Search Results (731)

Background/Objectives: Targeting individuals with certain characteristics provides improved precision in many healthcare applications. An alert mechanism for COPD exacerbations has recently been validated. It has been argued that its efficacy improves considerably with stratification. This paper provides an in-depth analysis of the cough data of the stratified cohort to identify options for and the feasibility of improved precision in the alert mechanism for the intended patient group. Methods: The alert system was extended using a system complementary to the existing one to accommodate observed rapid changes in cough trends. The designed system was tested in a post hoc analysis of the data. The trend data were inspected to consider their meaningfulness for patients and caregivers. Results: While stratification was effective in reducing misses, the augmented alert system improved the sensitivity and number of early alerts for the acute exacerbation of COPD (AE-COPD). The combination of stratification and the augmented mechanism led to sensitivity of 86%, with a false alert rate in the order of 1.5 per year in the target group. The alert system is rule-based, operating on interpretable signals that may provide patients or their caregivers with better insights into the respiratory condition. Conclusions: The augmented alert system operating based on cough trends has the promise of increased precision in detecting AE-COPD in the target group. Since the design and testing of the augmented system were based on the same data, the system needs to be validated. Signals within the alert system are potentially useful for improved self-management in the target group. Full article

This study presents a numerical and experimental evaluation of axial load transfer mechanisms in deep foundations constructed in stratified cohesive soils in İzmir, Türkiye. A full-scale bi-directional static load test equipped with strain gauges was conducted on a barrette pile to investigate depth-dependent mobilization of shaft resistance. A finite element model was developed and calibrated using field-observed load–settlement and strain data to replicate the pile–soil interaction and deformation behavior. The analysis revealed a shaft-dominated load transfer behavior, with progressive mobilization concentrated in intermediate-depth cohesive layers. Sensitivity analysis identified the undrained stiffness (E_u) as the most influential parameter governing pile settlement. A strong polynomial correlation was established between calibrated E_u values and SPT N₆₀, offering a practical tool for preliminary design. Additionally, strain energy distribution was evaluated as a supplementary metric, enhancing the interpretation of mobilization zones beyond conventional stress-based methods. The integrated approach provides valuable insights for performance-based foundation design in layered cohesive ground, supporting the development of site-calibrated numerical models informed by full-scale testing data. Full article

Our study was conducted in the Valle Nuevo National Park and included four habitat classes: tussock grass (Sabapa), pine forest (Pinoc), broadleaf forest (Boslat), and agricultural ecosystem (Ecoag). We had two main objectives: to comparatively describe millipede communities and to determine the relationships between population density/diversity and soil physicochemical variables. The research was cross-sectional and non-manipulative, with a descriptive and correlational scope; sampling followed a stratified systematic design, with eight transects and 32 quadrats of 1 m², covering 21.7 km. We found a sandy loam soil with an extremely acidic pH. The highest population density of millipedes was recorded in Sabapa, and the lowest in Ecoag. The highest alpha diversity was shared between Boslat (Margalef = 1.72) and Pinoc (Shannon = 2.53); Sabapa and Boslat showed the highest Jaccard similarity (0.56). The null hypothesis test using the weighted Shannon index revealed a statistically significant difference in diversity between the Boslat–Sabapa and Pinoc–Sabapa pairs. Two of the species recorded highly significant indicator values (IndVal) for two habitat classes. We found significant correlations (p < 0.05) between various soil physicochemical variables and millipede density and diversity. Full article

Background: Pigmented superficial skin lesions pose a persistent diagnostic challenge due to overlapping clinical and dermoscopic appearances between benign and malignant entities. While histopathology remains the gold standard, there is growing interest in non-invasive imaging models that can preoperatively stratify malignancy risk. This methodological pilot study was designed to explore the feasibility and initial diagnostic performance of a novel radiology-adapted logistic regression approach. To develop and preliminarily evaluate a new logistic model integrating both structural (lesion size, depth) and vascular (Doppler patterns) ultrasonographic features for non-invasive risk stratification of pigmented superficial skin lesions. Material and Methods: In this prospective single-center pilot investigation, 44 patients underwent standardized high-frequency grayscale and Doppler ultrasound prior to excisional biopsy. Lesion size, depth, and vascularity patterns were systematically recorded. Three logistic regression models were constructed: (1) based on lesion size and depth, (2) based on vascularity patterns alone, and (3) combining all parameters. Model performance was assessed via ROC curve analysis. Intra-observer reliability was determined by repeated measurements on a random subset. Results: The lesion size and depth model yielded an AUC of 0.79, underscoring the role of structural features. The vascularity-only model showed an AUC of 0.76. The combined model demonstrated superior discriminative ability, with an AUC of approximately 0.85. Intra-observer analysis confirmed excellent repeatability (κ > 0.80; ICC > 0.85). Conclusions: This pilot study introduces a novel logistic framework that combines grayscale and Doppler ultrasound parameters to enhance non-invasive malignancy risk assessment in pigmented superficial skin lesions. These encouraging initial results warrant larger multicenter studies to validate and refine this promising approach. Full article

Background: Social media platforms play a crucial role in contemporary healthcare, facilitating patient participation and enabling communication among healthcare workers, as well as serving as a platform for medical awareness and advocacy. Social media use among healthcare workers has increased to 91%, with 65% using it for health promotion purposes. Nonetheless, current studies have not properly and empirically explored its dimensions. Objectives: This study therefore examines social media dimensions and the productivity of healthcare workers. Methods: Leveraging the professional productivity theory and digital engagement theory, the study employs SPSS to analyze the gathered data through a partial least squares (PLS-SEM) approach to explore social media dimensions and productivity among healthcare workers in a Nigerian Tertiary Hospital. Based on a cross-sectional descriptive survey design and stratified random sampling method, 344 medical workers were analyzed. Findings: The study found that fear of missing out, information sharing, social influence, trust, and social media usage have a significant impact on the productivity of healthcare professionals. Conclusions: This research adds to the growing academic research on the capabilities of social media within the circular economic systems aimed at advancing healthcare delivery in developing economies. The research offers a method for maximizing the use of social media within healthcare settings to foster enhanced healthcare outcomes, particularly productivity. Full article

Background/Objectives: The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic resistance. Somatic alterations in key pathway genes—including KRAS, NRAS, BRAF, and EGFR—are pivotal to clinical decision-making in precision oncology. However, the integration of these genomic events with clinical and demographic data remains hindered by fragmented resources and a lack of accessible analytical frameworks. To address this challenge, we developed AI-HOPE-RTK-RAS, a domain-specialized conversational artificial intelligence (AI) system designed to enable natural language-based, integrative analysis of RTK-RAS pathway alterations in CRC. Methods: AI-HOPE-RTK-RAS employs a modular architecture combining large language models (LLMs), a natural language-to-code translation engine, and a backend analytics pipeline operating on harmonized multi-dimensional datasets from cBioPortal. Unlike general-purpose AI platforms, this system is purpose-built for real-time exploration of RTK-RAS biology within CRC cohorts. The platform supports mutation frequency profiling, odds ratio testing, survival modeling, and stratified analyses across clinical, genomic, and demographic parameters. Validation included reproduction of known mutation trends and exploratory evaluation of co-alterations, therapy response, and ancestry-specific mutation patterns. Results: AI-HOPE-RTK-RAS enabled rapid, dialogue-driven interrogation of CRC datasets, confirming established patterns and revealing novel associations with translational relevance. Among early-onset CRC (EOCRC) patients, the prevalence of RTK-RAS alterations was significantly lower compared to late-onset disease (67.97% vs. 79.9%; OR = 0.534, p = 0.014), suggesting the involvement of alternative oncogenic drivers. In KRAS-mutant patients receiving Bevacizumab, early-stage disease (Stages I–III) was associated with superior overall survival relative to Stage IV (p = 0.0004). In contrast, BRAF-mutant tumors with microsatellite-stable (MSS) status displayed poorer prognosis despite higher chemotherapy exposure (OR = 7.226, p < 0.001; p = 0.0000). Among EOCRC patients treated with FOLFOX, RTK-RAS alterations were linked to worse outcomes (p = 0.0262). The system also identified ancestry-enriched noncanonical mutations—including CBL, MAPK3, and NF1—with NF1 mutations significantly associated with improved prognosis (p = 1 × 10⁻⁵). Conclusions: AI-HOPE-RTK-RAS exemplifies a new class of conversational AI platforms tailored to precision oncology, enabling integrative, real-time analysis of clinically and biologically complex questions. Its ability to uncover both canonical and ancestry-specific patterns in RTK-RAS dysregulation—especially in EOCRC and populations with disproportionate health burdens—underscores its utility in advancing equitable, personalized cancer care. This work demonstrates the translational potential of domain-optimized AI tools to accelerate biomarker discovery, support therapeutic stratification, and democratize access to multi-omic analysis. Full article

In the current era of a dynamic environment, organizations need to continuously innovate and transform to remain competitive. Digital transformation is an essential driver across organizations, including small and medium-sized enterprises (SMEs), reshaping organizational agility. This research examines the interconnection among knowledge sharing, digital transformation, open innovation, organizational agility, and transformational leadership. A quantitative research design was employed, using an online survey with data collected from 543 participants selected through a stratified random sampling from SMEs in China. Data were analyzed by utilizing partial least squares structural equation modeling. The results include a significant impact of knowledge sharing on digital transformation, digital transformation on open innovation, and open innovation on organizational agility. Additionally, digital transformation and open innovation were found to significantly mediate the relationship between knowledge sharing and open innovation and organizational agility. Moreover, transformational leadership significantly moderated the impact of digital transformation on open innovation. The model explained 67.7% of the variation in organizational agility. The research provides a holistic model for SMEs aiming to leverage information sharing, technological integration, and leadership practice to improve flexible and innovative systems, contributing to theoretical understanding and practical solutions to sustainable resilience. Full article

In an era where online interactions increasingly shape social dynamics, the pervasive issue of cyberbullying poses a significant threat to the well-being of individuals, particularly among vulnerable groups. Despite extensive research on text-based cyberbullying detection, the rise of visual content on social media platforms necessitates new approaches to address cyberbullying using images. This domain has been largely overlooked. In this paper, we present a novel dataset specifically designed for the detection of visual cyberbullying, encompassing four distinct classes: abuse, curse, discourage, and threat. The initial prepared dataset (cyberbullying visual indicators dataset (CVID)) comprised 664 samples for training and validation, expanded through data augmentation techniques to ensure balanced and accurate results across all classes. We analyzed this dataset using several advanced deep learning models, including VGG16, VGG19, MobileNetV2, and Vision Transformer. The proposed model, based on DenseNet201, achieved the highest test accuracy of 99%, demonstrating its efficacy in identifying the visual cues associated with cyberbullying. To prove the proposed model’s generalizability, the 5-fold stratified K-fold was also considered, and the model achieved an average test accuracy of 99%. This work introduces a dataset and highlights the potential of leveraging deep learning models to address the multifaceted challenges of detecting cyberbullying in visual content. Full article

Background: Behavioural heterogeneity in obesity is increasingly recognised, but how specific dietary patterns, food preferences and physical activity vary between obesity classes remains poorly characterised. Methods: We analysed behavioural, dietary, and lifestyle data from 1366 adults attending a tertiary obesity clinic in Italy. Participants were stratified into five obesity classes defined by BMI. Age-adjusted regression models and chi-square tests with Bonferroni correction were used to examine associations between obesity severity and key behavioural outcomes, including food preferences, eating behaviours, physical activity, and self-reported sleep quality. Results: The prevalence of uncontrolled eating, skipping meals, and fast eating significantly increased with obesity severity after adjusting for age (all p < 0.05). Preference for yoghurt and legumes declined with increasing BMI, whereas preferences for meat and dairy remained stable. Age-adjusted sport participation decreased progressively, with significantly lower odds in Obesity I, II, and IIIA compared to the Overweight group. Sleep quality was highest among overweight participants and declined with obesity severity; night-time awakenings were most frequent in Obesity IIIB. Conclusions: Distinct behavioural and lifestyle traits, including lower sport participation, reduced preference for fibre-rich foods, and greater frequency of uncontrolled, fast, and irregular eating, showed overall trends across obesity classes. While these findings suggest the presence of behavioural phenotypes, their interpretation is limited by the cross-sectional design and the use of self-reported, non-validated measures. Future studies should incorporate objective assessments to inform targeted obesity interventions. Full article

Background/Objectives: The study aims to identify key cognitive and non-cognitive variables (e.g., clinical, neuroimaging, and genetic data) predicting cognitive decline in Parkinson’s disease (PD) patients using machine learning applied to a sample (N = 618) from the Parkinson’s Progression Markers Initiative database. Traditional research has mainly employed explanatory approaches to explore variable relationships, rather than maximizing predictive accuracy for future cognitive decline. In the present study, we implemented a predictive framework that integrates a broad range of baseline cognitive, clinical, genetic, and imaging data to accurately forecast changes in cognitive functioning in PD patients. Methods: An artificial neural network was trained on baseline data to predict general cognitive status three years later. Model performance was evaluated using 5-fold stratified cross-validation. We investigated model interpretability using explainable artificial intelligence techniques, including Shapley Additive Explanations (SHAP) values, Group-Wise Feature Masking, and Brute-Force Combinatorial Masking, to identify the most influential predictors of cognitive decline. Results: The model achieved a recall of 0.91 for identifying patients who developed cognitive decline, with an overall classification accuracy of 0.79. All applied explainability techniques consistently highlighted baseline MoCA scores, memory performance, the motor examination score (MDS-UPDRS Part III), and anxiety as the most predictive features. Conclusions: From a clinical perspective, the findings can support the early detection of PD patients who are more prone to developing cognitive decline, thereby helping to prevent cognitive impairments by designing specific treatments. This can improve the quality of life for patients and caregivers, supporting patient autonomy. Full article

Background/Objectives: Obesity, a major risk factor for cardiometabolic traits, is influenced by both genetic and environmental factors. Genetic studies have identified multiple single-nucleotide polymorphisms (SNPs) associated with obesity and related traits. This study aimed to examine the association between genetic risk score (GRS) and obesity-associated traits, while incorporating SNPs with established gene–diet interactions to explore their potential role in precision nutrition (PN) strategies. Methods: A total of 4279 participants were stratified into low- and intermediate-/high-GRS groups based on 18 SNPs linked to obesity and cardiometabolic traits. This study followed a case–control design, where cases included individuals with overweight/obesity, T2DM-positive (+), or CVD-positive (+) individuals and controls, which comprised individuals free of these traits. Logistic regression area under the curve (AUC) models were used to assess the predictive power of the GRS and traditional risk factors on BMI, T2DM and CVD. Results: Individuals in the intermediate-/high-GRS group had higher odds of being overweight or obese (OR = 1.23, CI: 1.03–1.48, p = 0.02), presenting as T2DM+ (OR = 1.56, CI: 1.03–2.49, p = 0.03) and exhibiting CVD-related traits (OR = 1.56, CI: 1.25–1.95, p < 0.0001), compared to the low-GRS group. The GRS was the second most predictive factor after age for BMI (AUC = 0.515; 95% CI: 0.462–0.538). The GRS also demonstrated a predictive power of 0.528 (95% CI: 0.508–0.564) for CVD and 0.548 (95% CI: 0.440–0.605) for T2DM. Conclusions: This study supports the potential utility of the GRS in assessing obesity and cardiometabolic risk, while emphasizing the potential of PN approaches in modulating genetic susceptibility. Incorporating gene–diet interactions provides actionable insights for personalized dietary strategies. Future research should integrate multiple gene–diet and gene–gene interactions to enhance risk prediction and targeted interventions. Full article

This paper examines the moderating role of corporate social responsibility (CSR) on the relationship between environmental, social, and governance (ESG) dimensions and investor decision-making in Jordan. Data were collected using a structured questionnaire designed for institutional investors and financial analysts, capturing perceptions of ESG, CSR, and investment behavior. A stratified random sample of 350 professionals across the financial, industrial, and service sectors was surveyed. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The findings show that environmental and social dimensions have positive effects on investor decisions, with governance dimensions having a negative effect. Notably, CSR has a negative moderating effect on the governance dimensions and investor decision, with no observed statistical moderating effect for environmental or social dimensions. This research unravels the multidimensional role of CSR in building the ESG-investor decision interface and identifies a counterintuitive negative moderating impact of CSR on governance, contributing to the existing literature on sustainability alignment in emerging markets. The results offer practical implications for companies aiming to attract sustainability-oriented investors by indicating the necessity for an integrated and genuine CSR and ESG approach. Full article

This research aims to establish the prevalence of imposter syndrome among Sultan Qaboos University (SQU) undergraduate students while assessing its association with depression symptoms and anxiety symptoms. A cross-sectional design recruited 504 undergraduate students selected through stratified random sampling. Data collection employed the Clance Imposter Phenomenon Scale (CIPS), the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 (GAD-7). Data analysis included Pearson’s correlation, chi-square tests, and logistic regression analyses. In total, 56% of participants had imposter syndrome. The CIPS scores showed a moderate relationship with depression (r = 0.486, p < 0.001) and anxiety (r = 0.472, p < 0.001). Students who experienced imposter syndrome showed a higher probability of developing depressive symptoms (χ² = 45.63, p < 0.001, OR = 3.49) and anxiety symptoms (χ² = 32.96, p < 0.001, OR = 2.86). The logistic regression analysis showed that depression (B = 0.096, p < 0.001) and anxiety (B = 0.075, p = 0.003) acted as significant predictors for imposter syndrome. This study reveals a strong link between imposterism, depression, and anxiety among students. This highlights the need for university counseling programs to address imposter feelings and the role of clinical psychology in managing this phenomenon in academic and clinical settings. Full article

Background: Large Language Models (LLMs) such as ChatGPT, Claude, and Gemini are being increasingly adopted in medicine; however, their reliability in cardiology remains underexplored. Purpose of the study: To compare the performance of three general-purpose LLMs in response to cardiology-related clinical queries. Study design: Seventy clinical prompts stratified by diagnostic phase (pre or post) and user profile (patient or physician) were submitted to ChatGPT, Claude, and Gemini. Three expert cardiologists, who were blinded to the model’s identity, rated each response on scientific accuracy, completeness, clarity, and coherence using a 5-point Likert scale. Statistical analysis included Kruskal–Wallis tests, Dunn’s post hoc comparisons, Kendall’s W, weighted kappa, and sensitivity analyses. Results: ChatGPT outperformed both Claude and Gemini across all criteria (mean scores: 3.7–4.2 vs. 3.4–4.0 and 2.9–3.7, respectively; p < 0.001). The inter-rater agreement was substantial (Kendall’s W: 0.61–0.71). Pre-diagnostic and patient-framed prompts received higher scores than post-diagnostic and physician-framed ones. Results remained robust across sensitivity analyses. Conclusions: Among the evaluated LLMs, ChatGPT demonstrated superior performance in generating clinically relevant cardiology responses. However, none of the models achieved maximal ratings, and the performance varied by context. These findings highlight the need for domain-specific fine-tuning and human oversight to ensure a safe clinical deployment. Full article

This study investigates the effect of pile diameter on the seismic performance of single piles using the kinematic interaction framework outlined in Method III of the Turkish Building Earthquake Code TBEC-2018. Pile diameters of 65 cm, 80 cm, and 100 cm were analyzed under four different soil profiles—soft clay, stiff clay, very loose sand-A, and very loose sand-B. The methodology integrated nonlinear spring modeling (P-y, T-z, Q-z) for soil behavior, one-dimensional site response analysis using DEEPSOIL, and structural analysis with SAP2000. The simulation results showed that increasing the pile diameter led to a significant rise in internal forces: the maximum bending moment increased up to 4.0 times, and the maximum shear force increased 4.5 times from the smallest to the largest pile diameter. Horizontal displacements remained nearly constant, whereas vertical displacements decreased by almost 50%, indicating improved pile–soil stiffness interaction. The depth of the maximum moment shifted according to the soil stiffness, and stress concentrations were observed at the interfaces of stratified layers. The findings underline the importance of considering pile geometry and soil layering in seismic design. This study provides quantitative insights into the trade-off between displacement control and force demand in seismic pile design, contributing to safer foundation strategies in earthquake-prone regions. Full article