Search Results (18,493)

Access to safe drinking water is critical for child growth and development. However, microbial contamination is a constant threat in many low- and middle-income countries. The current systematic review sets out to examine the evidence of drinking water quality and the physical and cognitive development of children aged 0 to 5 years. The review authors conducted a comprehensive search of SCOPUS, EBSCO, PubMed, the Cochrane Library, and Google Search for cohort studies and clinical trials conducted in English between the years 2010 and 2025. Of 222 studies, 15 were included in the review and the majority were conducted in low- and middle-income countries The findings consistently demonstrate that microbiological contamination, predominantly by Escherichia coli (the primary water quality indicator examined across studies), is associated with an increased risk of stunting (odds ratio up to 4.14) and reductions in height-for-age Z-scores (HAZ) (by 0.29–0.57). There is currently limited evidence in the studies reviewed that suggests a correlation between the presence of unsafe drinking water and a decrease in cognitive development; however, the evidence is insufficient and warrants further study. Integrated water, sanitation and hygiene (WASH) and nutrition programs had promising growth results, which varied depending on the initial sanitation coverage of the target population, adherence to the intervention, and the overall design of the program. To sum up, contaminated drinking water negatively affects physical and cognitive development during early childhood. Comprehensive WASH–nutrition strategies need to be implemented to reduce this impact and further progress towards Sustainable Development Goal (SDG) 6. Full article

This article presents an adaptive neural network method for the automated detection, reconstruction, and prioritisation of multi-stage criminal operations in the digital environment, aiming to protect human rights and ensure the legal security of digital evidence. The developed method combines multimodal temporal encoders, a graph module based on GNN for entity correlation, and a correlation head with a link-prediction mechanism and differentiable path recovery. Sliding time windows, logarithmic transformation of volumetric features, and pseudonymization of identifiers with the ability to utilise privacy-preserving procedures (federated learning, differential privacy) are used for data aggregation and normalisation. Unique features of the developed method include an integrated risk function combining an anomaly component and graph significance, a module for automated forensic packet generation with chain of custody recording, and a mechanism for incremental model updates. Experimental results demonstrate high diagnostic metric values (AUC ≈ 0.97, F1 ≈ 0.99 on the test dataset after balancing), robust recovery of priority paths (“path_probability” > 0.7 for top operations), and pipeline performance in PII leak prioritisation and human trafficking reconstruction scenarios. The study’s contribution lies in a practice-oriented neural network method that integrates detection, correlation, and the collection of legally applicable evidence. Full article

Background/Objectives: Wilms’ tumor (WT) is the most common malignant renal tumor in childhood, and although survival rates are high, a subset of patients with high-risk disease remain prone to treatment resistance and relapse. Increasing evidence suggests that the tumor microenvironment, particularly tumor-associated macrophages (TAMs), may influence tumor behavior and therapeutic response. This study aimed to characterize M1 and M2 macrophage infiltration in WT treated with neoadjuvant chemotherapy according to the International Society of Pediatric Oncology (SIOP) protocol and to evaluate their association with clinicopathological features. Methods: Tumor tissue samples from 46 pediatric patients were analyzed using double immunohistochemical staining for CD80 (M1) and CD163 (M2) macrophages. TAM density and M1/M2 ratios were quantified in viable tumor areas and correlated with clinical, laboratory, and pathohistological parameters. Results: Total TAM counts were significantly higher in tumors with a volume ≥500 mL and in unifocal tumors. Both M1 and M2 macrophages were more abundant in larger tumors; however, M2 macrophages predominated overall. The M1/M2 ratio was positively associated with total TAM counts and was significantly higher in tumors with larger volume, elevated serum neuron-specific enolase, and increased creatinine levels. Regressive histological subtypes exhibited a higher M1/M2 ratio compared with other subtypes. No significant associations were observed between macrophage infiltration and tumor stage or risk group. Conclusions: WT treated with neoadjuvant chemotherapy demonstrates low overall macrophage infiltration with a predominance of M2 macrophages. TAM polarization appears to be associated with tumor burden and selected biochemical parameters, highlighting the potential relevance of macrophages as biomarkers and therapeutic targets in WT. Full article

Left ventricular noncompaction cardiomyopathy (LVNC) is characterised by a two-layered ventricular wall with prominent trabeculations and deep recesses adjacent to a thinned compact layer. The phenotype spans from incidental findings to severe heart failure and malignant arrhythmias. More than 190 genes belonging to sarcomeric, cytoskeletal, mitochondrial, transcriptional and signalling pathways have been implicated, although only a subset reaches high gene disease validity in contemporary frameworks. Objectives: (i) Delineate the validated genetic landscape of LVNC; (ii) integrate developmental biology with cardiac genomics; (iii) translate genotype knowledge into diagnostic, prognostic and therapeutic guidance; (iv) outline a research agenda for precision cardiology. Methods: A narrative, pathway-oriented review of human and experimental studies (2000–July 2024). Results: Thirty-two genes meet definitive/strong validity thresholds and cluster in five biological networks. Oligogenic constellations account for ~4% of probands in recent cohorts. Imaging correlates (especially quantitative trabecular complexity and diffuse fibrosis metrics) provide complementary risk information. Conclusions: LVNC represents a convergence phenotype triggered by perturbations across developmental and structural networks; clinical management benefits from integrated genomics–imaging workflows and mechanism-informed trial design. Full article

Traditional mean–variance portfolio optimization proves inadequate for cryptocurrency markets, where extreme volatility, fat-tailed return distributions, and unstable correlation structures undermine the validity of variance as a comprehensive risk measure. To address these limitations, this paper proposes a unified entropy-based portfolio optimization framework grounded in the Maximum Entropy Principle (MaxEnt). Within this setting, Shannon entropy, Tsallis entropy, and Weighted Shannon Entropy (WSE) are formally derived as particular specifications of a common constrained optimization problem solved via the method of Lagrange multipliers, ensuring analytical coherence and mathematical transparency. Moreover, the proposed MaxEnt formulation provides an information-theoretic interpretation of portfolio diversification as an inference problem under uncertainty, where optimal allocations correspond to the least informative distributions consistent with prescribed moment constraints. In this perspective, entropy acts as a structural regularizer that governs the geometry of diversification rather than as a direct proxy for risk. This interpretation strengthens the conceptual link between entropy, uncertainty quantification, and decision-making in complex financial systems, offering a robust and distribution-free alternative to classical variance-based portfolio optimization. The proposed framework is empirically illustrated using a portfolio composed of major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—based on weekly return data. The results reveal systematic differences in the diversification behavior induced by each entropy measure: Shannon entropy favors near-uniform allocations, Tsallis entropy imposes stronger penalties on concentration and enhances robustness to tail risk, while WSE enables the incorporation of asset-specific informational weights reflecting heterogeneous market characteristics. From a theoretical perspective, the paper contributes a coherent MaxEnt formulation that unifies several entropy measures within a single information-theoretic optimization framework, clarifying the role of entropy as a structural regularizer of diversification. From an applied standpoint, the results indicate that entropy-based criteria yield stable and interpretable allocations across turbulent market regimes, offering a flexible alternative to classical risk-based portfolio construction. The framework naturally extends to dynamic multi-period settings and alternative entropy formulations, providing a foundation for future research on robust portfolio optimization under uncertainty. Full article

Urban hydrological risks are endangering vulnerable populations, particularly in densely populated metropolitan areas undergoing rapid land use transformation. This study uses geospatial analysis to identify zones in the Athens metropolitan area that are prone to surface water accumulation and stream flow development during extreme rainfall events. Two spatial indices were developed by integrating digital elevation models, flow accumulation, slope, aspect, the topographic wetness index, and classified road network data: a Surface Water Accumulation Index and a Stream flow Pathway Index. Roads were categorized based on their orientation relative to the direction of the slope, which allowed for an assessment of their influence on hydrological flow. Both indices were classified into five risk levels representing gradients of hydrological vulnerability. The spatial patterns revealed by this analysis show strong correlations with flood-prone areas and natural drainage systems. These insights are essential for guiding urban planning efforts aimed at reducing hydrological hazards, particularly for at-risk groups such as the homeless. This approach offers a valuable tool for promoting sustainable, socially inclusive landscape management. Full article

Safety production constitutes a core pillar of operational management for power grid enterprises. Assessing the safety production risks of target entities in mergers and acquisitions (M&A) is a prerequisite for strengthening safety governance, and it holds significant value for elevating the safety levels of power grids, equipment, and personnel. To address the issues of inconsistent assessment dimensions and over-reliance on empirical judgment in safety production risk evaluation during power grid M&A activities, this paper proposes an assessment model that integrates due diligence information with hybrid multi-attribute decision-making (MADM). By systematically identifying safety production risk factors throughout the M&A process, an indicator system encompassing four dimensions—physical constraints, management systems, historical performance, and dynamic adaptability—is established. A game-theoretic approach is adopted to combine the Level-Based Weight Assessment (LBWA) method and the Criteria Importance Through Inter-criteria Correlation (CRITIC) method for subjective–objective integrated weighting. Additionally, grey relational analysis (GRA) is introduced to refine the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) algorithm, enabling quantitative evaluation of risk levels. Case analysis results demonstrate that the proposed model can effectively distinguish risk discrepancies across different M&A scenarios with rational weight allocation for key indicators. Compared with traditional methods, it maintains ranking consistency while exhibiting higher discrimination efficiency, thus providing a scientific and effective risk assessment tool for power grid enterprises’ M&A decision-making. Full article

Epicardial adipose tissue (EAT) has previously been associated with coronary artery calcium scores, an increased burden of coronary artery disease (CAD), and features of plaque instability. These associations are likely mediated by endocrine and paracrine signaling from bioactive molecules secreted by EAT, which may contribute to coronary atherosclerosis. EAT can be non-invasively quantified on images obtained during coronary computed tomography angiography (CCTA). This study aimed to evaluate the potential association between EAT and non-calcified coronary plaques with severe stenosis using radiomic methodology. Materials and Methods: A total of 128 consecutive patients undergoing CCTA—both with and without contrast—for known or suspected CAD were retrospectively analyzed. EAT features were extracted from contrast scans. Coronary artery plaque features were evaluated using Coronary Artery Disease-Reporting and Data System (CAD-RADS). Results: EAT features showed a statistically significant positive correlation with non-calcified coronary plaques with severe grades of stenosis (CAD-RADS > 4). The Ensemble Machine Learning (EML) model combined with coronary plaque data showed a sensitivity of 1.00 and a specificity of 0.93, with a negative predictive value of 1.00 and a positive predictive value of 0.85, and an accuracy of 0.95 (95% CI: 0.9221–1) in internal validation. Conclusions: EAT may represent a novel imaging biomarker associated with the presence of actionable coronary plaques. Radiomic texture analysis of EAT could enhance the non-invasive prediction of coronary stenoses. These preliminary findings support the clinical utility of EAT evaluation via CCTA in patients with low to intermediate cardiovascular risk. Full article

This study analyzes the evolution and spatial distribution of cervical cancer mortality. Furthermore, it develops a dynamic simulation model for estimating the evolution of the disease up to 2040. This manuscript details an ecological and retrospective study that analyzed official mortality, morbidity, and population data from the 58 municipalities that constitute the state of San Luis Potosi. We used Moran’s index, linear correlation, structural equation modeling, Excel predictions, and Vensim PLE x64 simulation software to conduct this study. The evolution of deaths from cervical cancer shows a downward trend; mortality follows a clustered distribution pattern, and it is not random. The structural model showed standardized regression coefficients of 0.68 between syphilis cases and cervical cancer cases, with a coefficient of 0.35 for deaths; candidiasis cases with cervical cancer at a coefficient of 0.25 and with deaths from the same disease at a coefficient of 0.46. The coefficients of determination for cervical cancer cases and deaths were 0.74 and 0.91, respectively. This shows that these co-infections—syphilis and candidiasis—are a risk factor for cervical cancer mortality. The estimated mortality rates per 100,000 inhabitants for 2025, 2030, 2035, and 2040 were 5.5, 5.1, 4.8, and 4.4, respectively. The prediction indicates an increase in the number of CC cases and deaths from this cause. Full article

The integration of large language models (LLMs) such as GitHub Copilot, ChatGPT, and DeepSeek into programming education has introduced a new form of human–AI collaboration. These tools provide real-time code suggestions, debugging assistance, and design support, yet their effects on learning, trust, productivity, and coding practices remain underexplored. We surveyed 248 students to examine relationships among these constructs, usage patterns by programming experience and academic level, the most frequently used assistants and programming languages, group differences in perceived learning and coding practices, and the extent to which learning, trust, and coding practices predict productivity. Students reported high adoption of ChatGPT and Python, generally positive perceptions of learning and productivity, and significant positive correlations among all constructs. Kruskal–Wallis tests indicated no significant differences in perceived learning across Basic, Intermediate, and Expert programmers, nor in coding practices across academic years (Years 1–4). Multiple regression showed that learning, trust, and coding practices jointly explained a substantial proportion of productivity variance (R² = 0.628). These findings emphasize both opportunities and risks of AI integration and offer guidance for educators aiming to integrate AI tools while maintaining pedagogical rigor. Full article

Background: Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide. Soluble receptor for advanced glycation end products (sRAGE) has emerged as a candidate biomarker in metabolic, inflammatory, and malignant diseases, although its prognostic significance in LC remains uncertain. Methods: Serum sRAGE levels were prospectively measured at baseline and prior to the second cycle of treatment in patients with non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC). Associations of sRAGE with overall survival (OS), progression-free survival (PFS), clinical features, and other biomarkers were analyzed. Results: In total, 42 patients were enrolled in this study. sRAGE was detected in 16 patients (38.1%) at baseline and in 15 patients (37.5%) after the first cycle of treatment. Pre-treatment sRAGE levels were strongly correlated with post-treatment levels (Pearson’s r = 0.78; 95% CI, 0.61–0.88; p = 4.1 × 10⁻⁹) and moderately correlated with PD-L1 tumor proportion score in NSCLC patients (Spearman’s ρ = 0.4, p = 0.049). Pre-treatment sRAGE levels tended to be higher in patients with indeterminate/high risk of liver fibrosis compared to patients with low risk (Wilcoxon rank-sum test, p = 0.041). Post-treatment change in sRAGE levels was correlated with whole blood cell count-derived inflammatory markers. A preliminary association between decreased sRAGE and overall survival in SCLC patients was observed. Conclusions: Serum sRAGE shows potential as a blood-based biomarker reflecting metabolic, immune, and inflammatory status in lung cancer, warranting further investigation to clarify its prognostic and therapeutic relevance. Full article

The illegal wildlife trade remains a significant threat to biodiversity in South Africa. The poaching of native species in the country has increased over the years, primarily driven by the demand for abalone, rhino horns, and pangolin scales. This study analysed TRAFFIC wildlife crime records between 1984 and 2025 to identify hotspots, trends in enforcement over time, and the most affected species. We found that provinces such as Gauteng and KwaZulu-Natal have the highest diversity of species affected, while the Western Cape recorded the highest number of incidents, predominantly seizures. Seizure was the most common wildlife activity, followed by poaching and illegal harvesting, with fewer cases of smuggling, breeding, and prosecution. A total of 50 species across nine animal classes were impacted, with white rhinoceros (Ceratotherium simum (Burchell, 1817)), abalone (Haliotis midae (Linnaeus, 1758)), lion (Panthera leo (Linnaeus, 1758)), and ground pangolin (Smutsia temminckii (Smuts, 1832)) among the most frequently targeted. Correlation analysis revealed a strong positive relationship between seizures and arrests (Pearson’s r = 0.90, p = 0.001) across provinces. This indicates a substantial strengthening of law-enforcement activity across provinces, likely driven by enhanced detection or reporting, as reflected in a rising proportion of cases resulting in arrests. Species such as elephants and pangolins were associated with enforcement outcomes, particularly those involving horns, tusks, scales, and dead specimens. There is a need for targeted interventions in high-risk areas, and provinces must collaborate in combating the wildlife trade. Limitations in data completeness and species representation suggest the need for improved surveillance and reporting mechanisms to fully understand and combat illegal wildlife trade in South Africa. Full article

This paper examines the relationship between traditional gold (XAU) and its tokenized counterparts (PAXG and XAUT), providing an empirical assessment of how digital representations of real-world assets align with their underlying benchmarks. Using multi-year time series data, the study evaluates price deviations, tracking accuracy, correlations, and volatility across both weekday-only and 24/7 trading datasets, incorporating weekend effects and crypto-market microstructure. Results show that both tokenized assets exhibit strong long-term alignment with XAU, while short-term divergences arise from continuous crypto trading, liquidity fragmentation, and issuer-specific design features, with XAUT consistently tracking spot gold more closely than PAXG. Building on this analysis, the paper examines the role of tokenized gold within dynamic, smart contract-driven crypto portfolios that also include BTC, ETH, and cash. Portfolio simulations demonstrate that adaptive rebalancing strategies materially improve risk-adjusted performance, with XAUT serving as a stabilizing anchor and cash enabling rapid, automated repositioning during volatility spikes. The findings offer a dual contribution: they clarify the fidelity and market behavior of tokenized gold and provide evidence of its practical utility within automated, on-chain portfolio management, highlighting both its strengths and structural limitations in emerging digital financial systems. Full article

Regional financial integration in East Africa remains shallow, yet contagion risks persist due to market fragility and illiquidity. Using daily data from 2014 to 2025 from the Nairobi Securities Exchange (NSE), Dar es Salaam Stock Exchange (DSE), Rwanda Stock Exchange (RSE), and Uganda Securities Exchange (USE), this study examines volatility spillovers, dynamic connectedness, and contagion through autoregressive moving average – generalised autoregressive conditional heteroscedasticity (ARMA–GARCH) diagnostics, asymmetric dynamic conditional correlation (ADCC–GARCH) correlations, and the Diebold–Yilmaz framework. The results show weak spillovers and limited connectedness in tranquil periods, reflecting persistent segmentation. However, systemic stress triggers abnormal surges in correlations and connectedness, consistent with contagion as a temporary amplification of cross-market linkages. The NSE emerges as the dominant transmitter, driven by liquidity and cross-listings, while the USE acts as a passive absorber. The RSE and DSE alternate between marginal transmitters and receivers depending on conditions. These findings support the Adaptive Market and Financial Instability Hypotheses, underscoring the need for harmonised regulation, liquidity reforms, and adaptive risk management to bolster resilience. Full article

Background/Objectives: Postoperative delirium is an acute condition commonly seen in pediatric patients. It is often observed in intensive care units for patients undergoing general anesthesia. Characteristic symptoms include disturbances of consciousness, attention, perception, or disorientation. The occurrence of delirium can be assessed using the CAPD and PAED scales. Methods: A single-center observational cohort studyof the 2022–2024 results was conducted. A total of 89 patients of the Pediatric Anesthesiology and Intensive Care Unit undergoing procedures under general anesthesia were included in this study. The state of delirium just after the recovery from anesthesia was assessed using the CAPD and PAED scales. Results: A total of 60% of patients experienced delirium taking the results according to the CAPD scale, 39% according to the PAED scale. A score indicating delirium according to both the CAPD and PAED scales was recorded in 21% of the patients examined. The results of the correlation analyses indicate a strong relationship between the CAPD and PAED scales. The feature analysis indicated there was a correlation between the occurrence of delirium and chronic disease. One of the confirmed risk factors for the development of postoperative delirium in children is postoperative pain. The experience of postoperative delirium is associated with the observation of negative postoperative behavioral changes in children within 7 days of hospital discharge. Conclusions: The correlation analysis showed a significant positive relationship between CAPD scale and PAED scale. The feature analysis indicated a relationship between CAPD scores and the existence of chronic diseases. Full article