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A multidimensional risk prediction framework for malignant intestinal obstruction based on machine learning: computational model development and clinical validation
Association between oxidative balance score and psychological disorders in adults
Abstract Maintaining a favorable balance between antioxidants and pro-oxidants, rather than merely increasing antioxidant intake, may be critical for mental health. This study examined the association between Oxidative Balance Score (OBS), an integrated measure of both antioxidant and pro-oxidant exposures, and odds of psychological disorders. This cross-sectional analysis used baseline data from the Isfahan Functional Disorders (ISFUN) cohort. Dietary intakes were assessed using a self-administered, Willett-format, Dish-based, 106-item Semi-quantitative Food Frequency Questionnaire (DS-FFQ). OBS was calculated based on 16 dietary and 4 lifestyle factors. Depression and anxiety symptoms were evaluated using the Iranian-validated Hospital Anxiety and Depression Scale (HADS). After adjusting for confounding variables, higher OBS was significantly associated with reduced odds of depression (OR: 0.65; 95% CI: 0.49–0.86) and anxiety (OR: 0.70; 95% CI: 0.53–0.92). In sex-stratified analyses, a significant inverse association was found between OBS with depression (OR: 0.65; 95% CI: 0.45–0.93) and anxiety (OR: 0.64; 95% CI: 0.45–0.91) among women. However, there was no significant association between OBS with depression and anxiety among men. In conclusion, a higher OBS was associated with reduced odds of depression and anxiety in this population, with this inverse association being statistically significant only in women, highlighting the potential importance of a balanced antioxidant-pro-oxidant profile for mental health. Prospective studies are needed to confirm these findings.
Antibody repertoire associated with clinically diverse presentations of pediatric SARS-CoV-2 infection
Towards the expressive power of translating approach for knowledge graph completion
Abstract The pessimistic conclusions from previous research on the Expressive Power of translating approaches for knowledge graph completion are investigated and rethought. To this end, a novel model RosE is formulated by introducing two degrees of freedom and outperforms traditional translation-based models on widely used datasets such as , , , and . Every new freedom is a vector in the model, the operation of which multiplies with entity and relation embeddings, rotating them to a new position. Consequently, the head entity and relation embedding are equal to the tail entity. Fortunately, the intrinsic limitations merely exist in this research line when the model is trained in real vector space, not in other spaces such as trigonometric functions and complex. The experimental and theoretical results, together with the newly proposed model RosE, also confirm this conclusion. Therefore, the findings in this work do not discourage further exploration in this research line, but rather avoid those with discouraging outcomes. In short, this paper clarifies that the limitations of the translation approach for knowledge graph completion are specific conditions that only involve partial models. That is, the research line of translation approach is still promising when certain known pitfalls are avoided.
Clinical outcomes and determinants of neonatal hyperbilirubinemia among NICU admissions: a retrospective study in Ethiopia
Abstract Neonatal hyperbilirubinemia is a significant public health concern in Ethiopia, contributing to approximately 10% of neonatal mortality. This study aimed to identify the clinical outcomes and determinants of jaundice among neonates admitted to the Neonatal Intensive Care Unit (NICU) at Asella Referral and Teaching Hospital (ARTH) in South East Ethiopia. The objective of this study was to assess the magnitude of neonatal hyperbilirubinemia, its clinical outcomes, and the maternal and neonatal determinants among neonates admitted to the ARTH NICU. An institution-based retrospective cross-sectional study was conducted using systematic random sampling (K = 7), 183 neonates were selected randomly from a source population of 1,350 admissions. Data were analyzed using multivariate logistic regression to identify independent predictors of hyperbilirubinemia, with statistical significance set at p < 0.05. The prevalence of hyperbilirubinemia was 16.4%( n = 30; 95% CI: 11–21.8). Among these jaundiced neonates, 46.7% ( n = 14) presented with high admission total serum bilirubin levels ranging from 15 to 19 mg/dL, and 26.7% ( n = 8) required exchange transfusion. Inadequate breastfeeding (AOR 9.51, 95% CI: 1.24–72.67, p = 0.030) and neonatal sepsis (AOR 3.71, 95% CI: 1.34–10.28, p = 0.012) were the primary independent predictors. The high admission bilirubin levels and significant need for exchange transfusion highlight the urgent need for universal pre-discharge screening and robust lactation support.
Examining public acceptance intentions for government digital human through expectation confirmation and technology acceptance models
Physico-mechanical and chemical performance of Portland cement modified with eggshell powder for sustainable construction
Retrospective GIS-based spatial analysis of dengue case distribution across housing typologies in Malaysia
Workplace violence and body-worn camera use among emergency medical technicians in South Korea: a nationwide cross-sectional survey
Abstract Emergency medical technicians (EMTs) frequently experience workplace violence in prehospital settings; however, evidence on the role of body-worn cameras (BWCs) in this context is limited. This study examined the factors associated with frequent workplace violence, the perceived need for BWCs, and actual BWC use among South Korean EMTs, incorporating individual- and regional-level indicators. A nationwide cross-sectional survey was conducted among EMTs employed by provincial fire departments in South Korea. Survey data on sociodemographic characteristics, work conditions, workplace violence over the past year, and BWC-related perceptions and behaviors were linked to province-level assault indicators from the National 119 emergency medical services (EMS) Statistical Yearbook (2020–2024). The main outcomes included frequent workplace violence, high perceived need for BWCs, and BWC use. Regional assault burden was modeled as tertiles of a five-year mean provincial assault index and as a continuous measure per 10-unit increase. All primary outcomes were self-reported within a knowledge–attitude–practice (KAP) survey design. Multivariable logistic regression was performed to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). To address within-province clustering, Generalized Estimating Equations (GEE) were additionally used as a sensitivity analysis. Of 3,595 EMTs included in the descriptive analyses, 2,555 provided complete violence data. Nearly all respondents (99.7%) reported some workplace violence in the past year, and 17.3% were classified as experiencing frequent workplace violence. Overall, 64.4% reported using a BWC, and 87.9% expressed a high perceived need. Frequent violence was associated with younger age, longer EMS career, higher daily call volume, and working as an EMT rather than an ambulance driver, but not with regional assault burden. High perceived need for BWCs was more common among female EMTs and varied by age, again without clear associations with regional assault indices. In contrast, actual BWC use was associated with job position, shorter EMS career, lower call volume, and high perceived need, and was inversely associated with higher regional assault burden. Workplace violence was highly prevalent among South Korean EMTs, and the perceived need for BWCs was widespread. Individual and work-related factors, rather than province-level assault burden, were the key determinants of frequent violence and BWC-related perceptions, whereas BWC use was less common in regions with higher recorded assault indices.
Advanced hybrid nanocomposites for infrastructure renewal: enhanced shear and flexural performance in load-bearing applications
Factors associated with postpartum depression in mothers of the kangaroo mother care program in Southern Colombia: a cross-sectional study
Challenges of conventional iterative all-atom and coarse-grained multiscale molecular dynamics
Degree-based Inverse Prodeg indices linking graph theory and cheminformatics with sharp results and QSPR modeling
Diastereoselective and green synthesis of novel Hexahydro Imidazo [1,2-a] pyrimidine, from Azlactones, Brimonidine and Clonidine
Correction: Optimization and experimental analysis of a cleaning device for super rice with high impurity rates based on airflow field enhancement
Effect of changing kick frequency on inter-joint coordination during undulatory underwater swimming
Abstract Undulatory underwater swimming (UUS) contributes to race performance in competitive swimming, yet how inter-joint coordination changes with kick frequency remains unclear. This study examined the effects of kick frequency on trunk–hip and hip–knee inter-joint coordination during UUS using the Modified Vector Coding Technique. Eight male swimmers performed UUS trials at 80–120% of their optimal kick frequency, with kinematics captured using three-dimensional underwater motion analysis. Swimming velocity increased with kick frequency but plateaued beyond 100%. The proportion of the hip–knee anti-phase with distal dominance pattern ––hip and knee rotating in opposite directions with greater angular displacement at the knee–– increased with kick frequency, whereas the absolute durations of this pattern per cycle changed little, indicating that the time required for kick reversal remained nearly constant regardless of kick frequency. Under high-frequency conditions (110% and 120%), preserving the time for kick reversal within a shortened kick cycle was accompanied by constrained trunk undulation during the late downward kick. These findings suggest that when kick frequency is acutely increased beyond the optimal value, hip–knee reversal time is maintained but trunk undulation is impaired, likely contributing to the plateau in swimming velocity.
Optimization algorithm for improving the prediction accuracy of API solubility in green solvent
Abstract Supercritical carbon dioxide (SC-CO₂) is widely used as an environmentally friendly solvent in pharmaceutical processing, where accurate prediction of drug solubility is essential for efficient formulation design, extraction processes, and process optimization. However, predicting solubility behavior in supercritical systems remains challenging due to the nonlinear interactions between thermodynamic conditions and molecular properties. In this study, a hybrid artificial intelligence framework is developed to predict the solubility of active pharmaceutical ingredients (APIs) in SC-CO₂ using a curated dataset of more than 350 experimentally reported measurements. The proposed framework integrates interpretable deep learning (TabNet) and histogram-based gradient boosting (HGB) with three metaheuristic optimization algorithms, namely the Attack-Leave Optimizer (ALO), Energy Valley Optimizer (EVO), and Botox Optimization Algorithm (BOA), to improve hyperparameter tuning and predictive performance. Model evaluation was conducted using multiple statistical indicators, five-fold cross-validation, prediction interval bootstrapping, and multi-objective Pareto front analysis to assess accuracy and robustness. Among the evaluated configurations, the EVO-tuned TabNet model demonstrated the best predictive performance, achieving a coefficient of determination of $$\:{R}^{2}=0.982$$ along with narrow prediction intervals, indicating strong generalization capability within the studied thermodynamic domain. Statistical analysis using the Kruskal–Wallis test confirmed significant differences between optimizer performances ( $$\:p<0.05$$ ). These findings demonstrate that the proposed hybrid pipeline enhances predictive accuracy and interpretability within the thermodynamic domain represented by the compiled dataset. The framework therefore provides a statistically supported computational tool for assisting solvent selection and formulation analysis in supercritical systems, while broader generalization would benefit from future expansion of experimental solubility datasets.