Search Results (157,523)

Background: Insulin resistance (IR) is common in overweight or obese individuals. Dysregulation of trace elements such as cobalt (Co) and manganese (Mn) has been associated with obesity and IR markers in individuals with diabetes. However, their role in non-diabetic states is less understood. Objective: This study aimed to analyze the association between serum Co and Mn levels and IR in overweight and obese women without diabetes. Methods: A total of 112 overweight or obese women were evaluated for their anthropometric, metabolic, and biochemical characteristics. To estimate IR, the homeostatic model assessment of insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), triglyceride–glucose index (TyG), and triglyceride–glucose–body mass index (TyG-BMI) were calculated. Serum Co and Mn concentrations were quantified by inductively coupled plasma mass spectrometry (ICP-MS). Results: Our results show that 77% of participants exhibited central fat accumulation and a high prevalence of IR. Fasting insulin (FINS), HOMA-IR, and TyG-BMI were significantly higher in obese women, while adiponectin (Adpn) was lower. Moreover, Co was inversely associated with FINS (p = 0.003) and HOMA-IR (p = 0.011), and positively associated with QUICKI (p = 0.011) in obese women. In contrast, serum Mn levels showed negative correlations with fasting glucose (FG) (p = 0.021) and the TyG index (p = 0.048) in overweight women. Conclusions: Co serum levels were positively associated with FG and QUICKI and negatively associated with FINS and HOMA-IR in the obese group. Mn showed negative associations with FG and the TyG index, suggesting that these trace elements may play a role in the IR in people with obesity. Full article

Background/Objective: Childhood obesity and mental health disorders in preschool-aged children represent critical public health challenges with a rising global prevalence, closely linked to lifestyle behaviors and the family environment. This cross-sectional study aims to investigate the combined influence of maternal sociodemographic, socioeconomic, anthropometric characteristics, and lifestyle factors on the physical and mental health status of preschool-aged children. Methods: Validated questionnaires were administered to assess dietary habits, psychosocial parameters (depression, anxiety, stress), and sociodemographic, socioeconomic, and anthropometric variables among 200 preschool-aged children and their mothers, who served as the primary informants. Results: Maternal obesity was associated with a higher prevalence of childhood overweight/obesity (36.7% vs. 18.5% in children of non-obese mothers, p = 0.009). Maternal psychological factors, specifically depressive symptoms (B = 0.998, OR = 2.712, 95% CI: 1.222–6.020, p = 0.014) and anxiety (B = 1.676, OR = 5.346, 95% CI: 2.471–11.565, p < 0.001), were independently associated with an increased likelihood of child anxiety. Anthropometric measures, including waist circumference (p = 0.032) and hip circumference (p = 0.031), primarily influenced children’s physical health, whereas maternal psychological factors predominantly affected their emotional well-being. Conclusions: The findings underscore the necessity for targeted interventions focusing on enhancing maternal nutrition and mental health literacy, aiming to promote healthy dietary patterns, physical activity, and lifestyle behaviors. Such interventions are pivotal for preventing childhood obesity and fostering overall well-being at the population level. Full article

Background/Objectives: Academic burnout is an emerging problem among nursing students, characterized by emotional exhaustion, cynicism, and reduced academic efficacy. Sports interventions have been shown to have a positive effect on nurses as a preventive strategy against burnout. The aim of this study was to evaluate the effect of a nature sports program on the levels of academic burnout in nursing students. Methods: A randomized clinical trial was performed. The intervention was a 12-week nature exercise program with two sessions each week. The main dependent variables were burnout (measured using the Maslach Burnout Inventory—Student Survey), stress (measured using the Perceived Stress Scale), and anxiety and depression (measured using the Hospital Anxiety and Depression Scale). The post-intervention sample size was n = 58 in the control group and n = 48 in the intervention group. Results: After the intervention, significant differences were found in respect of emotional exhaustion (p < 0.001; Cohen’s D: 0.483), stress (p < 0.05; Cohen’s D: 0.456), and mean steps per day (p < 0.001; Cohen’s D: −1.09), with the mean values being reduced in the intervention group by around three points in emotional exhaustion and stress; the intervention group also achieved a higher mean number of daily steps compared to the control group. Conclusions: A nature sports program could help to reduce emotional exhaustion and stress, and increase the number of steps per day. Full article

Species distribution models were built for three short-palped crane fly species of the genus Dicranomyia: Dicranomyia affinis, Dicranomyia chorea, and Dicranomyia mitis. The main objective of this study was to assess potential habitat suitability in undersampled regions and explore differences in environmental space. Dicranomyia affinis was historically considered a variety of Dicranomyia mitis due to their morphological similarity. In contrast, Dicranomyia chorea is a widespread species. The biology and ecology of these species remain poorly understood. Models were developed using Maxent, a widely used tool. Our results indicate that Dicranomyia affinis and Dicranomyia chorea share highly similar predicted habitat suitability, with high suitability across the Mediterranean, Central, and Northern Europe, moderate suitability in Eastern Europe, and low suitability in Central Asia. In contrast, Dicranomyia mitis is predicted to have greater habitat suitability in Eastern Europe and Scandinavia, with lower suitability in Mediterranean regions. Analysis of variable importance revealed possible ecological differences between the species. While climatic factors primarily influenced the models for Dicranomyia affinis and Dicranomyia chorea, Dicranomyia mitis was more strongly influenced by the variable pH. These findings may provide insights into potential distributions in undersampled areas and improve our understanding of the species’ ecology. Full article

Background: Nowadays, there is a lack of reliable and minimally invasive diagnosis methods for the early detection of Alzheimer’s disease. The development and validation of such tools could significantly reduce the dependence on more invasive and costly confirmatory procedures, such as cerebrospinal fluid biomarkers analysis and neuroimaging techniques. Objectives: The main objective of this study is to validate the clinical performance of a previously developed diagnosis model based on plasma biomarkers from patients in a cognitive disorder unit. Methods: A new cohort of patients was recruited from the same cognitive disorder unit (n = 93). Specifically, demographic data (gender, age, and educational level), plasma biomarkers levels, and genotype (glial fibrillary acidic protein, phosphorylated Tau 181, amyloid-beta42/amyloid-beta40, apolipoprotein E) were collected to evaluate both approaches of the previous diagnosis model (one-cut-off, two-cut-off). Results: The one-cut-off approach showed a sensitivity of 74.3%, a specificity of 89.5%, and an area under the curve of 0.888, while the values for the two-cut-off approach were sensitivity of 66.7%, specificity of 99.9%, and area under the curve of 0.867. Conclusions: A multivariate diagnostic tool was temporally validated for implementation in a clinical unit. In fact, satisfactory results were obtained from both approaches (one-cut-off, two-cut-offs), but the two cut-offs approach was more consistent in correctly identifying non-Alzheimer’s disease cases, allowing us to identify a large number of cases with high specificity. Full article

Background/Objectives: Decision-making under socially evaluative stress engages a dynamic interplay between cognitive control, emotional appraisal, and motivational systems. Contemporary models of multi-level co-regulation posit that these systems operate in reciprocal modulation, redistributing processing resources to prioritise either rapid socio-emotional alignment or deliberate evaluation depending on situational demands. Methods: Adopting a neurofunctional approach, a novel dual-task protocol combining the MetaCognition–Stress Convergence Paradigm (MSCP) and the Social Stress Test Neuro-Evaluation (SST-NeuroEval), a simulated social–evaluative speech task calibrated across progressive emotional intensities, was implemented. Twenty professionals from an HR consultancy firm participated in the study, with concurrent recording of frontal-temporoparietal electroencephalography (EEG) and bespoke psychometric indices: the MetaStress-Insight Index and the TimeSense Scale. Results: Findings revealed that decision contexts with higher socio-emotional salience elicited faster, emotionally guided choices (mean RT difference emotional vs. cognitive: −220 ms, p = 0.026), accompanied by oscillatory signatures (frontal delta: F(1,19) = 13.30, p = 0.002; gamma: F(3,57) = 14.93, p ≤ 0.001) consistent with intensified socio-emotional integration and contextual reconstruction. Under evaluative stress, oscillatory activity shifted across phases, reflecting the transition from anticipatory regulation to reactive engagement, in line with models of phase-dependent stress adaptation. Across paradigms, convergences emerged between decision orientation, subjective stress, and oscillatory patterns, supporting the view that cognitive–emotional regulation operates as a coordinated, multi-level system. Conclusions: These results underscore the importance of integrating behavioural, experiential, and neural indices to characterise how individuals adaptively regulate decision-making under socially evaluative stress and highlight the potential of dual-paradigm designs for advancing theory and application in cognitive–affective neuroscience. Full article

In recent years, the demand for both high accuracy and real-time performance in 3D object detection has increased alongside the advancement of autonomous driving technology. While multimodal methods that integrate LiDAR and camera data have demonstrated high accuracy, these methods often have high computational costs and latency. To address these issues, we propose an efficient 3D object detection network that integrates three key components: a DepthWise Lightweight Encoder (DWLE) module for efficient feature extraction, an Efficient LiDAR Image Fusion (ELIF) module that combines channel attention with cross-modal feature interaction, and a Mixture of CNN and Point Transformer (MCPT) module for capturing rich spatial contextual information. Experimental results on the KITTI dataset demonstrate that our proposed method outperforms existing approaches by achieving approximately 0.6% higher 3D mAP, 7.6% faster inference speed, and 17.0% fewer parameters. These results highlight the effectiveness of our approach in balancing accuracy, speed, and model size, making it a promising solution for real-time applications in autonomous driving. Full article

Wetland mapping plays a crucial role in monitoring wetland ecosystems, water resource management, and habitat suitability assessment. Wetland classification remains significantly challenging due to the diverse types, intricate spatial patterns, and highly dynamic nature. This study proposed a dynamic hybrid method that integrated feature selection and object-oriented ensemble model construction to improve wetland mapping using Sentinel-1 and Sentinel-2 data. The proposed feature selection approach integrates the ReliefF and recursive feature elimination (RFE) algorithms with a feature evaluation criterion based on Shapley additive explanations (SHAP) values, aiming to optimize the feature set composed of various variables. During the construction of ensemble models (i.e., RF, XGBoost, and LightGBM) with features selected by RFE, hyperparameter tuning is subsequently conducted using Bayesian optimization (BO), ensuring that the selected optimal features and hyperparameters significantly enhance the accuracy and performance of the classifiers. The accuracy assessment demonstrates that the BO-LightGBM model with ReliefF-RFE-SHAP-selected features achieves superior performance to the RF and XGBoost models, achieving the highest overall accuracy of 89.4% and a kappa coefficient of 0.875. The object-oriented classification maps accurately depict the spatial distribution patterns of different wetland types. Furthermore, SHAP values offer global and local interpretations of the model to better understand the contribution of various features to wetland classification. The proposed dynamic hybrid method offers an effective tool for wetland mapping and contributes to wetland environmental monitoring and management. Full article

The quality of digital 3D models of fossils is important from the perspective of their further usage, either for scientific or didactical purposes. However, fidelity evaluation has rarely been attempted for digitized fossil objects. In the present research, a 3D triangulated model of the unique skull of Madygenerpeton pustulatum was built using an YXLON µCT device. The comparative analysis was performed using models obtained from seven optical surface-scanning systems. Methodology for accuracy assessment involved the determination of distances between the points in pairs of models, interchanging the reference and tested ones. Statistical significance testing using paired t-tests was performed. In particular, it was found that the YXLON µCT model was closest to the one obtained from AICON SmartScan, exhibiting an average distance of $\overline{{∆}_d}$ = −0.0183 mm with a standard deviation of σ{∆_d} = 0.0778 mm, which is close to the permissible error of 20 µm given in technical specifications for AICON scanners. It was demonstrated that the analysis maintained measurement validity even though the YXLON model consisted of 23.8 M polygons and the AICON model consisted of 13.9 M polygons. Comparison with other digital models demonstrated that the fidelity of the triangulated µCT model made it feasible for further research and dissemination purposes. Full article

Background/Objectives: Encouraged by the traditional use of Cotinus coggygria Scop. (European smoketree) for its anti-inflammatory and antioxidant properties, and considering the limitations of current therapies for recurrent aphthous stomatitis (RAS), we aimed to develop and evaluate a mucoadhesive oral gel containing C. coggygria stem bark extract. Methods: A thermosensitive gel was formulated using Carbopol^® 974P NF and poloxamer 407, enriched with 5% C. coggygria extract (CC gel), and characterized for its organoleptic properties, pH, electrical conductivity, and storage stability over six months. Therapeutic efficacy was assessed in a Wistar albino rat model of chemically induced oral ulcers. Animals were divided into three groups: untreated controls (CTRL), rats treated with gel base (GB), and those treated with CC gel over a 10-day period. Healing progression was monitored macroscopically (ulcer size reduction), biochemically (oxidative stress markers in plasma and tissue), and histologically. Results: The CC gel demonstrated satisfactory physicochemical stability and mucosal compatibility. Moreover, it significantly accelerated ulcer contraction and achieved complete re-epithelialization by day 6. Biochemical analyses revealed reduced TBARS and increased SOD, CAT, and GSH levels in ulcer tissue, indicating enhanced local antioxidant defense. Histological evaluation confirmed early resolution of inflammation, pronounced fibroblast activity, capillary proliferation, and full epithelial regeneration in the CC group, in contrast to delayed healing and persistent inflammatory infiltration in the GB and CTRL groups. Conclusions: These findings indicate that the CC gel has potential as a natural, topical formulation with antioxidant and regenerative properties for RAS, although further studies, including clinical evaluation, are required to confirm its overall efficacy and long-term safety. Full article

The rapid and reliable acquisition of canopy-related metrics is essential for improving decision support in viticultural management, particularly when monitoring individual vines for targeted interventions. This study presents a spatially explicit workflow that integrates Uncrewed Aerial System (UAS) imagery, 3D point-cloud analysis, and Object-Based Image Analysis (OBIA) to detect and monitor individual grapevines throughout the growing season. Vines are identified directly from 3D point clouds without the need for prior training data or predefined row structures, achieving a mean Euclidean distance of 10.7 cm to the reference points. The OBIA framework segments vine vegetation based on spectral and geometric features without requiring pre-clipping or manual masking. All non-vine elements—including soil, grass, and infrastructure—are automatically excluded, and detailed canopy masks are created for each plant. Vegetation indices are computed exclusively from vine canopy objects, ensuring that soil signals and internal canopy gaps do not bias the results. This enables accurate per-vine assessment of vigour. NDRE values were calculated at three phenological stages—flowering, veraison, and harvest—and analyzed using Local Indicators of Spatial Association (LISA) to detect spatial clusters and outliers. In contrast to value-based clustering methods, LISA accounts for spatial continuity and neighborhood effects, allowing the detection of stable low-vigour zones, expanding high-vigour clusters, and early identification of isolated stressed vines. A strong correlation (R² = 0.73) between per-vine NDRE values and actual yield demonstrates that NDRE-derived vigour reliably reflects vine productivity. The method provides a transferable, data-driven framework for site-specific vineyard management, enabling timely interventions at the individual plant level before stress propagates spatially. Full article

Background: Skin diseases of inflammatory origin, such as atopic dermatitis, psoriasis and acne, have a substantial prevalence in the world population. Natural products are particularly important at a topical level. Essential oils are examples of natural products and thyme in particular has been used for medicinal purposes due to its biological properties. Objectives: The aim of present work was to study the anti-inflammatory potential of Thymus mastichina essential oil, focusing on purified terpene-rich fractions. whose major compounds were thymol and linalool, eucalyptol and α-terpineol, and γ-terpinene and terpinolene, respectively. Additionally, a phytocannabinoid formulation containing cannabidiol (CBD) and cannabigerol (CBG) was evaluated to explore potential synergistic effects. Methods: Thymus mastichina essential oil was extracted and purified to obtain terpene-enriched fractions, which were used to develop three distinct formulations. These were screened for antioxidant activity using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay and assessed for cytotoxicity in HaCaT human keratinocytes. Anti-inflammatory potential was evaluated via gene expression. Selected thyme formulations—alone or in combination with CBD/CBG—were also tested in vivo using a mouse model of acute skin inflammation. Results: The antioxidant activity of the three formulations showed a reduction in DPPH radicals. In addition, the formulations demonstrated to be safe in vitro in the human keratinocyte cell model HaCaT. Under PMA-induced inflammatory stress, the fractions modulated-inflammatory gene expression to varying degrees While terpene fractions alone showed moderate activity, their combination with CBD/CBG enhanced the anti-inflammatory response. In vivo, the gel formulations reduced oedema in a mouse model of acute inflammation. Conclusions: The data support the safe and effective use of Thymus mastichina-derived terpene fractions for topical anti-inflammatory applications. The synergistic effect observed with CBD and CBG suggests that combining essential oil terpenes with phytocannabinoids may offer a novel therapeutic strategy for managing inflammatory skin disorders. Full article

Background: Pain is a disabling issue in athletes, with significant impact on performance and career longevity. Many randomized clinical trials (RCTs) have explored interventions to reduce pain, leading to multiple systematic reviews with meta-analysis, but their methodological rigor and clinical applicability remain unclear. Objective: To provide an overview of systematic reviews with meta-analysis on interventions aimed at alleviating pain intensity in athletes, identifying knowledge gaps and appraising methodological quality. Methods: CINAHL, Embase, Epistemonikos, PubMed, Scopus, SPORTDiscus, and Cochrane Library were searched from inception to February 2025. Systematic reviews with meta-analysis of RCTs evaluating interventions to manage pain in athletes were considered. Athletes without restrictions in terms of sports, clinical, and sociodemographic characteristics were included. Overlap between reviews was calculated using the corrected covered area. Results: Twelve systematic reviews met inclusion criteria. Physical exercise modalities (e.g., gait retraining, hip strengthening), acupuncture, photo biomodulation, and topical medication showed potential benefits in reducing pain intensity. Other interventions, such as certain manual therapy techniques, platelet-rich plasma, or motor imagery, did not show consistent effects. All reviews focused solely on pain intensity, with minimal stratification by sport or clinical condition which may affect the extrapolation of meta-analyzed findings to the clinical practice. Methodological quality was often low, with flaws in reporting funding sources, lists of excluded studies, and certainty of evidence (was mostly rated as low/very low). Overlap was variable across the interventions. Conclusions: Given low/sparse certainty and minimal sport-specific analyses, no strong clinical recommendations can be made; preliminary signals favor proximal hip strengthening, gait retraining, photo biomodulation (acute soreness), and topical NSAIDs pending higher-quality syntheses. Future reviews should consider mandatory GRADE; pre-registered protocols; sport- and condition-specific analyses; and core outcome sets including multi-dimensional pain. Full article

Background/Objective: Predicting pharmacological response in cancer remains a key challenge in precision oncology due to intertumoral heterogeneity and the complexity of drug–gene interactions. While machine learning models using multi-omics data have shown promise in predicting pharmacological response, selecting the features with the highest predictive power critically affects model performance and biological interpretability. This study aims to compare computational and biologically informed gene selection strategies for predicting drug response in cancer cell lines and to propose a feature selection strategy that optimizes performance. Methods: Using gene expression and drug response data, we trained models on both data-driven and biologically informed gene sets based on the drug target pathways to predict IC₅₀ values for seven anticancer drugs. Several feature selection methods were tested on gene expression profiles of cancer cell lines, including Recursive Feature Elimination (RFE) with Support Vector Regression (SVR) against gene sets derived from drug-specific pathways in KEGG and CTD databases. The predictability was comparatively analyzed using both AUC and IC₅₀ values and further assessed on proteomics data. Results: RFE with SVR outperformed other computational methods, while pathway-based gene sets showed lower performance compared to data-driven methods. The integration of computational and biologically informed gene sets consistently improved prediction accuracy across several anticancer drugs, while the predictive value of the corresponding proteomic features was significantly lower compared with the mRNA profiles. Conclusions: Integrating biological knowledge into feature selection enhances both the accuracy and interpretability of drug response prediction models. Integrative approaches offer a more robust and generalizable framework with potential applications in biomarker discovery, drug repurposing, and personalized treatment strategies. Full article

Background: Nurse-manager competencies shape workforce stability, yet role-based perception gaps between managers and staff may influence staff nurses’ turnover cognitions. Objectives: To (1) compare nurse managers’ self-ratings with staff nurses’ ratings of the same managers on the Nurse Manager Competency Inventory (NMCI); (2) compare both groups’ perceptions of staff nurses’ turnover intention (EMTIS); (3) examine domain-specific links between perceived competencies and perceived turnover intention; and (4) explore demographic influences (age, education, experience) on these perceptions. Methods: Cross-sectional dual-rater study with 225 staff nurses and 171 nurse managers in two tertiary hospitals in Saudi Arabia. Data were collected from August to November 2024. Managers completed NMCI self-ratings, and staff nurses rated their managers on the same NMCI domains; both groups rated staff nurses’ turnover intention using EMTIS. Between-group differences were tested with one-way ANOVA (two-tailed α = 0.05), and associations were examined with Pearson’s r (95% CIs). Findings: Managers consistently rated themselves higher than staff rated them across all nine NMCI domains; the largest descriptive gaps were in Promoting Staff Retention, Recruit Staff, Perform Supervisory Responsibilities, and Facilitate Staff Development (e.g., overall NMCI: managers M = 3.67, SD = 0.61 vs. staff M = 3.04, SD = 0.74; F = 0.114, p = 0.73)with comparatively smaller divergence for Ensure Patient Safety and Quality. Managers and staff did not differ significantly on EMTIS (overall EMTIS: managers M = 3.16, SD = 1.28 vs. staff M = 3.00, SD = 1.15; F = 21.32, p = 0.173). Specific competency domains—retention, supervision, staff development, safety/quality leadership, and quality improvement—showed small inverse correlations with EMTIS facets (typical r ≈ −0.11 to −0.19; p < 0.05), whereas the global NMCI–overall EMTIS correlation was non-significant (r = −0.077, p = 0.124). Effect sizes were modest and should be interpreted cautiously. Conclusions: Actionable signals reside at the domain (micro-competency) level rather than in global leadership composites. Targeted, continuous, unit-embedded development in human- and development-focused competencies—tracked with dual-lens (manager–staff) measurement and linked to retention KPIs—may help nudge turnover cognitions downward. Key limitations include the cross-sectional, perception-based design and two-site setting. Findings nonetheless align with international workforce challenges and may be transferable to similar hospital contexts. Full article