-induced intestinal dysfunction, accompanied by improved barrier-related indices, immune rebalancing, and microbial stabilization, thereby providing proof-of-concept evidence for their further evaluation in feline gastrointestinal health. Full article

Optimizing drug administration remains a central challenge in the development of modern therapies, especially in the context of conditions that require spatiotemporal control of active substance release. In this context, hydrogels have been intensively investigated as polymeric platforms for drug delivery, through their three-dimensional hydrophilic structure, tunable properties, and compatibility with biological environments. This analysis presents an integrated approach to hydrogels used in drug administration, addressing the physicochemical fundamentals, the constitutive polymeric materials, and the mechanisms of response to relevant physiological stimuli. Recent experimental studies have been discussed, which highlight the use of hydrogels based on natural, synthetic, and hybrid polymers for controlled and targeted release, in correlation with various administration routes, including oral, injectable, transmucosal, and topical ones. Advanced functionalization strategies that allow adaptive responses to pH, temperature, glucose, enzymes, and reactive oxygen species are also analyzed. Furthermore, emerging directions integrating hydrogels with biosensors, microdevices, and wireless communication systems for real-time monitoring and on-demand release are highlighted. Overall, the analysis emphasizes the role of smart hydrogels as multifunctional platforms for complex therapeutic strategies while also underlining the current challenges associated with clinical translation and long-term performance. Full article

Modern enterprise information systems must simultaneously support complex organizational structures, ensure robust security, and remain scalable and maintainable over time. Traditional Role-Based Access Control (RBAC) models, while effective for permission management, operate primarily as post-design security layers and do not provide a unified methodology for structuring system architecture. This paper introduces the Zoned Role-Based (ZRB) model, a mathematically formalized and comprehensive framework that integrates organizational modeling, system design, implementation, access control, and long-term maintenance. ZRB models an organization as a hierarchy of zones, each containing its own roles, applications, operations, and users, forming a recursive Zone Tree that directly mirrors real organizational semantics. Through formally defined role hierarchies, zone-scoped permission sets, and inter-zone inheritance mappings, ZRB provides a context-aware permission calculus that unifies authentication and authorization across all zones. The paper presents the theoretical foundations of ZRB, a multi-phase engineering methodology for constructing integrated enterprise systems, and a complete implementation architecture with permission inference, navigation design, administrative subsystems, and deployment models. Primary validation and evaluations across several developed systems demonstrate significant improvements in permission accuracy, administrative efficiency, scalability, and maintainability. ZRB thus offers a rigorously defined and practically validated framework for building secure, scalable, and organizationally aligned enterprise information systems. Full article

The escalating complexity of airport surface operations challenges traditional risk quantification methods. Conventional linear models often fail to capture the non-linear interactions within sociotechnical systems. While hybrid System-Theoretic Process Analysis (STPA) and Bayesian Network (BN) models provide an alternative, existing integrations are frequently constrained by ad hoc structural translations and rare-event data sparsity. To address these methodological limitations, this study proposes an enhanced STPA-BN framework. A formalized mapping mechanism (M1–M4) translates qualitative STPA scenarios into a BN topology to quantify non-linear causal dependencies across environmental precursors, operator cognitive states, unsafe control actions, and systemic hazards. Parameterization is achieved via a logic-guided strategy, fusing historical incident data mining with deterministic physical constraints to correct rare-event probabilities. The framework is validated through a reconstruction of the 2023 Austin–Bergstrom runway incursion incident. Results indicate that under low visibility and degraded surveillance, incursion probability escalates to 86%. Sensitivity analysis reveals that while restoring surveillance infrastructure reduces collision risk by ~13%, communication compliance improvements prove insufficient in sensory-deprived environments. These findings quantitatively demonstrate that administrative controls cannot substitute for robust engineering safeguards in complex operations. Full article

School classrooms represent complex, interconnected systems where indoor environmental quality critically influences student health, cognitive performance, and educational equity. Yet traditional approaches operate in disciplinary silos, creating systemic failures in design, operation, and maintenance. This narrative review adopts a systems engineering framework to demonstrate how integrated interventions—spanning policy, design, technology, and operations—create resilient, sustainable, and healthy classroom climates. Amid escalating climate change impacts (rising temperatures, heatwaves, wildfires) and emerging threats (airborne pathogens, urban pollution), reactive measures like school closures prove pedagogically counterproductive. This review synthesizes evidence on natural, mechanical, and mixed-mode ventilation systems optimized through advanced control strategies, smart technologies, and health-centred policies. Key findings reveal that synergistic integration of Policy, Management, Construction, Operation, and Smart Technologies, in a systems engineering framework, outperforms singular strategies. Critical interventions include hybrid ventilation coupled with layered defences (HEPA filtration, UVGI), AI-driven adaptive controls using IoT sensors and Model Predictive Control to optimize energy while managing pollutant concentrations, and mandatory IAQ standards rooted in stakeholder education. By framing classrooms as interconnected engineering systems, this work provides actionable insights for architects, engineers, policymakers, and administrators, positioning future school design toward resilience, sustainability, and human-centred health outcomes. Full article

Biomarker-based guided delivery of drugs is an emerging paradigm of precision medicine in which targeted therapeutic intervention is administered on the basis of certain biological markers in order to achieve maximal dosing, targeting, and time optimization. By utilizing quantifiable physiological or molecular signatures like the expression of transporters, enzymatic activities, metabolite levels, or disease-specific markers to tie in the correlation of drug disposition, these systems provide individualized intervention with optimized efficacy and safety. Oral administration of drugs is still the best route in patient compliance; however, several drugs are handicapped by suboptimal bioavailability secondary to poor solubility, limited permeability, efflux transporter participation, and enzymatic first-pass degradation. These result in variable therapeutic results in patient populations. Biomarker guidance in oral drug delivery provides a potent strategy for overcoming such challenges through site-specific release, real-time dose optimization, and adjustment of absorption pathways. Recent developments include pH-controlled formulations for gut-specific targeting, enzyme-activated nanocarriers, glucose-starved responsive devices for metabolic disease, and biomarker-driven transporters for permeability enhancement. Preclinical and early-phase clinical studies hold promising prospects for applications in oncology, infectious disease, inflammatory bowel disease, and metabolic disease. While promising momentum exists, transition to routine use in the clinic awaits rigorous biomarker validation, scalability in manufacture, and regulations harmonization. On the horizon, the integration of biomarker-guided oral drug delivery with nanotechnology, artificial intelligence, machine learning, and wearable biosensors holds promise for revolutionizing oral therapy into very personalized, responsive, and efficient treatment methods. Full article

Extremely preterm infants often require prolonged respiratory support due to lung immaturity and inflammation, placing them at high risk of lung injury and development of bronchopulmonary dysplasia (BPD). In many of these infants, systemic postnatal corticosteroids are used to reduce lung inflammation, facilitate mechanical ventilation (MV) weaning and extubation, and improve short-term pulmonary outcomes. However, despite decades of clinical use, substantial variation persists in timing, choice of agent and dosing. These inconsistencies reflect a lack of strong evidence and a limited understanding of the systemic and organ-specific effects of therapy for a highly heterogenous population usually exposed to this medication. This narrative review addresses these gaps by integrating current knowledge of the inflammatory and respiratory effects of postnatal corticosteroids in extremely preterm infants. We explore how corticosteroids modulate pulmonary inflammation, their effects on lung development, and how they affect key clinical outcomes such as extubation success and BPD severity. We also examine evolving approaches to corticosteroid administration and dosing, highlighting the importance of individualized strategies informed by developmental and disease-specific considerations. Comparative data from randomized controlled trials are reviewed, including the efficacy and side-effect profiles of commonly used regimens. Current evidence supports judicious use of late low-dose dexamethasone, while early prophylaxis with inhaled or intratracheal steroids remains experimental and is not routinely advised. In line with a physiology-driven approach, we also discuss emerging domain-specific monitoring tools that may enhance patient selection and optimize timing of intervention. By synthesizing mechanistic insights with clinical evidence, this review supports a more nuanced, individualized approach to postnatal corticosteroid therapy in extremely preterm infants, balancing therapeutic benefits with potential systemic trade-offs. Full article

In modern smart city and IoT environments, diverse sensors for traffic management, environmental monitoring, and energy systems continuously generate large volumes of heterogeneous events in real time. Efficiently processing these multi-source event streams requires a scalable and responsive computing architecture. However, many Kubernetes-hosted serverless Function-as-a-Service (FaaS) deployments operate within a single administrative cluster and provide limited user-level control over dynamic multicluster placement based on heterogeneous event types and real-time resource conditions. To address these limitations, this study proposes a generalized event-driven FaaS architecture capable of efficiently processing multi-event streams across multicluster environments. The proposed architecture was implemented on Kubernetes-based testbed by integrating a multicluster orchestrator, an event-processing engine, a workflow execution layer, and a serverless platform. Evaluation using a smart city-inspired scenario demonstrates that the proposed platform provides improved load distribution characteristics and maintains higher workflow success rates under increasing workloads compared to the evaluated single-cluster baseline. This research provides a scalable design approach for serverless platforms that can meet real-time event processing requirements in IoT and smart city applications. Full article

This paper proposes a novel method for synthesizing a discrete optimization algorithm based on the moth–flame paradigm for application to the architecture of deceptive systems incorporating decoys and traps. Unlike existing approaches that primarily rely on continuous search spaces or static deception strategies, the proposed method enables the formation of a discrete search space with a coordinate-based representation of deception objects and system states. A spiral search trajectory is synthesized by modeling the dynamic interaction between moths and flames, which allows the algorithm to balance exploration and exploitation effectively and to mitigate premature convergence to local optima. The problem of selecting subsequent operational steps of a deceptive system, which includes the control and reconfiguration of decoys and traps in response to detected events, is formulated as a discrete optimization problem. The objective of this optimization is to increase the effectiveness of cyberattack and malware detection in corporate network environments. The decision variables include the sequence of deception actions, process models, and architectural characteristics of the system, while the constraints are defined by the operational conditions, resource limitations, and structural features of corporate networks. The proposed method supports the identification of an optimal sequence of deception actions under dynamically changing conditions and provides mechanisms for operational adaptation to attacker behavior in real time. This adaptability enables the creation of deceptive systems capable of long-term autonomous operation without continuous administrative intervention, while simultaneously increasing their resistance to adversarial reconnaissance and reverse engineering of their operational principles. The experimental results confirm the feasibility and effectiveness of the proposed approach and demonstrate the potential of integrating population-based optimization algorithms into deceptive system architectures. Comparative analysis shows that the proposed method outperforms its closest competitor, the genetic algorithm, achieving an improvement of 4.82% in terms of the objective function value. Future research directions include deeper integration of population-based optimization methods into decoy-and-trap architectures and the development of a comprehensive framework for organizing their operation in accordance with the proposed conceptual model. Overall, the results contribute to enhancing the cyber-resilience of corporate networks through intelligent, adaptive, and autonomous systems for countering modern cyberattacks and malware. Full article

A thunderstorm is a weather system that can trigger severe natural disasters, characterized by sudden onset, short duration, and significant damage. Accurate forecasting of thunderstorms has long been a challenging task. Data-driven artificial intelligence (AI) technologies have provided new solutions, yet AI-driven thunderstorm forecasting still lacks high-quality thunderstorm training datasets. Leveraging lightning data from the China Meteorological Administration’s Advanced Direction and Time-of-Arrival Detecting (ADTD) network and the three-dimensional Very Low Frequency/Low Frequency (VLF/LF) lightning location data of the Institute of Electrical Engineering, Chinese Academy of Sciences, we have constructed an AI training dataset for thunderstorms over China (AITDTS) through four sequential procedures: rigorous data quality control, multi-source integration, thunderstorm-prone area labeling, and feature extraction. The AITDTS encompasses 85,071 thunderstorm events and 3,973,171 corresponding gridded samples at 10 min temporal resolution and 1 km × 1 km spatial resolution across China during 2016–2023. Each sample includes location labels, 38 radar-derived physical parameters with a 10-min temporal resolution and 62 environmental parameters with an hourly temporal resolution. We further quantified predictor information gain for thunderstorm forecasting: radar echo top/base heights, composite reflectivity, vertical integrated liquid water content and reflectivity at 10 km showed high information gain. Atmospheric instability, dynamic uplifting, moisture conditions and vertical wind shear at 1 km exhibited moderate information gain. The AITDTS can be directly applied to training and evaluation of AI-driven forecasting models, offering critical data for thunderstorm nowcasting. Full article

Insulin therapy remains essential for the management of diabetes mellitus; however, conventional subcutaneous injection continues to impose significant physical, psychological, and behavioral barriers that negatively affect treatment adherence and metabolic outcomes. Injection-related pain, fear of needles, local tissue complications, and psychological insulin resistance contribute to delayed insulin initiation, inadequate dose titration, and suboptimal glycemic control worldwide. In response, alternative insulin delivery routes (including oral, pulmonary, nasal, and transdermal strategies) have been explored to reduce invasiveness and improve patient experience. Among these, transdermal insulin delivery has emerged as a particularly promising approach due to its potential to bypass gastrointestinal degradation, provide controlled absorption, and enhance patient acceptance. Recent advances in microneedle-based systems and needle-free jet injectors have enabled effective transdermal insulin administration by overcoming the skin barrier while minimizing pain and discomfort. This narrative review synthesizes current evidence on insulin delivery technologies with a specific focus on transdermal and needle-free systems. We discuss the biological and physicochemical challenges of insulin transport, the mechanisms underlying emerging delivery platforms, and clinical evidence regarding metabolic efficacy, glycemic variability, and patient-reported outcomes. The integration of these technologies with continuous glucose monitoring is also explored. Finally, we address translational challenges and future perspectives, highlighting the role of needle-free insulin delivery as a patient-centered strategy to improve adherence and metabolic control in diabetes care. Full article

Diabetes mellitus (DM) is a chronic metabolic disorder characterized by persistent systemic inflammation, which contributes to progressive multi-organ dysfunction, particularly in metabolically active tissues such as the liver and kidneys. Bee bread (Perga), a fermented bee pollen product rich in bioactive compounds, has been reported to exert anti-inflammatory and organ-protective effects; however, its tissue-specific influence on inflammatory responses under diabetic conditions remains incompletely defined. Thirty-two male Wistar Albino rats were randomly assigned to four groups: Control, DM, DM + Perga, and Perga. Diabetes was induced by streptozotocin (STZ; 55 mg/kg, i.p.). Perga was administered orally at a dose of 0.5 g/kg/day for 28 days. Pro-inflammatory cytokine levels (CRP, TNF-α, IL-1β, and IL-6) were quantified in liver and kidney tissues using ELISA. Histopathological alterations were evaluated by hematoxylin and eosin staining. DM significantly increased the IL-1β, IL-6, and CRP levels in hepatic tissue and elevated TNF-α, IL-1β, IL-6, and CRP levels in renal tissue. Perga administration attenuated these inflammatory responses, particularly reducing IL-1β and IL-6 levels in the liver and all measured cytokines in the kidney. Histopathological analyses revealed hepatocyte degeneration and necrosis, sinusoidal dilatation, tubular epithelial degeneration, and glomerular damage in diabetic rats, whereas Perga treatment partially improved hepatic alterations and improved renal structural integrity. These findings indicate that Perga exerts tissue-specific anti-inflammatory and protective effects in experimental diabetes, with a more pronounced impact on renal inflammation than on hepatic responses. Although its effects on hepatic TNF-α and CRP levels were limited, Perga may act as a natural modulator of cytokine-mediated inflammatory processes. Further studies are warranted to elucidate the underlying molecular mechanisms. Full article