Search Results (3,483)

Background: Comprehensive Geriatric Assessment (CGA) is essential for evaluating older cancer patients, but significant gaps persist in both research and clinical practice. This study aimed (I) to identify the CGA elements that most influence anti-cancer treatment decisions in older patients and (II) to explore the predictive value of CGA components for mortality. Methods: This observational study included older patients with newly diagnosed, histologically confirmed solid or hematological cancers, recruited consecutively from 2003 to 2023. Participants were followed for four years. The data collected included CGA measures of functional (Activities of Daily Living-ADL), cognitive (Mini-Mental State Examination-MMSE), and emotional (Geriatric Depression Scale-GDS) domains. Patients were categorized into frail, vulnerable, or fit groups based on Balducci’s criteria. Statistical analyses included decision tree modeling and Cox regression to identify predictors of mortality. Results: A total of 7022 patients (3222 females) were included, with a mean age of 78.3 ± 12.9 years. The key CGA factors influencing treatment decisions were ADL (first step), cohabitation status (second step), and age (last step). After four years, 21.9% patients had died. Higher GDS scores (OR 1.04, 95% CI 1.01–1.07, p = 0.04) were independently associated with survival in men and living with family members (OR 1.67, 95% CI 1.35–2.07, p < 0.001) in women. Younger patients (<77 years) showed both MMSE and GDS as significant risk factors for mortality. Conclusions: Functional capacity, cohabitation status, and GDS scores are crucial for guiding treatment decisions and predicting mortality in older cancer patients, emphasizing the need for a multidimensional geriatric assessment. Full article

Background/Objectives: The objective of this study was to validate a new parent-reported scale for tracking Pediatric Acute-onset Neuropsychiatric Syndrome (PANS). PANS is a condition characterized by a sudden and severe onset of neuropsychiatric symptoms. To meet diagnostic criteria, an individual must present with either obsessive–compulsive disorder (OCD) or severely restricted food intake, accompanied by at least two additional cognitive, behavioral, or emotional symptoms. These may include anxiety, emotional instability, depression, irritability, aggression, oppositional behaviors, developmental or behavioral regression, a decline in academic skills such as handwriting or math, sensory abnormalities, frequent urination, and enuresis. The onset of symptoms is usually triggered by an infection or an abnormal immune/inflammatory response. Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections (PANDAS) is a subtype of PANS specifically linked to strep infections. Methods: We developed a 101-item PANS/PANDAS and Related Inflammatory Brain Disorders Treatment Evaluation Checklist (PTEC) designed to assess changes to a patient’s symptoms over time along 10 subscales: Behavior/Mood, OCD, Anxiety, Food intake, Tics, Cognitive/Developmental, Sensory, Other, Sleep, and Health. The psychometric quality of PTEC was tested with 225 participants. Results: The internal reliability of the PTEC was excellent (Cronbach’s alpha = 0.96). PTEC exhibited adequate test–retest reliability (r = 0.6) and excellent construct validity, supported by a strong correlation with the Health subscale of the Autism Treatment Evaluation Checklist (r = 0.8). Conclusions: We hope that PTEC will assist parents and clinicians in the monitoring and treatment of PANS. The PTEC questionnaire is freely available at neuroimmune.org/PTEC. Full article

Rapid urbanization has intensified global settlement density, significantly increasing the importance of urban street environmental quality, which profoundly affects residents’ physical and psychological well-being. Traditional methods for evaluating urban environmental quality have largely overlooked dynamic perceptual changes occurring throughout the day, resulting in incomplete assessments. To bridge this methodological gap, this study presents an innovative approach combining advanced deep learning techniques with time-series street view imagery (SVI) analysis to systematically quantify spatio-temporal variations in the perceived environmental quality of pedestrian-oriented streets. It further addresses two central questions: how perceived environmental quality varies spatially across sections of a pedestrian-oriented street and how these perceptions fluctuate temporally throughout the day. Utilizing Golden Street, a representative living street in Shanghai’s Changning District, as the empirical setting, street view images were manually collected at 96 sampling points across multiple time intervals within a single day. The collected images underwent semantic segmentation using the DeepLabv3+ model, and emotional scores were quantified through the validated MIT Place Pulse 2.0 dataset across six subjective indicators: “Safe,” “Lively,” “Wealthy,” “Beautiful,” “Depressing,” and “Boring.” Spatial and temporal patterns of these indicators were subsequently analyzed to elucidate their relationships with environmental attributes. This study demonstrates the effectiveness of integrating deep learning models with time-series SVI for assessing urban environmental perceptions, providing robust empirical insights for urban planners and policymakers. The results emphasize the necessity of context-sensitive, temporally adaptive urban design strategies to enhance urban livability and psychological well-being, ultimately contributing to more vibrant, secure, and sustainable pedestrian-oriented urban environments. Full article

Background/Objectives: Cardiovascular diseases (CVDs) encompass a wide range of heart and vascular conditions and remain the leading cause of death worldwide. Acceptance and Commitment Therapy (ACT) is a psychotherapeutic approach that integrates acceptance, mindfulness, and commitment to value-based actions. This systematic review aims to explore the current evidence on the potential role of ACT interventions in supporting psychological well-being among individuals with CVDs. Methods: A systematic review was conducted in accordance with PRISMA guidelines. A search of the literature was conducted through Scopus, PubMed, Web of Science, Cochrane, and PsycINFO databases. Six studies met the inclusion criteria. Results: The reviewed studies suggest that ACT may promote psychological flexibility, emotion regulation, and self-care behaviors in patients with CVDs. Reported outcomes include improved mindfulness, reduced distress, and enhanced quality of life. However, the evidence base is limited in both size and methodological rigor, with included studies varying in design and population. Conclusions: While preliminary findings indicate that ACT shows promise in addressing psychological aspects of CVDs, the current evidence remains insufficient to draw definitive conclusions. Further high-quality, large-scale studies are needed to evaluate the effectiveness and clinical applicability of ACT in cardiovascular populations. Full article

Background/Objectives. Social and Emotional Education (SEE) interventions during early childhood have shown considerable promise in enhancing children’s emotion understanding, social competence, and behavioural adjustments. However, few studies have examined their long-term impact, especially across the preschool-to-primary school transition. This study evaluated the effectiveness of a manualized SEE program, Timmy’s Trip to Planet Earth, in promoting emotional, behavioural, and social functioning over time. Methods. A quasi-experimental longitudinal design was adopted with pre- and post-test assessments conducted approximately 18 months apart. Participants were 89 typically developing children (aged 59–71 months), assigned to an experimental group (n = 45) or a waiting-list group (n = 44). The program combined teacher training, classroom-based lessons, home activities, and teachers’ ongoing implementation support. The effectiveness of the program was measured via the Test of Emotion Comprehension (TEC), the Strengths and Difficulties Questionnaire (SDQ), and the Social Competence and Behavior Evaluation (SCBE-30). Results. Significant Time × Group interactions were observed for the TEC External and Mental components, indicating greater improvements in emotion recognition and mental state understanding in the intervention group. The SDQ revealed significant reductions in conduct problems and increased prosocial behaviours. In the SCBE-30, a significant interaction effect was found for social competence, with the intervention group showing greater improvement over time compared to the control group. Conclusions. The findings suggest that SEE programs can produce meaningful and lasting improvements in children’s emotional and social skills across key educational transitions. Teacher training and family involvement likely played a critical role in supporting the program’s sustained impact. Full article

Acoustic quality is one of the aspects that contribute to the heritage of cultural and religious spaces. It is increasingly common to find scientific literature detailing the sound characteristics of places of worship, especially those with cultural and historical significance. This article presents a comprehensive acoustic characterization of three colonial heritage churches in Quito. It examines the relationship between objective and subjective parameters that influence the valuation of a space or sound environment. To analyze this relationship, we employed structural equation modeling (SEM) to evaluate three latent variables using perceptual acoustic indicators. The SEM results highlighted significant associations between physical acoustic parameters, emotional responses, and evaluative judgments, underscoring that traditional intelligibility metrics alone may not fully capture acoustic quality in these contexts. These findings provide a robust interdisciplinary framework that spans objective measures and human perception, offering valuable guidance for future heritage conservation efforts. Full article

Background/Objectives: Depression, anxiety and apathy are often associated with subjective cognitive complaints (SCCs) in people with Parkinson’s disease (PwPD) without cognitive impairment. Cognitive reserve (CR) enhances emotional resilience, allowing people to better cope with stress and emotional challenges, factors affecting quality of life. We aimed to explore the relationship between CR and mood/anxiety in cognitively intact PwPD with and without SCCs. Methods: In this cross-sectional study we enrolled 133 PwPD and normal cognitive function (age 59.8 ± 6.7 years; disease duration 9.0 ± 5.5 years; male/female 84/49). We assessed cognitive reserve (CR scale), subjective cognitive complaints (with PD-CFRS), QoL (PDQ8), mood, anxiety and apathy (BDI-II; STAI, PAS, Apathy scales). We used a t-test to compare groups (with/without SCC; M/F); correlations and moderation analysis to evaluate the relation between CR and behavioral features and the interplay between CR, behavioral discomfort and QoL. Results: The group with SCCs had significantly (p < 0.05) higher scores in PDQ8, Apathy, STAI, PAS-C and BDI-II scales than those with no SCCs. Males with SCCs had higher scores in PDQ8, Apathy scale and BDI-II while females differed in PDQ8 and Apathy scale scores. In the SCC group, late-life CR was negatively correlated with PAS-C (avoidance behavior) and BDI-II; correlations were confirmed in the male group where CR also correlated with PDQ-8 and PAS persistent anxiety. Conclusions: PwPD and SCCs are more depressed and anxious compared to people without SCCs. Furthermore, we found a relationship between depressive symptoms, anxiety and CR: PwPD with SCCs may rely on cognitive reserve to better cope with the feeling of anxiety and depression, especially in male gender. Full article

This study builds on Davis’ TAM by integrating environmental and psychological variables relevant to AI medical diagnostics. This study developed a mathematical theoretical model called the “AI medical diagnosis-acceptance evaluation model” (AMD-AEM) to better understand acceptance behavior intention. Using mathematical modeling, we established reflective measurement model indicators and structural equation relationships, where linear structural equations illustrate the interactions among latent variables. In 2025, we collected empirical data from 2380 patients and medical staff who have experience with AI diagnostic systems in teaching hospitals in central Taiwan. Smart PLS 3 was employed to validate the AMD-AEM model. The results reveal that perceived usefulness (PU) and information quality (IQ) are the primary predictors of acceptance behavior intention (ABI). Additionally, perceived ease of use (PE) indirectly influences ABI through PU and attitude toward use (ATU). AI emotional perception (AEP) notably shows a significant positive relationship with ATU, highlighting that warm and positive human–AI interactions are crucial for user acceptance. IQ was identified as a mediating variable, with variance accounted for (VAF) coefficient analysis confirming its complete mediation effect on the path from ATU to ABI. This indicates that information quality enhances user attitudes and directly increases acceptance behavior intention. The AMD-AEM model demonstrates an excellent fit, providing valuable insights for academia and the healthcare industry. Full article

Background/Objectives: Fibromyalgia syndrome (FMS) is a chronic pain condition in which executive function (EF) alterations have been reported, though strikingly, relationships between simple executive functions (EFs) (updating, inhibition, and mental flexibility) and high-order ones, such as planning and problem-solving, have not been addressed yet in this population. This research aimed to firstly explore how low-level EFs play a role in planning and problem-solving performances. Methods: Thirty FMS patients and thirty healthy participants completed a series of neuropsychological tests evaluating low- and high-order EFs. Clinical and emotional symptoms were assessed with self-report questionnaires, while pain and fatigue levels were measured with numerical scales. Importantly, specific drug restrictions were accounted for. Results: Patients scored lower in most neurocognitive tests, with statistical significance noted only for visuospatial working memory (WM) and two planning and problem-solving tests. Pain, fatigue, and sleep disturbances showed important effects on most of the cognitive outcomes. Multiple regression analyses reflected that planning and problem-solving were successfully and partially predicted by updating, inhibition, and mental flexibility (though differences emerged between tasks). Conclusions: Our study confirms the presence of cognitive impairments in FMS, especially in high-order EFs, supporting patients’ complaints. Clinical symptoms play a role in FMS dyscognition but do not explain it completely. For the first time, as far as the authors know, simple EF influences on planning and problem-solving tests have been described for FMS patients. These results might help in unraveling the dysexecutive profile in FMS to design more adjusted treatment options. Full article

Background/Objectives: Clinical Research Nurses (CRNs) have emerged as pivotal actors in the conduct, coordination, and oversight of clinical trials globally. Over the past three decades, the role of the CRN has evolved in response to the increasing complexity of research protocols, ethical standards, and regulatory frameworks. Originating as task-oriented support figures, CRNs have progressively assumed broader responsibilities that include patient advocacy, protocol integrity, ethical vigilance, and interprofessional coordination. By mapping the global literature on CRNs, this review will examine how their role has been defined, implemented, and evaluated over the past three decades. Methods: A scoping review was conducted using JBI methodology and PRISMA-ScR guidelines. The search covered the peer-reviewed and gray literature from 1990 to 2024 across major databases. Data analysis combined traditional extraction with topic modeling, Multiple Correspondence Analysis, and k-means clustering to identify key themes. Results: From the 128 included studies, four major themes emerged: clinical trial management, role perception and team integration, professional competencies and development, and systemic barriers. Despite formal competency frameworks, CRNs face inconsistencies in role recognition, unstable contracts, and limited career pathways. Emotional strain and professional isolation are recurrent. Over time, their functions have evolved from task execution to broader responsibilities, including advocacy and ethical oversight. However, no studies reported patient-level outcomes, revealing a critical gap in the evidence base. Conclusions: CRNs play a vital but undervalued role in clinical research. Persistent structural challenges hinder their development and visibility. Enhancing institutional support and generating outcome-based evidence are necessary steps toward fully integrating CRNs into research infrastructures. Full article

This study presents a data-driven, multi-objective optimization framework for user-centric product form design, integrating affective response modeling with coupled constraint satisfaction. Initially, morphological analysis and aesthetic evaluation are employed to extract critical design elements, while cluster analysis segments users based on preference data. Dominance-based rough set theory is then applied to derive group-specific affective patterns, which are subsequently modeled using Genetic Algorithm-optimized Backpropagation Neural Networks (GA-BPNN). The framework leverages Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate Pareto-optimal solutions, balancing aesthetic preferences and engineering constraints across user groups. A case study on SUV form design validates the proposed methodology, demonstrating its efficacy in delivering optimal, user-group-targeted design solutions while accommodating individual variability and constraint interdependencies. The results highlight the framework’s potential as a generalizable approach for emotion-aware, constraint-compliant product design. Full article

This research investigates topic-based sentiment classification in Greek educational-related data using transformer-based language models. A comparative evaluation is conducted on GreekBERT, XLM-r-Greek, mBERT, and Palobert using three original sentiment-annotated datasets representing parents of students with functional diversity, school directors, and teachers, each capturing diverse educational perspectives. The analysis examines both overall sentiment performance and topic-specific evaluations across four thematic classes: (i) Material and Technical Conditions, (ii) Educational Dimension, (iii) Psychological/Emotional Dimension, and (iv) Learning Difficulties and Emergency Remote Teaching. Results indicate that GreekBERT consistently outperforms other models, achieving the highest overall F1 score (0.91), particularly excelling in negative sentiment detection (F1 = 0.95) and showing robust performance for positive sentiment classification. The Psychological/Emotional Dimension emerged as the most reliably classified category, with GreekBERT and mBERT demonstrating notably high accuracy and F1 scores. Conversely, Learning Difficulties and Emergency Remote Teaching presented significant classification challenges, especially for Palobert. This study contributes significantly to the field of sentiment analysis with Greek-language data by introducing original annotated datasets, pioneering the application of topic-based sentiment analysis within the Greek educational context, and offering a comparative evaluation of transformer models. Additionally, it highlights the superior performance of Greek-pretrained models in capturing emotional detail, and provides empirical evidence of the negative emotional responses toward Emergency Remote Teaching. Full article

Background: Chronic primary orofacial pain (COFP) affects approximately 7% of the population and often leads to reduced quality of life. Patients frequently undergo multiple assessments and treatments across healthcare disciplines, often without a definitive diagnosis. The 2019 ICD-11 classification of chronic primary pain clusters together COFP subtypes based on chronicity and associated functional and emotional impairment. Objective: This study aimed to evaluate whether these subtypes of COFP share common underlying mechanisms by comparing neuroimaging findings. Methods: A systematic review was conducted in accordance with PRISMA guidelines. Searches were performed using Medline (OVID) and Scopus up to April 2025. Inclusion criteria focused on MRI-based neuroimaging studies of participants diagnosed with COFP subtypes. Data extraction included participant demographics, imaging modality, brain regions affected, and pain assessment tools. Quality assessment used a modified Coleman methodological score. Results: Fourteen studies met the inclusion criteria, all utilising MRI and including two COFP subtypes (temporomandibular disorder and burning mouth syndrome). Resting- and task-state imaging revealed overlapping alterations in several brain regions, including the thalamus, somatosensory cortices (S1, S2), cingulate cortex, insula, prefrontal cortex, basal ganglia, medial temporal lobe, and primary motor area. These changes were consistent across both TMD and BMS populations. Conclusions: The findings suggest that chronic primary orofacial pain conditions (TMD and BMS) may share common central neuroplastic changes, supporting the hypothesis of a unified pathophysiological mechanism. This has implications for improving diagnosis and treatment strategies, potentially leading to more targeted and effective care for these patients. Full article

The application of artificial intelligence has significantly advanced interactive storytelling. However, current research has predominantly concentrated on the content generation capabilities of AI, primarily following a one-way ‘input-direct generation’ model. This has led to limited practicality in AI story writing, mainly due to the absence of investigations into user-driven creative processes. Consequently, users often perceive AI-generated suggestions as unhelpful and unsatisfactory. This study introduces a novel creative tool named Story Forge, which incorporates a card-based interactive narrative approach. By utilizing interactive story element cards, the tool facilitates the integration of narrative components with artificial intelligence-generated content to establish an interactive story writing framework. To evaluate the efficacy of Story Forge, two tests were conducted with a focus on user engagement, decision-making, narrative outcomes, the replay value of meta-narratives, and their impact on the users’ emotions and self-reflection. In the comparative assessment, the participants were randomly assigned to either the experimental group or the control group, in which they would use either a web-based AI story tool or Story Forge for story creation. Statistical analyses, including independent-sample t-tests, p-values, and effect size calculation (Cohen’s d), were employed to validate the effectiveness of the framework design. The findings suggest that Story Forge enhances users’ intuitive creativity, real-time story development, and emotional expression while empowering their creative autonomy. Full article

Continuous blood pressure (BP) monitoring provides valuable insight into the body’s dynamic cardiovascular regulation across various physiological states such as physical activity, emotional stress, postural changes, and sleep. Continuous BP monitoring captures different variations in systolic and diastolic pressures, reflecting autonomic nervous system activity, vascular compliance, and circadian rhythms. This enables early identification of abnormal BP trends and allows for timely diagnosis and interventions to reduce the risk of cardiovascular diseases (CVDs) such as hypertension, stroke, heart failure, and chronic kidney disease as well as chronic stress or anxiety disorders. To facilitate continuous BP monitoring, we propose an AI-powered estimation framework. The proposed framework first uses an expert-driven feature engineering approach that systematically extracts physiological features from photoplethysmogram (PPG)-based arterial pulse waveforms (APWs). Extracted features include pulse rate, ascending/descending times, pulse width, slopes, intensity variations, and waveform areas. These features are fused with demographic data (age, gender, height, weight, BMI) to enhance model robustness and accuracy across diverse populations. The framework utilizes a Tab-Transformer to learn rich feature embeddings, which are then processed through an ensemble machine learning framework consisting of CatBoost, XGBoost, and LightGBM. Evaluated on a dataset of 1000 subjects, the model achieves Mean Absolute Errors (MAE) of 3.87 mmHg (SBP) and 2.50 mmHg (DBP), meeting British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed architecture advances non-invasive, AI-driven solutions for dynamic cardiovascular health monitoring. Full article