Search Results (345)

Background/Objectives: Perirenal fat (PRF) constitutes a critical yet understudied component of the tumor microenvironment in clear cell renal cell carcinoma (ccRCC). While radiomics enables non-invasive tissue characterization, whether PRF-derived features can simultaneously predict both TNM stage and histological grade, and whether optimal peritumoral distances differ between these distinct biological targets, remains unexplored in the literature. Methods: This multi-cohort retrospective study included 474 histopathologically confirmed ccRCC patients from three independent datasets (2007–2023). Automated nnU-Net segmentation delineated tumors and kidneys. Concentric PRF regions were systematically generated at 1–10 mm radial distances, yielding 18 distinct regions of interest. From each ROI, 1409 radiomic features were extracted using PyRadiomics. Sequential feature selection employed correlation filtering, SHAP-guided elimination, and LASSO regularization. Multiple machine learning classifiers underwent hyperparameter optimization with rigorous cross-cohort validation. Results: Systematic ROI screening revealed target-specific optimal distances: 4 mm PRF for TNM staging versus 10 mm PRF for histological grading. For staging, the integrated model (tumor + PRF radiomics + clinical variables) achieved AUC 0.829 (95% CI 0.781–0.877), sensitivity 80.2%, and specificity 67.8%. For grading, the combined model achieved AUC 0.780 (95% CI 0.598–0.962), sensitivity 79.7%, and specificity 63.3%, significantly outperforming all single-compartment models (DeLong p < 0.001). Conclusions: This study establishes that PRF radiomics enables accurate simultaneous non-invasive prediction of both TNM stage and histological grade in ccRCC. The novel discovery that optimal peritumoral distances differ substantially by prediction target (4 mm versus 10 mm) suggests distinct biological underpinnings for stage- and grade-related microenvironmental alterations, with important methodological implications for radiomic model development in oncology. Full article

Background/Objectives: Increasing training demands in women’s football have heightened interest in female-specific health characteristics, including menstrual health. The aim of this study was to describe menstrual-cycle characteristics and injury history in adult amateur female football players using selected items of the Low Energy Availability in Females Questionnaire (LEAF-Q), with particular focus on prolonged absence of menstrual bleeding and training-associated menstrual changes. Methods: A cross-sectional survey was conducted in 118 adult amateur (non-elite) female football players (mean age 24.41 ± 4.50 years). Participants reported mean weekly training hours of 4.88 ± 2.45, consistent with amateur-level competitive and recreational participation. Selected items of the LEAF-Q were used, rather than the complete questionnaire; therefore, findings should be interpreted as descriptive indicators of menstrual health and injury history rather than a comprehensive LEA screening. Results: Most participants reported normal menstruation (95.76%), and menarche most commonly occurred between 12 and 14 years of age (92.37%). A history of ≥3 consecutive months without menstrual bleeding (clinically meaningful amenorrhea) was reported by 12.71% of players, while 4.24% reported such an episode at the time of the survey. Training-associated changes in menstrual bleeding were reported by 52.54% of participants, most commonly shorter and lighter bleeding; less frequently, cessation of bleeding (8.93%) or heavier and prolonged bleeding (1.79%) was reported. Injuries in the preceding 12 months were common, with 71.19% reporting one or two injuries and 28.81% reporting three or four injuries. Conclusions: Despite a high prevalence of self-reported regular menstrual cycles, a notable proportion of adult amateur female football players reported episodes of prolonged absence of menstrual bleeding and training-associated changes in bleeding characteristics. These findings highlight the variability of menstrual-cycle characteristics in the context of football training and support the inclusion of routine, confidential menstrual-health monitoring as part of broader athlete health management in women’s football. Football-related injuries were common over the preceding 12 months, reflecting the substantial musculoskeletal demands of the sport. Full article

Background/Objectives: Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder characterized by reproductive and metabolic dysfunction. Vitamin D deficiency is common in women with PCOS and is linked to adverse metabolic and reproductive outcomes. However, the role of vitamin D supplementation in managing PCOS remains unclear due to the heterogeneous evidence available. This systematic review aimed to synthesize both observational and interventional studies to assess the association between vitamin D levels and PCOS, focusing on prevalence, metabolic outcomes, and reproductive parameters. Methods: A comprehensive search of PubMed, Web of Science, Scopus, and Embase was conducted in October 2025, identifying studies published between January 2000 and October 2025. Eligible studies included observational studies and randomized controlled trials (RCTs) evaluating serum 25-hydroxyvitamin D [25(OH)D] levels and/or the effects of vitamin D supplementation in women with PCOS. Studies were included if they used recognized diagnostic criteria for PCOS or sufficient diagnostic details to confirm the condition. Two reviewers independently performed screening, data extraction, and quality assessment according to PRISMA 2020 guidelines. Results: Eleven studies (nine RCTs, two observational) encompassing 1063 women with PCOS met the inclusion criteria. Observational studies demonstrated inverse associations between serum 25(OH)D levels and insulin resistance, body mass index (BMI), and leptin, but not with total testosterone. RCTs showed modest and inconsistent improvements in insulin sensitivity, with effects more apparent in some trials enrolling vitamin D-deficient women. Reproductive benefits (cycle regularity/ovulation) were observed only in selected trials, generally with small samples and short follow-up. Conclusions: Vitamin D deficiency is common in women with PCOS and correlates with metabolic and reproductive dysfunction. While vitamin D supplementation shows variable effects, it should not be considered a stand-alone therapy for PCOS. Correction of deficiency may complement existing treatments, but evidence remains insufficient to support routine vitamin D supplementation for fertility outcomes in PCOS. Full article

Martial arts have evolved from self-defense practices into structured competitive sports that demand high levels of neuromotor control, where improper execution remains a major source of injury. This study evaluates lower-limb control during the execution of the karate lateral kick using videogrammetry biomechanical analysis. Three participants were recorded during regular training sessions and selected according to their level of expertise. Each participant performed lateral kicks at three predefined distances (close, comfortable, and long), selected based on common training practice and individual biomechanical considerations. Videogrammetry data were generated using Kinovea version 0.9.5 software to extract sagittal ankle trajectories. Statistical analyses were carried out in MATLAB version 2025b using spatial coordinates to obtain kinematic data on the practitioner’s performance. The results revealed skill-dependent differences in movement control, characterized by temporal evolution of kinematic variables and their corresponding time–frequency representations. Novice practitioners exhibited limited control during the raising and recovery phases, despite reaching the target. In contrast, expert practitioners demonstrated consistent posture, controlled acceleration during impact, and stable limb trajectories during descent. These observations provide a foundation for data-driven classification of kick execution quality and outline potential applications in supervised learning, real-time feedback systems, and injury risk reduction during karate training. Full article

Background: The integration of multi-omics data, such as genomics and transcriptomics, into artificial intelligence models has advanced precision medicine. However, their clinical applicability remains limited due to model complexity. We integrated DNA mutation, RNA expression, and A>I(G) RNA editing data to develop a predictive model for drug response in breast cancer. Methods: We analyzed 104 patients from the Breast Cancer Genome-Guided Therapy Study (ClinicalTrials.gov: NCT02022202). Clinical variables, gene expression, tumor and germline DNA variants, and RNA editing features were integrated into machine learning models to predict therapy response. Generalized linear models (GLM), random forest (RF), and support vector machines (SVM) were trained and evaluated across multiple random 70/30 train-test splits. Feature selection was performed exclusively within the training set using LASSO regularization. Model performance was assessed using the F1-score on independent test sets. The additive effect of RNA editing was evaluated using paired comparisons across identical train/test splits. Results: We characterized the cohort using clinical, mutational, transcriptomic, and RNA editing profiles in 69 non-responders and 35 responders. Across repeated splits, adding RNA editing frequently maintained or modestly improved predictive performance, particularly in expression-based models, with paired analyses showing a statistically significant increase in F1-score. Conclusions: RNA editing represents a complementary molecular layer that can enhance multi-omic models for therapy response prediction in breast cancer, supporting further investigation of epitranscriptomic features in precision oncology. Full article

Background and Objectives: Cross-country skiing requires high levels of upper-body strength and efficient respiratory function to sustain performance during sport-specific movements. This study aimed to examine the effects of an eight-week ski ergometer-based training program on upper-extremity isokinetic muscle strength and pulmonary function in competitive cross-country skiers. Materials and Methods: A total of 20 cross-country skiers voluntarily participated in the study (experimental group: n = 10, control group: n = 10). The research was conducted using a quasi-experimental controlled design. During the eight-week training period, the experimental group performed ski ergometer training three times per week at an intensity of 80–90% of maximal heart rate, with a target distance of 2.5 km per session, in addition to their regular training program. Measurements were obtained before and after the intervention. Results: Following the ski ergometer training period, significant increases were observed in FVC (F = 18.565, p < 0.001, ηp² = 0.508) and FEV₁ (F = 8.789, p = 0.008, ηp² = 0.328), which were associated with enhanced respiratory muscle endurance and ventilatory capacity. Regarding the isokinetic strength parameters, the DPPE60 variable showed significant main effects of time (F = 33.770, p < 0.001, ηp² = 0.652) and time × group interaction (F = 18.590, p < 0.001, ηp² = 0.508), indicating higher upper-extremity strength values across the measurement period. Additionally, strong positive correlations were found between dominant and nondominant limbs (r = 0.79–0.92; p < 0.05), indicating balanced bilateral strength development and high neuromuscular coordination. Conclusions: Ski ergometer-based training was associated with improvements in upper-extremity peak power (DPPE60) and ventilatory capacity (FVC) beyond general training-related adaptations. These findings suggest that SkiErg training may be a useful complementary method for enhancing selected performance-related physiological parameters in cross-country skiers. Full article

This paper focuses on variable selection and parameter estimation for mixed-effects Poisson count regression models. To simultaneously select important variables in both fixed effects and random effects, we propose a double-penalized Poisson count regression model with the Smoothly Clipped Absolute Deviation (SCAD) penalty imposed on both components. To estimate the unknown parameters, we develop a new iterative algorithm called the Double SCAD–Local Quadratic Approximation (DSCAD-LQA) algorithm. Under regularity conditions, the consistency and Oracle property of the proposed estimator are established. Simulation studies are conducted under two types of penalty parameter selection criteria: the Schwarz Information Criterion (SIC) and the Generalized Approximate Cross-Validation (GACV). We evaluate the performance of the proposed method under different levels of correlation among explanatory variables and different covariance structures of random effects. Comparisons are also carried out with the non-penalized model, the single-penalized model, and the double LASSO-penalized model. The results demonstrate that the proposed double SCAD penalty method performs better than the other three methods in terms of important variable selection and coefficient estimation, and is especially effective for sparse models. Full article

Background: Despite the widespread implementation of treat-to-target strategies and modern disease-modifying antirheumatic drugs, a substantial proportion of patients with rheumatoid arthritis (RA) continue to report unmet needs (UNs), defined as a mismatch between patient expectations and symptom burden on the one hand and outcomes achieved with current care on the other. Patient–physician discordance in global assessments may reflect multidimensional influences, including pain mechanisms, psychosocial factors, functional impairment, and communication gaps, extending beyond inflammatory disease activity. Methods: In this cross-sectional study, 133 patients with RA and 57 healthy controls were included. UNs were operationalized as the signed difference between patient global assessment and physician global assessment (ΔPGA–PhGA). Clinical variables, patient-reported outcomes, and Short Form-36 (SF-36) domains were incorporated into two regularized partial correlation network models estimated using the extended Bayesian information criterion graphical least absolute shrinkage and selection operator (EBICglasso). Node centrality indices (strength, signed strength, betweenness, and closeness) were calculated. Network stability was evaluated using 2000 bootstrap resamples and correlation stability (CS) coefficients. Results: In the clinical network, pain intensity demonstrated the highest strength centrality and the strongest direct association with UNs. In contrast, Disease Activity Score in 28 joints with C-reactive protein (DAS28-CRP) showed no direct association with UNs after accounting for shared variance. In the SF-36-based quality-of-life network, UNs exhibited inverse associations, particularly with perceived health change and role–emotional functioning. Stability analyses indicated acceptable to good robustness (clinical network: CS = 0.59 for edge weights and 0.44 for strength; SF-36 network: CS = 0.59), supporting the reliability of the estimated network structures. Conclusions: UNs in RA are not solely determined by inflammatory disease activity but are embedded within interconnected clinical and psychosocial domains. Pain occupies a structurally central position in the clinical network, whereas perceived health change and emotional role limitations characterize the quality-of-life context of UNs. These findings underscore the importance of multidimensional and patient-centered assessment strategies in RA management. Full article

Accurate engine fault classification with limited labeled data is critical for the safety and reliability of rotating machinery. This task is challenging because operating regimes are time-varying, and key variables must satisfy physical constraints, under which traditional feature classifier pipelines degrade and deep networks tend to overfit. We propose PhysGMM-MoE, a physics-aware Gaussian Mixture Model (GMM)-Mixture-of-Experts (MoE) framework for small-sample engine fault classification. At the data level, PhysGMM-MoE fits class-conditional, regime-aware GMMs and performs physically constrained, distance-based quality control to selectively augment minority classes while preserving engine operating semantics. At the model level, a heterogeneous pool of lightweight statistical experts and a lightweight Transformer-based deep expert (ECFT-Transformer) capture complementary neighborhood cues and high order multi-sensor correlations, and an L2-regularized logistic regression meta-learner fuses expert outputs via stacking. We evaluate fault classification on the 3500-DEFault diesel-engine dataset using the adopted eight-class cylinder-fault labeling (H, F1–F7) built from in-cylinder pressure statistics and torsional-vibration harmonics; although severity levels exist in the dataset, this study focuses on classification rather than severity estimation. With 40 training samples per class, PhysGMM-MoE achieves a mean accuracy of 0.9875, exceeding SMOTE+XGBoost by 0.0086, and attains the best macro precision/recall/F1 of 0.9878/0.9826/0.9889, demonstrating strong performance under the adopted small-sample setting. Full article

Background: Transtibial prostheses are commonly classified as endoskeletal or exoskeletal and differ in weight, adaptability, and mechanical response, which may influence gait performance. This study examined whether prosthesis type affects overground walking movement structure and neuromuscular control and assessed the relationship between walking speed and neuromuscular control. Methods: Principal component analysis (PCA) was applied to kinematic marker data from 20 unilateral transtibial amputees using either endoskeletal (n = 10; 54.7 ± 6.1 years) or exoskeletal prostheses (n = 10; 57.9 ± 8.7 years) during self-selected overground walking. Principal movements (PMs) were extracted to represent functionally meaningful gait components. Movement structure was evaluated using the relative explained variance of PM positions (rVAR), whereas neuromuscular control was quantified using the root mean square of PM accelerations (RMS; acceleration magnitude) and the number of zero crossings (N; regularity/predictability). Group differences were examined using covariate-adjusted analyses, controlling for preferred walking speed. Results: No significant differences in walking movement structure were found between prosthetic types. Unadjusted analyses suggested greater swing-phase acceleration (PM₂) and lower neuromuscular variability across PM₁–PM₄ in the endoskeletal group; however, these effects were no longer significant after adjusting for BMI and walking speed. Walking speed showed strong associations with neuromuscular control (p ≤ 0.003), with faster speeds linked to greater swing-phase acceleration and reduced variability. Conclusions: Walking movement structure and neuromuscular control were comparable between prosthetic types, while walking speed emerged as a key factor in gait evaluation among transtibial amputees. Full article

Industry 5.0 emphasizes human-centered integration of artificial intelligence in industrial contexts, yet successful adoption depends critically on workforce perception and acceptance. This research develops and validates a machine learning framework for predicting AI-related perceptions and expected impacts in the construction industry under small sample constraints typical of specialized industrial surveys. Specifically, the study aims to develop and empirically validate a predictive AI decision support model that estimates the expected impact of AI adoption in the construction sector based on digital competencies, ICT utilization, AI training and experience, and AI usage at both individual and organizational levels, operationalized through a composite AI Impact Index and two process-oriented outcomes (perceived task automation and perceived cost reduction). Using a dataset of 51 survey responses from Slovak construction professionals collected in 2025, we implement a methodologically rigorous approach specifically designed for limited-data regimes. The framework encompasses ordinal target simplification from five to three classes, dimensionality reduction through theoretically grounded composite indices reducing features from 15 to 7, exclusive deployment of low variance regularized models, and leave-one-out cross-validation for unbiased performance estimation. The optimal model (Lasso regression with recursive feature elimination) predicts cost reduction perception with R² = 0.501, MAE = 0.551, and RMSE = 0.709, while six classification targets achieve weighted F1 = 0.681, representing statistically optimal performance given sample constraints and perception measurement variability. Comparative evaluation confirms regularized models outperform high variance alternatives: random forest (R² = 0.412) and gradient boosting (R² = 0.292) exhibit substantially lower generalization performance, empirically validating the bias-variance trade-off rationale. Key methodological contributions include explicit bias-variance optimization preventing overfitting, feature selection via RFE reducing input space to six predictors (personal AI usage, AI impact on budgeting, ICT utilization, AI training, company size, and age), and demonstration that principled statistical approaches achieve meaningful predictions without requiring large-scale datasets or complex architectures. The framework provides a replicable blueprint for perception and impact prediction in data-constrained Industry 5.0 contexts, enabling targeted interventions, including customized training programs, strategic communication prioritization, and resource allocation for change management initiatives aligned with predicted adoption patterns. Full article

Background: Hearing aid (HA) outcome is a multidimensional construct that requires not only the analysis of auditory function improvement, but also a subjective evaluation of benefits from HAs. Indeed, subjective satisfaction of patients with HAs is not entirely predictable from audiometric outcomes such as real ear gain or functional gain. In light of this possible discrepancy the 1990 Consensus Statement for “Recommended Components of a Hearing Aid Selection Procedure for Adults” suggested that verification of hearing aids benefit also incorporate the subjective satisfaction with amplification. Objectives: The aim of this study was to test the validity and reliability of the Italian version of International Outcome Inventory for Hearing Aids (IOI-HA-It). Methods: Ninety-eight outpatients were randomly recruited to participate in this study. They all made regular use of HAs and were supplied with three different self-administered questionnaires. The International Outcome Inventory for Hearing Aids (IOI-HA), the Hearing Handicap Inventory for Adults (HHIA) or for elderly (HHIE) and the Italian translation of the MOS 36-Item Short Form Health Survey (SF-36). The epidemiological features and results were analyzed as descriptive statistics. Continuous variables were expressed as means with standard deviations (SDs). Reliability of the Italian version was assessed by the following two parameters: internal and test–retest consistencies. Internal consistency reliability was measured by Cronbach’s alpha coefficient. Results and Conclusions: This study evidenced that the IOI-HA-It is proved to offer adequate subjective outcome measures to better appreciate the integral evaluation of a patient’s rehabilitative experience. Furthermore, since it is a very brief questionnaire with low demand on time and cost involved in its compilation, it should be recommended in clinical practice. Full article

Hydrogen embrittlement poses a critical threat to the durability of metallic components in emerging hydrogen energy infrastructure. Reliable fatigue life assessment in hydrogen-rich environments is, however, severely constrained by the high cost and low throughput of high-pressure testing, resulting in characteristically sparse experimental datasets. Conventional empirical fatigue models struggle to capture hydrogen–mechanical coupling effects, while purely data-driven approaches often suffer from severe overfitting under data-scarce conditions. To address this challenge, this study develops a physics-enhanced learning framework that integrates established fracture mechanics principles with machine learning. Using high-strength GS80A steel as a case study, two complementary strategies are introduced. First, a physically augmented input strategy reformulates raw experimental variables into dimensionless physical descriptors derived from the Basquin and Goodman relations, thereby reducing the complexity of the learning space. Second, a physics-regularized ensemble strategy combines deterministic physical predictions with neural network outputs through a dual-pathway inference scheme, ensuring physically admissible behavior during extrapolation. An automated hyperparameter selection module is further employed to establish a robust data-driven baseline. Comparative evaluation against optimized multi-layer perceptron and support vector regression models demonstrates that the proposed framework significantly improves predictive robustness in small-sample regimes. Specifically, the coefficient of determination ($R^2$) exceeds 0.975, with the root mean square error (RMSE) reduced by approximately 70% compared to the pure data-driven baseline. By systematically embedding mechanistic priors into the learning process, the proposed approach provides a reliable and interpretable tool for fatigue assessment of metallic components operating in hydrogen environments. Full article

This study aimed to characterize hunting dog packs (rehalas) and identify management typologies within a Mediterranean mountain system (Sierra Morena region of Córdoba). An ethno-demographic survey was designed and completed by 30 breeders. Descriptive statistics were used for general characterization, while variability assessment and typology identification were performed using multiple correspondence analysis and hierarchical clustering. The typical dog pack breeder was a 48-year-old man with extensive experience (28.5 years) and basic formal education. Dog packs comprised an average of 51.9 dogs, predominantly of the Large-sized Podenco Andaluz breed, participating in approximately 40 hunting events per year. Feeding practices commonly combine commercial feed with supplementary food items. Health management included routine deworming every six months, and 43% of breeders reported concern about leishmaniasis. Training generally began at around 14.5 months of age and followed regular weekly routines. Ten factors explained 82.4% of the observed variability, allowing the identification of three typologies: traditional, pragmatic, and non-organized. These findings underline the cultural, genetic, and socio-ecological relevance of dog packs as working groups in Mediterranean rural systems. The long-term sustainability of these systems depends on reinforcing selective breeding, improving health management, and safeguarding traditional practices adapted to each identified typology. Full article

We introduce a novel Bayesian approach for feature (variable) selection using Gaussian process regression, which is crucial for enhancing interpretability and model regularization. Our method employs nearest neighbor Gaussian processes as scalable approximations to classical Gaussian processes. Feature selection is performed by conditioning the process mean and covariance function on a random set representing the indices of relevant variables. A priori beliefs regarding this set control the feature selection, while reference priors are assigned to the remaining model parameters, ensuring numerical robustness in the process covariance matrix. For model inference, we propose a Metropolis-within-Gibbs algorithm. The effectiveness of the proposed feature selection approach is demonstrated through evaluation on simulated data, a computer experiment approximation, and two real-world data sets. Full article