Search Results (17,513)

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

Introduction: Immune checkpoint inhibitors (ICIs) have transformed the treatment of metastatic melanoma; however, predictive markers of therapeutic response remain poorly defined. This study systematically assesses clinical, histological, and molecular predictors associated with survival outcomes in melanoma patients treated with ICIs. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines, a systematic search was conducted in MEDLINE, Web of Science, and the Cochrane Central Register of Controlled Trials (CENTRAL) for studies published between January 2018 and October 2025. Eligible studies reported associations between predictive factors and overall survival (OS) or progression-free survival (PFS) in adult melanoma patients receiving ICIs. Pooled hazard ratios (HRs) with corresponding 95% confidence intervals (CIs) from univariate (UVA) and multivariate analyses (MVA) were synthesized using random-effects meta-analyses. Results: Sex was not a consistent predictor (contradictory effects; PFS heterogeneity I² ≈ 90%), whereas older age predicted worse OS (MVA continuous: HR 1.05, 95% CI 1.02–1.08; UVA ≥ 65 vs. <65: HR 1.70, 95% CI 1.36–2.12). Poor performance status, assessed using the Eastern Cooperative Oncology Group (ECOG) scale, strongly predicted inferior outcomes (ECOG ≥ 1 vs. 0: MVA OS HR 2.01, 95% CI 1.61–2.51; MVA PFS HR 1.49, 95% CI 1.18–1.88; ECOG ≥ 2 vs. <2: MVA OS HR 2.24, 95% CI 1.79–2.81). Elevated lactate dehydrogenase (LDH) was consistently associated with poorer survival (MVA OS HR 1.71, 95% CI 1.53–1.91; MVA PFS HR 1.61, 95% CI 1.41–1.85), whereas body mass index (BMI) > 25 kg/m² was associated with improved OS (HR 0.82, 95% CI 0.68–0.98). Higher disease burden predicted worse prognosis (Stage IV vs. III: MVA OS HR 1.57, 95% CI 1.16–2.13; >2 metastatic sites vs. ≤2: MVA OS HR 2.38, 95% CI 1.40–4.07; brain metastases: MVA OS HR 1.69, 95% CI 1.30–2.20; MVA PFS HR 1.52, 95% CI 1.00–2.33). Histologic and molecular factors showed prognostic value: ulceration worsened OS (UVA HR 2.08, 95% CI 1.25–3.44) and PFS (UVA HR 2.97, 95% CI 1.39–6.32); acral subtype had poorer OS than cutaneous melanoma (MVA HR 2.99, 95% CI 1.63–5.48); high tumor mutational burden (TMB) improved PFS (UVA HR 0.47, 95% CI 0.33–0.70); and cutaneous immune-related adverse events (irAEs) were associated with favorable outcomes (skin disorders: UVA OS HR 0.26, 95% CI 0.14–0.47; UVA PFS HR 0.50, 95% CI 0.34–0.74). In contrast, detectable circulating tumor DNA (ctDNA) predicted markedly worse PFS (MVA HR 4.72, 95% CI 2.31–9.65) and a non-significant trend toward worse OS (MVA HR 3.34, 95% CI 0.96–11.67). Liver metastases and programmed death-ligand 1 (PD-L1) expression were not significantly associated with survival. Discussion: This meta-analysis synthesizes evidence on clinicopathologic, laboratory, and histopathologic predictors of immunotherapy outcomes in metastatic melanoma. Performance status, age, LDH, BMI, and metastatic burden consistently correlated with prognosis, while ulceration, disease stage, and TMB emerged as key histologic determinants. Conversely, PD-L1 and gender showed no consistent predictive value, whereas cutaneous immune-related adverse events and ctDNA reflected favorable and poor outcomes, respectively. These findings highlight the multifactorial nature of immunotherapy response and support the further development of integrated prognostic models to refine patient stratification and optimize treatment outcomes. Full article

Background: Hilar cholangiocarcinoma (HC) is a highly aggressive malignancy with a poor prognosis, highlighting the urgent need to elucidate its molecular drivers. This study aimed to systematically identify and functionally validate key genes and pathways driving HC pathogenesis. Methods: RNA sequencing (RNA-seq) was performed on paired primary HC tumors and matched adjacent non-tumorous tissues to identify differentially expressed genes (DEGs). Subsequent bioinformatic analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein–protein interaction (PPI) network construction, were conducted to characterize the functional landscape and identify hub genes. Transwell assays and orthotopic metastatic models were used to investigate the functions of Contactin-1 (CNTN1) in HC invasion in vitro and metastasis in vivo. Results: RNA-seq analysis identified 35 DEGs in HC, mainly involved in cell adhesion, cytoskeletal regulation, and axon development. PPI network analysis identified six hub genes, including CNTN1, NCAM1, PLP1, GPM6B, SLC1A3, and PMP2. Furthermore, we demonstrated that CNTN1, a neuronal membrane glycoprotein, was markedly up-regulated in HC at both mRNA and protein levels, and its elevated expression correlated with poor prognosis. Gain- and loss-of-function studies demonstrated that CNTN1 promotes HC cell invasion in vitro and metastasis in vivo. Mechanistically, CNTN1 exerts its pro-invasive effects by activating the PI3K-AKT signaling pathway and inducing epithelial–mesenchymal transition (EMT). Conclusions: Our integrated analysis identifies CNTN1 as a critical oncogenic driver in HC, promoting metastasis through PI3K-AKT-mediated EMT. These findings nominate CNTN1 as a potential prognostic biomarker and therapeutic target in HC. Full article

This study aims to examine the impact of empathetic leadership in corporate communication, focusing on its role in enhancing employee well-being and fostering a positive workplace culture. It explores how empathetic communication contributes to trust, engagement, and long-term organizational success. Based on an international survey conducted among communication professionals in Canada and the United States (n = 1055), our analyses revealed significant gender disparities in the perception of empathy among senior communication leaders, with male professionals reporting higher perceived empathy compared to female professionals. Additionally, hierarchical position influenced perceptions, with higher-ranking employees reporting stronger empathic leadership. Perceptions of increased empathic communication over the past year were notably higher among men, older employees, and those with more experience. Empathetic leadership demonstrated a strong positive correlation with employee engagement and organizational commitment but did not significantly impact burnout. Findings from this study contribute to a broader understanding of how leadership empathy varies across professional environments and demographic groups, underscoring the complex dynamics of gender and organizational structure in shaping workplace experiences. Findings in our study contribute to both the advancement of leadership theory and the improvement of corporate communication practice. Full article

In this work, a study of the chemical composition of the compound Sb₂(S_1−xSe_x)₃ used in thin-film solar cell fabrication, based on correlating data obtained from XRD and XPS analyses, is presented. This approach enables us to propose a mathematical expression for evaluating stoichiometric variations in the material, showing how the variable x evolves as a function of the diffraction angle 2θ. To establish this model, we analyzed the most intense diffraction peak, corresponding to the (221) plane. To validate the proposed method, a series of Sb₂(S_1−xSe_x)₃ thin films with different compositions were synthesized using RF-magnetron sputtering followed by conventional heat treatments in a controlled-atmosphere reaction furnace. The XRD results reveal a systematic 2θ shift in the crystalline diffraction peaks toward the positions of the binary precursor phases—from Sb₂Se₃ to Sb₂S₃—caused by the increased sulfur content during synthesis. XPS measurements confirm the presence of Sb, Se, and S, and high-resolution spectra indicate a decrease in selenium content as the sulfur fraction increases. These results allowed us to elucidate the stoichiometric behavior of antimony sulfoselenide Sb₂(S_1−xSe_x)₃ using trend curves fitted to the characterization data. Full article

Retaining the green workforce, employees driving sustainability and environmental innovation, is essential for organizational resilience and long-term environmental goals. While prior Green HRM research has primarily relied on survey-based methodologies and theoretical frameworks to examine retention factors, these approaches lack predictive capability and fail to provide actionable, employee-specific insights. This study advances beyond descriptive and correlational analyses by employing explainable artificial intelligence (XAI) to develop a transparent, data-driven framework for identifying attrition drivers and quantitatively evaluating retention strategies. Unlike existing studies that rely on self-reported perceptions, our approach leverages objective HR data and machine learning to predict individual-level attrition risk with calibrated probabilities. Leveraging the IBM HR Analytics dataset as a proxy for sustainability-focused roles, we construct an interpretable logistic regression model with strong predictive performance and isotonic regression calibration. Global and local interpretability techniques, including SHAP, LIME, and permutation importance, show that non-monetary factors, such as excessive overtime, frequent business travel, and limited promotion opportunities, have a greater impact on turnover risk than salary levels. These findings align with Green Human Management (Green HRM) principles, which emphasize work–life balance and employee well-being. Crucially, our policy simulation framework, absent from prior Green HRM studies, demonstrates that eliminating overtime could reduce predicted attrition probability by 17.35% for affected employees, potentially retaining 31 staff members, substantially outperforming modest salary adjustments. This work expands the value of predictive AI into HR analytics by consolidating HR analytics with Green HRM through a novel methodology that bridges the gap between prediction and actionable intervention. It represents the first systematic integration of XAI-based predictive modeling with counterfactual policy simulation in environmentally conscious sustainable organizations. Full article

Objective: This study aimed to develop the Infectious Diseases Knowledge Questionnaire (IDKQ) and evaluate its psychometric properties for use in community settings. Methods: This methodological study was conducted with 533 adults aged ≥ 18 years. Data were collected using a sociodemographic information form and the IDKQ. Content validity was assessed by expert evaluation. Construct validity was examined using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Reliability was evaluated through item–total correlations, internal consistency (KR-20), test–retest reliability, and intraclass correlation coefficients (ICC). Data analyses were performed using SPSS 25.0 and AMOS 21.0. Results: Content validity index values ranged from 0.94 to 1.00. EFA revealed a four-factor structure consisting of 17 items, explaining 45.66% of the total variance (KMO = 0.784; Bartlett’s test, p < 0.001). CFA demonstrated good model fit (χ²/df = 2.329, RMSEA = 0.074, CFI = 0.946, AGFI = 0.847, GFI = 0.887, SRMR = 0.045). The KR-20 coefficient was 0.735, the test–retest correlation was 0.604, and the ICC was 0.781. Conclusions: The IDKQ demonstrates acceptable internal consistency and moderate temporal stability, providing preliminary evidence of reliability and construct validity. It may serve as a tool for assessing infectious disease knowledge, although further validation in independent samples is recommended. Full article

Background: Artificial intelligence chatbots are increasingly used by patients to obtain health information, including for prostate cancer. While these platforms offer accessible and conversational responses, concerns remain regarding the quality, usability, and clinical relevance of AI-generated content. This study comparatively evaluated patient-directed prostate cancer information generated by commonly used AI chatbots. Methods: Standardised prostate cancer-related prompts were developed using Google Trends and authoritative healthcare resources. Identical queries were submitted to five publicly accessible AI chatbots: ChatGPT 5.2, Google Gemini, Claude AI, Microsoft Copilot, and Perplexity. Responses were independently assessed by two blinded reviewers using the DISCERN instrument for information quality and the Patient Education Materials Assessment Tool for printable materials (PEMAT-P) for understandability and actionability. Inter-rater reliability was assessed using intraclass correlation coefficients (ICCs). Readability was evaluated using the Flesch–Kincaid Reading Ease score. Descriptive statistics were used for comparative and pooled analyses. Results: Overall information quality was moderate, with a pooled median (interquartile range [IQR]) DISCERN score of 56.5 (53.0–61.0). Higher mean DISCERN scores were observed for ChatGPT 5.2 and Microsoft Copilot, whereas lower scores were observed for Claude and Perplexity. PEMAT-P understandability was consistently high across platforms, with a pooled median (IQR) score of 91.7% (83.3–91.7%). In contrast, PEMAT-P actionability was uniformly poor, with a pooled median (IQR) score of 0% (0–0%). Readability analysis demonstrated moderate complexity, with a pooled median (IQR) Flesch–Kincaid Reading Ease score of 50.4 (49.2–52.5) and a median word count of 666 (657–1022). Inter-rater reliability was good for PEMAT understandability (ICC 0.841) and moderate for DISCERN (ICC 0.712). Conclusions: AI chatbots provide highly understandable but only moderately high-quality patient-directed prostate cancer information, with a consistent lack of actionable guidance. Although variation in content quality was observed across platforms, significant limitations remain in evidence transparency and practical patient support. Future development should prioritise integration of evidence-based resources and actionable decision-support tools to enhance the role of AI chatbots in prostate cancer education. Full article

Colorectal cancer (CRC) is a prevalent malignant tumour, with its incidence and mortality rates consistently ranking among the highest and exhibiting an upward trend. Extensive screening and early diagnosis are crucial for managing CRC progression and improving patient prognosis. This study aims to construct a novel analytical framework for integrating the sequencing data from tumour tissue and peripheral blood. By integrating and analysing the multi-omics data and clinical data from tumour tissues and peripheral blood, we confirmed that the LTB4R gene is significantly upregulated not only in tumour tissues but also in the peripheral blood of CRC patients. Further single-cell RNA sequencing (scRNA-seq) and immune cell correlation analyses revealed that Leukotriene B4 receptor 1 (LTB4R) is primarily expressed in macrophages, T cells, and other immune cells, with a significant negative correlation observed with M1-type macrophages, suggesting its potential pro-tumourigenic role in CRC by suppressing M1 macrophage. Additionally, simulated gene knockout analysis (scTenifoldKnk) demonstrated that LTB4R knockout significantly impacts immune-related pathways, including immune response and immune receptor activity. These findings not only highlight the potential of LTB4R as a peripheral blood diagnostic marker for CRC but also elucidate its involvement in tumour progression, offering novel insights for early clinical diagnosis and tumour screening systems. Full article

Background: Spirulina supplementation combined with structured exercise may improve obesity-related metabolic dysfunctions. This research examined whether this combination enhances body composition, glucose levels, lipid profile, and cardiorespiratory fitness in overweight and obese adults. Methods: Following PRISMA 2020 guidelines, a systematic search of Scopus, PubMed, and Web of Science identified randomized controlled trials (RCTs) evaluating spirulina (1–6 g/day) combined with structured exercise in individuals with overweight and obesity (BMI ≥ 25). The search retrieved 91 records, of which 10 studies met the inclusion criteria and were included in the systematic review. Nine studies provided sufficient post-intervention data and were included in the quantitative meta-analysis using a random-effects model, with heterogeneity assessed using τ², Q, and I² statistics. Publication bias was evaluated using rank correlation, regression-based tests, trim-and-fill, and fail-safe N analyses. Results: Combined spirulina supplementation and structured exercise (6–12 weeks) was associated with reductions in BMI (−1.34 kg/m²), body fat percentage (−3.03%), fasting glucose (−14.47 mg/dL), LDL-C (−12.68 mg/dL), and triglycerides (−9.81 mg/dL), along with increases in VO2max (3.25 mL/kg/min) and HDL-C (4.21 mg/dL). Effect estimates were generally larger in combined exercise–spirulina subgroups, particularly in HIITsupp and R-AEsupp conditions, whereas supplementation-only comparisons demonstrated smaller and less consistent changes. Inflammatory markers and adipokines (CRP, TNF-α, MCP-1, IL-6, IL-8) showed favorable directional changes in individual trials. Conclusions: Spirulina combined with structured exercise was associated with changes in anthropometric, glycemic, cardiorespiratory, and lipid parameters in individuals with overweight or obesity. Full article

Background: Disability progression in multiple sclerosis (MS) is increasingly recognized as a consequence of large-scale brain network disruption rather than isolated regional damage. Although diffusion tensor imaging (DTI) is the reference method for assessing structural connectivity, its limited availability restricts widespread clinical application. There is therefore a critical need for alternative approaches capable of capturing network-level alterations using routinely acquired MRI data. Objective: This study aimed to determine whether synthetic structural connectivity matrices derived from standard regional volumetric MRI can capture clinically meaningful network alterations in MS and predict subsequent functional progression, particularly upper limb decline. Methods: Regional brain volumetry was obtained from routine T1-weighted MRI using an automated, clinically approved volumetric pipeline. Synthetic structural connectivity matrices were generated by integrating principles of structural covariance, distance-dependent connectivity, and disease-specific vulnerability patterns. Graph-theoretical network metrics were extracted to characterize global and regional topology. Machine learning models including logistic regression, support vector machines, random forests, and gradient boosting were trained to predict clinical progression defined by worsening on the 9-Hole Peg Test. Dimensionality reduction was performed using principal component analysis, and model performance was evaluated using balanced accuracy, AUC-ROC, and resampling-based validation. Feature importance analyses were conducted to identify network vulnerability patterns. Results: Synthetic connectivity networks exhibited biologically plausible properties, including preserved but attenuated small-world organization. Global efficiency showed a strong inverse correlation with disability severity (EDSS). Patients with clinical progression demonstrated marked reductions in network integration and segregation, alongside increased characteristic path length. Machine learning models achieved robust prediction of upper limb functional decline, with ensemble-based methods performing best (balanced accuracy > 80%, AUC-ROC up to 0.85). A limited subset of connections accounted for a disproportionate share of predictive power, predominantly involving frontoparietal associative networks, thalamocortical pathways, and inter-hemispheric connections. In a longitudinal subset, network-level alterations preceded measurable clinical deterioration by several months. Conclusions: Synthetic structural connectivity derived from routine volumetric MRI captures clinically relevant network-level disruption in multiple sclerosis and enables accurate prediction of functional progression. By bridging network neuroscience with widely accessible imaging data, this framework provides a pragmatic alternative for connectomic analysis when diffusion imaging is unavailable and supports a network-based understanding of disease evolution in MS. Full article

As emerging pollutants, nanoplastics (NPs) are profoundly threatening aquatic ecosystems. However, the systematic response mechanisms of aquatic floating macrophytes to NP stress and the mitigation strategies of nanoselenium (Se) remain poorly understood. This study used P. stratiotes, a dominant species in freshwater ecological restoration, as the research object. By intervening in NP stress via foliar application of Se, the study systematically deciphered the plant’s response and mitigation mechanisms to NPs pollution through integrating physiological and biochemical analyses, ultrastructural observation of cells, and transcriptomic and metabolomic multi-omics techniques. The results showed that NP stress significantly reduced photosynthetic pigment concentration and inhibited photosystem function in Pistia stratiotes L., disrupted energy metabolism homeostasis, and simultaneously induced an outburst of reactive oxygen species (ROS). It activated non-enzymatic antioxidant substances such as flavonoids and glutathione (GSH), as well as enzymatic defense systems including catalase (CAT) and peroxidase (POD), promoting the reprogramming of the plant’s metabolic strategy from growth priority to defense dominance. At the transcriptomic level, NP stress significantly altered the gene expression profile, with core pathways enriched in photosynthesis antenna proteins and phenylpropanoid biosynthesis. Metabolomic analysis revealed significant differences in metabolites, with markedly upregulated contents of defense-related metabolites such as lipids and terpenoids. The intervention of NPs-Se effectively restored photosynthetic pigment contents and enzyme activities, alleviated cell membrane damage by repairing the photosynthetic apparatus, optimizing ribosome-mediated protein synthesis pathways, and strengthening the antioxidant defense network. Meanwhile, it regulated the expression of specific genes and the accumulation of core differential metabolites, reconstructed the balance between energy supply and defense investment, enabling the plant to achieve more efficient adaptive regulation. Multi-omics correlation analysis further confirmed that the responses of P. stratiotes to NPs and NPs-Se exhibited characteristics of coordinated regulation, highlighting the modular regulatory patterns of nano-stress responses. In conclusion, Se can effectively alleviate the stress damage of nanoplastics to P. stratiotes through multi-dimensional regulation, providing a key experimental basis and theoretical support for the ecological restoration of NP-polluted water bodies and ecological risk assessment. Full article

Background/Objectives: Although Ki-67 is not formally incorporated into the grading system of pulmonary neuroendocrine neoplasms (PNENs), it is widely used as an adjunct marker to reflect proliferative activity and support diagnostic stratification. Manual Ki-67 assessment is subject to interobserver variability and methodological limitations. This study aimed to evaluate the reliability and performance of two artificial intelligence (AI)-based image analysis systems in Ki-67 index assessment and to compare their results with expert pathologist evaluation in pulmonary neuroendocrine tumors. Methods: A total of 63 pulmonary neuroendocrine neoplasm cases, including typical carcinoid (n = 29), atypical carcinoid (n = 13), and large cell neuroendocrine carcinoma (n = 21), were retrospectively analyzed. Ki-67 proliferation indices were independently assessed by four pathologists within predefined hotspot regions, counting approximately 2000 tumor cells per case. The same regions were analyzed using two AI-based image analysis systems (Roche uPath Ki-67 and Virasoft Virasight Ki-67). Interobserver agreement among pathologists was evaluated using the intraclass correlation coefficient (ICC), and concordance between manual and AI-based assessments was assessed using Spearman’s correlation and linear regression analyses. To account for potential scanner/platform effects, slides were digitized using two different whole-slide scanners (VENTANA DP^® 600 and Leica Aperio AT2), and color normalization and quality control procedures were applied prior to AI-based analysis. For clinical interpretability, Ki-67 indices were stratified into categorical groups based on tumor subtype-specific thresholds (0–<10%: low, 10–25%: intermediate, >25%: high), and agreement between manual and AI-based categorical scoring was evaluated using Cohen’s kappa coefficient. Results: Among the 63 pulmonary neuroendocrine neoplasm cases, Ki-67 proliferation indices varied across tumor subtypes, with typical carcinoids showing low, atypical carcinoids intermediate, and large cell neuroendocrine carcinomas high proliferative activity. Interobserver agreement among four pathologists was excellent (ICC = 0.998, 95% CI: 0.996–0.998). Strong correlations were observed between manual Ki-67 assessments and AI-derived indices, with Spearman correlation coefficients of 0.961 (95% CI: 0.918–0.982) for Roche AI and 0.904 (95% CI: 0.821–0.949) for Virasoft AI, and 0.926 (95% CI: 0.842–0.968) between the two AI systems. Bland–Altman analyses demonstrated minimal mean differences and most cases within the 95% limits of agreement, indicating high concordance without systematic bias. Categorical agreement analysis, using subtype-specific Ki-67 thresholds (0–<10%: low; 10–25%: intermediate; >25%: high), showed excellent concordance between manual and AI-based scoring (Cohen’s kappa 0.877 for Roche AI and 0.827 for Virasoft AI; p < 0.001), confirming the clinical interpretability and reproducibility of AI-based Ki-67 assessment. Conclusions: AI-based Ki-67 index assessment shows strong concordance with expert pathologist evaluation and reflects biologically relevant differences among pulmonary neuroendocrine neoplasm subtypes. These results suggest that AI-assisted Ki-67 analysis may serve as a reproducible and objective adjunct to routine diagnostic practice in pulmonary neuroendocrine tumors. Full article

Background: In Southern Italy, metabolic dysfunction-associated fatty liver disease (MASLD) is rising despite adherence to traditional Mediterranean diets. Accurate assessment of physical activity (PA) and sedentary behavior is critical for effective non-pharmacological management but remains methodologically challenging. Methods: We compared subjective and objective PA measures in 133 adults (mean age 49.0 ± 9.8 years; BMI 35.7 ± 4.9 kg/m²) with moderate-to-severe MASLD. Participants completed the International Physical Activity Questionnaire–Short Form (IPAQ-SF) and wore an ActiGraph GT9X wrist accelerometer for seven days. Results: The IPAQ-SF significantly underestimated moderate PA by 865 min/week (p < 0.001) and reported 33.16 ± 14.78 min/week of vigorous activity not detected by accelerometry. Sedentary time was slightly underestimated (0.45 h/day, p = 0.05), with better overall agreement. Stratified analyses showed significant underestimation of sedentary behavior among women and participants <50 years. Spearman correlations were weak (rho = 0.14 for moderate PA; rho = 0.36 for sedentary behavior). Bland–Altman plots confirmed poor agreement for moderate PA but acceptable limits for sedentary estimates. Conclusions: In high-risk Southern Italian populations with MASLD, reliance on self-reported PA may lead to inaccurate clinical guidance. Integrating objective monitoring with subjective tools is essential to deliver precise, individualized exercise prescriptions beyond generic “move more” recommendations. Full article

Background/Objectives: Antisocial personality disorder (ASPD) is strongly associated with violence, substance use, criminal behavior, and elevated suicide risk. Although inflammatory and metabolic dysregulation have been implicated in severe psychiatric disorders, the biological correlates of impulsivity, aggression, and suicide risk in forensic ASPD populations remain unclear. This study aimed to investigate whether routine hematological, inflammatory, and metabolic parameters are associated with these clinical features. Methods: This cross-sectional study included 57 male individuals diagnosed with antisocial personality disorder (ASPD) who had committed crimes and were referred to the Forensic Psychiatry Department of Elazığ Fethi Sekin City Hospital in Turkey by the court, and 56 age-matched healthy controls. Participants completed standardized assessments of impulsivity (BIS-11), aggression (BPAQ), and suicide probability (SPS). Hematological indices, inflammatory markers, and routine biochemical parameters were analyzed. Group comparisons, correlation analyses, and multivariable logistic regression were performed. Results: Compared with age-matched controls, individuals with ASPD showed markedly higher impulsivity, aggression, and suicide probability, alongside substantially higher rates of substance use, imprisonment history, and suicide attempts (all p < 0.001). Hematological and inflammatory analyses revealed lower red blood cell (RBC) counts and elevated mean corpuscular volume (MCV), red cell distribution width (RDW), C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), and CRP–albumin ratio (CAR) in the ASPD group (all p < 0.05). Biochemical profiling showed reduced glucose, total protein, albumin, HDL, ALT, and vitamin B12 levels, with increased uric acid levels in ASPD (p < 0.05). Multivariable analysis indicated that being married and having higher education were protective against ASPD, whereas higher uric acid and CAR levels were associated with increased risk. Conclusions: The findings indicate that criminal offenders with ASPD show increased inflammatory markers and altered hematological and biochemical profiles. Routine blood parameters, combined with psychometric assessments, may help identify individuals at higher behavioral risk and support early risk stratification in forensic psychiatric settings, although causal relationships cannot be inferred from this cross-sectional study. Full article