Search Results (5,877)

Background: Reducing dietary salt intake is a global public health priority. However, empirical evidence is needed to clarify whether higher levels of health literacy (HL) and eHealth literacy (eHL), together with the use of diversified information dissemination channels, are positively associated with better salt-related knowledge, attitudes, and practices (KAP). This study examined the indirect associations via eHL and the number of sources of salt-reduction information (NSSI) in the relationship between HL and salt-related KAP. Methods: A cross-sectional survey was conducted from 2022 to 2023 using multistage stratified random sampling among residents aged 15–69 in 22 counties/districts of Jiangxi Province, China. Data on sociodemographic characteristics, HL, eHL, NSSI, and salt-related KAP were collected through face-to-face household interviews using a standardized electronic questionnaire system. Spearman correlation analysis and multiple linear regression were applied to assess associations among HL, eHL, NSSI, and salt-related KAP. Path analysis was employed to evaluate the indirect associations between HL and salt-related KAP via eHL and NSSI. Results: A total of 5396 residents participated, of whom 51.50% were male. Participants aged 15–34, 35–54, and 55–69 years accounted for 13.10%, 42.96%, and 43.94% of the sample, respectively. After adjustment for covariates, individuals with adequate HL, adequate eHL, and a greater NSSI had significantly higher total salt-related KAP scores (p < 0.001). In the path analysis, the standardized direct association of HL with the total salt-related KAP was 0.229 (p < 0.001). The standardized indirect associations via NSSI and eHL were 0.089 (95% CI: 0.069 to 0.111, p < 0.001) and 0.057 (95% CI: 0.033 to 0.089, p < 0.001), respectively, accounting for 23.73% and 15.20% of the total association. Conclusions: High levels of HL and eHL, together with increased exposure to multiple salt-reduction information sources, are associated with improved salt-related KAP. Both eHL and NSSI partially explain the association between HL and salt-related KAP. Future salt-reduction interventions should integrate conventional health education with mobile health technologies to expand information dissemination channels and support sustained salt-reduction behaviors. Full article

This study examined the ‘too central to fail’ concept in the South African equity sector. We employed the Granger causality framework and PageRank algorithm to generate the centrality scores of the sectors on the Johannesburg Stock Exchange under extreme market conditions. Using the realized volatilities of sectoral returns for the full sample period (3 January 2006–31 December 2021), as well as during the global financial crisis (GFC), European debt crisis (EDC), COVID-19 pandemic, and US–China trade war sub-periods, we analyzed the sectors’ interconnections and calculated each sector’s centrality score across the entire sample and under different extreme market conditions. This allowed us to rank sectors relative to their centrality scores. The results indicate that, in the full sample, the insurance sector has the highest PageRank centrality score, suggesting it is too central to fail. This implies that the insurance sector acts as a systemic receiver of risks and provides stability within the network of sectors. However, the sub-period analyses reveal that General Industrial and Automobiles emerged as the key sectors with the highest PageRank centrality scores, and shocks from other sectors can disproportionately affect these industries during crisis periods. Underperformance in these sectors could have destabilizing effects on the South African economy. The findings have significant implications for regulators and policymakers, portfolio and fund managers, local and international investors, and researchers in the field of finance. Full article

Background: Non-invasive brain stimulation (NIBS) is increasingly used as an adjunctive option in late-life depression (≥60 years), a condition frequently complicated by multimorbidity and incomplete response to standard treatments. Comparative real-world evidence between repetitive Transcranial Magnetic Stimulation (rTMS) and transcranial Direct Current Stimulation (tDCS), particularly including functional outcomes, remains limited. Methods: We conducted a retrospective, naturalistic comparative study of 104 depressed inpatients (≥60 years), either unipolar or bipolar, treated with rTMS (n = 48) or tDCS (n = 56) as part of routine care. Depression severity was assessed with the 21-item Hamilton Depression Rating Scale (HDRS21) at baseline, 2 weeks, and 1 month; response was defined as ≥50% HDRS21 score reduction and remission as HDRS21 < 7 at 1 month. Global Assessment of Functioning (GAF) was assessed at admission and discharge (baseline and 1 month). Longitudinal changes were examined using covariate-adjusted mixed-effects models; categorical outcomes were compared using χ² tests. Propensity score matching was applied as an additional approach to reduce confounding due to the observational design. Results: At 1 month, response and remission rates were significantly higher in the rTMS group than in the tDCS group (87.5% vs. 55.4%, p < 0.001; 62.5% vs. 41.1%, p = 0.047, respectively). rTMS showed greater HDRS21 score reductions at 2 weeks and 1 month (Time × Treatment, p < 0.001). GAF scores significantly improved over time in both groups (Time effect, p < 0.001) without between-technique differences (Time × Treatment, p = 0.56), and GAF scores did not differ by response/remission status. Conclusions: In this cohort of inpatients aged ≥ 60 years with depressive episodes, rTMS was associated with greater short-term reductions in HDRS21 scores compared with tDCS, whereas both modalities showed comparable improvements in GAF from admission to discharge. Full article

Background: Hepatocellular carcinoma (HCC) stands as a prevalent global health issue with increasing incidence and mortality rates. Hepatocellular carcinoma (HCC) exhibits profound molecular and clinical heterogeneity, which limits the effectiveness of current therapeutic strategies. Circadian rhythm disruption has been implicated in metabolic reprogramming, proliferation, and immune modulation in cancer, but its role in shaping HCC heterogeneity remains poorly defined. Methods: Four public HCC transcriptomic cohorts (TCGA-LIHC, CHCC, LIRI, LICA) were integrated using RMA normalization and ComBat for batch correction. Consensus clustering based on 31 core circadian clock genes (CCGs) identified robust molecular subtypes. Multi-omics characterization—including genomic alterations, pathway activity (GSEA/GSVA), immune microenvironment profiling (CIBERSORT, EPIC, MCP-counter, xCell), and drug-sensitivity prediction (pRRophetic/oncoPredict)—was performed to delineate subtype-specific biological properties. A nine-gene CCG-based RiskScore model was constructed using LASSO Cox regression to internally validate subtype robustness and intra-subtype risk stratification. Results: Using consensus clustering of 31 core CCGs in TCGA-LIHC and three independent validation cohorts (CHCC, LIRI, LICA), we identified three reproducible subtypes—Cluster-1 (metabolic–quiescent), Cluster-2 (transition–intermediate), and Cluster-3 (proliferation–inflammatory)—which were recapitulated across cohorts and showed distinct overall survival (Cluster-3 worst; log-rank p values significant across datasets). Multi-omic characterization revealed that Cluster-3 exhibits the highest tumor mutational burden and CNV burden with enrichment of TP53/AXIN1/TERT alterations, strong activation of cell-cycle, E2F, and G2M programs, and an immune-hot yet immunosuppressed microenvironment enriched for TAMs, Tregs and MDSCs. By contrast, Cluster-1 shows relative genomic stability, dominant hepatic metabolic signatures (fatty-acid oxidation, bile-acid and xenobiotic metabolism) and an immune-cold phenotype. Single-cell mapping linked ALAS1 expression to malignant hepatocytes predominating in Cluster-1, whereas NONO and CSNK1D localized to stromal (CAFs/TECs) and both malignant/immune compartments respectively in Cluster-3, providing a cellular mechanism for subtype-specific metabolism, angiogenesis and immune modulation. Finally, a nine-gene CCG-based RiskScore validated prognostic stratification and drug-sensitivity predictions indicated subtype-specific therapeutic vulnerabilities (notably increased predicted TKI sensitivity in Cluster-3). Conclusion: In conclusion, this study proposes a robust circadian rhythm-based molecular classification of hepatocellular carcinoma, revealing three biologically and clinically distinct subtypes characterized by divergent genomic alterations, metabolic programs, immune microenvironment states, and prognostic patterns. By integrating bulk and single-cell transcriptomic data, we identify subtype-specific roles of key circadian regulators—including ALAS1, NONO, and CSNK1D—in shaping tumor metabolism, proliferation, stromal remodeling, and immune suppression. These findings highlight circadian dysregulation as a potential upstream factor associated with HCC heterogeneity and provide a conceptual framework for developing subtype-tailored mechanistic studies and circadian-informed therapeutic strategies. Full article

The effective recognition of risky driving behaviors holds technical potential for supporting accident prevention and sustainable transportation. However, existing intelligent algorithms for optimizing deep learning models in this field often suffer from slow convergence and high errors. This study proposes a novel hybrid model (ICPO-XGBoost) for risky driving behavior classification. The improved crested porcupine optimizer (ICPO) was developed using logistic-tent composite mapping for population initialization, a hybrid mechanism combining refraction opposition-based learning and Cauchy mutation to avoid local optima, and an adaptive variable spiral search with inertia weight to balance global and local search. The ICPO was then employed to optimize the hyperparameters of the XGBoost classifier. The ICPO demonstrated superior optimization accuracy and convergence speed compared to benchmark algorithms. The ICPO-XGBoost model achieved accuracy, precision, recall, and F1 scores of 96.2%, 95.4%, 95.8%, and 95.6%, respectively, for classifying and identifying risky driving behaviors. Compared to various benchmark models, these results represent increases of 12.7–24.8%, 14.8–31.8%, 14.9–31.0%, and 15.0–32.4%, respectively. For specific driving behavior categories (normal driving, slow driving, short-distance tailgating, sudden acceleration/deceleration, frequent lane changing, and forced lane changing), the precision, recall, and F1 scores of the ICPO-XGBoost model fell within the ranges of 84.8–99.2%, 87.5–100.0%, and 86.2–99.2%, respectively. Compared to benchmark models, these metrics show increases of 1.5–75.8%, 5.8–68.1%, and 3.3–72.6%, respectively. Notably, the model significantly improved accuracy in identifying sudden acceleration/deceleration behaviors. The results of this model facilitate the classification and early warning of risky driving behaviors, thereby reducing the frequency of such behaviors, lowering the risk of traffic accidents, and enhancing road traffic safety. Full article

Background: Perfectionism is a multidimensional disposition marked by exceptionally high standards and self-worth contingent on flawless performance. In university settings, academic demands may amplify perfectionistic pressure, with maladaptive outcomes most consistently linked to socially prescribed expectations and self-critical failure processing. This study profiled perfectionism dimensions in Italian university students and examined their associations with perceived relational and self-related correlates (Roots). Methods: A multicenter cross-sectional study was conducted with Italian university students. Participants completed two validated tools: the 14-item Multidimensional Perfectionism Scale—Revised (MPS-R) and the 16-item Roots questionnaire. Descriptive statistics, Spearman correlations, and non-parametric group comparisons were performed. Results: Self-oriented perfectionism was the most prominent dimension, while socially prescribed perfectionism (SPP) was comparatively lower but showed the clearest links with vulnerability-related correlates. Lower perceived parental and interpersonal trust was associated with stronger failure-based self-appraisals and perceived excessive demands from others. Higher SPP was observed among women and younger students and in more evaluative study contexts. Conclusions: Perfectionism in this sample was predominantly self-directed, yet risk-relevant profiles were characterized by SPP and self-critical failure processing in conjunction with lower perceived trust/acceptance. These findings support screening approaches that move beyond global scores and inform prevention strategies targeting fear of mistakes, contingent self-worth, and perceived evaluative pressure to promote student well-being. Longitudinal and intervention studies are needed to test temporal pathways and scalable, targeted prevention strategies. Full article

Rural revitalization has emerged as a core agenda in the global pursuit of sustainable development, with its success fundamentally hinging on enhancing the resilience of rural households to withstand shocks and restore their livelihoods. In contrast to mainstream research that primarily examines whether Medical Financial Assistance (MFA) reduces medical burden, this paper focuses on MFA as ex-post cash compensation and investigates whether and how it affects the sustainable livelihood recovery of low-income rural households following health shocks, thereby providing empirical evidence for understanding the foundational role of health security in rural revitalization. A quasi-natural experiment is constructed by leveraging the institutional feature that MFA eligibility is activated by exogenous health shocks. Using two-wave balanced panel data (2021–2022) from a nationally designated deep poverty-stricken county in Hebei Province, China, the Propensity Score Matching–Difference-in-Differences (PSM-DID) method and mediation models are employed for causal identification and mechanism testing. The findings indicate that (1) MFA significantly promotes household income recovery. It enables recipient households to recover per capita net income by an average of approximately 13.2% (p < 0.01), demonstrating a protective recovery effect, and simultaneously recovers per capita non-farm labor income by an average of approximately 13.8% (p < 0.05), revealing a developmental recovery effect. The latter is partially mediated by the non-farm labor participation rate (mediation ratio 51.7%, Sobel Z = 2.10). This finding validates the “time release effect,” demonstrating that MFA stimulates endogenous dynamics by restoring health capital and releasing labor previously constrained by family care responsibilities. It thereby extends the application of health capital theory from the individual to the household level. (2) Mechanism analysis shows that the protective recovery effect is fully mediated by the amount of MFA received (mediation ratio 326.7%, Sobel Z = 12.85), providing empirical evidence for precautionary saving theory in the context of targeted social assistance and revealing the potential productive attributes of the social safety net. (3) Heterogeneity analysis reveals clear group targeting and shock thresholds. The protective effect is concentrated among elderly households, while the developmental effect is primarily evident in middle-aged households. Both recovery effects manifest significantly only for households experiencing major disease shocks, confirming the theoretical expectation of “conditional effectiveness,” namely that policy effects are systematically moderated by household life-cycle characteristics and the severity of health shocks. This study demonstrates that MFA serves both as a safety net and an empowerment tool, but its effectiveness is highly contingent upon household characteristics and shock severity. By uncovering the foundational mechanisms through which health security contributes to rural household resilience, this study provides empirical evidence from China for building sustainable poverty prevention systems in the global process of rural revitalization. Full article

While infrared–visible image object detection exhibits advantages in complex scenarios, existing methods still suffer from issues such as static frequency-domain modeling, severe cross-modal interference, and insufficient local detail perception. To address these problems, this paper proposes an infrared and visible image object detection based on a spatial-channel cross-frequency guided fusion network. First, we construct a frequency residual selective transformer to realize local inductive bias and a global receptive field for infrared and visible image feature extraction. Furthermore, the spatial-channel frequency fusion mechanism based on the Homogeneous Frequency Refined Block and the Heterogeneous Spatial-Channel Frequency Fusion Block is proposed to achieve modality-consistent feature fusion. Finally, the frequency reconstruction guided decoder selects high-frequency components to sharpen object boundaries. The SCFusion network was evaluated on four public benchmark datasets (VT821, VT1000, VT5000, and VI-RGBT1500), with test sets containing 411, 400, 2500, and 600 pairs of infrared–visible images, respectively. Following the standard training and testing protocol, the network achieved Mean Absolute Error (MAE) scores of 0.025 (VT821), 0.016 (VT1000), 0.023 (VT5000), and 0.020 (VI-RGBT1500). Full article

Organizational productivity and workforce management are highly affected by employee attrition. Thus, an employee attrition prediction system may allow human resource management to enhance the workplace by minimizing attrition. This study proposes a new and interpretable ensemble learning framework for employee attrition prediction. The model integrates SHapley Additive exPlanations (SHAP)-based feature selection, Optuna hyperparameter optimization, and dual explainability using SHAP and Local Interpretable Model-agnostic Explanations (LIME). Random oversampling (ROS) is used to address class imbalance. The proposed framework allows for both global and local interpretability, enabling actionable insights into retention drivers. It was assessed using two benchmark datasets: the Kaggle HR Analytics dataset (14,999 records) and the IBM HR dataset (1470 records). The results revealed that the most impactful factors on employee attrition are promotion history, tenure, job satisfaction, workload, average monthly hours, overtime, and financial incentives. Furthermore, the proposed model achieved exceptional performance on both datasets. On the Kaggle dataset, it reached an accuracy of 98.72%, an F1-score of 97.29%, and an ROC–AUC of 0.994, while on the IBM dataset, it produced an accuracy of 97.72%, an F1-score of 97.74%, and an ROC–AUC of 0.995. Moreover, the proposed approach shows high computational efficiency, demonstrating that it is suitable for real-world deployment. These findings indicate that integrating explainable AI techniques, resampling tools, and automated hyperparameter tuning can achieve robust, accurate, and actionable employee attrition predictions, supporting HR managers’ decision-making. Full article

Background: Elderly patients with heart failure with reduced ejection fraction (HFrEF) who experience worsening heart failure (wHF) remain at high residual risk despite optimal medical therapy (OMT), and data on cognitive function and comprehensive geriatric assessment (CGA) in this setting are lacking. This study evaluated the association between 12-month treatment with vericiguat and changes in cardiac, functional and geriatric parameters in elderly patients with recent wHF. Methods and results: In this single-center prospective observational study, 55 patients (45 men, mean age 76.4 ± 5.1 years) with HFrEF on OMT and a recent episode of wHF were treated with vericiguat and followed for 12 months. Clinical assessment, CGA and echocardiography including speckle-tracking were performed at baseline, 6, and 12 months. At 12 months, the mean vericiguat dose was 5.5 ± 2.9 mg/day. NT-proBNP levels decreased from 980 (467–2106) to 654 (274–1762) pg/mL (p < 0.0001), while left ventricular ejection fraction increased from 36.8 ± 3.1% to 43.4 ± 5.7% (p < 0.0001). Global longitudinal strain improved from −9.2 ± 1.7% to −11.5 ± 2.1% (p = 0.008), with parallel improvements in right ventricular function and pulmonary pressures. Cognitive performance improved (MMSE 25.1 ± 1.7 to 26.2 ± 2.1 points, p < 0.0001), as did depressive symptoms (GDS 7.8 ± 2.0 to 5.4 ± 1.6 points, p < 0.0001), physical performance (SPPB 6.7 ± 1.1 to 8.4 ± 0.9 points, p < 0.0001), and gait speed (0.70 ± 0.10 to 0.83 ± 0.06 m/s, p < 0.0001). Conley score decreased from 5.2 ± 2.3 to 2.4 ± 1.8 points (p < 0.0001), suggesting a lower risk of falls. Loop diuretic and MRA use were significantly reduced during follow-up. Conclusions: In this elderly HFrEF cohort with recent wHF on contemporary OMT, 12-month treatment with vericiguat was associated with consistent improvements in cardiac structure and function, biomarkers, and multidimensional geriatric status. These hypothesis-generating findings support the integration of CGA into future controlled studies of vericiguat in frail older patients with HFrEF. Given the observational design and lack of a control group, causal inference is not possible. Full article

Background/Objectives: Joubert syndrome is a rare neurodevelopmental disorder characterized by congenital cerebellar and brainstem malformations affecting networks involved in predictive motor control, sensorimotor integration, and autonomic regulation, resulting in a heterogeneous motor phenotype. Functional impairment is typically described using global gross motor scores, which may not adequately reflect axial control, postural organization, musculoskeletal alignment, or respiratory–postural interactions. The objective of this descriptive pilot case series was to provide a multidimensional functional characterization of children with Joubert syndrome by integrating standardized motor assessments with postural, musculoskeletal, and thoracoabdominal measures. Methods: Six children with genetically and radiologically confirmed Joubert syndrome underwent a single standardized assessment session conducted by the same examiner. This cross-sectional, non-controlled study was based on feasibility sampling, and no a priori power calculation was performed. Gross motor function and postural control were evaluated using the Gross Motor Function Measure-88 and the Balance Assessment Rating Scale. Additional measures included joint range of motion, sacral inclination angle, thoracic configuration, thoracic excursion during quiet breathing, and respiratory rate. Analyses were limited to descriptive statistics. Results: Gross motor performance varied widely across participants, whereas postural control scores did not parallel gross motor performance levels within the cohort. Inter-individual variability was observed in joint mobility, pelvic alignment, and thoracoabdominal configuration, including among children with relatively preserved gross motor scores. Thoracic excursion during quiet breathing demonstrated a relatively narrow and low within-cohort range. Conclusions: In this small exploratory case series, functional characteristics observed in this cohort extended beyond global motor scores. Axial control, postural organization, and thoracoabdominal configuration may represent relevant descriptive domains of functional presentation within a multidimensional framework. Larger, longitudinal, and controlled studies are required to determine their clinical and neurodevelopmental significance. Full article

Manual microscopy for malaria diagnosis is labor-intensive and prone to inter-observer variability. This study presents an automated binary classification approach for detecting malaria ring-form infections in thin blood smear single-cell images using a parallel neural network framework. Utilizing a balanced Kaggle dataset of 27,558 erythrocyte crops, images were standardized to 128 × 128 pixels and subjected to on-the-fly augmentation. The proposed architecture employs a dual-branch fusion strategy, integrating a convolutional neural network for local morphological feature extraction with a multi-head self-attention branch to capture global spatial relationships. Performance was rigorously evaluated using 10-fold stratified cross-validation and an independent 10% hold-out test set. Results demonstrated high-level discrimination, with all models achieving an ROC–AUC of approximately 0.99. The primary model (Model#1) attained a peak mean accuracy of 0.9567 during cross-validation and 0.97 accuracy (macro F1-score: 0.97) on the independent test set. In contrast, increasing architectural complexity in Model#3 led to a performance decline (0.95 accuracy) due to higher false-positive rates. These findings suggest that moderate-capacity feature fusion, combining convolutional descriptors with attention-based aggregation, provides a robust and generalizable solution for automated malaria screening without the risks associated with over-parameterization. Despite a strong performance, immediate clinical use remains limited because the model was developed on pre-segmented single-cell images, and external validation is still required before routine implementation. Full article

In-depth research on marine biodiversity is essential for understanding and protecting marine ecosystems, where semantic segmentation of marine species plays a crucial role. However, segmenting microscopic zooplankton images remains challenging due to highly variable morphologies, complex boundaries, and the scarcity of high-quality pixel-level annotations that require expert knowledge. Existing semi-supervised methods often rely on single-model perspectives, producing unreliable pseudo-labels and limiting performance in such complex scenarios. To address these challenges, this paper proposes a consistency-driven dual-teacher framework tailored for zooplankton segmentation. Two heterogeneous teacher networks are employed: one captures global morphological features, while the other focuses on local fine-grained details, providing complementary and diverse supervision and alleviating overfitting under limited annotations. In addition, a dynamic fusion-based pseudo-label filtering strategy is introduced to adaptively integrate hard and soft labels by jointly considering prediction consistency and confidence scores, thereby enhancing supervision flexibility. Extensive experiments on the Zooplankton-21 Microscopic Segmentation Dataset (ZMS-21), a self-constructed microscopic zooplankton dataset demonstrate that the proposed method consistently outperforms existing semi-supervised segmentation approaches under various annotation ratios, achieving mIoU scores of 64.80%, 69.58%, 70.32%, and 73.92% with 1/16, 1/8, 1/4, and 1/2 labeled data, respectively. Full article

Background and Aims: The rising global incidence of cholangiocarcinoma (CCA) coincides with epidemics of type 2 diabetes (T2D) and chronic hepatitis B virus (HBV) infection. Although both are established independent risk factors, the shared molecular mechanisms by which they contribute to cholangiocarcinogenesis remain poorly understood. We hypothesized that T2D and HBV converge on a state of chronic metabolic inflammation (“metaflammation”) that drives CCA progression through a conserved transcriptomic network. Methods: We performed an integrative bioinformatics analysis of transcriptomic data from public repositories, including samples of CCA (TCGA-CHOL, n = 45; GSE107943, n = 163), T2D-affected liver (GSE23343, n = 20), and HBV-infected liver (GSE58208, n = 102). Acknowledging that the T2D and HBV datasets were derived from whole-liver tissue, whereas CCA originates in the biliary epithelium, we identified differentially expressed genes (DEGs) across conditions and defined a core gene set shared among them. Subsequent analyses included functional enrichment, construction of protein–protein interaction (PPI) networks, survival analysis, and protein validation. Results: We identified a core metaflammation signature comprising 156 genes that were consistently dysregulated across T2D, HBV, and CCA. Pathway analysis revealed significant enrichment in PPAR signaling, cytokine–cytokine receptor interaction, PI3K-Akt, and TNF signaling pathways. Protein–protein interaction (PPI) network analysis identified IL6, TNF, AKT1, STAT3, and PPARG as the top hub genes. These hubs were functionally modularized into clusters associated with inflammatory signaling, metabolic regulation, and cell growth and survival. In the TCGA CCA cohort, high expression of IL6, TNF, AKT1, and STAT3 and low expression of PPARG correlated with advanced tumor stage and poorer overall survival (e.g., IL6: ρ = 0.42, p = 0.01). A metaflammation score derived from these hubs (weighted combination of the five genes) emerged as an independent prognostic factor (HR = 2.8, p < 0.001). Protein-level dysregulation of these hubs was confirmed via immunohistochemistry. Conclusions: This study defines a conserved metaflammation network that links T2D and HBV to CCA, identifying key hub genes and pathways. This signature provides a mechanistic explanation for epidemiological risks, serves as a novel prognostic tool, and offers a rationale for targeting metaflammation in prevention and therapy for high-risk populations. Full article

Background: Despite the widespread implementation of treat-to-target strategies and modern disease-modifying antirheumatic drugs, a substantial proportion of patients with rheumatoid arthritis (RA) continue to report unmet needs (UNs), defined as a mismatch between patient expectations and symptom burden on the one hand and outcomes achieved with current care on the other. Patient–physician discordance in global assessments may reflect multidimensional influences, including pain mechanisms, psychosocial factors, functional impairment, and communication gaps, extending beyond inflammatory disease activity. Methods: In this cross-sectional study, 133 patients with RA and 57 healthy controls were included. UNs were operationalized as the signed difference between patient global assessment and physician global assessment (ΔPGA–PhGA). Clinical variables, patient-reported outcomes, and Short Form-36 (SF-36) domains were incorporated into two regularized partial correlation network models estimated using the extended Bayesian information criterion graphical least absolute shrinkage and selection operator (EBICglasso). Node centrality indices (strength, signed strength, betweenness, and closeness) were calculated. Network stability was evaluated using 2000 bootstrap resamples and correlation stability (CS) coefficients. Results: In the clinical network, pain intensity demonstrated the highest strength centrality and the strongest direct association with UNs. In contrast, Disease Activity Score in 28 joints with C-reactive protein (DAS28-CRP) showed no direct association with UNs after accounting for shared variance. In the SF-36-based quality-of-life network, UNs exhibited inverse associations, particularly with perceived health change and role–emotional functioning. Stability analyses indicated acceptable to good robustness (clinical network: CS = 0.59 for edge weights and 0.44 for strength; SF-36 network: CS = 0.59), supporting the reliability of the estimated network structures. Conclusions: UNs in RA are not solely determined by inflammatory disease activity but are embedded within interconnected clinical and psychosocial domains. Pain occupies a structurally central position in the clinical network, whereas perceived health change and emotional role limitations characterize the quality-of-life context of UNs. These findings underscore the importance of multidimensional and patient-centered assessment strategies in RA management. Full article