Search Results (9,166)

Background/Objectives: Although antimicrobial resistance (AMR) is recognized as a critical global health threat across human, animal, and environmental domains, evidence from AMR economic evaluations remains limited. This study systematically reviewed available studies, emphasizing existing evidence and reported limitations in AMR-related economic evaluations. Methods: A comprehensive review of peer-reviewed empirical studies was conducted, including publications up to July 2023 without temporal restrictions, but limited to English-language articles. Literature searches were undertaken in PubMed and Cochrane using a search strategy centered on the terms “economic evaluations” and “antimicrobial resistance.” Screening and data extraction were performed by two reviewers independently, with disagreements resolved through consensus or consultation with a third reviewer. Findings were synthesized narratively. Results: Of the 3682 records screened, 93 studies were included. Evidence gaps were identified across income and geographic regions, particularly in low- and middle-income countries (LMICs) and the African, Southeast Asian, and Eastern Mediterranean regions. Studies were comparatively more numerous in high-income countries (HICs) and the European and Americas regions. Substantial gaps also existed in one health approach and community-based evaluations. Nine major study limitations were identified, with many interlinked. The most frequent issues included limited generalizability primarily due to inadequate sampling approaches (n = 16), and single-center studies (n = 11), alongside errors in cost estimation (n = 4), and lack of consideration for essential features or information (n = 3). Conclusions: The review highlights persistent evidence gaps and recurring methodological shortcomings in AMR economic evaluations. Addressing these limitations, particularly in LMICs, will strengthen the evidence base and better inform policy implementation to combat AMR effectively. Full article

Gene regulatory networks (GRN) govern cellular identity and function through precise control of gene transcription. Single-cell technologies have provided powerful means to dissect regulatory mechanisms within specific cellular states. However, existing computational approaches for modeling single-cell RNA sequencing (scRNA-seq) data often infer local regulatory interactions independently, which limits their ability to resolve regulatory mechanisms from a global perspective. Here, we propose a deep learning framework (Planet) based on diffusion models for constructing cell-specific GRN, thereby providing a systems-level view of how protein regulators orchestrate transcriptional programs. Planet jointly optimizes local network structures in conjunction with gene expression profiles, thereby enhancing the structural consistency of the resulting networks at the global level. Specifically, Planet decomposes GRN generation into a series of Markovian evolution steps and introduces a Triple Hybrid-Attention Transformer to capture long-range regulatory dependencies across diffusion time-steps. Benchmarks on multiple scRNA-seq datasets demonstrate that Planet achieves competitive performance against state-of-the-art methods and yields only a slight improvement over DigNet under comparable conditions. Compared with conventional diffusion models that rely on fixed sampling schedules, Planet employs a fast-sampling strategy that accelerates inference with only minimal accuracy trade-off. When applied to mouse-lung Cd8⁺Gzmk⁺ T cells, Planet successfully reconstructs a cell-type-specific GRN, recovers both established and previously uncharacterized regulators, and delineates the dynamic immunoregulatory changes that accompany ageing. Overall, Planet provides a practical framework for constructing cell-specific GRNs with improved global consistency, offering a complementary perspective to existing methods and new insights into regulatory dynamics in health and disease. Full article

This study addresses the limitations of traditional coal calorific value prediction models, which primarily rely on linear regression and single-source proximate analysis data. Based on 465 Chinese coal samples and integrating proximate analysis, ultimate analysis, and constructed derived indicators (combustible content—CC, carbon–hydrogen index—CHI, carbon in combustibles—CIC), a nonlinear modeling method combining mean impact value (MIV) feature selection and support vector regression (SVR) is proposed. The results show that the Pearson correlation coefficients between the derived indicators and net calorific value (NCV) all exceed 0.93, outperforming the original items. Using CC–CHI–CIC–FC_ad as characteristic variables, the established SVR model achieved a mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R²) of 1.838%, 0.544 MJ/kg, and 0.962, respectively, with exceptionally high statistical significance (F = 1485.96, p < 0.001). The predictive accuracy of this model is significantly superior to traditional linear models, while the proposed linear model based on the derived indicators (R² > 0.900) can serve as an alternative for rapid estimation. This method effectively enhances the accuracy and robustness of coal calorific value prediction. Full article

The increasing complexity of the Edge–Cloud Continuum (ECC), driven by the rapid expansion of the Internet of Things (IoT) and data-intensive applications, necessitates implementing innovative methods for automated and efficient system management. In this context, recent studies focused on the utilization of self-* capabilities that can be used to enhance system autonomy and increase operational proactiveness. Separately, anomaly detection and adaptive sampling techniques have been explored to optimize data transmission and improve systems’ reliability. The integration of those techniques within a single, lightweight, and extendable self-optimization module is the main subject of this contribution. The module was designed to be well suited for distributed systems, composed of highly resource-constrained operational devices (e.g., wearable health monitors, IoT sensors in vehicles, etc.), where it can be utilized to self-adjust data monitoring and enhance the resilience of critical processes. The focus is put on the implementation of two core mechanisms, derived from the current state-of-the-art: (1) density-based anomaly detection in real-time resource utilization data streams, and (2) a dynamic adaptive sampling technique, which employs Probabilistic Exponential Weighted Moving Average. The performance of the proposed module was validated using both synthetic and real-world datasets, which included a sample collected from the target infrastructure. The main goal of the experiments was to showcase the effectiveness of the implemented techniques in different, close to real-life scenarios. Moreover, the results of the performed experiments were compared with other state-of-the-art algorithms in order to examine their advantages and inherent limitations. With the emphasis put on frugality and real-time operation, this contribution offers a novel perspective on resource-aware autonomic optimization for next-generation ECC. Full article

Background/Objectives: Medical nutrition therapy (MNT) is a crucial component of type 1 diabetes (T1D) management; however, the effectiveness of specific dietary approaches in adults remains unclear due to variations in study design, terminology, and reported outcomes. This scoping review summarizes evidence published between 2015 and 2025 on dietary interventions in adults with T1D, focusing on metabolic and psychosocial outcomes and adherence to international nutritional guidelines. Methods: We searched PubMed, Web of Science, Scopus, and Google Scholar, following the PRISMA-ScR recommendations, to identify observational studies, randomized clinical trials (RCTs), and guidelines involving adults (≥18 years) with T1D. Extracted data included metabolic outcomes (glycated hemoglobin A1c (HbA1c), glycemic variability (GV), insulin dose (ID), lipids, blood pressure, body weight, and others), as well as psychosocial indicators (i.e., quality of life, diabetes-related stress, and fear of hypoglycemia). Results: In total, 41 studies met the inclusion criteria, comprising 18 observational, 14 randomized, and 9 studies that evaluated psychosocial aspects. A low-carbohydrate diet (LCD) reduced HbA1c by 0.3–0.9% and total ID by approximately 15–20% without increasing the incidence of severe hypoglycemia. A low-fat vegan diet and structured carbohydrate counting (CC) programs also improved glycemic and lipid profiles. The Mediterranean diet (MedDiet) and plant-based diet mainly improved diet quality and well-being. The results showed an association between better metabolic control and lower carbohydrate (CHO) intake, as well as higher intakes of fiber and protein. In contrast, a Western diet and high intake of sweets were linked to poorer outcomes. Conclusions: Combining an LCD with education, CC, and modern diabetes technology provides the most consistent benefits for adults with type 1 diabetes (T1D adults). The MedDiet and plant-based diet support diet quality and psychosocial well-being, although current evidence remains limited, primarily due to small sample sizes and short follow-up periods. Full article

We propose a novel meta-learning enhanced 3D CNN-LSTM framework for durability prediction. The framework integrates 3D microstructural data from micro-CT scanning with environmental time-series data through a dual-branch architecture: a 3D CNN branch extracts spatial degradation patterns from volumetric data, while an LSTM network processes temporal environmental factors. To address data scarcity, we incorporate a prototypical network-based meta-learning module that learns class prototypes from limited support samples and generalizes predictions to new corrosion scenarios through distance-based probability estimation. Additionally, we develop a dynamic feature fusion mechanism that adaptively combines spatial, environmental, and mechanical features using trainable attention coefficients, enabling context-aware representation learning. Finally, an interface damage visualization component identifies critical degradation zones and propagation trajectories, providing interpretable engineering insights. Experimental validation on laboratory specimens demonstrates superior accuracy (74.6% in 1-shot scenarios) compared to conventional methods, particularly in aggressive corrosion environments where data scarcity typically hinders reliable prediction. The visualization system generates interpretable 3D damage maps with an average Intersection-over-Union of 0.78 compared to ground truth segmentations. This work establishes a unified computational framework bridging microstructure analysis with macroscopic durability assessment, offering practical value for infrastructure maintenance decision-making under uncertainty. The modular design facilitates extension to diverse interface types and environmental conditions. Full article

Background: Standard imaging in neurosurgery often fails to visualize infiltrative tumor regions that extend beyond contrast enhancement. Metabolic imaging using hyperpolarized 13C-MRI may offer new intraoperative insights into tumor biology. Objective: To systematically assess the clinical and technical evidence on hyperpolarized MRI for metabolic tumour characterization in patients with malignant brain tumors. Eligibility criteria: We included original human studies reporting on hyperpolarized 13C-MRI for perioperative and diagnostic use in brain tumor patients. Reviews, animal studies, and technical-only reports were excluded. Information sources: Searches were conducted in PubMed, Embase, and Web of Science on 26 December 2024. Risk of bias: Methodological quality was assessed using the QUADAS-2 tool. Synthesis of results: A qualitative synthesis was performed, and where feasible, random-effects meta-analysis was used to calculate standardized mean differences (SMDs) and heterogeneity statistics. Results: Three studies (n = 15 patients) met inclusion criteria. The bicarbonate-to-pyruvate ratio showed a significant difference between tumor and non-tumour brain (SMD = 1.34, p = 0.002), whereas pyruvate-to-lactate ratio (kPL) values showed minimal difference (SMD = 0.06, p = 0.730). Asmall effect was observed for kPL between tumor and normal-appearing white matter (SMD = –0.33). One study provided qualitative data only. Overall heterogeneity was high (I² = 69.4%). Limitations: Limitations include small sample sizes, heterogeneous methodologies, and limited availability of patient-level data. Interpretation: Hyperpolarized 13C-MRI shows metabolic differentiation between tumor and healthy tissue in certain parameters, especially bicarbonate metabolism. While promising, the technology requires further clinical validation before routine intraoperative application. Full article

Pavement condition assessment is a fundamental aspect of airport pavement management systems (APMS) for ensuring safe and efficient airport operations. However, conventional methods, which rely on extensive on-site inspections and complex calculations, are often time-consuming and resource-intensive. In response, Industry 4.0 has introduced machine learning (ML) as a powerful tool to streamline these processes. This study explores five ML algorithms (Linear Regression (LR), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM)) for predicting the Pavement Condition Index (PCI). Using basic alphanumeric distress data from three international airports, this study predicts both numerical PCI values (on a 0–100 scale) and categorical PCI values (3 and 7 condition classes). To address data imbalance, random oversampling (SMOTE—Synthetic Minority Oversampling Technique) and undersampling (RUS) were used. This study fills a critical knowledge gap by identifying the most effective algorithms for both numerical and categorical PCI determination, with a particular focus on validating class-based predictions using relatively small data samples. The results demonstrate that ML algorithms, particularly Random Forest, are highly effective at predicting both the numerical and the three-class PCI for the original database. However, accurate prediction of the seven-class PCI required the application of oversampling techniques, indicating that a larger, more balanced database is necessary for this detailed classification. Using 10-fold cross-validation, the successful models achieved excellent performance, yielding Kappa statistics between 0.88 and 0.93, an error rate of less than 7.17%, and an area under the ROC curve greater than 0.93. The approach not only significantly reduces the complexity and time required for PCI calculation, but it also makes the technology accessible, enabling resource-limited airports and smaller management entities to adopt advanced pavement management practices. Full article

Contrast-enhanced ultrasound (CEUS) is increasingly applied in veterinary medicine as a safe, rapid, and non-invasive imaging technique for assessing renal disorders. Despite its expanding use, the literature on its application in feline renal trauma remains scarce. This retrospective study aimed to describe CEUS findings in cats with suspected traumatic renal injuries. Medical records were reviewed for cats that underwent both B-mode ultrasonography and CEUS, with findings confirmed by follow-up, surgery, or cytology. Three cats met the inclusion criteria. Two presented focal or multifocal renal lesions ranging from 10 to 20 mm in diameter, with heterogeneous echotexture, distortion of renal contours, and non-enhancing areas on CEUS consistent with hematomas or lacerations. The third cat showed a circumferential subcapsular halo (approximately 3–5 mm thick) with evidence of contrast leakage, compatible with limited active hemorrhage. CEUS appeared effective in identifying and characterizing renal injuries, offering valuable information to support clinical decision-making and guide both conservative and surgical management. Nevertheless, due to the limited sample size and the absence of quantitative data, these results should be considered preliminary. Further prospective studies are warranted to confirm the diagnostic performance and clinical utility of CEUS in feline renal trauma. Full article

Background/Objectives: Furosemide is frequently used in preterm neonates for respiratory and fluid management but has been linked to nephrocalcinosis (NC), a renal complication with unclear long-term impact. Clarifying this association is crucial for safe diuretic use. Methods: A focused literature review included observational studies published between 1982 and 2025 reporting NC incidence by renal ultrasound in preterm infants receiving furosemide. Data on sample size, gestational age, birth weight, NC prevalence, and furosemide dosing/duration were extracted. Results were synthesized descriptively. Results: Twenty-two studies with 1489 infants were included. NC prevalence ranged 6–83%, higher in infants <32 weeks’ gestation or <1500 g. Across studies, incidence clustered at 17–41% between 4 weeks and term-equivalent age. Cumulative furosemide doses were generally three- to fourfold higher in NC groups (10–19 mg/kg cumulative vs. ≤5 mg/kg cumulative, p < 0.001). A dose-dependent risk was noted, with odds ratios increasing above a cumulative dose of 10 mg/kg. Some studies found no significant dose–response, indicating variability and confounding factors. NC was detected during NICU stay or around term-equivalent age; ~60% resolved after discontinuation, while persistent cases were associated with prolonged exposure and renal dysfunction. A recent multicenter, dose-escalation randomized trial showed that carefully dosed furosemide (≤2 mg/kg/day for 28 days) did not increase NC risk, though electrolyte disturbances were more frequent. Conclusions: Evidence supports a dose-related association between furosemide and NC in preterm infants. When administered cautiously within defined limits, risk may be mitigated. Careful dosing, monitoring, and further studies are essential for safe use. Full article

This paper presents a data-driven decision-support framework for distribution system operations using logistic regression (LR) on the Voltage Margin Index (VMI). Treating VMI as the sole explanatory feature, the proposed two-stage workflow first fits an inferential LR model to establish statistical significance and perform valid statistical inference on the coefficients. Next, it trains a performance-optimized LR classifier with class-balanced sample weighting to produce calibrated violation probabilities. LR maps VMI to violation probability and analytically converts a calibrated probability threshold into an operator-ready VMI decision boundary. Applying 5-fold group cross-validation to 8816 node-level samples generated from a 22.9 kV Jeju Island model yields performance- and safety-oriented probability thresholds (θ_opt = 0.7891, θ_safe = 0.6880), which correspond to VMI decision boundaries VMI_DB,opt = 0.7893 and VMI_DB,safe = 0.8101. On an unseen 20% test set, the LR classifier achieves 99.94% accuracy (F₁ = 0.9977) under θ_opt and 100% recall under θ_safe. A random forest (RF) benchmark confirms comparable accuracy (=99.72%) but lacks analytical invertibility and transparency. This framework offers distribution system operators (DSOs) and virtual power plant (VPP) operators clear, evidence-based criteria for routine planning and risk-averse decision-making, and it can be applied directly to any distribution system with node-level voltage measurements and known regulation limits. Full article

Background/Aims: Parkinson’s disease (PD) is a progressive neurodegenerative disorder caused by the degeneration of dopaminergic neurons, leading to motor and non-motor symptoms that significantly impair quality of life (QoL). Oxidative stress (OS) and neuroinflammation play a key role in its progression. The ketogenic diet (KD) may have neuroprotective effects by reducing these factors through ketosis. The primary aim of this narrative review is to examine the impact of the ketogenic diet on the quality of life and symptomatology of patients with PD, evaluating its effects on motor and non-motor symptoms, as well as on certain metabolic parameters. Secondary aims included assessing the feasibility of and adherence to the diet, as well as its tolerability and safety. Methods: A search of PubMed, Scopus, Embase, CINAHL and Cochrane databases up to June 2025 was performed. Eligible studies included adults with PD following a KD regimen. Data were extracted regarding QoL outcomes, adverse events, and risk of bias included for synthesis. Results: A total of 152 patients were included across 6 studies. KD showed a small to moderate effect size on QoL improvements, particularly in non-motor domains such as fatigue and sleep quality. However, findings were inconsistent across studies. Risk of bias was rated moderate to high due to small sample sizes, heterogeneous methodologies, and lack of blinding. The most frequently reported adverse events were gastrointestinal disturbances (nausea, constipation), weight loss, and transient fatigue. Conclusions: Although preliminary evidence suggests a potential benefit of KD on QoL in PD patients, the small number of participants, short follow-up, and high heterogeneity significantly limit generalizability. Further large, controlled trials with rigorous methodology are warranted before relevant conclusion benefits can be drawn. Full article

This study investigates the mineralogical and physical properties of serpentinite from the Monteferrato area (Tuscany, Italy) to evaluate its potential use in Tuscany architectural restoration. The research addresses the need to identify replacement materials compatible with historic stones while preserving their original features. Representative specimens from the Bagnolo quarry were analysed through physical testing and a wide range of mineralogical and geochemical techniques, including polarised light microscopy, X-ray diffraction, electron probe micro-analysis, whole-rock chemistry, and fibre quantification. The results show a mineralogical composition dominated by serpentine-group minerals and magnetite, with physical properties generally consistent across samples. Measured capillary water absorption ranges from 3.27 to 5.27 g/m²·s^0.5, open porosity from 5.25% to 8.93%, apparent densities range from 2.49 to 2.56 g/cm³, and imbibition coefficient from 2.16% to 3.71%. Comparative analysis with serpentinite from historic sources (Figline di Prato quarry, Tuscany) and from monuments (Baptistery of San Giovanni, Florence) demonstrates close compositional and textural affinities, supporting the suitability of the rock from the studied quarry for restoration purposes in Tuscany monuments. However, chrysotile concentrations up to 14,153 mg/kg, exceeding Italian regulatory thresholds, represent a critical limitation. This not only requires the implementation of strict safety measures but also raises serious concerns regarding the practical feasibility of using this stone in conservation projects. More broadly, the presence of asbestiform minerals in serpentinites highlights a significant and often underestimated health risk associated with their extraction, processing, and use. Despite its importance, detailed fibre count data are rarely published or made publicly accessible, hindering both transparent risk assessment and informed decision-making. By integrating petrographic, mineralogical, and physical–mechanical characterisation with fibre quantification, this study not only assesses the technical suitability of Monteferrato serpentinites for restoration of Tuscan monuments but also contributes to a more responsible and evidence-based approach to their use, emphasising the urgent need for transparency and health protection in conservation practices. Full article

Background: Nursing interventions are important in improving patient outcomes, especially in acute care units where patients encounter severe and complicated health problems. However, multiple barriers can hinder the accurate assessment of the effectiveness of such interventions. Aim: The aim of this study was to explore the barriers to evaluating the impact of nursing interventions on patient outcomes in acute care settings. Methods: This study employed a qualitative exploratory research design. This study was carried out in the acute care departments of a governmental tertiary hospital in the Western Region, Saudi Arabia. A purposive sample of 20 nurses was considered. Data were collected using a semi-structured interview guide. Thematic analysis was employed for data analysis. Results: The thematic analysis results identified five major themes: a lack of a standardised evaluation tool, time constraints, resource limitations, patient variability, and a lack of interdisciplinary collaboration. Conclusions: The results reveal that there are obstacles to evaluating nursing interventions in acute care. Such obstacles hinder the introduction of evidence-based changes in nursing practice and, consequently, affect the quality of care provided to patients. Healthcare settings should therefore focus on addressing the identified barriers and enabling nurses to effectively evaluate their care interventions. Full article

In a cellular maritime communication system, ocean buoys are essential to enable environmental monitoring, offshore platform management, and disaster response. Therefore, energy-efficient transmission from the buoys is a key requirement to prolong their operational time. A fixed uplink beamforming can be considered to save energy by leveraging its beam gain while managing the target link reliability. However, the dynamic condition of ocean waves causes buoys’ random orientation, leading to frequent misalignment of their predefined beam direction aimed at the base station, which degrades both the link reliability and energy efficiency. To address this challenge, we propose a wave-adaptive beamforming framework to satisfy data-rate demands within limited power budgets. This strategy targets scenarios where sea state information is unavailable, such as in network-assisted systems. We propose a Two-Dimensional Thompson Sampling (2DTS) scheme that jointly selects beamwidth and transmit power to satisfy the target-rate constraint with minimal power consumption and thus achieve maximal energy efficiency. This adaptive learning approach effectively balances exploration and exploitation, enabling efficient operation in uncertain and changing sea conditions. In simulation, under a moderate sea state, 2DTS achieves an energy efficiency of $1.26 \times {10}^4$ bps/Hz/J at round 600, which is $73.7\%$ of the ideal ($1.71 \times {10}^4$), and yield gains of $96.9\%$ and $447.8\%$ over exploration-based TS and conventional TS, respectively. Under a harsh sea state, 2DTS attains $3.09 \times {10}^4$ bps/Hz/J ($85.6\%$ of the ideal $3.61 \times {10}^4$), outperforming the exploration-based and conventional TS by $83.9\%$ and $113.1\%$, respectively. The simulation results demonstrate that the strategy enhances energy efficiency, confirming its practicality for maritime communication systems constrained by limited power budgets. Full article