Search Results (763)

Background: Oxidative stress is a critical factor contributing to male infertility, impairing spermatogonial stem cells (SSCs) and disrupting normal spermatogenesis. This study aimed to isolate and characterize human SSCs and to investigate oxidative stress-related gene expression, protein interaction networks, and developmental trajectories involved in SSC function. Methods: SSCs were enriched from human orchiectomy samples using CD49f-based magnetic-activated cell sorting (MACS) and laminin-binding matrix selection. Enriched cultures were assessed through morphological criteria and immunocytochemistry using VASA and SSEA4. Transcriptomic profiling was performed using microarray and single-cell RNA sequencing (scRNA-seq) to identify oxidative stress-related genes. Bioinformatic analyses included STRING-based protein–protein interaction (PPI) networks, FunRich enrichment, weighted gene co-expression network analysis (WGCNA), and predictive modeling using machine learning algorithms. Results: The enriched SSC populations displayed characteristic morphology, positive germline marker expression, and minimal fibroblast contamination. Microarray analysis revealed six significantly upregulated oxidative stress-related genes in SSCs—including CYB5R3 and NDUFA10—and three downregulated genes, such as TXN and SQLE, compared to fibroblasts. PPI and functional enrichment analyses highlighted tightly clustered gene networks involved in mitochondrial function, redox balance, and spermatogenesis. scRNA-seq data further confirmed stage-specific expression of antioxidant genes during spermatogenic differentiation, particularly in late germ cell stages. Among the machine learning models tested, logistic regression demonstrated the highest predictive accuracy for antioxidant gene expression, with an area under the curve (AUC) of 0.741. Protein oxidation was implicated as a major mechanism of oxidative damage, affecting sperm motility, metabolism, and acrosome integrity. Conclusion: This study identifies key oxidative stress-related genes and pathways in human SSCs that may regulate spermatogenesis and impact sperm function. These findings offer potential targets for future functional validation and therapeutic interventions, including antioxidant-based strategies to improve male fertility outcomes. Full article

Educational buildings, including schools, nurseries and universities, face stricter regulation and design control on indoor air quality (IAQ) and thermal conditions than other built environments, as these may affect children’s health and wellbeing. In this scoping review, wide-ranging health, performance, and absenteeism consequences of poor—and benefits of good—IAQ and thermal conditions are evaluated, focusing on source control, ventilation and air purification interventions. Economic impacts of interventions in educational buildings have been evaluated to enable the assessment of tangible building-related costs and savings, alongside less easily quantifiable improvements in educational attainment and reduced healthcare. Key recommendations are provided to assist decision makers in pathways to provide clean air, at an optimal temperature for students’ learning and health outcomes. Although the role of educational buildings can be challenging to isolate from other socio-economic confounders, secondary short- and long-term impacts on attainment and absenteeism have been demonstrated from the health effects associated with various pollutants. Sometimes overlooked, source control and repairing existing damage can be important cost-effective methods in minimising generation and preventing ingress of pollutants. Existing ventilation standards are often not met, even when mechanical and hybrid ventilation systems are already in place, but can often be achieved with a fraction of a typical school budget through operational and maintenance improvements, and small-scale air-cleaning and ventilation technologies, where necessary. Full article

Digital hoarding (DH) is an emerging behavior with potential implications for psychological well-being and daily functioning. While traditionally associated with physical hoarding disorder, DH presents unique challenges in digital environments, particularly among university students increasingly immersed in technology. This study examines the relationship between DH and academic performance, proposing a theoretical model in which academic engagement and academic burnout act as mediating mechanisms. Drawing on the Job Demands–Resources Theory, we provide evidence that DH contributes to a health impairment process that negatively affects student outcomes. Our findings reveal DH as a novel predictor of academic burnout, highlighting its detrimental impact on academic performance. These results carry significant theoretical and practical implications, offering new insights into the role of technology-related anxiety disorders in educational settings. From a practical perspective, our study underscores the need for higher education institutions to implement targeted interventions focused on emotional regulation and learning strategies to mitigate the negative effects of DH. Despite limitations related to sample specificity and cross-sectional data, this research opens avenues for future longitudinal studies and interventions aimed at addressing DH in both academic and professional contexts. By linking digital behaviors to mental health and performance, this work aligns with public health interests in understanding technology’s impact on youth well-being. Full article

Background: Hypertension is a serious global health issue affecting over one billion adults and leading to severe complications if left unmanaged. Despite medical advancements, only a fraction of patients effectively have their hypertension under control. Among the factors that hinder adherence to hypertensive drugs are the debilitating side effects of the drugs. The lack of adherence results in poorer patient outcomes as patients opt to live with their condition, instead of having to deal with the side effects. Hence, there is a need to discover new hypertension drug molecules with better side effects to increase patient treatment options. To this end, computational methods such as artificial intelligence (AI) have become an exciting option for modern drug discovery. AI-based computational drug discovery methods generate numerous new lead antihypertensive drug molecules. However, predicting their potential side effects remains a significant challenge because of the complexity of biological interactions and limited data on these molecules. Methods: This paper presents a machine learning approach to predict the potential side effects of computationally synthesised antihypertensive drug molecules based on their molecular properties, particularly functional groups. We curated a dataset combining information from the SIDER 4.1 and ChEMBL databases, enriched with molecular descriptors (logP, PSA, HBD, HBA) using RDKit. Results: Gradient Boosting gave the most stable generalisation, with a weighted F1 of 0.80, and AUC-ROC of 0.62 on the independent test set. SHAP analysis over the cross-validation folds showed polar surface area and logP contributing the largest global impact, followed by hydrogen bond counts. Conclusions: Functional group patterns, augmented with key ADMET descriptors, offer a first-pass screen for identifying side-effect risks in AI-designed antihypertensive leads. Full article

Reinforcement learning models often rely on uncertainty estimation to guide decision-making in dynamic environments. However, the role of memory limitations in representing statistical regularities in the environment is less understood. This study investigated how limited memory capacity influence uncertainty estimation, potentially leading to misestimations of outcomes and environmental statistics. We developed a computational model incorporating active working memory processes and lateral inhibition to demonstrate how relevant information is selected, stored, and used to estimate uncertainty. The model allows for the detection of contextual changes by estimating expected uncertainty and perceived volatility. Two experiments were conducted to investigate limitations in information availability and uncertainty estimation. The first experiment explored the effect of cognitive load on memory reliance for uncertainty estimation. The results show that cognitive load diminished reliance on memory, lowered expected uncertainty, and increased perceptions of environmental volatility. The second experiment assessed how outcome exposure conditions affect the ability to detect environmental changes, revealing differences in the mechanisms used for environmental change detection. The findings emphasize the importance of memory constraints in uncertainty estimation, highlighting how misestimation of uncertainties is influenced by individual experiences and the capacity of working memory (WM) to store relevant information. These insights contribute to understanding the role of WM in decision-making under uncertainty and provide a framework for exploring the dynamics of reinforcement learning in memory-limited systems. Full article

This study explores the potential of immersive virtual reality (IVR) to enhance knowledge transfer in vocational education, particularly in bridging the gap between academic learning and practical workplace application. The focus lies on relevant predictors for actual learning transfer, namely knowledge acquisition and the transfer-related self-efficacy. Additionally, the Cognitive Affective Model of Immersive Learning (CAMIL) is used to investigate potential predictors in IVR learning. This approach allows for empirical testing of the CAMIL and validation of its assumptions using empirical data. To address the research questions, a quasi-experimental field study was conducted with 141 retail trainees at a German vocational school. Participants were assigned to either an IVR group or a control group receiving traditional instruction. The intervention spanned four teaching sessions of 90 min each, focusing on the design of a retail sales area based on sales-promoting principles. To assess subject-related learning outcomes, a domain-specific knowledge test was developed. In addition, transfer-related self-efficacy and other relevant constructs were measured using Likert-scale questionnaires. The results show that IVR-based instruction significantly improves knowledge acquisition and transfer-related self-efficacy compared to traditional teaching methods. In terms of the CAMIL-based mechanisms, significant correlations were found between transfer-related self-efficacy and factors such as interest, motivation, academic self-efficacy, embodiment, and self-regulation. Additionally, correlations were found between knowledge acquisition and relevant predictors such as interest, motivation, and self-regulation. These findings underscore IVR’s potential to facilitate knowledge transfer in vocational school, highlighting the need for further research on its long-term effects and the actual application of learned skills in real-world settings. Full article

Background: Brazilian Jiu-Jitsu (BJJ) is a physically demanding sport associated with a notable risk of musculoskeletal injuries. Understanding injury patterns, rehabilitation approaches, and psychological readiness to return to sport (RTS) is essential for prevention and management strategies. This study aimed to investigate injury characteristics among Italian BJJ athletes, assess their rehabilitation processes and psychological recovery, and identify key risk factors such as belt level, body mass index (BMI), and training load. Methods: A cross-sectional survey was conducted among members of the Italian BJJ community, including amateur and competitive athletes. A total of 360 participants completed a 36-item online questionnaire. Data collected included injury history, rehabilitation strategies, RTS timelines, and responses to the Injury-Psychological Readiness to Return to Sport (I-PRRS) scale. A Random Forest machine learning algorithm was used to identify and rank potential injury risk factors. Results: Of the 360 respondents, 331 (92%) reported at least one injury, predominantly occurring during training sessions. The knee was the most frequently injured joint, and the action “attempting to pass guard” was the most reported mechanism. Most athletes (65%) returned to training within one month. BMI and age emerged as the most significant predictors of injury risk. Psychological readiness scores indicated moderate confidence, with the lowest levels associated with playing without pain. Conclusions: Injuries in BJJ are common, particularly affecting the knee. Psychological readiness, especially confidence in training without pain, plays a critical role in RTS outcomes. Machine learning models may aid in identifying individual risk factors and guiding injury prevention strategies. Full article

Background/Objectives: Aging is often accompanied by physical and cognitive decline, affecting older adults’ mobility. Virtual reality (VR) offers innovative opportunities to safely practice everyday tasks, such as street crossing. This study was designed as a feasibility and pilot study to explore acceptance, usability, and preliminary effects of a VR-based road-crossing intervention for older adults. It investigates the use of virtual reality (VR) as an innovative training tool to support senior citizens in safely navigating everyday challenges such as crossing roads. By providing an immersive environment with realistic traffic scenarios, VR enables participants to practice in a safe and controlled setting, minimizing the risks associated with real-world road traffic. Methods: A VR training application called “Wegfest” was developed to facilitate targeted road-crossing practice. The application simulates various scenarios commonly encountered by older adults, such as crossing busy streets or waiting at traffic lights. The study applied a single-group pre-post design. Outcomes included the Timed Up and Go test (TUG), Falls Efficacy Scale-International (FES-I), and Montreal Cognitive Assessment (MoCA). Results: The development process of “Wegfest” demonstrates how a highly realistic street environment can be created for VR-based road-crossing training. Significant improvements were found in the Timed Up and Go test (p = 0.002, d = 0.784) and fall-related self-efficacy (FES-I, p = 0.005). No change was observed in cognitive function (MoCA, p = 0.56). Participants reported increased subjective safety (p < 0.001). Discussion: The development of the VR training application “Wegfest” highlights the feasibility of creating realistic virtual environments for skill development. By leveraging immersive technology, both physical and cognitive skills required for road-crossing can be effectively trained. The findings suggest that “Wegfest” has the potential to enhance the mobility and safety of older adults in road traffic through immersive experiences and targeted training interventions. Conclusions: As an innovative training tool, the VR application not only provides an engaging and enjoyable learning environment but also fosters self-confidence and independence among older adults in traffic settings. Regular training within the virtual world enables senior citizens to continuously refine their skills, ultimately improving their quality of life. Full article

Background/Objectives: Autism spectrum disorder (ASD) is a neurodevelopmental disorder distinguished by an extensive range of symptoms, including reduced social interaction, communication difficulties and tiresome behaviors. Early detection of ASD is important because it allows for timely intervention, which significantly improves developmental, behavioral, and communicative outcomes in children. However, traditional diagnostic procedures for identifying autism spectrum disorder (ASD) typically involve lengthy clinical examinations, which can be both time-consuming and costly. This research proposes leveraging automated machine learning (AUTOML) to streamline the diagnostic process and enhance its accuracy. Methods: In this study, by collecting data from various rehabilitation centers across Pakistan, we applied a specific AUTOML tool known as Tree-based Pipeline Optimization Tool (TPOT) for ASD detection. Notably, this study marks one of the initial explorations into utilizing AUTOML for ASD detection. The experimentations indicate that the TPOT provided the best pipeline for the dataset, which was verified using a manual machine learning method. Results: The study contributes to the field of ASD diagnosis by using AUTOML to determine the likelihood of ASD in children at prompt stages of evolution. The study also provides an evaluation of precision, recall, and F1-score metrics to confirm the correctness of the diagnosis. The propose TPOT-based AUTOML framework attained an overall accuracy 78%, with a precision of 83%, a recall of 90%, and an F1-score of 86% for the autistic class. Conclusions: In summary, this research offers an encouraging approach to improve the detection of autism spectrum disorders (ASD) in children, which could lead to better results for affected individuals and their families. Full article

Reflexivity is a metacognitive process traditionally applied to tasks and actions. Although emotions are a significant component of work life, the application of reflexivity to the emotional domain has received limited attention. This study addresses this gap by critically reviewing empirical evidence on reflexivity and emotions, aiming to understand this relationship and its outcomes in the workplace. A systematic literature review on Scopus and PsycINFO identified 722 records resulting in a final sample of 15 studies that met the PICO inclusion criteria and were included. These studies were analyzed according to recursively developed criteria. The findings showed that reflexivity affects emotions by considering them as the application domain; emotions, in turn, can trigger reflexivity. The outcomes of this relationship concern organizational learning and the workers’ role and identity. This relationship was more frequently investigated in high-emotion professional contexts and with a focus on specific professional roles. Due to the limited number of studies, the findings cannot be generalized. However, this study helps to define the role of reflexivity as a metacognitive competence applicable to emotions. Developing reflexivity within professional and organizational settings may help professionals regulate their own and others’ emotions by learning to detect, make sense of, and question critical emotional episodes. Full article

Background: The Nerve Growth Factor (NGF) is essential for neuronal survival and function and represents a key therapeutic target for pain and inflammation-related disorders, as well as for neurodegenerative diseases. Small-molecule antagonists of human NGF (hNGF) offer advantages over monoclonal antibodies, including oral availability and reduced immunogenicity. However, their development is often hindered by solubility challenges, necessitating the use of solvents like dimethyl sulfoxide (DMSO). This study investigates whether DMSO directly interacts with hNGF and affects its receptor-binding properties. Methods: Integrative/hybrid computational and experimental biophysical approaches were used to assess DMSO-NGF interaction by combining machine-learning tools and Nuclear Magnetic Resonance (NMR), Fourier Transform Infrared (FT-IR) spectroscopy, Differential Scanning Fluorimetry (DSF) and Grating-Coupled Interferometry (GCI). These techniques evaluated binding affinity, conformational stability, and receptor-binding dynamics. Results: Our findings demonstrate that DMSO binds hNGF with low affinity in a specific yet non-disruptive manner. Importantly, DMSO does not induce significant conformational changes in hNGF nor affect its interactions with its receptors. Conclusions: These results highlight the importance of considering solvent–protein interactions in drug discovery, as these low-affinity yet specific interactions can affect experimental outcomes and potentially alter the small molecules binding to the target proteins. By characterizing DMSO-NGF interactions, this study provides valuable insights for the development of NGF-targeting small molecules, supporting their potential as effective alternatives to monoclonal antibodies for treating pain, inflammation, and neurodegenerative diseases. Full article

Genetic factors play an important role in the risk of developing lung cancer, a disease that disproportionately affects African American (AA) individuals who smoke. Accumulating evidence suggests that specific ancestry-informative genetic markers are predictive of lung cancer risk in AA individuals who smoke. Although testing for, and communication of, genetic risk to patients should impact health and screening, results are mixed. The goal of this study was to evaluate the effects of genetic risk communication that also included ancestry-specific risk information among African American individuals who smoke. Using an experimental design, African American individuals who smoke (n = 166) were assigned randomly to receive hypothetical genetic test results that indicated (1) low vs. high genetic risk for lung cancer (“Risk”) and (2) European vs. African Ancestry (“Ancestry”). We hypothesized that participants who had been told that they were both at high risk for lung cancer based on genetic markers prominent in African persons at risk of lung cancer, and that they have African ancestry, would exhibit increases in cognitive (perceived lung cancer risk), emotional (cancer worry and psychological distress), and motivational (motivation to quit smoking) factors shown to predict longer-term health behavior change. Results revealed significant and moderate-to-large effects of Risk for all outcomes. There was also a significant Ancestry effect on perceived lung cancer risk: increased risk perceptions among participants who learned that they have high African genetic heritage. Path analytic modeling revealed that cognitive and emotional factors mediated the effects of both Risk and Ancestry feedback on motivation to quit smoking. Findings further highlight the importance of incorporating ancestry-specific genetic risk information into genetic counseling sessions, especially in underserved populations, as doing so may impact key cognitive, emotional, and motivational factors critical to behavior change. Full article

Objectives: To investigate whether a Multimodal Rehabilitation Program (MRP) affects the change in visual–spatial abilities, especially attention, information-processing speed, visual–spatial learning, the severity of depression, and strategies for coping with pain in Complex Regional Pain Syndrome (CRPS) participants. Methods: The study was conducted between October 2021 and February 2023, with a 4-week rehabilitation program that included individual physiotherapy, manual and physical therapy, and psychological intervention such as psychoeducation, relaxation, and Graded Motor Imagery therapy. Twenty participants with CRPS and twenty healthy participants, forming a control group, were enlisted. The study was a 2-arm parallel: a CRPS group with MRP intervention and a healthy control group matched to the CRPS group according to demographic variables. Before and after, the MRP participants in the CRPS group were assessed for visual–spatial learning, attention abilities, severity of depression, and pain-coping strategy. The healthy control group underwent the same assessment without intervention before two measurements. The primary outcome measure was Reproduction on Rey–Osterrieth’s Complex Figure Test assessing visual–spatial learning. Results: In the post-test compared to the pre-test, the participants with CRPS obtained a significantly high score in visual–spatial learning (p < 0.01) and visual information-processing speed (p = 0.01). They made significantly fewer omission mistakes in visual working memory (p = 0.01). After the MRP compared to the pre-test, the CRPS participants indicated a decrease in the severity of depression (p = 0.04) and used a task-oriented strategy for coping with pain more often than before the rehabilitation program (p = 0.02). Conclusions: After a 4-week MRP, the following outcomes were obtained: an increase in visual–spatial learning, visual information-processing speed, a decrease in severity of depression, and a change in the pain-coping strategies—which became more adaptive. Full article

This study develops two machine learning models to predict the medal performance of countries at the 2028 Olympic Games while systematically analyzing and quantifying the impacts of the host effect and exceptional coaching on medal gains. The dataset encompasses records of total medals by country, event categories, and athletes’ participation from the Olympic Games held between 1896 and 2024. We use K-means clustering to analyze medal trends, categorizing 234 nations into four groups (${\alpha }_1$, ${\alpha }_2$, ${\alpha }_3$, ${\alpha }_4$). Among these, ${\alpha }_1$, ${\alpha }_2$, ${\alpha }_3$ represent medal-winning countries, while ${\alpha }_4$ consists of non-medal-winning nations. For the $\alpha$1, $\alpha$2, and $\alpha$3 groups, 2–3 representative countries from each are selected for trend analysis, with the United States serving as a case study. This study extracts ten factors that may influence medal wins from the dataset, including participant data, the number of events, and medal growth rates. Factor analysis is used to reduce them into three principal components: Factor analysis condenses ten influencing factors into three principal components: the event scale factor (F1), the medal trend factor (F2), and the gender and athletic ability factor (F3). An ARIMA model predicts the factor coefficients for 2028 as 0.9539, 0.7999, and 0.2937, respectively. Four models (random forest, BP Neural Network, XGBoost, and SVM) are employed to predict medal outcomes, using historical data split into training and testing sets to compare their predictive performance. The research results show that XGBoost is the optimal medal predicted model, with the United States projected to win 57 gold medals and a total of 135 medals in 2028. For non-medal-winning countries (${\alpha }_4$), a three-layer fully connected neural network (FCNN) is constructed, achieving an accuracy of 85.5% during testing. Additionally, a formula to calculate the host effect and a Bayesian linear regression model to assess the impact of exceptional coaching on athletes’ medal performance are proposed. The overall trend of countries in the $\alpha$1 group is stable, but they are significantly affected by the host effect; the trend in the $\alpha$2 group shows an upward trend; the trend in the $\alpha$3 group depend on the athletes’ conditions and whether the events they excel in are included in that year’s Olympics. In the $\alpha$4 group, the probabilities of the United Arab Republic (UAR) and Mali (MLI) winning medals in the 2028 Olympic Games are 77.47% and 58.47%, respectively, and there are another four countries with probabilities exceeding 30%. For the eight most recent Olympic Games, the gain rate of the host effect is 74%. Great coaches can bring an average increase of 0.2 to 0.5 medals for each athlete. The proposed models, through an innovative integration of clustering, dimensionality reduction, and predictive algorithms, provide reliable forecasts and data-driven insights for optimizing national sports strategies. These contributions not only address the gap in predicting first-time medal wins for non-medal-winning nations but also offer guidance for policymakers and sports organizations, though they are constrained by assumptions of stable historical trends, minimal external disruptions, and the exclusion of unknown athletes. Full article

Mixed reality (MR) laboratories combine physical elements with virtual components, providing convenient experiential environments for testing engineering concepts. This article reports the design, validation, and implementation of an MR laboratory for engineering students to practice the implementation of control algorithms in microcontrollers. First, the design of the MR lab is described in detail. In this, a seesaw electromechanical system is emulated, being synchronized with electrical signals that represent sensors’ measurements and actuators’ commands. Thus, a control algorithm implemented by the students in a microcontroller can affect the simulated system in real time. The real seesaw system was used to validate the simulated plant in the MR lab, finding that the same control algorithm effectively controls both the simulated and physical seesaw systems. A practice, designed based on Kolb’s experiential learning cycle, where the students must implement P, PI, and PID controllers in the MR lab, was implemented. A survey was conducted to assess the students’ motivation, and a post-test was administered to evaluate their learning outcomes. Full article