Search Results (21,841)

(1) Background: IgA nephropathy (IgAN) is a leading cause of chronic kidney disease worldwide. Despite its prevalence, the molecular mechanisms of IgAN remain poorly understood, partly due to limited research scale. Identifying key genes involved in IgAN’s pathogenesis is critical for novel diagnostic and therapeutic strategies. (2) Methods: We identified differentially expressed genes (DEGs) by analyzing public datasets from the Gene Expression Omnibus. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to elucidate the biological roles of DEGs. Hub genes were screened using weighted gene co-expression network analysis combined with machine learning algorithms. Immune infiltration analysis was conducted to explore associations between hub genes and immune cell profiles. The hub genes were validated using receiver operating characteristic curves and area under the curve. (3) Results: We identified 165 DEGs associated with IgAN and revealed pathways such as IL-17 signaling and complement and coagulation cascades, and biological processes including response to xenobiotic stimuli. Four hub genes were screened: three downregulated (FOSB, SLC19A2, PER1) and one upregulated (SOX17). The AUC values for identifying IgAN in the training and testing set ranged from 0.956 to 0.995. Immune infiltration analysis indicated that hub gene expression correlated with immune cell abundance, suggesting their involvement in IgAN’s immune pathogenesis. (4) Conclusion: This study identifies FOSB, SLC19A2, PER1, and SOX17 as novel hub genes with high diagnostic accuracy for IgAN. These genes, linked to immune-related pathways such as IL-17 signaling and complement activation, offer promising targets for diagnostic development and therapeutic intervention, enhancing our understanding of IgAN’s molecular and immune mechanisms. Full article

Persistent scatterer (PS) pixel selection is crucial in the PS-InSAR technique, ensuring the quality and quantity of PS pixels for accurate deformation measurements. However, traditional methods like the amplitude dispersion index (ADI)-based method struggle to balance the quality and quantity of PS pixels. To adequately select high-quality PS pixels, and thus improve the deformation measurement performance of PS-InSAR, the multi-temporal feature extraction network (MFN) is constructed in this paper. The MFN combines the 3D U-Net and the convolutional long short-term memory (CLSTM) to achieve time-series analysis. Compared with traditional methods, the proposed MFN can fully extract the spatiotemporal characteristics of complex SAR images to improve PS pixel selection performance. The MFN was trained with datasets constructed by reliable PS pixels estimated by the ADI-based method with a low threshold using ∼350 time-series Sentinel-1A SAR images, which contain man-made objects, farmland, parkland, wood, desert, and waterbody areas. To test the validity of the MFN, a deformation measurement experiment was designed for Tongzhou District, Beijing, China with 38 SAR images obtained by Sentinel-1A. Moreover, the similar time-series interferometric pixel (STIP) index was introduced to evaluate the phase stability of selected PS pixels. The experimental results indicate a significant improvement in both the quality and quantity of selected PS pixels, as well as a higher deformation measurement accuracy, compared to the traditional ADI-based method. Full article

Thorough investigation into Tesla valve (TV) design was conducted across a large design of experiments (DOE) consisting of four varying geometric parameters and six different Reynolds number regimes in order to develop an optimized pressure drop device utilizing machine learning (ML) methods. A non-standard TV design was geometrically parameterized, and an automation suite was created to cycle through numerous combinations of parameters. Data were collected from completed computational fluid dynamics (CFD) simulations. TV designs were tested in the restricted flow direction for overall differential pressure, and overall minimum pressure with consideration to the onset of cavitation. Qualitative observations were made on the effects of each geometric parameter on the overall valve performance, and particular parameters showed greater influence on the pressure drop compared to classically optimized parameters used in previous TV studies. The overall minimum pressure demonstrated required system pressure for a valve to be utilized such that onset to cavitation would not occur. Data were utilized to train an ML model, and an optimized geometry was selected for maximized pressure drop. Multiple optimization efforts were made to meet design pressure drop goals versus traditional diodicity metrics, and two geometries were selected to develop a final design tool for overall pressure drop component development. Future work includes experimental validation of the large dataset, as well as further validation of the design tool for use in industry. Full article

Background/Objectives: Developing high-performance artificial intelligence (AI) models for rare diseases like malignant bone tumors is limited by scarce annotated data. This study evaluates same-modality cross-domain transfer learning by comparing an AI model pretrained on chest radiographs with a model trained from scratch for detecting malignant bone tumors on knee radiographs. Methods: Two YOLOv5-based detectors differed only in initialization (transfer vs. scratch). Both were trained/validated on institutional data and tested on an independent external set of 743 radiographs (268 malignant, 475 normal). The primary outcome was AUC; prespecified operating points were high-sensitivity (≥0.90), high-specificity (≥0.90), and Youden-optimal. Secondary analyses included PR/F1, calibration (Brier, slope), and decision curve analysis (DCA). Results: AUC was similar (YOLO-TL 0.954 [95% CI 0.937–0.970] vs. YOLO-SC 0.961 [0.948–0.973]; DeLong p = 0.53). At the high-sensitivity point (both sensitivity = 0.903), YOLO-TL achieved higher specificity (0.903 vs. 0.867; McNemar p = 0.037) and PPV (0.840 vs. 0.793; bootstrap p = 0.030), reducing ~17 false positives among 475 negatives. At the high-specificity point (~0.902–0.903 for both), YOLO-TL showed higher sensitivity (0.798 vs. 0.764; p = 0.0077). At the Youden-optimal point, sensitivity favored YOLO-TL (0.914 vs. 0.892; p = 0.041) with a non-significant specificity difference. Conclusions: Transfer learning may not improve overall AUC but can enhance practical performance at clinically crucial thresholds. By maintaining high detection rates while reducing false positives, the transfer learning model offers superior clinical utility. Same-modality cross-domain transfer learning is an efficient strategy for developing robust AI systems for rare diseases, supporting tools more readily acceptable in real-world screening workflows. Full article

Objective: Mental fatigue (MF) worsens soccer performance. Further knowledge is needed to understand MF’s effects on soccer players and its underlying mechanisms. Our aim was to analyze the subjective, objective, and neural MF-related outcomes induced by different type of tasks. Methods: A randomized crossover experimental design with repeated measures was used. Thirteen amateur soccer players (M_age = 23 ± 5.43) completed three conditions: cognitive (30 min. Stroop.), physical (30 min. cycling), or combined (30 min. Stroop while cycling). Ratings of mental fatigue (measured via the Visual Analogue Scale), electroencephalographical signals (electroencephalography), and psychomotor performance (Brief-Psychomotor Vigilance Test) were measured pre- and post-condition. Soccer-related decision-making (TacticUP^® test) was assessed post-condition. Results: Linear Mixed Models analysis revealed increments in perceived mental fatigue in all conditions, especially cognitive (p = 0.004) and combined (p < 0.0001) conditions. Psychomotor performance worsened, especially for cognitive (p = 0.039) and combined (p = 0.009) conditions. The Individual Alpha Peak Frequency was lower after the cognitive task (p = 0.040) and compared with the physical task (p = 0.021). The Alpha midline power increased after the cognitive task in the central-frontal (p = 0.047) and central-posterior brain regions (p = 0.043). Conclusions: Cognitive and combined conditions were found to be more mentally demanding and fatiguing than single physical tasks. This was also reflected by an impaired reaction time. Based on the neural activity recorded, the performance impairments caused by mental fatigue were caused by reduced brain readiness (i.e., a lower Alpha Peak Frequency). However, non-significant changes were found in soccer-related decision-making. Coaches should consider the type of training tasks they recommend in light of their different effects on mental fatigue and performance. Full article

Campylobacter spp. are leading causes of bacterial gastroenteritis globally. Swine are recognized as an important reservoir for this pathogen. The emergence of antimicrobial resistance (AMR) and multidrug resistance (MDR) in Campylobacter is a global health concern. Traditional methods for detecting AMR and MDR, such as phenotypic testing or whole-genome sequencing, are resource-intensive and time-consuming. In the present study, we developed and validated a supervised machine learning model to predict MDR status in Campylobacter isolates from swine, using publicly available phenotypic AMR data collected by NARMS from 2013 to 2023. Resistance profiles for seven antimicrobials were used as predictors, and MDR was defined as resistance to at least one agent in three or more antimicrobial classes. The model was trained on 2013–2019 isolates and externally validated using isolates from 2020, 2021, and 2023. Random Forest showed the highest performance (accuracy = 99.87%, Kappa = 0.9962) among five evaluated algorithms, which achieved high balanced accuracy, sensitivity, and specificity in both training and external validation. Our feature importance analysis identified erythromycin, azithromycin, and clindamycin as the most influential predictors of MDR among Campylobacter isolates from swine. Our temporally validated, interpretable model provides a robust, cost-effective tool for predicting MDR in Campylobacter spp. and supports surveillance and early detection in food animal production systems. Full article

Unaccustomed eccentric exercise is well established to induce exercise-induced muscle damage (EIMD), characterized by transient strength loss, delayed onset muscle soreness (DOMS), reduced range of motion, and proprioceptive disturbances. While limb dominance has been proposed as a potential modulator of susceptibility to EIMD, evidence remains inconclusive. This exploratory study aimed to compare alterations in muscle damage indices between dominant and non-dominant knee extensors 48 h after eccentric isokinetic exercise. Eighteen physically active young women (23 ± 2 years) completed two eccentric exercise sessions (5 × 15 maximal contractions at 60°/s), one per limb, with sessions separated by 24–30 days. For all participants, testing was conducted during the early follicular phase. Muscle strength (isometric and eccentric peak torque), DOMS (palpation and pain pressure threshold), range of motion, fatigue index, and position sense were assessed pre- and 48 h post-exercise. Significant reductions in isometric and eccentric peak torque, increased DOMS, impaired position sense, and altered fatigue index were observed 48 h post-exercise in the exercised limb (p < 0.001), with no differences between dominant and non-dominant limbs across all indices. These findings demonstrate that limb dominance does not influence the magnitude of EIMD in knee extensors of young women. Practical implications include equal consideration of both limbs in eccentric training, rehabilitation, and injury prevention programs. Full article

Recently, cognitive training programs using digital content with visuoperceptual stimulation have been developed and commercialized. In particular, digital intervention content for children with attention deficit hyperactivity disorder (ADHD) has been developed as games, enhancing motivation and accessibility for the target population. Active stimulation is required to elicit positive effects on self-regulation training, including attention control and impulse inhibition, through task-based content. Common forms of stimulation include emotional stimuli, such as praise and encouragement, and economic stimuli based on a self-directed token economy system. Economic stimulation can serve as active reinforcement because the child directly engages as the primary agent within the task content. This study applied and validated a token economy intervention using digital therapeutic content in children with ADHD. Behavioral assessments were conducted using the Comprehensive Attention Test (CAT) and the Korean version of the Child Behavior Checklist (K-CBCL). The developed digital intervention content implemented a user-centered token economy based on points within the program. In the CAT Flanker Task, the experimental group (0.84 ± 0.40) showed significantly higher sensitivity factor scores than the control group (0.72 ± 0.59) after 4 weeks, with a large effect size (F = 4.76, p = 0.038, partial η² = 0.150). Additionally, the rate of change in externalizing behavior scores on the K-CBCL showed a significant difference between the two groups (t = 2.35, p = 0.026, Cohen’s d = 0.860), demonstrating greater improvement in externalizing symptoms in the experimental group than in the control group. Therefore, this study suggests that the participant-centered implementation model using token economy mechanisms in digital intervention content may serve as a novel and effective therapeutic approach for children with ADHD. Full article

Beliefs about personhood held by healthcare professionals and care partners influence care outcomes, satisfaction, and the well-being of persons living with dementia (PLWD). This study examined differences in personhood beliefs based on demographic and contextual factors, including the Social Vulnerability Index (SVI), using registration data from the Bravo Zulu care partner training program (n = 540). Guided by the Ring Theory of Personhood, eight factors were analyzed: age, sex, race, socioeconomic status, professional discipline, healthcare experience, prior care partner training, and SVI. One-way ANOVA and independent t-tests were used to examine group-level differences, and multiple linear regression was conducted to assess the extent to which these factors predicted personhood beliefs. Race, age (borderline significance) professional discipline, and prior training as a care partner were significant predictors of personhood beliefs. Subscale analyses using ANOVA and t-test showed that beliefs about psychosocial engagement varied by SVI and healthcare experience with small effect size; however, these factors did not significantly predict of overall personhood beliefs in the regression model. Findings underscore the importance of recognizing how background characteristics shape personhood beliefs about PLWD. Promoting self-reflection and expanding culturally responsive training may support person- and relationship-centered care and improve satisfaction in multicultural care settings. Full article

The object detection model for synthetic aperture radar (SAR) images needs to have strong generalization ability and more stable detection performance due to the complex scattering mechanism, high sensitivity of the orientation angle, and susceptibility to speckle noise. Visual large models possess strong generalization capabilities for natural image processing, but their application to SAR imagery remains relatively rare. This paper attempts to introduce a visual large model into the SAR object detection task, aiming to alleviate the problems of weak cross-domain generalization and poor adaptability to few-shot samples caused by the characteristics of SAR images in existing models. The proposed model comprises an image encoder, an attention module, and a detection decoder. The image encoder leverages the pre-trained Segment Anything Model (SAM) for effective feature extraction from SAR images. An Adaptive Channel Interactive Attention (ACIA) module is introduced to suppress SAR speckle noise. Further, a Dynamic Tandem Attention (DTA) mechanism is proposed in the decoder to integrate scale perception, spatial focusing, and task adaptation, while decoupling classification from detection for improved accuracy. Leveraging the strong representational and few-shot adaptation capabilities of large pre-trained models, this study evaluates their cross-domain and few-shot detection performance on SAR imagery. For cross-domain detection, the model was trained on AIR-SARShip-1.0 and tested on SSDD, achieving an mAP50 of 0.54. For few-shot detection on SAR-AIRcraft-1.0, using only 10% of the training samples, the model reached an mAP50 of 0.503. Full article

The potential of Virtual Reality (VR) in enhancing learning and training is being widely explored. The relationship of immersion, as one of the core technical features of VR, with knowledge types has not been fully explored. This study aims to investigate how VR immersion levels (high vs. low) affect the acquisition of declarative and procedural knowledge, as well as related cognitive and affective factors. A 2 × 2 mixed design was adopted, with 64 college students who had no VR experience and no background in professional medical knowledge being randomly assigned to either a high-immersion group (using HTC Vive Pro headsets) or a low-immersion group (using desktop monitors). Participants completed learning tasks on thyroid and related diseases (declarative knowledge) and cardiopulmonary resuscitation (procedural knowledge), followed by knowledge tests and self-report questionnaires to measure presence, motivation, self-efficacy, cognitive load, and emotional states. Results showed that high immersion significantly improved learning outcomes for both types of knowledge with large effect sizes. In both knowledge domains, high immersion also enhanced presence, intrinsic motivation, self-efficacy, and positive emotions. However, cognitive load was reduced only for declarative knowledge, and no significant effects were observed for self-regulation. These findings highlight the differential impact of VR immersion on knowledge acquisition and provide insights for optimizing VR-based educational interventions. Full article

The structural parameters of Stewart platforms play a critical role in enhancing dynamic performance, improving motion accuracy, and enabling effective control strategies. However, practical applications face several key limitations, including the metric balancing for optimization, the limited singularity-free workspace, and low computational efficiency. To overcome those shortcomings, this work proposes a multi-objective optimal design of the structural parameters for Stewart platform based on Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Kolmogorov–Arnold Network (KAN). Firstly, under the stroke constraints of the Stewart platform, this work focuses on optimizing the platform’s key structural parameters. This approach enables both the optimization of existing equipment and the design of new devices. Secondly, this work employs KAN to establish a model that characterizes the relationship between the structural parameters and diverse postures within the maximum singularity-free workspace. This approach not only enhances computational efficiency but also ensures high precision. Finally, this study proposes six performance metrics and utilizes NSGA-III to optimize the structural parameters, thereby achieving a trade-off among these diverse objectives. Simulation and experimental results demonstrate that KAN significantly outperforms the Multi-Layer Perceptron (MLP) in predicting workspace postures. Compared with MLP, KAN achieves higher prediction accuracy and lower error rates across both training and test datasets. When comparing NSGA-III with NSGA-II, the proposed approach demonstrates modest improvements in most performance metrics while preserving acceptable trade-offs between the optimization objectives. Full article

The presented research aims to find a data-driven formula for the compressive stress–strain behaviour of closed-cell aluminium foams with respect to the apparent density of the material. This is a continuation and new development of an earlier study by the authors. In the previous step, 500 artificial neural network models were built and trained on experimental results from compression tests and then evaluated based on, among other factors, mean absolute relative errors for training and verification stages. In this step, the evaluation of networks is amended, and criteria are introduced that are connected with the mechanical characteristics of the material, i.e., the plateau stress and quasi-elastic gradient. A weighted condition of all measures is proposed. Based on the amended conditions, a neural network model with a weighted mean absolute relative error of ≅5% is chosen and presented, together with the mathematical equation for its stress–strain–density relationship σ = f(ε, ρ) over a range of material apparent densities ρ ∈ <0.2; 0.3> g/cm³. Experimental relationships for compressive strength and plateau stress are also presented. Full article

Skin cancer is one of the most prevalent and potentially lethal cancers worldwide, highlighting the need for accurate and timely diagnosis. Convolutional neural networks (CNNs) have demonstrated strong potential in automating skin lesion classification. In this study, we propose a multi-class classification model using EfficientNet-B0, a lightweight yet powerful CNN architecture, trained on the HAM10000 dermoscopic image dataset. All images were resized to 224 × 224 pixels and normalized using ImageNet statistics to ensure compatibility with the pre-trained network. Data augmentation and preprocessing addressed class imbalance, resulting in a balanced dataset of 7512 images across seven diagnostic categories. The baseline model achieved 77.39% accuracy, which improved to 89.36% with transfer learning by freezing the convolutional base and training only the classification layer. Full network fine-tuning with test-time augmentation increased the accuracy to 96%, and the final model reached 97.15% when combined with Monte Carlo dropout. These results demonstrate EfficientNet-B0’s effectiveness for automated skin lesion classification and its potential as a clinical decision support tool. Full article

Background: Chronic wrist pain is becoming increasingly recognized among athletes engaging in wrist-loading activities such as handstands. However, its prevalence and associated risk factors in handstand practitioners have not been systematically studied. This study aimed to investigate the prevalence of chronic wrist pain and to explore associated factors such as discipline, training habits, and pain management strategies. Methods: This cross-sectional study aimed to investigate the prevalence and associated factors of chronic wrist pain among handstand practitioners. Eligible participants were individuals aged 18 years or older, of any gender, who practiced handstands regularly (defined as at least once per week). Participants were recruited via a combination of open invitations on social media (Facebook, WhatsApp, Instagram) and direct outreach to movement studios and training communities. The survey was administered online using Google Forms and remained open for two months. Participation was voluntary and anonymous. Descriptive statistics were used to present sociodemographic characteristics, including age group, gender, sport discipline, and weekly training hours. Participants reported training habits, equipment use, pain history, and management strategies via a self-developed questionnaire designed for this study. Chronic pain was defined as recurring or persistent wrist pain. Descriptive statistics were used to summarize responses. Associations between chronic wrist pain and survey variables were analyzed using Chi-square or Fisher’s exact tests for nominal data, and Chi-square test for trend for ordinal data. A p-value < 0.05 was considered statistically significant. Results: A total of 321 participants were included in the study. The most represented age group was 25–34 years, comprising 123 (38.3%) of the participants. Gender distribution was 174 (54.2%) males and 147 (45.8%) females. The most common sport disciplines were Yoga (88, 27.4%), Capoeira (60, 18.7%), and Movement (52, 16.2%). Chronic wrist pain was reported by 182 (56.7%) of participants. Younger age was significantly associated with higher pain prevalence (p = 0.042). No significant associations were observed between chronic pain and weekly training hours, warm-up routines, brace use, or grip device use. Female participants demonstrated more proactive pain management behaviors (p = 0.016). Sport discipline and training practices showed non-significant trends toward pain differences. Conclusions: Chronic wrist pain is common among handstand practitioners, particularly among younger athletes. These findings suggest that injury risk may relate more to training intensity and biomechanics than to simple training volume. Further research incorporating objective diagnostics and standardized intervention protocols is warranted. Full article