Search Results (342)

Background: Enterobiasis, caused by Enterobius vermicularis, is recognized as a common intestinal helminthiasis worldwide. Despite multiple surveys in Thailand, no pooled synthesis at the country level has been carried out to evaluate prevalence patterns, temporal trends, or vulnerable groups. Therefore, this systematic review and meta-analysis were undertaken to provide an updated and comprehensive estimate of the prevalence of E. vermicularis in Thailand and to identify high-risk populations for targeted interventions. Methods: The systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (PROSPERO: CRD420251053217). Studies reporting E. vermicularis infection in Thailand were systematically searched in international and Thai databases. Pooled prevalence and odds ratios (ORs) were calculated using random-effects models. Subgroup analyses and meta-regression were performed according to year, region, age, population type, and diagnostic method. Results: A total of 56 studies, including 52,765 participants, were analyzed. The overall pooled prevalence was estimated at 3.6% (95% confidence interval [CI]: 2.1–5.9%), with a decline observed in the subgroup analysis by publication year, from 4.75% in 2000–2009 to 1.15% in 2020–2023. The highest prevalence was reported in Central Thailand (7.93%). High infection rates were found among immigrant children (25.2%), hilltribe children (19.9%), Karen students (15.5%), and children in orphanages (11.4%). A markedly higher prevalence was detected by the Scotch tape method compared with direct smear/concentration (12.9% vs. 0.33%). No significant difference in infection risk was observed between males and females (OR = 1.03, p = 0.65). Conclusions: The pooled prevalence of E. vermicularis in Thailand was estimated at 3.6%, but this figure should be interpreted with caution due to high heterogeneity across studies. More meaningful insights were identified in subgroup analyses, which revealed a temporal decline in prevalence, geographic clustering in Central Thailand, and disproportionately high infection rates among socioeconomically disadvantaged child populations. No statistically significant association was found between gender and risk of infection. These patterns underscore the need for targeted screening, deworming, and hygiene interventions, along with the standardized use of the Scotch tape technique for accurate surveillance and comparability of future studies. Full article

Avian chlamydiosis, primarily caused by Chlamydia psittaci (C. psittaci), poses significant threats to poultry and avian trade. Emerging species such as Chlamydia gallinacea (C. gallinacea), Chlamydia avium (C. avium), and Chlamydia ibidis (C. ibidis) have recently been detected in birds. However, the prevalence and genetic diversity of avian chlamydia in birds within Guangxi remain unknown. In this study, 1744 samples collected from apparently healthy birds were screened, revealing an overall positivity rate of 28.20% (95% CI, 27.58–28.90%, 492/1744) for avian chlamydia. Among poultry, pigeons had the highest positivity rate at 62.30% (95% CI, 55.37–68.69%, 152/244), followed by chickens at 25.05% (95% CI, 21.25–29.23%, 128/511), geese at 18.12% (95% CI, 12.93–24.82%, 29/160), and ducks at 14.14% (95% CI, 11.57–17.26%, 82/580). Additionally, pet and wild birds exhibited positivity rates of 40.35% (95% CI, 34.20–46.83%, 92/228) and 42.86% (95% CI, 24.52–61.83%, 9/21), respectively. Phylogenetic analysis based on the outer-membrane protein A gene indicated that chicken samples belonged to genotypes B of C. psittaci and C. gallinacea. In ducks, genotypes A and B of C. psittaci and C. gallinacea were identified, representing the first documented occurrence of C. psittaci genotypes B and C. gallinacea in ducks in China. The nucleotide sequences from goose samples were initially clustered into genotype A group, while those from pigeons were clustered within genotype B. Furthermore, positive samples from pet birds were classified into genotypes A and B, as well as the C. gallinacea group. Similarly, samples from wild birds were classified into genotypes A and B. These findings suggest that diverse avian chlamydia genotypes are circulating among bird populations in Guangxi, with an expanding host range indicating potential cross-species transmission. Moreover, certain strains derived from waterfowl were found to cluster with those linked to recent psittacosis outbreaks, highlighting the zoonotic potential of avian chlamydia. Therefore, sustained surveillance for avian chlamydia in bird populations and monitoring its genetic evolutionary characteristics are essential to decrease public health risks. Full article

The development of highly selective and sensitive gas sensors is essential for detecting toxic pollutants, such as carbon monoxide (CO), which pose severe health and environmental risks. In this work, the adsorption of CO molecules on Ni_6−xCu_x (x = 0–6) clusters supported on hexagonal boron nitride quantum dots with nitrogen vacancies (h-BN_VQDs) is explored through density functional theory (DFT) calculations. For this purpose, the stability of the metallic clusters supported on the boron nitride sheet was calculated, and the adsorption properties of the CO molecule on the Ni_6−xCu_x (x = 0–6)/h-BN_VQDs composite were determined. The results demonstrated a high binding energy between Ni_6−xCu_x (x = 0–6) clusters and the h-BN_VQDs sheets, suggesting that Ni-Cu clusters are highly stable on h-BN_VQDs sheets. For CO adsorption, adsorption energy and charge transfer calculations indicated that the Ni₆ and Ni_6−xCu_x (x = 2 and 3) clusters exhibit the strongest CO binding and highest charge transfer, suggesting them as good candidates for CO gas sensing. These findings provide theoretical insights into the rational design of bimetallic catalysts for gas-sensing applications. Full article

The escalating frequency and severity of natural disasters present significant challenges to the stability and sustainability of global financial systems, with the US stock market being especially vulnerable. This study examines sector-level exposure and contagion dynamics during climate-related disaster events, providing insights essential for sustainable investing and resilient financial planning. Using an advanced econometric framework—dynamic conditional correlation GARCH (DCC-GARCH) augmented with Generalized Hyperbolic Processes (GHPs) and an asymmetric specification (ADCC-GARCH)—we model daily stock returns for 20 publicly traded US companies across five sectors (insurance, energy, automotive, retail, and industrial) between 2017 and 2022. The results reveal considerable sectoral heterogeneity: insurance and energy sectors exhibit the highest vulnerability, with heavy-tailed return distributions and persistent volatility, whereas retail and selected industrial firms demonstrate resilience, including counter-cyclical behavior during crises. GHP-based models improve tail risk estimation by capturing return asymmetries, skewness, and leptokurtosis beyond Gaussian specifications. Moreover, the ADCC-GHP-GARCH framework shows that negative shocks induce more persistent correlation shifts than positive ones, highlighting asymmetric contagion effects during stress periods. The results present the insurance and energy sectors as the most exposed to extreme events, backed by the heavy-tailed return distributions and persistent volatility. In contrast, the retail and select industrial firms exhibit resilience and show stable, and in some cases, counter-cyclical, behavior in crises. The results from using a GHP indicate a slight improvement in model specification fit, capturing return asymmetries, skewness, and leptokurtosis indications, in comparison to standard Gaussian models. It was also shown with an ADCC-GHP-GARCH model that negative shocks result in a greater and more durable change in correlations than positive shocks, reinforcing the consideration of asymmetry contagion in times of stress. By integrating sector-specific financial responses into a climate-disaster framework, this research supports the design of targeted climate risk mitigation strategies, sustainable investment portfolios, and regulatory stress-testing approaches that account for volatility clustering and tail dependencies. The findings contribute to the literature on financial resilience by providing a robust statistical basis for assessing how extreme climate events impact asset values, thereby informing both policy and practice in advancing sustainable economic development. Full article

Background and Objectives: Professional burnout threatens special education teachers’ well-being and educational service quality through three psychological dimensions: emotional exhaustion, depersonalization, and personal achievement. Limited studies have employed behavioral data analysis to examine burnout patterns in special education and their relationships with demographic factors and contemporary stressors. This study aimed to (1) identify burnout levels among Greek special education teachers, (2) determine demographic risk factors, and (3) examine relationships between burnout dimensions and COVID-19 psychological impact. Materials and Methods: A cross-sectional study surveyed 114 special education teachers from Achaia and Aitoloakarnania prefectures, Greece (response rate: 87.7%), using the Maslach Burnout Inventory–Educators Survey (MBI-ES) and demographic questionnaires. Behavioral data analysis integrates traditional statistics with advanced techniques, including cluster analysis and classification modeling. Results: Four distinct burnout profiles emerged: Low Burnout (36.8%), Moderate Emotional Exhaustion (30.7%), High Risk (21.9%), and Depersonalization-Dominant (10.5%). Overall burnout prevalence was low, with 73.7% showing minimal depersonalization and 67.5% maintaining high personal achievement. Employment status emerged as the strongest predictor of burnout risk. Emotional exhaustion was the primary predictor of COVID-19 psychological impact (r = 0.547, p < 0.001), explaining 29.9% of pandemic-related distress variance. Male substitute teachers demonstrated the highest vulnerability to depersonalization, while experienced female permanent teachers showed resilience patterns. Conclusions: Behavioral data analysis revealed distinct burnout patterns enabling personalized interventions. Emotional exhaustion serves as both a key vulnerability factor and primary intervention target. These findings support targeted approaches to occupational health with implications for educational policy. Limitations include cross-sectional design and regional sampling. Future longitudinal studies should validate these patterns across diverse educational contexts. Full article

Land-use change and its interaction with petrochemical accident risk are critical for sustainable coastal development. This study established a multi-source data-integrated risk assessment framework, employing fuzzy C-means clustering to stratify petrochemical accident risk into six distinct levels. The analysis revealed the relationship between these risk levels and land-use type changes. Furthermore, the Takagi–Sugeno fuzzy dynamic model was applied to evaluate potential risks at representative coastal petrochemical enterprises. The findings were as follows: (1) Risk concentrates in small-to-medium private, newly established firms, primarily as explosion accidents. (2) The highest risk occurs in Bohai Bay, followed by Jiangsu, Zhejiang, and Guangdong; national policies have reduced affected zones from 352.61 km² (2019) to 43.67 km² (2022). (3) The total potential risk zone spans 2986.21 km², with high-risk cores in Hebei, Zhejiang, and Fujian (36.52%) and medium-risk in Shandong Peninsula (32.01%). (4) Risk primarily affects farmland and construction land; urban expansion has increased affected built-up areas from 16.36% (2012) to 47.02% (2022), shifting effects from ecological to combined socio-ecological consequences. These findings provide critical theoretical support and actionable management recommendations for integrating coastal land-use planning, urban expansion control, and coordinated petrochemical risk governance. Full article

Grassland degradation poses a serious threat to ecosystem stability and the sustainable development of human societies. In this study, we propose a framework for grassland degradation risk assessments and early warning based on key ecological factors (KEFs) in Xilingol. The NDVI, NPP, and grass yield were selected as KEFs to represent vegetation coverage, ecosystem productivity, and actual biomass, respectively. By constructing a grassland degradation index (GDI) and integrating K-means clustering, the average curvature, and a gravity center shift analysis, we quantified the degradation risk levels and identified the threshold values for different grassland types. The results showed the following: (1) the grass yield was the most sensitive indicator of grassland degradation in Xilingol, with high-risk thresholds decreasing from 115.67 g·m⁻² in the temperate meadow steppes (TMSs) to 73.27 g·m⁻² in the temperate typical steppes (TTSs), and further to 32.30 g·m⁻² in the temperate desert steppes (TDSs); (2) the TDSs exhibited the highest curvature value (2.81 × 10⁻⁴) in the initial stage, indicating a higher likelihood of rapid early-stage degradation, whereas the TMSs and TTSs reached peak curvature in the latest stages; and (3) the TTSs had the largest proportion of high-risk areas (33.02%), with a northeast–southwest distribution and a probable westward expansion trend. This study provides a practical framework for grassland degradation risk assessments and early warning, offering valuable guidance for ecosystem management and sustainable land use. Full article

This study investigates the concentrations of essential and trace elements (Ni, Cu, Fe, Zn, Mn, and Al) in packaged ice cream samples collected from markets in Kırşehir province, located in Central Anatolia, Turkey, aiming to assess potential health risks associated with their consumption. Among the detected trace elements, Al (3.21–16.6 mg/kg) and Fe (2.03–24.0 mg/kg) had the highest concentrations, followed by Zn (0.56–3.00 mg/kg), Ni (0.84–4.84 mg/kg), Cu (1.15–3.46 mg/kg), and Mn (0.18–1.56 mg/kg). To explore the relationships between trace elements and identify possible contamination sources, chemometric approaches including principal component analysis, correlation matrices, and hierarchical cluster analysis (Ward’s method) were applied. Human health risk assessment was conducted by calculating Estimated Daily Intake (EDI), Target Hazard Quotient (THQ), Hazard Index (HI), and Carcinogenic Risk (CR), with uncertainty evaluated through Monte Carlo Simulation (10,000 iterations). HI values above 1 in children and adults indicate that trace element exposure through ice cream consumption may pose a health risk. High Al-THQ and Ni-CR values in children may require stricter monitoring and regulatory measures in case of long-term and regular consumption. Full article

Background/Objectives: Gut microbial metabolism of choline and related quaternary amines to trimethylamine (TMA) is the first step in the production of trimethylamine N-oxide (TMAO), a circulating metabolite that contributes to the development of atherosclerosis and other forms of cardiovascular disease (CVD). No data exist on regional differences in TMA production within the colon due to difficulties studying gut regions in vivo. A better understanding of TMA production by gut microbiota is needed to develop strategies to limit TMA production in the gut and TMAO levels in circulation with the goal of reducing CVD risk. Methods: We employed our novel three-compartment MiGut in vitro model, which establishes three distinct microbial ecologies mimicking the proximal, mid, and distal colon, to study conversion of choline to TMA by human gut microbiota using isotopically labelled substrate. Results: Choline-d₉ was almost completely converted to TMA-d₉ in vessels 2–3 (mimicking the mid and distal colon) within 6–8 h, but little conversion occurred in vessel 1 (mimicking the proximal colon). Abundance of cutC, part of the cutC/D gene cluster responsible for choline conversion to TMA, was highest in vessel 1 vs. 2–3, suggesting that its expression or activity may be suppressed in the proximal colon. Another possibility is that the viability/activity of bacteria expressing cutC could be suppressed in the same region. Conclusions: This novel finding suggests that while bacteria capable of converting choline to TMA exist throughout the colon, their activity may be different in distinct colon regions. The regional specificity of TMA production, if confirmed in vivo, has implications for both basic microbial ecology related to CVD and the development of strategies to control TMA and TMAO production, with the goal of lowering CVD risk. These findings warrant further study in vitro and in vivo. Full article

Background/Objectives: Nonsteroidal anti-inflammatory drugs (NSAIDs) are commonly associated with gastrointestinal (GI) adverse events. This study aimed to assess the incidence and patterns of NSAID-induced GI disorders using the FDA Adverse Event Reporting System (FAERS) database and to compare the risks among different NSAIDs. Methods: NSAID-related reports were extracted from FAERS, focusing on 21 ulcer-related GI events with ≥1000 reports each, based on MedDRA v26.0. The number of reports, reporting odds ratios, and p-values were calculated and visualized using a volcano plot. Principal component analysis(PCA) was carried out to reduce the dimensionality of the dataset and revealed under-lying patterns in the data.PCA was performed to identify patterns related to risk, severity, and injury site, whereas hierarchical clustering was used to group NSAIDs based on these patterns. Hierarchical cluster analysis is a method of grouping similar data to generate a classification. Results: Statistically significant signals were identified for 19 of the 21 GI-related adverse events, including the serious condition of perforation. PCA revealed that the first component represented risk, the second severity, and the third the site of injury (upper vs. lower GI tract). Cyclooxygenase-2 (COX-2) selective NSAIDs (e.g., celecoxib, rofecoxib) were associated with a lower incidence but greater severity, primarily in the upper GI tract. Conversely, nonselective NSAIDs (e.g., acetylsalicylic acid, lornoxicam) showed higher incidence rates, though the events were generally milder. In our dataset, acetylsalicylic acid had the highest incidence, whereas meloxicam showed the highest severity. Clustering analysis revealed three distinct NSAID groups with differing patterns in risk, severity, and affected GI site. Mild adverse events may be underreported in FAERS. Dosage-related effects were not assessed in this study. Conclusions: NSAIDs differ significantly in their gastrointestinal adverse event profiles, attributable to COX selectivity. When selecting an NSAID, both the likelihood and the nature of potential GI harm should be considered. Full article

Background: Hearing impairment is a significant public health issue globally, yet national data for Saudi Arabia remain sparse. Methods: Using data from the 2017 Disability Survey, we analysed 12 hearing-related indicators across 13 administrative regions. Descriptive statistics, logistic regression, cluster analysis, and residual mapping were applied to identify socio-demographic disparities and service gaps. Findings: Among 20,408,362 Saudi nationals, about 1,445,723 (7.1%) reported at least one functional difficulty. Of these, 289,355 individuals (1.4%) had hearing impairment, either alone or with other difficulties—229,541 (1.1%) had hearing impairment combined with other disabilities, while 59,814 (0.3%) had only hearing impairment. Females and males were equally affected. Notably, educational attainment and marital status significantly influenced device uptake; less-educated and divorced individuals were particularly underserved. Regionally, southern provinces (Al-Baha, Jazan, and Najran) demonstrated the highest unmet need due to geographic barriers, limited audiological resources, and socioeconomic constraints, reflecting compounded risks from consanguinity and rural isolation. Cluster analyses identified provinces requiring urgent attention, recommending mobile audiology units, tele-audiology services, and means-tested vouchers to enhance coverage. Conclusions: Despite Saudi Arabia’s existing public audiology services and a National Newborn Hearing Screening programme achieving 96% coverage, substantial gaps remain in follow-up care and specialist distribution, underscoring the necessity for systematic workforce tracking and enhanced rural incentives. International evidence from India and Brazil underscores the feasibility and cost-effectiveness (approximately USD 5200/QALY) of these recommended interventions. Implementing targeted provincial strategies, integrating audiological screening into routine healthcare visits, and aligning resource allocation with the WHO and Vision 2030 benchmarks will significantly mitigate hearing impairment’s health, social, and economic impacts, enhancing the quality of life and societal inclusion for affected individuals. Full article

Locational errors and sampling bias may produce unrepresentative species distribution models. To decompose the influence of errors, I modeled species distributions of 31 mammal species from georeferenced records and random samples from range maps, with potential sources of errors added or removed, using the random forests algorithm. Errors included the addition of (1) cities, (2) administrative centers, (3) records flagged as potential errors (e.g., outliers), and (4) urban records to range map samples; the removal of (5) flagged records and (6) urban records from georeferenced records; and the addition of (7) random points and (8) clustered points to georeferenced records. I also examined separation between thinned and unthinned (i.e., locally concentrated) records and ocean and land areas. Errors generally did not perturb species distributions, particularly if errors were located within species ranges. The greatest departure relative to unaltered models (mean niche overlap values of 0.96 out of 1) was due to the addition of administrative centers at a 13% error rate. Because locational errors overall do not occur in modern georeferenced records, outliers may provide important samples from undersampled areas. Delineating land from ocean coordinates may require a land layer at the highest available resolution and buffered to match the distance of locational uncertainty for georeferenced records. Predicted areas for species distributions increased along the spectrum of models from concentrated georeferenced records, thinned records, and random samples from range maps. Species distributions modeled with all georeferenced records will have the greatest sampling concentration (to differentiate from bias, because predictive modeling is not hypothesis testing), resulting in model locational precision, whereas species distribution models from random samples of range maps will have locational generality (rather than errors). The risk of removing samples of suitable conditions is the generation of unrepresentative models whereas the benefit of sample removal is slightly more generalized models, but which also may represent overpredictions. Full article

Understanding the spatial dynamics and drivers of armed conflict is crucial for anticipating risk and informing targeted interventions. However, current research rarely considers the spatio-temporal clustering characteristics of armed conflicts. Here, we assess the distribution dynamics and driving factors of armed conflict from the perspective of armed conflict clusters, employing complex network dynamic community detection methods and interpretable machine learning approaches. The results show that conflict clusters vary in terms of regional distribution. Sub-Saharan Africa boasts the highest number of conflict clusters, accounting for 37.9% of the global total and covering 40.4% of the total cluster area. In contrast, South Asia and Afghanistan, despite having a smaller proportion of clusters at 12.1%, hold the second-largest cluster area, which is 18.1% of the total. The characteristics of different conflict networks are influenced by different factors. Historical exposure, socio-economic deprivation, and spatial structure are the primary determinants of conflict patterns, while climatic variables contribute less prominently as part of a broader system of environmental vulnerability. Moreover, the influence of driving factors shows spatial heterogeneity. By integrating cluster-level analysis with interpretable machine learning, this study offers a novel perspective for understanding the multidimensional characteristics of armed conflicts. Full article

The changing global climate and rapid urbanization make extreme rainstorm events frequent, and the flood disaster caused by rainstorm has become a prominent problem of urban public safety in China, which severely restricts the healthy and sustainable development of social economy. The weight calculation method of traditional risk assessment model is single and ignores the difference of multi-dimensional information space involved in risk analysis. This study constructs a flood risk assessment model by incorporating natural, social, and economic factors into an indicator system structured around four dimensions: hazard, exposure, vulnerability, and disaster prevention and mitigation capacity. A combination of the Analytic Hierarchy Process (AHP) and the entropy weight method is employed to optimize both subjective and objective weights. Taking the central urban area of Wuhan with a high flood risk as an example, based on the risk assessment values, spatial autocorrelation analysis, cluster analysis, outlier analysis, and hotspot analysis are applied to explore the spatial clustering characteristics of risks. The results show that the overall assessment level of flood hazard in central urban area of Wuhan is medium, the overall assessment level of exposure and vulnerability is low, and the overall disaster prevention and mitigation capability is medium. The overall flood risk levels in Wuchang and Jianghan are the highest, while some areas in Qingshan and Hanyang have the lowest levels. The spatial characteristics of each dimension evaluation index show obvious autocorrelation and spatial differentiation. These findings aim to provide valuable suggestions and references for reducing urban disaster risks and achieving sustainable urban development. Full article

Background and objectives: Adducins are cytoskeletal proteins essential for membrane stability, actin–spectrin network organization, and cell signaling. Mutations in the genes ADD1, ADD2, and ADD3 have been linked to hypertension, neurodevelopmental disorders, and cancer. However, no comprehensive in silico saturation mutagenesis study has systematically evaluated the pathogenic potential and structural consequences of all possible missense mutations in adducins. This study aimed to identify high-risk variants and their potential impact on protein stability and function. Methods: We performed computational saturation mutagenesis for all possible single amino acid substitutions across the adducin proteins family. Pathogenicity predictions were conducted using four independent tools: AlphaMissense, Rhapsody, PolyPhen-2, and PMut. Predictions were validated against UniProt-annotated pathogenic variants. Predictive performance was assessed using Cohen’s Kappa, sensitivity, and precision. Mutations with a prediction probability ≥ 0.8 were further analyzed for structural stability using mCSM, DynaMut2, MutPred2, and Missense3D, with particular focus on functionally relevant domains such as phosphorylation and calmodulin-binding sites. Results: PMut identified the highest number of pathogenic mutations, while PolyPhen-2 yielded more conservative predictions. Several high-risk mutations clustered in known regulatory and binding regions. Substitutions involving glycine were consistently among the most destabilizing due to increased backbone flexibility. Validated variants showed strong agreement across multiple tools, supporting the robustness of the analysis. Conclusions: This study highlights the utility of multi-tool bioinformatic strategies for comprehensive mutation profiling. The results provide a prioritized list of high-impact adducin variants for future experimental validation and offer insights into potential therapeutic targets for disorders involving ADD1, ADD2, and ADD3 mutations. Full article