Search Results (7,800)

This study formulates and verifies a network-based evaluation methodology for appraising the sustainable competitiveness of community enterprises. Based on Social Capital Theory, the Resource-Based View (RBV), and Network Theory, the model defines high-quality networks as structural relational circumstances that facilitate resource sharing and knowledge sharing, serving as mediating mechanisms that improve competitive outcomes. A quantitative study approach was utilized, gathering survey data from 451 representatives of community enterprises around Thailand, and Structural Equation Modeling (SEM) was applied to assess both measurement features and structural relationships. The model demonstrates satisfactory internal reliability, convergent validity, and discriminant validity, affirming measurement adequacy. Empirical evidence indicates that high-quality networks are positively correlated with sustainable competitiveness, both directly and indirectly, with 49.2% of the overall effect conveyed through resource and knowledge exchange, emphasizing the practical value of network-based processes. The suggested model offers practical utility for policymakers and development agencies in search of evidence-based instruments to enhance competitiveness, network capacity, and long-term resilience in community enterprises. The cross-sectional methodology and lack of contextual control variables restrict causal inference and external generalizability, highlighting the necessity for longitudinal or quasi-experimental expansions. By emphasizing model creation and empirical validation, this study develops a systematic and reproducible methodological framework for assessment. Full article

Background: Intimate partner violence (IPV) represents a major public health problem in Europe, with significant physical, psychological, and social consequences. Nurses are often the first professionals capable of detecting early signs of IPV, yet they lack validated instruments to assess their clinical competency in detection, evaluation, documentation, and intervention. This study aimed to develop and validate the Intimate Partner Violence Nursing Competency Scale (IPVNCS), aligned with the Nursing Intervention Classification (NIC 6403). Methods: A cross-sectional psychometric study was conducted among registered nurses in the Community of Madrid. A 30-item Likert-type self-administered instrument (1–5 scale) was developed based on NANDA, NIC 6403, and NOC frameworks. A total of 202 nurses participated. Reliability was assessed through Cronbach’s alpha. Construct validity was examined using exploratory factor analysis (EFA) with Promax rotation and confirmatory factor analysis (CFA) using AMOS 26. Ethical approval was obtained (CEU San Pablo, code 843/24/104). Results: After item refinement, 26 items remained across four dimensions: (1) Intervention and Referral, (2) Detection and Assessment, (3) Documentation and Recording-keeping, (4) Psychosocial Support. The instrument showed excellent reliability ($\alpha$ = 0.97). KMO was 0.947 and Bartlett’s test was significant (p < 0.001). CFA demonstrated satisfactory fit: ${\chi }^2$/df = 2.066, RMSEA = 0.073, CFI = 0.92, TLI = 0.91, NFI = 0.86. The final model adequately represented the latent structure. After debugging, its psychometric properties were significantly improved. Four redundant items were eliminated, achieving internal consistency ($\alpha$ = 0.97), a KMO value of 0.947 and a significant Bartlett’s test of sphericity. It showed a better fit, according to ${\chi }^2$/df = (2.066); Parsimony = (720.736); RMR (0.0529; RMSEA (0.073); NFI (0.860); TLI (0.910) and CFI (0.920). The final model provides an adequate representation of the latent structure of the data. This study provides initial evidence of construct validity and internal consistency reliability of the IPVNCS. Conclusions: The IPVNCS is a valid and reliable tool to assess nursing competencies for clinical management of IPV. It supports structured evaluation across four core nursing domains, enabling improved educational planning, clinical decision-making, and quality of care for victims. The scale fills a gap in clinical nursing assessment tools and can support protocol development in emergency, primary care, and hospital settings. Full article

This study documents the ethnomedicinal knowledge of Konak (Malatya, Eastern Anatolia, Türkiye), a region with rich plant diversity but no prior comprehensive research. The aim of the study is to systematically document and analyze the ethnomedicinal practices of Konak village, focusing on plant taxa (species, subspecies and varieties) used, preparation methods, and therapeutic applications. Data were collected through semi-structured interviews with 68 local informants. Quantitative analysis was performed using Informant Consensus Factor (F_IC) and Use Value (UV) indices. Plant specimens were collected, identified, and deposited in the herbarium. The study documented 86 plant taxa from 35 families used in 230 therapeutic applications. Lamiaceae, Asteraceae, and Rosaceae were the most represented families. High F_IC values were recorded for colds (F_IC = 0.95), stomach pain (F_IC = 0.92), and inflammation (F_IC = 0.90), indicating strong community consensus. The most frequently cited species were Origanum vulgare subsp. gracile, Mentha spp., and Rosa canina. There are novel or locally specific uses, with 13 taxa having no previously recorded ethnomedicinal applications in the reviewed literature. The findings reveal Konak as a significant repository of ethnomedicinal knowledge. High-F_IC taxa represent prime candidates for phytochemical and pharmacological research to validate traditional uses and support evidence-based phytotherapy. This study enriches regional ethnopharmacological data and highlights candidate taxa for pharmacological validation. Full article

Objectives: Mindfulness has been proposed as an important health outcome and an indicator of mental well-being. This study aimed to evaluate the psychometric properties of the Five Facet Mindfulness Questionnaire (FFMQ-15) in two samples of mental health service users with diverse cultural and linguistic backgrounds (English- and Chinese-speaking). The study addresses the conceptual gap regarding the limited validation of the FFMQ-15 in Chinese-speaking clinical populations and examines the implications of measurement invariance. This study aimed at (1) confirming the reliability and validity of the FFMQ-15 in mental health service users; (2) assessing the validity of the FFMQ-15 in Chinese-speaking populations, where evidence is limited; and (3) examining measurement invariance across English- and Chinese-speaking groups to ensure cross-cultural applicability and comparable score interpretation. Methods: Participants were recruited using snowball sampling and social media advertising, targeting adults aged 18 or older who could read and write English or Chinese and had received mental health services. The English-speaking sample comprised 115 adults, and the Chinese-speaking sample included 118 adults. Exploratory factor analysis was used to identify structural dimensions, while confirmatory factor analysis was conducted for both samples to evaluate the five-factor structure of the FFMQ-15. Results: The EFA showed literature-aligned results supporting the 5-factor structure model, while the CFA model demonstrated acceptable fit: χ²/df = 159.50/80 = 1.99, p < 0.001; CFI = 0.927; TLI = 0.904; RMSEA = 0.065 (90% CI [0.050, 0.080]); SRMR = 0.060, BIC = 10,843.067, meeting established thresholds, and the non-significant measurement variance indicated the measurement model’s consistency among clinical patients and across different cultural contexts. Conclusions: The FFMQ-15 shows strong psychometric properties for measuring mindfulness in English- and Chinese-speaking mental health service users, supporting its value in clinical research and practice. Full article

Modern healthcare systems increasingly depend on wearable Internet of Medical Things (IoMT) devices for the continuous monitoring of patients’ physiological parameters. It remains challenging to differentiate between genuine physiological anomalies, sensor faults, and malicious cyber interference. In this work, we propose a hybrid fusion framework designed to attribute the most plausible source of an anomaly, thereby supporting more reliable clinical decisions. The proposed framework is developed and evaluated using two complementary datasets: CICIoMT2024 for modelling security threats and a large-scale intensive care cohort from MIMIC-IV for analysing key vital signs and bedside interventions. The core of the system combines a supervised XGBoost classifier for attack detection with an unsupervised LSTM autoencoder for identifying physiological and technical deviations. To improve clinical realism and avoid artefacts introduced by quantised or placeholder measurements, the physiological module incorporates quality-aware preprocessing and missingness indicators. The fusion decision policy is calibrated under prudent, safety-oriented constraints to limit false escalation. Rather than relying on fixed fusion weights, we train a lightweight fusion classifier that combines complementary evidence from the security and clinical modules, and we select class-specific probability thresholds on a dedicated calibration split. The security module achieves high cross-validated performance, while the clinical model captures abnormal physiological patterns at scale, including deviations consistent with both acute deterioration and data-quality faults. Explainability is provided through SHAP analysis for the security module and reconstruction-error attribution for physiological anomalies. The integrated fusion framework achieves a final accuracy of 99.76% under prudent calibration and a Matthews Correlation Coefficient (MCC) of 0.995, with an average end-to-end inference latency of 84.69 ms (p95 upper bound of 107.30 ms), supporting near real-time execution in edge-oriented settings. While performance is strong, clinical severity labels are operationalised through rule-based proxies, and cross-domain fusion relies on harmonised alignment assumptions. These aspects should be further evaluated using realistic fault traces and prospective IoMT data. Despite these limitations, the proposed framework offers a practical and explainable approach for IoMT-based patient monitoring. Full article

This systematic review examines 205 studies on the use of Large Language Models (LLMs) in psychiatry, psychology, psychotherapy, and clinical workflows. Furthermore, studies that directly evaluated at least one LLM in a mental health context were included in the extended detailed analysis. GPT-4 and GPT-3.5 were the most commonly assessed models. Although LLMs showed promising short-term performance across domains, most evaluations relied on small, non-longitudinal datasets and single-session testing, limiting generalizability. The evidence indicates rapid growth but significant methodological inconsistency, emphasizing the need for more diverse datasets, standardized evaluation, and long-term validation before clinical integration. This review also examines how LLMs are being incorporated into mental health practice, outlining key challenges, limitations, and emerging opportunities. Ethical, clinical, and technological considerations are proposed to guide responsible adoption. Given the complexity of mental health care, a multidisciplinary, human-centered approach remains essential to ensure that future LLM applications augment—rather than replace—professional expertise. Full article

Prostate cancer diagnostics have evolved substantially with the integration of multiparametric magnetic resonance imaging (mpMRI), refined prostate-specific antigen (PSA) metrics, and targeted biopsy techniques. While mpMRI has become a central gatekeeper in biopsy decision-making, it is not infallible. Clinically significant prostate cancer may therefore remain undetected, particularly in patients with elevated PSA density, adverse PSA kinetics, or MRI-occult disease. This narrative review synthesizes contemporary evidence on PSA interpretation, mpMRI performance, and biopsy strategy selection, highlighting the limitations of single-parameter approaches. We discuss the diagnostic yield and clinical implications of targeted, systematic, and combined biopsy techniques, emphasizing scenarios in which systematic sampling remains necessary despite negative or equivocal imaging findings. Emerging data support combined targeted and systematic biopsy as the most robust strategy for maximizing the detection of clinically significant disease while limiting overdiagnosis in most biopsy-naive and high-risk patients. By integrating PSA dynamics, prostate volume, imaging findings, and individual risk profiles, a structured, risk-adapted diagnostic pathway can be achieved. The proposed framework is intended as a conceptual, expert-derived clinical aid to support risk-adapted decision-making. It should be interpreted alongside established guidelines, and prospective validation in future studies is warranted. Full article

Background: Large language models (LLMs) are becoming progressively integrated into clinical practice; however, their role in cardiovascular (CV) prevention remains unclear. This review synthesizes current evidence on LLM applications in preventive cardiology and proposes a governance framework for their safe translation into practice. Methods: We conducted a comprehensive narrative review of literature published between January 2015 and November 2025. Evidence was synthesized across three functional domains: (1) patient applications for health literacy and behavior change; (2) clinician applications for decision support and workflow efficiency; and (3) system applications for automated data extraction, registry construction, and quality surveillance. Results: Evidence suggests that while LLMs generate empathetic, guideline-concordant patient education, they lack the nuance required for unsupervised, personalized advice. For clinicians, LLMs effectively summarize clinical notes and draft documentation but remain unreliable for deterministic risk calculations and autonomous decision-making. System-facing applications demonstrate potential for automated phenotyping and multimodal risk prediction. However, safe deployment is constrained by hallucinations, temporal obsolescence, automation bias, and data privacy concerns. Conclusions: LLMs could help mitigate structural barriers in CV prevention but should presently be deployed only as supervised “reasoning engines” that augment, rather than replace, clinician judgment. To guide the transition from in silico performance to bedside practice, we propose the C.A.R.D.I.O. framework (Clinical validation, Auditability, Risk stratification, Data privacy, Integration, and Ongoing vigilance) as a roadmap for responsible integration. Full article

Cardiac transplantation remains an important therapy for end-stage heart failure, although allograft rejection continues to pose significant clinical challenges. This review evaluates both established and emerging blood-based biomarkers for noninvasive monitoring of rejection in heart transplant recipients. Donor-derived cell-free DNA (ddcfDNA) and gene expression profiling (GEP) represent well-validated, commercially available molecular tools that demonstrate strong discriminative capacity for acute rejection episodes. Additionally, microRNAs (miRs) and extracellular vesicles (EVs) show considerable potential as novel biomarkers, although further validation is required. In contrast, conventional biomarkers such as B-type natriuretic peptide (BNP), cardiac troponins, and creatine kinase-MB (CK-MB) offer limited specificity in the context of rejection. This review synthesizes current evidence on the clinical utility, methodological challenges, and integration strategies of these biomarkers, highlighting a shift toward molecular-based approaches for improving post-transplant surveillance and patient outcomes. Full article

In the last decade, the interdisciplinary field of neuroarchitecture has grown significantly, revealing measurable links between architectural features and human neural processing. This review synthesizes current research at the nexus of neuroscience and architecture, with a focus on how emerging virtual reality (VR) and artificial intelligence (AI) technologies are being utilized to understand and enhance human spatial experience. We systematically reviewed literature from 2015 to 2025, identifying key empirical studies and categorizing advances into three themes: core components of neuroarchitectural research; the use of physiological sensors (e.g., EEG, heart rate variability) and virtual reality to gather data on occupant responses; and the integration of neuroscience with AI-driven analysis. Findings indicate that built environment elements (e.g., geometry, curvature, lighting) influence brain activity in regions governing emotion, stress, and cognition. VR-based experiments combined with neuroimaging and physiological measures enable ecologically valid, fine-grained analysis of these effects, while AI techniques facilitate real-time emotion recognition and large-scale pattern discovery, bridging design features with occupant emotional responses. However, the current evidence base remains nascent, limited by small, homogeneous samples and fragmented data. We propose a four-domain framework (somatic, psychological, emotional, cognitive-“SPEC”) to guide future research. By consolidating methodological advances in VR experimentation, physiological sensing, and AI-based analytics, this review provides an integrative roadmap for replicable and scalable neuroarchitectural studies. Intensified interdisciplinary efforts leveraging AI and VR are needed to build robust, diverse datasets and develop neuro-informed design tools. Such progress will pave the way for evidence-based design practices that promote human well-being and cognitive health in built environments. Full article

Background: Hashimoto’s thyroiditis (HT) is a prevalent autoimmune disorder, often diagnosed late due to its asymptomatic or nonspecific presentation. Emerging evidence suggests that gut-derived lipopolysaccharides (LPS) may contribute to autoimmune activation. Objective: The primary objective of this study was to assess circulating LPS concentrations and dietary patterns in patients with Hashimoto’s thyroiditis compared to healthy controls. Methods: A hospital-based case–control study was conducted involving 105 HT patients and 25 healthy controls. Serum LPS concentrations, thyroid hormone profiles, and autoantibody levels were assessed. Dietary patterns were evaluated using the validated KomPAN questionnaire. Results: HT patients exhibited significantly higher serum LPS levels, particularly those with elevated anti-TPO and TRAB antibodies. A positive correlation was found between LPS and the fT3/fT4 ratio (r = 0.247, p = 0.006), and a negative correlation with fT4 (r = −0.314, p < 0.001). Dietary analysis revealed lower Pro-Healthy Diet Index scores in HT patients (3.94 vs. 5.34, p = 0.001), with increased consumption of processed foods and reduced intake of whole grains and oats. Conclusions: Elevated levels of lipopolysaccharides (LPS) and unhealthy dietary patterns may play a role in the development of thyroid autoimmunity. Taken together, these observations are consistent with a multifactorial model that potentially involves gut barrier dysfunction, endotoxemia, and nutritional factors in HT pathogenesis. Full article

Trichomoniasis is the most prevalent non-viral sexually transmitted infection worldwide and is caused by Trichomonas vaginalis. The development of resistance against the standard treatment, metronidazole, highlights the need for alternative therapeutic approaches. The role of innate immune cells is crucial for understanding trichomoniasis; however, the contribution of monocytes remains poorly characterized. We previously reported that the antimicrobial peptides LL-37 and its derivative KR-20 are trichomonacidal. In other systems, LL-37 displays immunomodulatory effects. Nevertheless, whether these peptides modulate monocyte responses in the presence of T. vaginalis remains unknown, which was the aim of this study. U937 monocytes were co-incubated with LL-37 or KR-20 (3 h), with or without parasite. Monocyte metabolic activity, nitric oxide production, and relative expression of innate immune genes were assessed. LL-37 decreased monocyte metabolic activity and upregulated TNF-α expression (10 and 5 μM, respectively) in parasite-challenged monocytes. Meanwhile, KR-20 (2.5–10 μM) preserved metabolic activity, bound microbial components (LPS), reduced parasite-induced nitric oxide production, and downregulated the expression of IL-8, TNF-α, IL-1β, and COX-2 in infected monocytes. This work provides initial evidence that KR-20 modulates innate immune response in monocytes during T. vaginalis infection, suggesting its potential—yet to be fully validated—as an immunomodulatory candidate for trichomoniasis. Full article

Background: Depression remains a major global mental health concern, significantly intensified during the COVID-19 pandemic. As social media usage surged during this period, it emerged as a valuable source for identifying early signs of depression. Artificial intelligence (AI) offers powerful tools to analyze large volumes of user-generated content, enabling timely and effective detection of depressive symptoms. This review aims to preliminarily explore and compare evidence on the use of AI models for detecting depression in social content across the pre-, during, and post-pandemic phases, assessing their effectiveness and limitations. Methods: A narrative literature review was conducted using PubMed and Scopus, following the SANRA guidelines to ensure methodological quality and reproducibility. The study was pre-registered in the OSF database and employed the PICOS framework for the strategy. Inclusion criteria comprised studies in English from the past 10 years that analyzed depression detection via AI, machine learning (ML), and deep learning (DL) applied to textual data, images, and social metadata. This review addresses the following four research questions: (1) whether AI models improved effectiveness in detecting depression during/after the pandemic vs. pre-pandemic; (2) whether textual, visual, or multimodal data approaches became more effective during the pandemic; (3) whether AI models better addressed technical challenges (data quality/diversity) post-pandemic; and (4) whether strategies for responsible AI implementation improved during/after the pandemic. Results: Out of 349 identified records, nine primary studies were included, as most excluded articles had a predominantly technical focus and did not meet the clinical relevance criteria. AI models demonstrated strong potential in detecting depression, particularly through text-based classification and social content analysis. Several studies reported high predictive performance, with notable improvements in accuracy and sensitivity during and after the pandemic, although evidence remains limited. Conclusions: Our preliminary analysis suggests that AI-based depression detection on social media shows potential for clinical use, highlighting interdisciplinary collaboration, ethical considerations, and patient-centered approaches. These findings require confirmation and validation through larger, well-designed systematic reviews. Full article

Background/Objectives: Irritable Bowel Syndrome (IBS) is a widely prevalent chronic disorder of brain–gut interaction which represents a clinical challenge due to its complex underlying causes and the lack of a standardized treatment approach. This cross-sectional research collected real-world data (RWD) on the effectiveness, safety, and usage pattern of a natural substance-based medical device, Colilen IBS, indicated for the treatment of IBS. Methods: Surveys were conducted both in Italy and Germany with 6101 participants, including 4425 patients, 1014 pharmacists, and 662 physicians using a structured GxP web platform that allows voluntary participants to share their experiences with the device. The validated platform was designed to comply with post-market surveillance requirements of EU Regulation 2017/745. Statistical analyses included descriptive evaluations of responses to gauge overall effectiveness and safety of the device. Results: The effectiveness reported with the medical device was judged extreme or great by 79.2% of patients, with 89.2% of whom observed symptom improvement within one month. Both safety and tolerability were rated extreme or great by 90.7% of patients. Healthcare professionals reported a similar rate on the overall effectiveness, with 94.9% of pharmacists and 95.9% of physicians indicating it extreme or great. Similarly, the safety profile was corroborated by nearly all pharmacists (97.0%) and physicians (98.2%) reporting extreme or great satisfaction with both safety and tolerability of the medical device. Conclusions: This research provides RWD supporting the effectiveness and safety of the product for treating IBS. The strong coherence among patients, pharmacists, and physicians in positively rating the device’s performance suggests that this medical device represents a therapeutic option that effectively addresses patient needs while minimizing safety concerns. Continuous RWD collection is essential, as it offers insights into real-world practice and ensures ongoing confirmation of the product’s safety and effectiveness. Ultimately, this will advance IBS patient care by integrating real-world evidence into clinical management. Full article

Field cancerization is a fundamental paradigm in tumorigenesis, emphasizing that carcinogenesis begins long before the appearance of clinically detectable lesions and often precedes recognizable premalignant changes. A direct manifestation of this process is the molecular dysregulation observed in the peritumoral mucosa—histologically normal-appearing tissue that nonetheless exhibits genetic and epigenetic alterations similar to those of the adjacent tumor. This review summarizes current evidence on the molecular alterations shared between tumor tissue and peritumoral mucosa in HNSCC and evaluates their potential as biomarkers for defining molecular margins and improving surgical precision. A literature search was conducted in PubMed using combinations of the keywords “peritumor,” “laryngeal”, “HNSCC,” and “field cancerization.” Studies were included if they directly compared tumor tissue with peritumoral mucosa and, preferably, a third set of distant normal control samples. Only nine studies met the inclusion criteria, highlighting the scarcity of focused research in this area. Reported biomarkers exhibiting comparable dysregulation in both tumor and peritumor tissues include MDM2, E2F2, CDKN2A/p16, ETS-1, MGMT, and multiple microRNAs (e.g., miR-21, miR-96-5p, miR-145-5p). These molecular signatures demonstrate the presence of a biologically altered field extending beyond histologically defined tumor margins. Peritumoral mucosal dysregulation, as a consequence of field cancerization, underscores the need to redefine surgical margins at the molecular level. The identification and validation of biomarkers reflecting this continuum could enable the establishment of molecular margins—improving risk assessment, reducing local recurrence, and advancing personalized oncologic surgery in HNSCC. Standardizing definitions and sampling protocols for “normal adjacent tissue” remains essential for future translational research. Full article