Search Results (463)

Background/Objectives: Aging is associated with a gradual decline in physical capabilities, often leading to impaired balance and reduced functional status, which are major contributors to falls in older adults. Although many studies have assessed these variables independently, a limited amount of research has explored the direct relationship between balance and functional status in a healthy geriatric population. The aim of this study was to investigate the relationship between balance and functional capacity and to assess the influence of demographic factors such as age, comorbidities, smoking status, and history of falls. Methods: A sample of community-dwelling older adults (19 women, 16 men) (n = 35), aged 60 years and above (M = 78 years; SD = 9.23) from Sparta, Greece, took part in the present study. Participants were assessed using three validated tools: (a) the Five Times Sit-to-Stand test, (b) the Timed Up-and-Go test, and (c) the Berg Balance Scale. Spearman’s rank correlation coefficient was used for statistical analysis (α = 0.05). Results: Age was positively correlated with poorer performance in the Five Times Sit-to-Stand (r = 0.40; p < 0.01) and the Timed Up-and-Go test (r = 0.47; p < 0.01) and negatively correlated with Berg Balance Scale scores (r = −0.51; p < 0.01). Comorbidities and smoking were also associated with the Berg Balance Scale. A strong negative correlation was observed between balance and the other two functional tests (Five Times Sit-to-Stand: r = −0.51; Timed Up-and-Go: r = −0.66; both p < 0.01). Conclusions: The findings highlight the importance of evaluating both balance and functional capacity in older adults as interrelated factors that can significantly influence quality of life and fall risk. Future research with larger and more diverse populations is recommended to confirm the present findings and to use exercise programs to prevent falls in the geriatric population. Full article

The ongoing secular changes in human movement development means that an assessment of primitive reflexes is now required not only in disabled but also in neurotypical children. This study had three aims: (1) presenting the TASHUN test battery as suitable for the assessment of primitive reflex activity in normal children and child athletes; (2) analyzing reflex characteristics of neurotypical children; (3) verifying validity and reliability of tests. Spearman’s rank correlation and ROC analysis were used for validation. Intraclass Correlation Coefficient and RM ANOVA analyzed reliability. The test on 242 schoolgirls has demonstrated that retained primitive reflexes are present in almost every individual (84.7–95.7%). Correlations showed strong positive association, with all values exceeding 0.8, and ROC analysis demonstrated excellent predictive strength (AUC values over 0.9). Interobserver reliability showed excellent agreement (ICC values above 0.9). No significant offset was present among the scoring by evaluators. Therefore, testing for primitive reflexes is necessary in neurotypical children in order to obtain a realistic image about the physiology of reflexes and their role in motor development. Our screening could be useful for practicing sport professionals, researchers and academics, to identify deficiencies, to further explore reflexes and to train future PE teachers and trainers. Full article

Atomic force microscopy (AFM)-based nanoindentation enables investigation of the mechanical response of biological materials at a subcellular scale. However, quantitative estimates of mechanical parameters such as the elastic modulus (E) remain unreliable because the influence of sample roughness on E measurements at the nanoscale is still poorly understood. This study re-examines the interpretation of roughness from a more rigorous perspective and validates an experimental methodology to extract roughness at each nanoindentation site—i.e., the local roughness γ_s—with which the corresponding E value can be accurately correlated. Cortical regions of a murine tibia cross-section, characterized by complex nanoscale morphology, were selected as a testbed. Eighty non-overlapping nanoindentations were performed using two different AFM tips, maintaining a maximum penetration depth of 10 nm for each measurement. Our results show a slight decreasing trend of E versus γ_s (Spearman’s rank correlation coefficient ρ = −0.27187). A total of 90% of the E values are reliable when γ_s < 10 nm (coefficient of determination R² > 0.90), although low γ_s values are associated with significant dispersion around E (γ_s = 0) = E₀ = 1.18 GPa, with variations exceeding 50%. These findings are consistent with a qualitative tip-to-sample contact model that accounts for the pronounced roughness heterogeneity typical of bone topography at the nanoscale. Full article

Background: Preparing patients for surgery considers assessing the patient’s somatic health, for example by the American Society of Anesthesiology (ASA) scale or the Revised Cardiac Risk Index (RCRI), known as the Lee index. This process usually ignores mental functioning (personality and anxiety), which is known to influence health. The purpose of this study is to analyze the existence of a relationship between personality traits (the Big Five model and trait-anxiety) and anesthesia scales (ASA scale, Lee index) used for the preoperative evaluation of patients. Methods: The study group comprised 102 patients (59 women, 43 men) scheduled for hip replacement surgery. Patients completed two psychological questionnaires: the NEO-FFI (NEO Five Factors Inventory) and the X-2 STAI (State-Trait Anxiety Inventory) sheet. Next, the presence and possible strength of the relationship between personality traits and demographic and medical variables were analyzed using Spearman’s rho rank correlation coefficient. Results: Patients with a high severity of trait anxiety are classified higher on the ASA scale (rs = 0.359; p < 0.001). Neuroticism, defined according to the Big Five model, significantly correlates with scales of preoperative patient assessment: the ASA classification (rs = 0.264; p < 0.001) and the Lee index (rs = 0.202; p = 0.044). A hierarchical regression model was created to test the possibility of predicting ASA scores based on personality. It explained more than 34% of the variance and was a good fit to the data (p < 0.05). The controlled variables of age and gender accounted for more than 23% of the variance. Personality indicators (trait anxiety, neuroticism) additionally accounted for slightly more than 11% of the variance. Trait anxiety (Beta = 0.293) proved to be a better predictor than neuroticism (Beta = 0.054). Conclusions: These results indicate that inclusion of personality screening in the preoperative patient evaluation might help to introduce a more individualized approach to patients, which could result in better surgical outcomes. Full article

Air pollution monitoring in ungauged zones presents unique challenges yet remains critical for understanding environmental health impacts and socioeconomic dynamics in the Eastern Mediterranean region. This study investigates air pollution patterns in northwestern Syria during 2019–2024, analyzing NO₂ and CO concentrations using Sentinel-5P TROPOMI satellite data processed through Google Earth Engine. Monthly concentration averages were examined across eight key locations using linear regression analysis to determine temporal trends, with Spearman’s rank correlation coefficients calculated between pollutant levels and five meteorological parameters (temperature, humidity, wind speed, atmospheric pressure, and precipitation) to determine the influence of political governance, economic conditions, and environmental sustainability factors on pollution dynamics. Quality assurance filtering retained only measurements with values ≥ 0.75, and statistical significance was assessed at a p < 0.05 level. The findings reveal distinctive spatiotemporal patterns that reflect the region’s complex political-economic landscape. NO₂ concentrations exhibited clear political signatures, with opposition-controlled territories showing upward trends (Al-Rai: 6.18 × 10⁻⁸ mol/m²) and weak correlations with climatic variables (<0.20), indicating consistent industrial operations. In contrast, government-controlled areas demonstrated significant downward trends (Hessia: −2.6 × 10⁻⁷ mol/m²) with stronger climate–pollutant correlations (0.30–0.45), reflecting the impact of economic sanctions on industrial activities. CO concentrations showed uniform downward trends across all locations regardless of political control. This study contributes significantly to multiple Sustainable Development Goals (SDGs), providing critical baseline data for SDG 3 (Health and Well-being), mapping urban pollution hotspots for SDG 11 (Sustainable Cities), demonstrating climate–pollution correlations for SDG 13 (Climate Action), revealing governance impacts on environmental patterns for SDG 16 (Peace and Justice), and developing transferable methodologies for SDG 17 (Partnerships). These findings underscore the importance of incorporating environmental safeguards into post-conflict reconstruction planning to ensure sustainable development. Full article

This study investigates the weathering crust composite of serpentine, pyroxenite and granite in the Niangniangmiao area, the weathering crusts inside and outside the mining area were compared respectively, systematically revealing the distribution patterns, migration pathways, and ecological element activity characteristics of high-background elements (e.g., chromium (Cr) and nickel (Ni)) through precise sampling, the Tessier five-step sequential extraction method, and a migration coefficient model. Key findings include: (1) Element distribution and controlling mechanisms: The average Cr and Ni contents in the serpentinite profile are significantly higher than those in pyroxenite. However, the semi-weathered pyroxenite layer exhibits an inverted Cr enrichment ratio in relation to serpentinite, 1.8× and 1.2×, respectively, indicating that mineral metasomatic sequences driven by hydrothermal alteration dominate element differentiation; the phenomenon of inverted enrichment of high-background elements occurs in the weathering crust profiles of the two basic rocks. (2) Dual impacts of mining activities on heavy metal enrichment: Direct mining increases topsoil Cr content in serpentinite by 40% by disrupting parent material homology, while indirect activities introduce exogenous Zn and Cd (Spearman correlation coefficients with Cr/Ni are from ρ = 0.58 to ρ = 0.72). Consequently, the bioavailable fraction ratio value of Ni outside the mining area (21.14%) is significantly higher than that within the area (14.30%). (3) Element speciation and ecological element activity: Over 98% of Cr in serpentine exists in residual fractions, whereas the Fe-Mn oxide-bound fraction (F3) of Cr in extra-mining pyroxenite increases to 5.15%. The element activity in ecological systems ranking of Ni in soil active fractions (F1 + F2 = 15%) follows the order: granite > pyroxenite > serpentine. Based on these insights, a scientific foundation for targeted remediation in high-background areas (e.g., prioritizing the treatment of semi-weathered pyroxenite layers) can be provided. Full article

This paper proposes an integrated tree model architecture and a low-cost data construction method based on an improved Stacking strategy. It systematically analyzes the importance of safety indicators for mountainous sharp bends in plateau regions and conducts safety evaluation and optimization-strategy research for ten typical sharp-bend road segments in Tibet. In response to the challenges of traditional data collection in Tibet’s unique geographical and policy constraints, we innovatively use drone aerial video as the data source, integrating Tracker motion trajectory analysis, SegFormer road segmentation, and CAD annotation techniques to construct a dataset covering multi-dimensional features of “human–vehicle–road–environment” for mountainous plateau sharp-bend highways. Compared with similar studies, the cost of this dataset is significantly lower. Based on the strong interpretability of tree models and the excellent generalization ability of ensemble learning, we propose an improved Stacking strategy tree model structure to interpret the importance of each indicator. The Spearman correlation coefficient and TOPSIS algorithm are used to conduct safety evaluation for ten sharp-bend roads in Tibet. The results show that the output of the improved Stacking strategy and the sensitivity analysis of the three tree models indicate that curvature variation rate and acceleration are the most significant factors influencing safety, while speed and road width are secondary factors. The study also provides a safety ranking for the ten selected sharp-bend roads, offering a reference for the 318 Quality Improvement Project. From the perspective of indicator importance, curvature variation rate, acceleration, vehicle speed, and road width are crucial for the safety of mountainous plateau sharp-bend roads. It is recommended to implement speed limits for vehicles and widen the road-bend radius. The technical framework constructed in this study provides a reusable methodology for safety assessment of high-altitude roads in complex terrains. Full article

Accurate grid frequency prediction is essential for maintaining the stability and reliability of power systems. However, the complex dynamic characteristics of grid frequency and the nonlinear correlations among massive time series data make it challenging for traditional time series prediction methods to balance efficiency and accuracy. In this paper, we propose a Dynamic Significance–Correlation Weighting (D-SCW) method, which generates dynamic weight coefficients that evolve over time. This is achieved by constructing a joint screening mechanism of feature time series correlation analysis and statistical significance test, combined with the LightGBM gradient-boosting decision tree (GBDT) framework; accordingly, high-precision prediction of grid frequency time series data is realized. To verify the effectiveness of the D-SCW method, this study conducts comparative experiments on two actual grid operation datasets (including typical scenarios with wind/photovoltaic (PV) installations, accounting for 5–35% of the grid); additionally, the Spearman’s rank correlation coefficient method, mutual information (MI), Lasso regression, and the feature screening method of recursive feature elimination (RFE) are selected as the baseline control; root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are adopted as assessment indicators. The results show that the D-SCW-LightGBM framework reduces the root mean squared error (RMSE) by 5.2% to 10.4% and shortens the dynamic response delay by 52% compared with the benchmark method in high renewable penetration scenarios, confirming its effectiveness in both prediction accuracy and computational efficiency. Full article

Background/Objectives:Arterial hypertension, increased carotid intima-media thickness (cIMT), and arterial stiffness (AS) are recognized predictors of cardiovascular disease (CVD). Emerging evidence suggests that vascular remodeling may precede the full development of hypertension. Furthermore, body mass index (BMI), fat mass percentage (FM%), and visceral adipose tissue (VAT), are significant risk factors for cardiovascular events. Conversely, adherence to the Mediterranean diet is associated with reduced cardiovascular risk due to its beneficial effects on lipid metabolism, inflammation, and vascular health. Methods: This observational study explored the association between nutritional care and cardiovascular risk in 55 Italian adults (27 women, 28 men) consecutively evaluated at the Section of Clinical Nutrition and Nutrigenomics, University of Rome “Tor Vergata”, in 2024. Nutritional and vascular assessments, including BMI, waist-to-hip ratio (WHR), BIA, DXA, lab tests, blood pressure (BP), pulse wave velocity (PWV), and cIMT, were recorded. Spearman’s rank correlation coefficient was used to evaluate the relationship between vascular and nutritional parameters. Wilcoxon rank sum test; Fisher’s exact test; and Pearson’s Chi-squared test were performed for statistical analysis. Participants were divided into two groups based on cIMT > 0.90 mm and ≤0.90 mm. Results: Significant correlations emerged between higher MEDAS scores and BMI (r = −0.53, p < 0.01), FM% (r = −0.49, p < 0.01), VAT (r = −0.63, p < 0.01), and cIMT (r = −0.88, p < 0.01). Higher WHR and VAT were associated with increased brachial and central BP and PWV. Notable dietary differences were significantly higher between cIMT groups. Total cholesterol/C-HDL, C-LDL/C-HDL, the Atherogenic Index of Plasma, and the HOMA Index differed significantly between groups. Significant differences were also observed in the left ventricular diastolic function (p = 0.04), LVM/BSA, and LVM/h^2.7 in individuals with subclinical atherosclerosis (p < 0.05). Conclusions: These innovative findings underline the importance of multidisciplinary approaches to prevent CVD and suggest long-term benefits of Mediterranean diet adherence on vascular health. Full article

In this paper, we generate random graphs for a specific area, namely, models of real communication networks. We propose a method that determines the “best” invariant; the corresponding basic algorithm is as follows. For the generated set of graphs, we calculate the numerical values of each of the pre-selected invariants (i.e., indexes of Graovac–Ghorbani, Randic̀, Wiener, global clustering coefficients and the vector of second-order degrees). For all graphs, we arrange these numerical values in descending order, after which, for each of the 10 pairs of invariants, we calculate the rank correlation of these orders; for such calculations, we use 5 different variants of rank correlation algorithms (i.e., usual pair correlation, Spearman’s algorithm, Kendall’s algorithm and its improved version, and the algorithm proposed by the authors). In such a way, we get 10 pairs of rank correlation values, then we arrange them as the values of 10 independent elements of the 5 × 5 table (rows and columns of this table correspond to the 5 invariants under consideration). If the rank correlation values are negative, we record the absolute value of this value in the table. The basic idea is that the “most independent” invariant of the graph gets the minimum sum when summing 4 values of its row, i.e., less than for other invariants (other rows). For our subject area, we obtained the same result for 5 different variants of calculating the rank correlation: the value obtained for the vector of second-order degrees is significantly better than all the others, and among the usual invariants, the global clustering coefficients invariant is significantly better than others ones. This fact corresponds to our previous calculations, in which we ordered the graph invariants according to completely different algorithms. Full article

Background and Objectives: Gliomas are characterized by high disability rates, frequent recurrence, and low survival rates, posing a significant threat to human health. Accurate grading of gliomas is crucial for treatment plan selection and prognostic assessment. Previous studies have primarily focused on the binary classification (i.e., high grade vs. low grade) of gliomas. In order to perform the four-grade (grades I, II, III, and IV) glioma classification preoperatively, we constructed an artificial neural network (ANN) model using magnetic resonance imaging data. Materials and Methods: We reviewed and included patients with gliomas who underwent preoperative MRI examinations. Radiomics features were derived from contrast-enhanced T1-weighted images (CE-T₁WI) using Pyradiomics and were selected based on their Spearman’s rank correlation with glioma grades. We developed an ANN model to classify the four pathological grades of glioma, assigning training and validation sets at a 3:1 ratio. A diagnostic confusion matrix was employed to demonstrate the model’s diagnostic performance intuitively. Results: Among the 362-patient cohort, the ANN model’s diagnostic performance plateaued after incorporating the first 19 of the 530 extracted radiomic features. At this point, the average overall diagnostic accuracy ratings for the training and validation sets were 91.28% and 87.04%, respectively, with corresponding coefficients of variation (CVs) of 0.0190 and 0.0272. The diagnostic accuracies for grades I, II, III, and IV in the training set were 91.9%, 89.9%, 92.1%, and 90.7%, respectively. The diagnostic accuracies for grades I, II, III, and IV in the validation set were 88.7%, 87.1%, 86.5%, and 86.9%, respectively. Conclusions: The MRI radiomics-based ANN model shows promising potential for the four-type classification of glioma grading, offering an objective and noninvasive method for more refined glioma grading. This model could aid in clinical decision making regarding the treatment of patients with various grades of gliomas. Full article

Objectives: Our aim was to study whether the Modified Dental Anxiety Scale (MDAS) is reliable and valid for use in children aged 9 to 12 years. Methods: The study population was a convenient sample of Finnish comprehensive school pupils in the third and sixth grades (N = 57 and N = 69, respectively). Dental fear and anxiety (DFA) were measured with the Finnish validated adult version of MDAS, the modified Child Fear Survey Schedule—Dental Subscale (CFSS-DS-M), and a single question. Reliability was evaluated using Cronbach’s alpha. The criterion validity of MDAS was assessed using Spearman rank correlation coefficients against CFSS-DS-M and the single question. Construct validity was assessed by examining the ability of MDAS to find differences according to gender and treatment procedures using the chi-square test for categorized and the Mann–Whitney and Jonckheere–Terpstra tests for continuous variables. Results: The Cronbach alphas were 0.841, 0.708, and 0.778 for MDAS total, anticipatory, and treatment-related DFA, respectively. Correlations between MDAS and CFSS-DS-M total and subscale scores were moderate to strong (ρ = 0.559–0.794), supporting criterion validity. Girls in third grade had lower mean MDAS anticipatory DFA (3.4, SD = 1.44) than boys (4.5, SD = 2.21, p = 0.051). In sixth graders, girls had higher mean MDAS treatment-related DFA (8.4, SD = 3.17) than boys (6.9, SD = 2.61, p = 0.067). Children reporting orthodontic treatment had lower anticipatory DFA (mean = 3.4, SD = 2.13) than children not reporting (mean = 4.0, SD = 1.83; p = 0.009), supporting construct validity. Conclusions: The Finnish version of the MDAS showed good reliability, good criterion validity, and acceptable construct validity, supporting its use in children aged 9–12 years. Full article

Background/Objectives: A case-control study was conducted to determine the association of the −238 G>A and −308 G>A TNF-alpha (TNFA) promoter polymorphisms with mRNA and protein expression in 180 Systemic Lupus Erythematosus (SLE) patients and 186 control subjects (CS) from western Mexico. Methods: Genotyping was performed using the PCR-RFLP method. TNFA mRNA expression was assessed by real-time quantitative PCR, and soluble TNF-α (sTNF-α) levels were quantified by ELISA. For comparison groups, Chi-square, Mann–Whitney U, or Kruskal–Wallis tests were used. Spearman’s rank correlation coefficient determined the correlation between variables. The Area Under the Curve was used to determine the diagnosing performance of sTNF-α. Results: No differences were found in the genotype distribution of −238 G>A and −308 G>A TNFA polymorphisms between SLE patients and CS. However, the −238A allele was associated with increased SLE susceptibility (OR 1.18 CI 95% 1.02–3.50, p = 0.037). Also, logistic regression analysis showed that LN risk was significantly higher in carriers of the −308A allele (OR 3.11 IC95% 1.15–6.43; p = 0.002). On the other hand, the TNFA mRNA expression was 3.3-fold higher in SLE compared to CS. SLE patients with −308 GG genotype showed higher TNFA mRNA expression compared to GA+AA genotype carriers (p < 0.01). Regarding sTNFa levels, SLE patients showed higher concentration than CS, mainly in lupus nephritis (LN), with a weak negative correlation with estimated Glomerular Filtration Rate and an acceptable accuracy for diagnosing SLE and LN, with areas under the curve of 0.61 and 0.65, respectively. Conclusions: The −238 A allele and −308 A allele of the TNFA gene are linked to a higher risk of susceptibility to SLE and LN in the western Mexican population. Additionally, SLE patients exhibited increased TNF-alpha gene expression and sTNF-α, particularly in LN, demonstrating acceptable diagnostic performance. Full article

Scatter plots are widely recognized as fundamental tools for illustrating the relationship between two numerical variables. Despite this, based on solid theoretical foundations, scatter plots generated from pairs of continuous random variables may not serve as reliable tools for assessing dependence. Sklar’s theorem implies that scatter plots created from ranked data are preferable for such analysis, as they exclusively convey information pertinent to dependence. This is in stark contrast to conventional scatter plots, which also encapsulate information about the variables’ marginal distributions. Such additional information is extraneous to dependence analysis and can obscure the visual interpretation of the variables’ relationship. In this article, we delve into the theoretical underpinnings of these ranked data scatter plots, hereafter referred to as rank plots. We offer insights into interpreting the information they reveal and examine their connections with various association measures, including Pearson’s and Spearman’s correlation coefficients, as well as Schweizer–Wolff’s measure of dependence. Furthermore, we introduce a novel visualization ensemble, termed a d-plot, which integrates rank plots, empirical copula diagnostics, and traditional summaries to provide a comprehensive visual assessment of dependence between continuous variables. This ensemble facilitates the detection of subtle dependence structures, including non-quadrant dependencies, that might be overlooked by traditional visual tools. Full article

Background: Cancer survivors who do not engage in regular physical activity often experience persistent psychological distress and fatigue, which can significantly impact their quality of life. While handgrip strength (HGS) is recognized as an indicator of overall health and physical resilience, the combined role of HGS and physical inactivity in predicting psychological distress and fatigue in this population remains unclear. This study aimed to examine the relationships between self-reported physical inactivity, HGS, and psychological distress, specifically depressive symptoms, anxiety, and cancer-related fatigue (CRF), in physically inactive cancer survivors. Methods: This cross-sectional study included 42 physically inactive cancer survivors (mean age = 63.2 years, SD = 8.96) recruited from the Cancer Institute (IRCCS) in Bari, Italy. Physical inactivity was quantified based on self-reported weekly physical activity minutes, with all participants engaging in less than 150 min per week. The participants underwent HGS assessment and completed validated psychological measures, including the Beck Depression Inventory (BDI), the State-Trait Anxiety Inventory (STAI-Y1 and STAI-Y2), and the Fatigue Severity Scale (FSS). Results: Bivariate correlations were examined via Spearman’s rank correlation coefficients, and multiple linear regression analyses were performed to identify independent predictors of psychological distress and fatigue, adjusting for covariates such as age, sex, cancer type, and time since treatment completion. Both lower HGS and greater physical inactivity were significantly correlated with greater depressive symptoms (HGS: ρ = −0.524, p < 0.001; physical inactivity: ρ = −0.662, p < 0.001), greater fatigue severity (HGS: ρ = −0.599, p < 0.001; physical inactivity: ρ = −0.662, p < 0.001), and increased trait anxiety (HGS: ρ = −0.532, p < 0.001; physical inactivity: ρ = −0.701, p < 0.001). No significant associations were found between physical inactivity or HGS and state anxiety (p > 0.05). Multiple regression analyses confirmed that both HGS and physical inactivity independently predicted depressive symptoms (HGS: β = −0.435, p = 0.009; physical inactivity: β = −0.518, p = 0.002), trait anxiety (HGS: β = −0.313, p = 0.038; physical inactivity: β = −0.549, p < 0.001), and fatigue (HGS: β = −0.324, p = 0.033; physical inactivity: β = −0.565, p < 0.001), even after adjusting for covariates. Low physical activity and reduced muscle strength independently predict psychological distress and fatigue in cancer survivors. Conclusions: These findings highlight the potential exacerbating role of physical inactivity in both physical and psychological vulnerability, underscoring the need for interventions promoting regular exercise. Integrating strength assessments and structured physical activity programs may be key strategies in survivorship care to improve mental well-being and overall quality of life. Full article