Search Results (25,111)

Objective: Physical joint examination is fundamental in rheumatoid arthritis (RA) assessment. This study evaluated the diagnostic accuracy and agreement between standardized and non-standardized physical joint examinations in RA patients using musculoskeletal ultrasound as the reference standard. Methods: We assessed the joints for tenderness and swelling, calculating sensitivity, specificity, and predictive values. Musculoskeletal ultrasound was used as the reference standard, with adjustment for imperfect reference bias. Agreement between the methods was evaluated using the average kappa coefficient. Results: A total of 1496 joints were evaluated. Without adjustment for imperfect reference bias, standardized examination showed higher sensitivity for detecting pain and swelling than non-standardized examination. Specificity was similar for pain but higher for swelling in standardized examination. After bias adjustment, standardized examination sensitivity improved for pain (93.8% vs. 77.3%; 95% CI: 0.14–0.19) and swelling (91.9% vs. 60.0%; 95% CI: 0.29–0.34). Tenderness specificity remained comparable (standardized examination: 75.4%, non-standardized examination: 76.3%), while the non-standardized examination maintained superior swelling specificity (85.7% vs. 77.1%). Standardized joint examination demonstrated significantly higher concordance than non-standardized assessment in evaluating joint tenderness; standardized assessment yielded significantly greater average kappa coefficients under both false-positive-prioritized (0.44 vs. 0.37; p = 0.01) and false-negative-prioritized scenarios (0.59 vs. 0.45; p < 0.0001). For joint swelling, standardized evaluation showed significantly higher concordance when false negatives were considered more critical (0.59 vs. 0.37; p < 0.0001), whereas differences under false-positive prioritization were not statistically significant. Conclusions: Standardization of the physical joint examination significantly improves diagnostic accuracy and agreement in detecting joint tenderness and swelling in patients with rheumatoid arthritis. Implementing a standardized physical examination protocol may enhance disease activity diagnosis and optimize clinical management of RA. Full article

Since the end of the Civil War, Cambodia has pursued economic development to enhance livelihoods, particularly in rural areas, where land is a critical resource. Previous studies have indicated that the country has changed land use and land cover. However, they have not explained how these changes can improve the livelihoods of local communities, thereby mitigating their negative impacts through an asset-based approach. Battambang Province, in the northwestern region, was the battleground until political integration in 1996. Since then, the province has been home to immigrants exploring the lands for livelihood. Thus, this study aims to examine agricultural livelihoods in the villages of Dei Kraham and Ou Toek Thla, located west of Battambang Town. These were selected because of their common characteristics. Adopting a quantitative approach and a sustainable livelihood framework, this study employed stratified random sampling to select 123 families for interviews across three population subgroups: old settlers, new settlers, and young settlers. In situ information was collected using structured questionnaires and analyzed using Kruskal–Wallis tests to assess the livelihood assets underlying the physical, natural, human, financial, and social capital. The statistical analysis results reveal no significant differences (p-value = 0.079) in livelihood assets between the strata at the village level. Meanwhile, significant differences were observed in physical, human, and financial capital between old and young settlers when examining the subgroups (p-value 0.000). The extent of the land resources held by old settlers was associated with household income and livelihoods related to agriculture. Based on livelihood asset scores, nearly half of the new settlers (0.49–0.5) and a quarter of the young settlers (0.47) are vulnerable groups requiring support. The youth will soon face an uncertain future if they do not prioritize education. Full article

Due to their nutritional value and sustainability, edible insect-based foods are gaining popularity in Europe. Their use is regulated by EU legislation, which defines authorised species and sets labelling requirements. Molecular tools are being developed to authenticate such products. In this study, yellow mealworm (Tenebrio molitor) larvae authorised for human consumption were fed wheat flour-based diets containing varying proportions of house cricket (Acheta domesticus) flour for 21 days. This was followed by a 48 h starvation period to assess the persistence of insect DNA in the digestive tract. Two novel, species-specific, single-copy markers were designed: ampd gene for the Acheta domesticus and MyD88 gene for the Tenebrio molitor. These were applied using qPCR and ddPCR. Both methods successfully detected cricket DNA in the guts of starved larvae. Linear regression analysis revealed a strong, statistically significant correlation between the proportion of Acheta domesticus flour in the diet and the normalised relative quantity of DNA. ddPCR proved to be more sensitive than qPCR, particularly in the detection of low DNA levels. These results suggest that the presence of DNA from undeclared insect species in edible insects may be indicative of their diet rather than contamination or adulteration. This highlights the importance of contextual interpretation in food authenticity testing. Full article

Accurate and spatially consistent rainfall estimation is essential for hydrological modeling and flood risk mitigation, especially in mountainous tropical regions with sparse observational networks and highly heterogeneous rainfall. This study presents a comparative analysis of six radar–gauge merging methods, including three statistical approaches—Quantile Adaptive Gaussian (QAG), Empirical Quantile Mapping (EQM), and radial basis function (RBF)—and three geostatistical approaches—external drift kriging (EDK), Bayesian Kriging (BAK), and Residual Kriging (REK). The evaluation was conducted over the Huong River Basin in Central Vietnam, a region characterized by steep terrain, monsoonal climate, and frequent hydrometeorological extremes. Two observational scenarios were established: Scenario S1 utilized 13 gauges for merging and 7 for independent validation, while Scenario S2 employed all 20 stations. Hourly radar and gauge data from peak rainy months were used for the evaluation. Each method was assessed using continuous metrics (RMSE, MAE, CC, NSE, and KGE), categorical metrics (POD and CSI), and spatial consistency indicators. Results indicate that all merging methods significantly improved the accuracy of rainfall estimates compared to raw radar data. Among them, RBF consistently achieved the highest accuracy, with the lowest RMSE (1.24 mm/h), highest NSE (0.954), and strongest spatial correlation (CC = 0.978) in Scenario S2. RBF also maintained high classification skills across all rainfall categories, including very heavy rain. EDK and BAK performed better with denser gauge input but required recalibration of variogram parameters. EQM and REK yielded moderate performance and had limitations near basin boundaries where gauge coverage was sparse. The results highlight trade-offs between method complexity, spatial accuracy, and robustness. While complex methods like EDK and BAK offer detailed spatial outputs, they require more calibration. Simpler methods are easier to apply across different conditions. RBF emerged as the most practical and transferable option, offering strong generalization, minimal calibration needs, and computational efficiency. These findings provide useful guidance for integrating radar and gauge data in flood-prone, data-scarce regions. Full article

Reinforcement learning models often rely on uncertainty estimation to guide decision-making in dynamic environments. However, the role of memory limitations in representing statistical regularities in the environment is less understood. This study investigated how limited memory capacity influence uncertainty estimation, potentially leading to misestimations of outcomes and environmental statistics. We developed a computational model incorporating active working memory processes and lateral inhibition to demonstrate how relevant information is selected, stored, and used to estimate uncertainty. The model allows for the detection of contextual changes by estimating expected uncertainty and perceived volatility. Two experiments were conducted to investigate limitations in information availability and uncertainty estimation. The first experiment explored the effect of cognitive load on memory reliance for uncertainty estimation. The results show that cognitive load diminished reliance on memory, lowered expected uncertainty, and increased perceptions of environmental volatility. The second experiment assessed how outcome exposure conditions affect the ability to detect environmental changes, revealing differences in the mechanisms used for environmental change detection. The findings emphasize the importance of memory constraints in uncertainty estimation, highlighting how misestimation of uncertainties is influenced by individual experiences and the capacity of working memory (WM) to store relevant information. These insights contribute to understanding the role of WM in decision-making under uncertainty and provide a framework for exploring the dynamics of reinforcement learning in memory-limited systems. Full article

Background/Objectives: Keratoconus (KC) is a bilateral asymmetric corneal ectasia characterized by progressive corneal thinning, irregular astigmatism, and impaired visual acuity. The National Eye Institute (NEI) developed the Visual Function Questionnaire (VFQ-25) to assess the impact of visual impairment on quality of life. This study aimed to evaluate the effect of penetrating keratoplasty (PKP) on quality of life and visual acuity in KC patients one year postoperatively. Methods: A retrospective study was conducted between January 2018 and December 2022 at the Ophthalmology Department of Saint Barbara Hospital, Trauma Center, Sosnowiec, Poland. A total of 71 patients (86 eyes) diagnosed with KC underwent PKP. The VFQ-25 questionnaire and visual acuity measurements were assessed preoperatively and one year postoperatively. Results: The study cohort included 71 patients (20 females, 28.17%; 51 males, 71.83%). Preoperative visual acuity ranged from less than 0.05 on the Snellen chart to 0.5. Postoperatively, visual acuity improved to a range of 0.1–1.0. A visual acuity of 1.0 was achieved in 21 eyes (24.42%; 5 females, 24%; 16 males, 76%), with a statistically significant improvement (p < 0.01). The mean VFQ-25 composite score increased from 57.96 (±17.58) preoperatively to 81.42 (±14.66) postoperatively (p < 0.001). Domains with the lowest preoperative scores were “role difficulties,” “general vision,” and “mental health,” while “color vision” scored highest. Conclusions: PKP significantly enhances both objective visual acuity and subjective quality of life in KC patients, as reflected in VFQ-25 questionnaire outcomes. Full article

Background: Comprehensive Geriatric Assessment (CGA) is essential for evaluating older cancer patients, but significant gaps persist in both research and clinical practice. This study aimed (I) to identify the CGA elements that most influence anti-cancer treatment decisions in older patients and (II) to explore the predictive value of CGA components for mortality. Methods: This observational study included older patients with newly diagnosed, histologically confirmed solid or hematological cancers, recruited consecutively from 2003 to 2023. Participants were followed for four years. The data collected included CGA measures of functional (Activities of Daily Living-ADL), cognitive (Mini-Mental State Examination-MMSE), and emotional (Geriatric Depression Scale-GDS) domains. Patients were categorized into frail, vulnerable, or fit groups based on Balducci’s criteria. Statistical analyses included decision tree modeling and Cox regression to identify predictors of mortality. Results: A total of 7022 patients (3222 females) were included, with a mean age of 78.3 ± 12.9 years. The key CGA factors influencing treatment decisions were ADL (first step), cohabitation status (second step), and age (last step). After four years, 21.9% patients had died. Higher GDS scores (OR 1.04, 95% CI 1.01–1.07, p = 0.04) were independently associated with survival in men and living with family members (OR 1.67, 95% CI 1.35–2.07, p < 0.001) in women. Younger patients (<77 years) showed both MMSE and GDS as significant risk factors for mortality. Conclusions: Functional capacity, cohabitation status, and GDS scores are crucial for guiding treatment decisions and predicting mortality in older cancer patients, emphasizing the need for a multidimensional geriatric assessment. Full article

This study applies Gradient Boosting Machines (GBMs) and principal component regression (PCR) to forecast the closing price of the Johannesburg Stock Exchange (JSE) All-Share Index (ALSI), using daily data from 2009 to 2024, sourced from the Wall Street Journal. The models are evaluated under three training–testing split ratios to assess short-term forecasting performance. Forecast accuracy is assessed using standard error metrics: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute scaled error (MASE). Across all test splits, the GBM consistently achieves lower forecast errors than PCR, demonstrating superior predictive accuracy. To validate the significance of this performance difference, the Diebold–Mariano (DM) test is applied, confirming that the forecast errors from the GBM are statistically significantly lower than those of PCR at conventional significance levels. These findings highlight the GBM’s strength in capturing nonlinear relationships and complex interactions in financial time series, particularly when using features such as the USD/ZAR exchange rate, oil, platinum, and gold prices, the S&P 500 index, and calendar-based variables like month and day. Future research should consider integrating additional macroeconomic indicators and exploring alternative or hybrid forecasting models to improve robustness and generalisability across different market conditions. Full article

Rapid urbanization and escalating disaster risks necessitate resilient urban governance systems. Smart city initiatives that leverage digital technologies—such as the internet of things (IoT), big data analytics, and artificial intelligence (AI)—demonstrate transformative potential in enhancing emergency management capabilities. However, empirical evidence regarding their causal impact and underlying mechanisms remains limited, particularly in developing economies. Drawing on panel data from 275 Chinese prefecture-level cities over the period 2006–2021 and using China’s smart city pilot policy as a quasi-natural experiment, this study applies a multi-period difference-in-differences (DID) approach to rigorously assess the effects of smart city construction on emergency management capabilities. Results reveal that smart city construction produced a statistically significant improvement in emergency management capabilities, which remained robust after conducting multiple sensitivity checks and controlling for potential confounding policies. The benefits exhibit notable heterogeneity: emergency management capability improvements are most pronounced in central China and in cities at the extremes of population size—megacities (>10 million residents) and small cities (<1 million residents)—while effects remain marginal in medium-sized and eastern cities. Crucially, mechanism analysis reveals that digital technology application fully mediates 86.7% of the total effect, whereas factor allocation efficiency exerts only a direct, non-mediating influence. These findings suggest that smart cities primarily enhance emergency management capabilities through digital enablers, with effectiveness contingent upon regional infrastructure development and urban scale. Policy priorities should therefore emphasize investments in digital infrastructure, interagency data integration, and targeted capacity-building strategies tailored to central and western regions as well as smaller cities. Full article

Background: Hemiplegic migraine (HM) is a rare and severe subtype of migraine with a complex genetic basis. Although pathogenic variants in CACNA1A, ATP1A2, and SCN1A explain some familial cases, a significant proportion of patients remain genetically undiagnosed. Increasing evidence points to an overlap between migraine and cerebral small vessel disease (SVD), implicating vascular dysfunction in HM pathophysiology. Objective: This study aimed to identify rare or novel variants in genes associated with SVD in a cohort of patients clinically diagnosed with HM who tested negative for known familial hemiplegic migraine (FHM) pathogenic variants. Methods: We conducted a case-control association analysis of whole exome sequencing (WES) data from 184 unrelated HM patients. A targeted panel of 34 SVD-related genes was assessed. Variants were prioritised based on rarity (MAF ≤ 0.05), location (exonic/splice site), and predicted pathogenicity using in silico tools. Statistical comparisons to gnomAD’s Non-Finnish European population were made using chi-square tests. Results: Significant variants were identified in several SVD-related genes, including LRP1 (p.Thr4077Arg), COL4A1 (p.Pro54Leu), COL4A2 (p.Glu1123Gly), and TGFBR2 (p.Met148Leu and p.Ala51Pro). The LRP1 variant showed the strongest association (p < 0.001). All key variants demonstrated pathogenicity predictions in multiple computational models, implicating them in vascular dysfunction relevant to migraine mechanisms. Conclusions: This study provides new insights into the genetic architecture of hemiplegic migraine, identifying rare and potentially deleterious variants in SVD-related genes. These findings support the hypothesis that vascular and cellular maintenance pathways contribute to migraine susceptibility and may offer new targets for diagnosis and therapy. Full article

Objectives: This study sought to assess the impact of diabetes and hypertension on wound healing and recovery in orthopedic patients, with an emphasis on laboratory correlations. Materials and Methods: This study included 67 orthopedic patients, divided into a geriatric group (n = 49, ≥65 years) and a control group (n = 18). Clinical and laboratory assessments were performed at admission and discharge. Data were analyzed statistically. Results: Geriatric patients showed a higher triglyceride glucose-body mass index (TyG-BMI), glucose, cholesterol, C-reactive protein (CRP), interleukin-6 (IL-6), and leukocytes and lower hemoglobin and platelets (PLTs), with poorer healing and well-being. Elevated CRP, IL-6, and urea and decreased protein and hemoglobin persisted in this group. Diabetes improved outcomes in older adults, while hypertension worsened them in younger patients. Favorable outcomes correlated with higher triglycerides, fibrinogen, hemoglobin, and red blood cells (RBCs), while they did not correlate with elevated CRP, IL-6, leptin, urea, creatinine, and white blood cells (WBCs). Conclusions: Key predictors of healing and well-being included CRP, hemoglobin, RBC, and hematocrit in older patients and hypertension, CRP, hemoglobin, and leptin in younger individuals. Age-specific metabolic and inflammatory profiles influence recovery trajectories and may be used to predict problems in both recovery and patients’ well-being. Further research is required to better understand the correlations between these factors. Full article

The prediction and management of water quality are critical to ensure sustainable water resources, particularly in regions like Malaysia, where rivers face increasing pollution from industrialisation, agriculture, and urban expansion. This review aims to provide a comprehensive analysis of machine learning (ML) models and statistical methods applied in forecasting and classification of water quality. A particular focus is given to hybrid models that integrate multiple approaches to improve predictive accuracy and robustness. This study also reviews water quality standards and highlights the environmental context that necessitates advanced predictive tools. Statistical techniques such as residual analysis, principal component analysis (PCA), and feature importance assessment are also explored to enhance model interpretability and reliability. Comparative tables of model performance, strengths, and limitations are presented alongside real-world applications. Despite recent advancements, challenges remain in data quality, model interpretability, and integration of spatio-temporal and fuzzy logic techniques. This review identifies key research gaps and proposes future directions for developing transparent, adaptive, and accurate models. The findings can also guide researchers and policymakers towards the development of smart water quality management systems that enhance decision-making and ecological sustainability. Full article

Background: Environmental exposures, such as per- and polyfluoroalkyl substances (PFAS), in conjunction with social and behavioral factors, can significantly impact liver health. This research investigates the combined effects of PFAS (perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), alcohol consumption, smoking, income, and education on liver function among the U.S. population, utilizing data from the 2017–2018 National Health and Nutrition Examination Survey (NHANES). Methods: PFAS concentrations in blood samples were analyzed using online solid-phase extraction combined with liquid chromatography–tandem mass spectrometry (LC-MS/MS), a highly sensitive and specific method for detecting levels of PFAS. Liver function was evaluated using biomarkers such as alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), gamma-glutamyltransferase (GGT), total bilirubin, and the fatty liver index (FLI). Descriptive statistics and multivariable linear regression analyses were employed to assess the associations between exposures and liver outcomes. Bayesian Kernel Machine Regression (BKMR) was utilized to explore the nonlinear and interactive effects of these exposures. To determine the relative influence of each factor on liver health, Posterior Inclusion Probabilities (PIPs) were calculated. Results: Linear regression analyses indicated that income and education were inversely associated with several liver injury biomarkers, while alcohol use and smoking demonstrated stronger and more consistent associations. Bayesian Kernel Machine Regression (BKMR) further highlighted alcohol and smoking as the most influential predictors, particularly for GGT and total bilirubin, with posterior inclusion probabilities (PIPs) close to 1.0. In contrast, PFAS showed weaker associations. Regression coefficients were small and largely non-significant, and PIPs were comparatively lower across most liver outcomes. Notably, education had a higher PIP for ALT and GGT than PFAS, suggesting a more protective role in liver health. People with higher education levels tend to live healthier lifestyles, have better access to healthcare, and are generally more aware of health risks. These factors can all help reduce the risk of liver problems. Overall mixture effects demonstrated nonlinear trends, including U-shaped relationships for ALT and GGT, and inverse associations for AST, FLI, and ALP. Conclusion: These findings underscore the importance of considering both environmental and social–behavioral determinants in liver health. While PFAS exposures remain a long-term concern, modifiable lifestyle and structural factors, particularly alcohol, smoking, income, and education, exert more immediate and pronounced effects on hepatic biomarkers in the general population. Full article

Objective: This study aimed to compare the clinical and radiological outcomes of dynamic rod stabilization with and without transforaminal lumbar interbody fusion (TLIF) in patients undergoing surgery for degenerative lumbar instability. Specifically, we evaluated the prognostic value of hybrid systems in reducing adjacent segment disease (ASD), enhancing fusion rates, and improving functional outcomes. Methods: A retrospective analysis was conducted on 62 patients treated between 2019 and 2022. Group 1 (n = 34) underwent dynamic rod stabilization alone, while Group 2 (n = 28) received dynamic stabilization combined with TLIF. Radiological assessments included disk height index (DHI) and fusion rates. Clinical outcomes were measured using the Visual Analog Scale (VAS) for back and leg pain at baseline, 12, and 24 months. Statistical analysis was performed using Jamovi^® software (version 2.4.1). Results: The hybrid group (dynamic + TLIF) demonstrated significantly higher anterior fusion rates (p < 0.001) and greater improvement in VAS scores for back (p = 0.005) and leg pain (p < 0.001) at 12 months. Although operative time was longer (p = 0.002), there was no significant difference in hospital stay (p = 0.635). No significant differences were observed in ASD development (p = 0.11) or pseudoarthrosis (p = 0.396). The hybrid group maintained better lumbar lordosis and higher adjacent segment DHI. Conclusions: Hybrid dynamic stabilization combined with TLIF provides superior clinical outcomes and fusion rates compared to dynamic stabilization alone, without significantly increasing the risk of ASD. These findings support the use of hybrid constructs as a balanced strategy for treating degenerative lumbar instability. Full article

Background/Objectives: The field of dietary supplements is changing and evolving quickly. Registered Dietitian Nutritionists are recognized as experts in nutrition and familiarity with the usage of dietary supplements is expected. However, education on the use of dietary supplements is not equal across accredited dietetic education programs, which can lead to disparities in dietitians’ feelings of preparedness, attitudes, and consequently experience of comfort regarding dietary supplements. The purpose of this study was to create the D-SCOPE Questionnaire (Dietitians’ Survey of Comfort, Opinions, and Preparedness in Education in Supplements) and validate it. This questionnaire assesses Registered Dietitian Nutritionists’ feelings of preparedness, comfort with use, and general attitudes in the field of dietary supplements. Methods: Face and content validity was established with dietitian, nutritionist, and statistician input. For recruitment, 2000 national randomly selected emails were obtained from the Commission on Dietetic Registration. Registered Dietitian Nutritionists (n = 248) responded to the survey email request. Descriptive statistics (reported as means ± standard deviation), principal axis factoring (exploratory factor analysis) with a direct oblimin rotation and Cronbach’s a reliability analysis were used for validation techniques. Results: Five factors were created, which explained about 63% of the variance in the questionnaire. The questionnaire was generally reliable, but the factor structure could change with a non-US population. Conclusions: As a unit, the D-SCOPE Questionnaire shows validity and reliability in assessing Registered Dietitian Nutritionists’ perceptions of preparedness and attitudes in the area related to dietary supplements. Full article