Search Results (4,331)

Background: This study presents a preliminary evaluation of a six-day outpatient psychotherapeutic training based on the Duc in Altum Therapy (DIAT) approach, examining its effectiveness in reducing symptoms of anxiety, depression, and stress among adults. Given the growing demand for brief, effective mental health interventions, this format aims to provide intensive support within a condensed timeframe. Methods: The intervention consisted of supportive and experiential group therapy delivered over six days and included 109 participants. Outcomes were assessed at three time points—pre-intervention, post-intervention, and three-month follow-up—using the Direct Behavior Rating Scale Items, the Brief Screen for Depression (BSD), and the Perceived Stress Scale (PSS-10). Results: Participants reported lower levels of anxiety (B = −1.435, p < 0.0001), depression (B = −0.717, p < 0.001), and stress (B = −1.882, p < 0.001) after training, reflecting statistically significant within-group changes. These improvements were maintained at the three-month follow-up. Conclusions: Although the absence of a control group limits causal inference, the findings provide preliminary support for the effectiveness of DIAT as a brief psychotherapeutic intervention. Participants reported lower levels of anxiety, depression, and stress after the training, reflecting statistically significant within-group changes. These improvements were maintained at the three-month follow-up. Full article

The global burden of ischemic stroke requires a search for new factors that affect its risk and outcomes. Recent studies have shown that triiodothyronine could potentiate favorable stroke outcomes, but the reason for this is still unclear. To clarify the effects of the free triiodothyronine (fT3) level on stroke outcomes, we adopted a Mendelian randomization (MR) approach to evaluate their causal relationship. The genetic variants associated with the free triiodothyronine (fT3) level were obtained from the ThyroidOmics Consortium. Thirteen single-nucleotide polymorphisms, genetically predicting the fT3 level with a significance of p < 10⁻⁷, were adopted for MR analysis. Summary-level data for ischemic stroke outcomes (reported as a continuous variable, namely the modified Rankin score 3 months after stroke) was obtained from the GISCOME network. MR analyses were performed using the TwoSampleMR framework. The inverse-variance weighting method of MR analysis showed that a genetically predicted increase in fT3 level is associated with a reduction in ordinal Modified Rankin Scale scores (OR = 0.581, 95% CI 0.37–0.92, p = 0.0183). This study showed that higher fT3 levels could be causally associated with more favorable ischemic stroke outcomes and provides more evidence for the possibility of using thyroid hormone (TH) analogs to improve stroke outcomes. Full article

Background: Mobility plays a fundamental role in causal reasoning (causal inference or cause–effect learning), which is essential for brain development at early ages. Children naturally develop causal reasoning through interaction with their environment. Therefore, children with severe motor disabilities (GMFCS levels IV–V), who face limited opportunities for interaction, often show delays in causal reasoning. Objective: This study investigates how a wheelchair-mounted, semi-autonomous mobility platform operated via a simple switch may enhance causal learning in children with severe disabilities, compared with traditional therapies. However, due to the scarcity of participants who meet the inclusion criteria and the need for long-term evaluation, recruitment poses a significant challenge. This study aims to provide an initial assessment of the platform and collect preliminary data to estimate the required sample size and number of sessions for future studies. Methods: We conducted a pilot randomized controlled trial (RCT) to assess platform usability and its effect on reaction time and keystroke accuracy. Four children, aged 8.5 ± 2.38, participated in seven 30 min sessions. They were randomly assigned in equal numbers, with two participants in the intervention group (using the platform) and two in the control group (receiving standard therapy). Usability was evaluated through a questionnaire completed by two therapists. Key outcome measures included the System Usability Scale (SUS), reaction time (RT), and keystroke accuracy (NIS). Results: Despite the small sample size and recruitment challenges, the data allowed for preliminary estimates of the sample size and number of sessions required for future studies. Therapists reported positive usability scores. Children using the platform showed promising trends in RT and NIS, suggesting improved engagement with cause–effect tasks. Conclusions: The findings support the feasibility and usability of the mobility platform by therapists, although some improvements should be implemented in the future. No conclusive evidence was found regarding the platform’s effectiveness on causal learning, despite a positive trend over time. This pilot study also provides valuable insights for designing larger, statistically powered trials, particularly focused on NIS. Full article

Introduction: Polio supplementary immunisation activities (SIA) are implemented to rapidly increase vaccination coverage and interrupt the transmission of poliovirus in a specified geographical area. Polio SIA complements routine immunisation and is crucial for the eradication of the disease by increasing population immunity. However, several contextual factors (i.e., implementation determinants) can influence the success or failure of polio SIA implementation; as such, understanding their dynamics can enhance proactive planning for practice improvement. This study aimed to explore and map the contextual factors of polio SIA implementation in the African region using a critical systems thinking approach. Methods: A rapid review of published and grey literature was conducted. The search included the Global Polio Eradication Initiative library for programmatic reports and two databases (PubMed and Google Scholar). Data extraction was performed using a structured tool. Thematic analysis was performed to categorise the identified contextual factors according to the domains and constructs of the Consolidated Framework for Implementation Research (CFIR). Then, a causal loop diagram (CLD) was used to map the linkages between the identified factors. Results: A total of seventy-eight contextual factors across the five CFIR domains were identified: three for innovation, twenty for outer setting, sixteen for inner setting, twenty-six for individuals, and thirteen for the implementation process. A system map of all the factors using CLD revealed multiple contingent connections, with eleven reinforcing loops and four balancing loops. Conclusions: This study identified the multilevel nature of the contextual factors that influence polio SIA, including their dynamics. The integration of CLD and CFIR in this study offers critical insights into the potential feedback loops that exists between the contextual factors which can be used as leverage points for policy and practice improvements, including tailoring strategies to enhance polio campaign implementation effectiveness, especially with the expanded use of the novel Oral Polio Vaccine type 2 (nOPV2) across countries in the region. Full article

Background/Objectives: Natural products with claimed adaptogenic and/or immunomodulatory effects are widely used in traditional medicine systems across Eurasia. These include herbal remedies (e.g., Panax ginseng), fungi (e.g., Ganoderma lucidum), and animal-derived substances (e.g., propolis from Apis mellifera). Despite their popularity, the safety profiles of these products—particularly concerning adverse events (AEs) and serious adverse events (SAEs)—remain insufficiently understood. This study aimed to assess the safety profiles of adaptogenic and immunomodulatory natural products through a scoping review of published human studies and an analysis of individual case safety reports (ICSRs) from the WHO-UMC VigiBase database. Methods: A scoping review was conducted using PubMed (1980–2024) in line with PRISMA-ScR guidelines. Eligible studies included randomized and non-randomized clinical trials and case reports in humans focused on safety outcomes. Data extraction followed the Joanna Briggs Institute (JBI) standardized template. ICSRs from VigiBase were analyzed by product type, AE type and seriousness, and demographic characteristics. The data were further organized to highlight the 15 most frequently reported products and their top five System Organ Classes (SOCs) and Preferred Terms (PTs). Results: The scoping review identified 51 natural products with reported adaptogenic and/or immunomodulatory properties. This included 285 clinical trials and 119 case studies on single-ingredient products and 54 clinical trials and 21 case studies on multi-ingredient preparations. Common AEs included gastrointestinal, dermatological, hepatic, cardiovascular, and immunological reactions. SAEs were rare but reported for Echinacea purpurea, Silybum marianum, and Camellia sinensis. From Vigibase, 45,042 ICSRs were retrieved for 49 natural products: 10,702 for single-ingredient and 34,340 for multi-ingredient products. Among 7856 reports listing a single-ingredient product as the sole suspect, 15.8% were SAEs, including eight fatal cases. However, the causality remained unclear due to insufficient data. Ganoderma lucidum, Viscum album, and Silybum marianum were most frequently associated with SAEs. In multi-ingredient products, propolis was frequently linked to hypersensitivity and skin reactions. Conclusions: This study provides a comprehensive overview of the safety profiles of adaptogenic and immunomodulatory natural products. Variability in product composition, lack of standardization, incomplete reporting in clinical studies, and underreporting in pharmacovigilance databases complicate accurate risk assessment. For multi-ingredient products, attributing specific AEs to specific components remains difficult. Further high-quality clinical research and improved pharmacovigilance are needed, along with clear safety warnings to reduce risks for consumers. Full article

Workplace health and safety issues have long plagued the construction industry. While safety efforts have traditionally focused on physical risks, increasing attention is being paid to mental health and work-related stressors, which can negatively affect both productivity and safety. In Saudi Arabia, the construction sector presents a unique context because of its highly diverse, multinational workforce. Workers of different nationalities often operate on the same job site, leading to potential communication barriers, cultural misunderstandings, and inconsistent safety practices, all of which may amplify stress and safety risks. This research aims to investigate the influence of work-related stressors on construction workers’ safety in Saudi Arabia and identify which stressors most significantly contribute to the risk of injury. A structured questionnaire was distributed to 349 construction workers across 16 job sites in Saudi Arabia. The survey measures ten key stressors identified in the literature, including job site demand, job control, job certainty, skill demand, social support, harassment and discrimination, conflict with supervisors, interpersonal conflict, and job satisfaction. Data were analyzed using logistic regression and Pearson correlation to examine relationships between stressors and self-reported injuries. The findings indicated that work-related stressors significantly predict workplace injury. While the first regression model showed a modest effect size, it was statistically significant. The second model identified job site demand and job satisfaction as the most influential predictors of injury risk. Work-related stressors, particularly high job demands and low job satisfaction, substantially increase the likelihood of injury among construction workers. These findings emphasize the importance of incorporating psychosocial risk management into construction safety practices in Saudi Arabia. Future studies should adopt longitudinal designs to explore causal relationships over time and include qualitative methods such as interviews to gain a deeper understanding. Additionally, factors such as nationality, organizational policies, and management style should be investigated to better understand their moderating effects on the stress–injury relationship. Full article

Background: Osteoporosis is the most prevalent metabolic bone disease, characterized by decreased bone mineral density (, which leads to increased bone fragility, back pain, impaired postural stability, and a heightened risk of fractures. Proprioceptive exercises have been identified as an effective approach for reducing the risk of falls and adverse events. Objective: Our aim was to conduct a pilot exploratory study evaluating the effectiveness of proprioceptive training in improving coordination and balance, and in reducing chronic thoracolumbar back pain in older women diagnosed with osteoporosis. Methods: Quantitative ultrasound bone densitometry was performed on 144 women over the age of 60, followed by the implementation of a proprioceptive training program. The One-Leg Stance balance test and the Visual Analog Scale for pain intensity were administered before and after a six-month training intervention. Results: ANOVA revealed significant improvements in balance, with the OLS duration increasing from 2.49 s at baseline to 7.31 s following the intervention. Participants aged over 70 years demonstrated a positive, though comparatively lower increase in stability and balance. Chi-squared (χ²) analysis indicated that 83.9% of the variance in OLS performance was attributable to proprioceptive training (Cramer’s V = 0.839, p = 0.001). A significant reduction in VAS pain scores was observed, with 48.1% of the variance explained by the moderate effect of proprioceptive training (Cramer’s V = 0.481, p = 0.001). Conclusions: Proprioceptive training has the potential to improve postural stability, balance, and coordination and stimulate pain intensity in the thoracolumbar region. Despite promising results, the absence of a control group limits our ability to draw definitive causal conclusions. Full article

As a typical cold region, Northeast China is characterized by its unique climate, hydrological conditions, and land systems, which collectively shape the diversity and complexity of regional ecosystem services (ESs). This review systematically examines research on ESs in Northeast China from 1997 to 2025, with particular emphasis on recent advances in service classification and spatiotemporal patterns, trade-offs and synergies among ESs, the identification of driving mechanisms, regulatory pathways, and policy effectiveness. The findings reveal obvious spatial heterogeneity and distinct stage-wise changing patterns in ESs across the region, with particularly pronounced trade-offs between food production and regulating services. The primary driving factors are concentrated in natural and human activities dimensions, whereas region-specific variables and policy-related drivers remain underexplored. Current research predominantly employs methods such as correlation analysis and geographically weighted regression; however, the capacity to uncover causal mechanisms and nonlinear interactions remains limited. Future research should strengthen the simulation of ecological processes in cold regions, improve the balance between ES supply and demand, improve policy scenario assessments, and develop dynamic feedback mechanisms. Compared with previous studies focusing on single services or regions, this review provides a multidimensional perspective by synthesizing multiple ES categories, integrating spatiotemporal comparative analysis, and incorporating modeling strategies specific to cold-region dynamics. These efforts will help shift ES research beyond static description toward more systematic regulation and management, providing both theoretical support and practical guidance for sustainable development and ecological governance in Northeast China. Full article

This study aims to empirically investigate the contemporaneous, bidirectional causal relationship between innovation and entrepreneurial activities in China by constructing a dynamic simultaneous equation system. Using panel data from 31 provincial administrative regions from 2000 to 2022, our empirical results demonstrate a robust two-way causal relationship: vigorous innovation activities significantly stimulate the emergence and subsequent growth of entrepreneurial ventures, while entrepreneurial dynamism similarly promotes regional innovation. These findings remain stable and consistent after rigorous robustness checks. Further, employing a Panel Vector Autoregression (PVAR) approach in extended analyses, we find clear evidence of a stable positive feedback loop between innovation and entrepreneurship, characterized by progressive and cumulative effects. Additionally, regional heterogeneity analysis indicates that macroeconomic disparities significantly influence the bidirectional relationship between innovation and entrepreneurship. Specifically, differences in regional resource endowments and economic conditions largely account for variations in innovation–entrepreneurship dynamics across regions. Consequently, local governments should tailor innovation and entrepreneurship policies to regional contexts to maximize economic outcomes effectively under China’s current development paradigm. Full article

We developed and validated an animation-based assessment (ABA) method for evaluating high school students’ inquiry competencies in Taiwan’s 12-Year Curriculum. Contextualized in atmospheric chemistry involving methane and hydroxyl radicals, ABA integrated dynamic simulations, tiered multiple-choice and open-ended tasks, and process tracking on the CloudClassRoom platform, the assessment focused on measuring two inquiry skills: causal reasoning and critical thinking. The results of 26,823 students revealed that the ABA effectively differentiated student performance across ability levels and academic disciplines, with open-ended items sensitive to higher-order reasoning. Gender difference was not observed, indicating the gender-free design of the developed ABA. While the ABA supports diagnostic insights, limitations need to be addressed, including the underassessment of modeling and creative experimentation skills. Therefore, it is necessary to include open modeling tasks and AI-powered semantic scoring. The developed ABA contributes a scalable, competency-aligned framework for inquiry-based science assessments. Full article

Accurate prediction of soil phosphorus (P) adsorption capacity is essential for efficient fertilizer management and environmental protection. Traditional isotherm models, such as the Langmuir equation, have been widely used to quantify P sorption, but they do not adequately capture the nonlinear and multivariate nature of soil systems. This study evaluates the performance of a multi-output XGBoost regression model trained on laboratory-measured P adsorption data from 147 soils, representing a wide range of textures, pH levels, and CaCO₃ contents. The model was developed to simultaneously predict P adsorption at five different equilibrium concentrations (1, 2, 4, 6, and 10 mg/L). SHAP analysis and causal discovery via DirectLiNGAM revealed that initial Olsen P concentration and sand content are the primary factors reducing P adsorption. The multi-output XGBoost model was compared against classical Langmuir isotherms using an extended dataset of 10,389 soil samples. The extended dataset was binned into four groups based on Olsen P concentrations and four groups based on sand content. This binning was based on the identification of these variables as highly influential by the XGBoost model, and on their demonstrated causal relationship with soil P sorption capacity through causal inference analysis. The XGBoost model outperformed the Langmuir model in capturing the effect of Olsen P and sand content, as it predicted a 12.6% drop in P adsorption in the very high Olsen P group and a 19.2% drop in the very high sand content groups, which are substantially higher than the reductions estimated by Langmuir isotherms. These results demonstrate that machine learning models, trained on well-designed experimental data, offer a superior alternative to classical isotherms for modeling P sorption dynamics. Full article

Fine-grained aircraft classification in remote sensing is a critical task within the field of remote sensing image processing, aiming to precisely distinguish between different types of aircraft in aerial images. Due to the high visual similarity among aircraft targets in remote sensing images, accurately capturing subtle and discriminative features becomes a key technical challenge for fine-grained aircraft classification. In this context, we propose a Normalized Coordinate Attention-Based Counterfactual Classification Network (NCC-Net), which emphasizes the spatial positional information of aircraft targets and effectively captures long-range dependencies, thereby enabling precise localization of various aircraft components. Furthermore, we analyze the proposed network from a causal perspective, encouraging the model to focus on key discriminative features of the aircraft while minimizing distraction from the surrounding environment and background. Experimental results on three benchmark datasets demonstrate the superiority of our method. Specifically, NCC-Net achieves Top-1 classification accuracies of 97.7% on FAIR1M, 95.2% on MTARSI2, and 98.4% on ARSI120, outperforming several state-of-the-art methods. These results highlight the effectiveness and generalizability of our proposed method for fine-grained remote sensing target recognition. Full article

Background/Objectives: The role of direct oral anticoagulants (DOACs) vs. vitamin K antagonists (VKAs) in preventing recurrent thrombosis in patients with antiphospholipid syndrome (APS) remains uncertain. Using real-world data, we aimed to evaluate the effectiveness and internal validity of a digital twin (DT) approach for modeling thrombotic recurrence risk in APS patients treated with DOACs or VKAs. Methods: We conducted a multicenter observational study that included thrombotic APS patients treated with DOACs or VKAs. Clinical data were used to generate DT via conditional generative adversarial networks (CGANs), incorporating a directed acyclic graph (DAG) to preserve causal relationships. Validation metrics included absolute standardized mean differences (ASMD), mean ASMD (MASMD), and Spearman correlation matrices to assess structural fidelity. Treatment effects were estimated in a CGAN-conditioned cohort matched on key covariates. Results: Eighty-nine thrombotic APS patients were included: 70 (78.7%) received VKAs and 19 (21.3%) received DOACs. Thrombotic recurrences occurred in 5 DOAC patients (26.3%) and 17 AVK patients (24.3%). The CGAN-generated synthetic cohort closely mirrored the original data (MASMD = 0.073 ± 0.041), with 85.4% of pairwise correlations differing by <0.1 in absolute value. In the conditioned DT cohort, predicted recurrence was 24.2% for DOACs and 19.9% for VKAs. Recurrence risk increased with antibody burden, reaching 41.3% in triple-positive patients and 46.8% in those with index arterial thrombosis treated with DOACs. Conclusions: DT technology accurately replicated the clinical structure of APS patients, supporting its application for simulating counterfactual scenarios and estimating individualized treatment effects. Full article

Accurate estimation of heterogeneous treatment effects (HTEs) serves as a cornerstone of personalized decision-making, especially in observational studies where treatment assignment is not randomized. However, the presence of confounding and complex covariate structures poses significant challenges to reliable inference. In this study, we develop an innovative model averaging framework, which leverages proximity-based matching to enhance the accuracy of HTE estimation. The method constructs pseudo-outcomes via proximity score matching and subsequently applies an optimal model averaging procedure to these matched samples. We demonstrate that the proposed estimator achieves asymptotic optimality when the standard regularity conditions are met. Simulation studies, adapted from benchmark settings for evaluating HTE model averaging, confirm its superior finite-sample performance. Compared to standard HTE estimation approaches, the proposed method achieves consistently lower estimation errors and reduced variability. The method is further validated on a clinical dataset from the CPCRA trial, demonstrating its practical value for individualized causal inference. Full article

The strategic transformation of increasing data volumes into managerial decisions is critical for organisational performance and sustainability; yet, it faces hurdles like poor data quality, technological deficiencies, and skill gaps. This study investigates the causal interdependencies among key factors influencing data-driven decision-making within data-driven organisations. Utilising the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, a robust structural and Multi-Attribute Decision-Making (MADM) technique, expert judgments from five management-level professionals were analysed to construct direct and total-relation matrices. The results classify Data Analytics Literacy (DAL) and Business-Strategy Alignment (BSA) as primary causal factors, while Data Quality (DQ), Data Infrastructure & Technology (DIT), and Data Culture & Governance (DCG) emerge as effect factors. These findings provide a structured framework for prioritising managerial interventions, suggesting that strengthening foundational elements (DAL and BSA) will significantly enhance analytical capabilities and strategic alignment. A limitation is the small, expert-based sample, indicating the potential for future validation with larger, more diverse panels or Fuzzy-DEMATEL applications. Full article