Search Results (14,497)

This study seeks to explore the relationship between science teachers’ computational thinking skills, collaborative learning attitudes, and their creativity in the context of sustainable education. The study adopted an explanatory sequential design, which is one of the designs used in mixed-method research. A total of 369 science teachers were included in the quantitative phase of the study. Quantitative data were collected using three different scales. These scales included the “Computational Thinking Scale”, “Online Cooperative Learning Attitude Scale (OCLAS)”, and “Creative Self-Efficacy Scale”. Structural Equation Modelling (SEM), confirmatory factor analysis, and path analysis were conducted to analyze the quantitative data. The qualitative phase of the study consisted of nine science teachers. Data were collected with a semi-structured interview form by considering the scores obtained from the scales. Qualitative data were analyzed through descriptive analysis. It was found that science teachers’ computational thinking skills and collaborative learning attitudes significantly predicted their creativity within the framework of sustainable education. As a result of the interviews conducted, it was concluded that science teachers lacked computational thinking skills. It is critical to provide teachers with guidance on how to integrate computational thinking skills into their subject areas. Science teachers’ knowledge of computational thinking skills can be enhanced, and computational thinking skills can be included in all teacher education programs. Full article

Background: Brain metastases (BM) from gastric cancer (GC) are rare but associated with poor prognosis, significantly impacting patient survival and quality of life. The objective of this systematic review and meta-analysis is to consolidate existing research on BM from GC, evaluate the incidence and clinical outcomes, and explore the effectiveness of treatment options. Methods: A systematic search was conducted across the Medline, Web of Science, and Scopus databases, following PRISMA guidelines. Eighteen high-quality studies, as per the Newcastle–Ottawa Quality Assessment Scale, were included, encompassing 70,237 GC patients, of whom 621 developed BM. Data on progression-free survival (PFS), overall survival (OS), neurological symptoms, and HER2 status were analyzed using a random-effects model. Results: The incidence of BM in GC patients was found to be 2.29% (95% CI: 1.06–3.53%), with the range extending from 0.47% to 7.79% across studies. HER2-positive status was significantly associated with a higher likelihood of developing BM, with an odds ratio of 43.24 (95% CI: 2.05–913.39; p = 0.02), although this finding was based on limited data. The surgical resection of BM was linked to significantly improved survival outcomes, with a mean difference in OS of 12.39 months (95% CI: 2.03–22.75; p = 0.02) compared to non-surgical approaches. Conclusions: The surgical resection of brain metastases in GC patients significantly enhances overall survival, while HER2-positive patients may show a higher risk for developing BM. These findings underscore the importance of tailored therapeutic approaches for GC patients with BM. Full article

A youth-centric participatory mapping approach was employed to monitor the lower Mill Creek, an urban waterway located in Cincinnati, Ohio, by collecting geospatial data points on surface water quality and ecological assets. Utilizing the ArcGIS Field Maps application, a digital survey-based tool was developed to identify key areas related to ecological assets and urban water management challenges. The purpose of this citizen science approach was to allow researchers to capture and understand community perspectives and insights while engaging in scientific research that focuses on identifying geographic vulnerability areas and ecological assets. The primary objective was to empower local community groups and residents in an environmental justice neighborhood to understand the current opportunities and constraints of the adjacent waterbody, enabling informed decision-making for future planning initiatives that benefit both conservation and remediation efforts aligned with local values and needs. A youth-centric participatory mapping approach was employed to monitor the lower Mill Creek, an urban waterway in Cincinnati, Ohio, through the collection of geospatial data on surface water quality and ecological assets. The findings, based on hotspot analysis, revealed significant spatial clustering of heavy debris near the barrier dam and the lower portion of Mill Creek, where it converges with the Ohio River. This accumulation is attributed to the structural features of the barrier dam’s inner flood catchment area, which traps debris during rainfall events. Although no areas showed spatial significance for perceived ecological services, students identified specific areas with esthetic and biodiversity value, particularly at Mill Creek’s confluence with the Ohio River and along the northern stretch of the stream corridor. These findings provide valuable insights for guiding future conservation and remediation efforts that reflect both community values and environmental priorities. Full article

Background: The use of beta-blockers in the perioperative period has been widely investigated due to their potential to reduce the risk of major adverse cardiovascular and cerebrovascular events (MACCE) and mortality; yet their overall impact on various postoperative outcomes remains debated. This study constitutes a systematic review and meta-analysis of the impact of beta-blockers on mortality, MI, stroke, and other adverse effects such as hypotension and bradycardia in patients undergoing non-cardiac surgery. Methods: A comprehensive systematic review and meta-analysis were conducted according to PRISMA 2020 guidelines. Searches were performed across PubMed, Cochrane, Web of Science, Scopus, EMBASE, and CINAHL databases; we included randomized controlled trials and cohort and case-control studies published from 1999 to 2024. Results: This meta-analysis included data from 28 studies encompassing 1,342,430 patients. Perioperative beta-blockers were associated with a significant increase in stroke risk (RR 1.42, 95% CI: 1.03 to 1.97, p = 0.03, I² = 62%). However, no statistically significant association was found between beta-blocker use and mortality (RR 0.62, 95% CI: 0.38 to 1.01, p = 0.05, I² = 100%). Subgroup analyses revealed a protective effect on mortality for patients with high risks, such as patients with a history of atrial fibrillation, chronic heart failure, and other arrhythmias. For myocardial infarction (RR 0.82, 95% CI: 0.53 to 1.28, p = 0.36, I² = 86%), a reduction in events was observed in the subgroup of randomized controlled trials. Beta-blockers significantly increased the risk of hypotension (RR 1.46, 95% CI: 1.26 to 1.70, p < 0.01, I² = 25%) and bradycardia (RR 2.26, 95% CI: 1.37 to 3.74, p < 0.01, I² = 64%). Conclusions: Perioperative beta-blockers show increasing rates of stroke events following non-cardiac surgery but do not significantly impact the incidence of MI or mortality. The increased risks of hypotension and bradycardia necessitate careful patient selection and monitoring. Future research should aim to refine patient selection criteria and optimize perioperative management to balance the benefits and risks of beta-blocker use in surgical settings. Full article

Background/Objectives: The objective of this meta-analysis was to determine the impact of bariatric surgery on phase angle (PhA) and other bioimpedance measures among adults with obesity, comparing the effects of Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG). Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines, including studies up to May 2024 from MEDLINE, Scopus, Cochrane Library, and Web of Science. Eligible studies assessed PhA changes pre- and post-bariatric surgery in adults with BMI ≥ 30 kg/m². Data on PhA, fat mass (FM), fat-free mass (FFM), body cell mass (BCM), weight, and BMI were extracted and analyzed. Results: Thirteen studies with a total of 1124 patients were included. Significant PhA reductions were observed at 6 months post-surgery (effect size: −1.00; 95% CI: −1.11 to −0.89; p < 0.001), with a more substantial reduction in RYGB patients compared to SG. FM and FFM decreased significantly at 12 months (FM: −27.58; 95% CI: −32.58 to −22.57; p < 0.001; FFM: −10.51; 95% CI: −12.81 to −8.94; p < 0.001). Weight and BMI showed marked reductions at 6 months (Weight: −31.42 kg; 95% CI: −37.28 to −25.26; p < 0.001; BMI: −11.39; 95% CI: −12.60 to −10.18; p < 0.001), with sustained decreases at 12 and 24 months. Conclusions: Bariatric surgery significantly reduces PhA, FM, FFM, weight, and BMI, with initial greater impacts observed in RYGB compared to SG. PhA shows potential as a marker for monitoring post-surgical recovery and nutritional status. Further long-term studies and standardized measurement protocols are recommended to optimize patient management. Full article

Background: Anti-phospholipid syndrome (APS) has emerged as a significant issue in autoimmune diseases over recent decades. Its hallmark feature is thromboembolic events, potentially affecting any vascularized area including the microcirculation of the inner ear. Since the first case report of APS-related audiovestibular dysfunction described in 1993, numerous reports have explored the association between APS-related antibodies and audiovestibular dysfunction. These studies indicate a higher prevalence of APS-related antibodies in patients with sensorineural hearing loss compared to healthy controls. Unlike other idiopathic hearing loss disorders, audiovestibular dysfunction associated with APS may respond to appropriate treatments, highlighting the importance of timely recognition by clinicians to potentially achieve favorable outcomes. Therefore, this systematic review aims to consolidate current evidence on the characteristics, pathophysiology, assessment, and management of audiovestibular dysfunction linked to APS. Methods: This systematic review utilized electronic searches of the PubMed, Embase, ClinicalKey, Web of Science, and ScienceDirect online platforms. The initial search was performed on 27 January 2024, with the final update search completed on 20 June 2024. Results: Based on theoretical pathophysiology, anticoagulation emerges as a pivotal treatment strategy. Additionally, drawing from our preliminary data, we propose a modified protocol combining anticoagulants, steroids, and non-invasive brain stimulation to offer clinicians a novel therapeutic approach for managing these symptoms. Conclusions: Clinicians are encouraged to remain vigilant about the possibility of APS and its complex audiovestibular manifestations, as prompt intervention could stabilize audiovestibular function effectively. Full article

Background/Objectives: Directed acyclic graphs (DAGs) inform the epidemiologic statistical modeling confounders to determine close to true causal relationships in a study context. They inform the inclusion of the predictive model variables that affect the causal relationship. Non-small cell lung cancer (NSCLC) is frequently diagnosed, aggressive, and the second leading cause of cancer deaths in the United States. Determining factors affecting both the guideline-concordant treatment receipt and survival outcomes for early-stage lung cancer will help inform future statistical models aiming to achieve a close to true causal relationship. Methods: Peer-reviewed original research published during 2002–2023 was identified through PubMed, Embase, Web of Sciences, Clinical trials registry, and the gray literature. DAGitty version 3.1, an online software program, developed implied DAGs and integrated DAG graphics. The evidence synthesis for constructing directed acyclic graphs (ESC-DAGs) protocol was utilized to guide DAG development. The conceptual models utilized were Andersen and Aday for factors affecting treatment receipt and Shi and Steven for survival outcome factors. Results: A total of 36 studies were included in the DAG synthesis out of 9421 retrieved across databases. Eight studies served in the synthesis of treatment receipt DAG, while 28 studies were used for the survival outcomes DAG. There were 10 causal paths and 13 covariates for treatment receipt and 2 causal pathways and 32 covariates for survival outcomes. Conclusions: There are very few studies reporting on factors affecting early-stage NSCLC guideline-concordant care receipt compared to factors affecting its survival outcomes in the past two decades of original research. Future investigations can utilize data extracted in the current study to develop a meta-analysis informing effect size. Full article

Teachers’ professional learning often includes online components. This study examined how a case of 37 teachers utilized a specific online asynchronous professional learning platform designed to support teachers’ growth in learning to teach statistics and data science in secondary schools in the United States. The platform’s features and learning materials were designed based on effective online learning designs, supports for self-guided learning, and research on the teaching and learning of statistics and data science. We paid particular attention to the features we designed into the platform to support self-regulation and personalizing the experiences to meet their preferred learning goals such as allowing for free choice of learning materials, flexibility of when and how long to engage, providing personal recommendations based on user input, internal systems to track progress, and generating certificates of completion. In this study, we used a case study with both quantitative and qualitative data to examine whether teachers had gains in meeting learning goals related to their development in teaching statistics and data science, had sustained engagement, and found the features for personalization supportive for their learning. Results showed, overall, positive growth towards meeting learning goals and making small changes towards improved classroom practice. Most teachers were generally engaged in sustained ways across the study period, though we found six different patterns of completion that highlight ways in which teachers’ goal-directed and self-regulated learning occurred within the busy schedules of educators. Several personalized features, especially the recommendations and tracking system, were highly utilized and perceived as supportive of teachers’ learning. Full article

In mice, the fetal brain is dependent upon the placenta for factors that guide its early development. This linkage between the two organs has given rise to the term, the placenta–brain axis. A similar interrelationship between the two organs may exist in humans. We hypothesize that extracellular vesicles (EVs) released from placental trophoblast (TB) cells transport small RNA and other informational biomolecules from the placenta to the brain where their contents have pleiotropic effects. Here, EVs were isolated from the medium in which human trophoblasts (TBs) had been differentiated in vitro from induced pluripotent stem cells (iPSC) and from cultured iPSC themselves, and their small RNA content analyzed by bulk RNA-seq. EVs derived from human TB cells possess unique profiles of miRs, including hsa-miR-0149-3p, hsa-302a-5p, and many long non-coding RNAs (lncRNAs) relative to EVs isolated from parental iPSC. These miRs and their mRNA targets are enriched in neural tissue. Human neural progenitor cells (NPCs), generated from the same iPSC, were exposed to EVs from either TB or iPSC controls. Both sets of EVs were readily internalized. EVs from TB cells upregulate several transcripts in NPCs associated with forebrain formation and neurogenesis; those from control iPSC upregulated a transcriptional phenotype that resembled glial cells more closely than neurons. These results shed light on the possible workings of the placenta–brain axis. Understanding how the contents of small RNA within TB-derived EVs affect NPCs might yield new insights, possible biomarkers, and potential treatment strategies for neurobehavioral disorders that originate in utero, such as autism spectrum disorders (ASDs). Full article

Autism Spectrum Disorder (ASD) is a complex developmental condition marked by challenges in social interaction, communication, and behavior, often involving restricted interests and repetitive actions. The diversity in symptoms and skill profiles across individuals creates a diagnostic landscape that requires a multifaceted approach for accurate understanding and intervention. This study employed advanced machine-learning techniques to enhance the accuracy and reliability of ASD diagnosis. We used a standard dataset comprising 1054 patient samples and 20 variables. The research methodology involved rigorous preprocessing, including selecting key variables through data mining (DM) visualization techniques including Chi-Square tests, analysis of variance, and correlation analysis, along with outlier removal to ensure robust model performance. The proposed DM and logistic regression (LR) with Shapley Additive exPlanations (DMLRS) model achieved the highest accuracy at 99%, outperforming state-of-the-art methods. eXplainable artificial intelligence was incorporated using Shapley Additive exPlanations to enhance interpretability. The model was compared with other approaches, including XGBoost, Deep Models with Residual Connections and Ensemble (DMRCE), and fast lightweight automated machine learning systems. Each method was fine-tuned, and performance was verified using k-fold cross-validation. In addition, a real-time web application was developed that integrates the DMLRS model with the Django framework for ASD diagnosis. This app represents a significant advancement in medical informatics, offering a practical, user-friendly, and innovative solution for early detection and diagnosis. Full article

Background/Objectives: The presence of enamel on the tooth surface is crucial for the long-term success of minimally invasive adhesive restorations such as dental veneers. Our study aims to evaluate the enamel thickness in the incisal, middle, and cervical portions of the labial surface of the upper central incisors using cone beam computed tomography (CBCT). This imaging method provides detailed and accurate three-dimensional images with a low radiation dose, allowing an accurate assessment of enamel thickness. The analysis aims to identify variations in enamel thickness depending on the age and different levels of the labial tooth surface. Methods: 800 CBCT scans performed for diagnostic or therapeutic purposes on patients aged 18–60 years were analyzed. The data were gathered from the imaging archives of private practitioners from Targu Mures and the “George Emil Palade” University of Medicine, Pharmacy, Science, and Technology of Targu Mures. Enamel thickness measurements were conducted using the OnDemand3D Communicator CBCT evaluation program, with subsequent statistical analysis performed using GraphPad Instat Prism software. Results: Results showed significant variation in enamel thickness between the incisal, middle, and cervical segments of the labial surface of the upper central incisors. A decrease in enamel thickness with age has been observed. In patients aged 18–40, mean values of enamel thickness 1 mm and 3 mm above the cementoenamel junction (CEJ) were 0.48 ± 0.092, respectively, 0.819 ± 0.158. In patients over 40, the mean values were 0.454 ± 0.116 and 0.751 ± 0.067 at 1 mm, respectively, 3 mm above the CEJ. Statistically significant differences were found between the two age groups at 1 mm and 3 mm above the CEJ, with p < 0.0001 and p = 0.0214. Conclusions: A statistically significant decrease can be observed in enamel thickness in almost the entire labial surface of the upper central incisors with aging. The varied thickness of the enamel at different tooth levels requires individualized planning for each patient to maximize the long-term aesthetic and functional results. Full article

Underwater object detection (UOD) presents substantial challenges due to the complex visual conditions and the physical properties of light in underwater environments. Small aquatic creatures often congregate in large groups, further complicating the task. To address these challenges, we develop Aqua-DETR, a tailored end-to-end framework for UOD. Our method includes an align-split network to enhance multi-scale feature interaction and fusion for small object identification and a distinction enhancement module using various attention mechanisms to improve ambiguous object identification. Experimental results on four challenging datasets demonstrate that Aqua-DETR outperforms most existing state-of-the-art methods in the UOD task, validating its effectiveness and robustness. Full article

Background: Nursing students face unique challenges during their university education, making them vulnerable to mental health problems. Psychological resilience has been identified as a protective factor against these issues. However, previous reviews have identified gaps in the literature on resilience and mental health among nursing students. Objectives: This scoping review aims to identify and map studies on psychological resilience and mental health in undergraduate nursing students, synthesize current evidence on their relationship, identify interventions for enhancing resilience, and highlight gaps in the existing literature. Eligibility criteria: Studies published between January 2019 and April 2024 in English, Portuguese, and Spanish addressing resilience and mental health in undergraduate nursing students will be included. Primary studies, secondary studies, clinical guidelines, and grey literature will be considered. Sources of evidence: Searches will be conducted in multiple databases including EMBASE, ERIC, PubMed, Science Direct, Web of Science, DOAJ, ELSEVIER, EMERALD, and WILEY ONLINE LIBRARY. Grey literature sources will also be searched. Charting methods: Data will be extracted using a standardized form and synthesized narratively. Thematic analysis will be conducted using MAXQDA software ((Verbi GmbH, 24 version, 2023). Quantitative summaries, visual mapping, subgroup analyses, and trend analyses will be performed where appropriate. Results: As this is a protocol, results are not yet available. The review will present a comprehensive map of the current literature on psychological resilience and mental health in nursing students, including identified interventions and research gaps. Conclusions: This scoping review will provide valuable insights to guide curriculum development, support services, and policy-making in nursing education. The findings may support actions to strengthen resilience and prevent mental health problems among future nursing professionals. Full article

This review provides a comprehensive analysis of the transformative role of artificial intelligence (AI) in predicting and preventing sports injuries across various disciplines. By exploring the application of machine learning (ML) and deep learning (DL) techniques, such as random forests (RFs), convolutional neural networks (CNNs), and artificial neural networks (ANNs), this review highlights AI’s ability to analyze complex datasets, detect patterns, and generate predictive insights that enhance injury prevention strategies. AI models improve the accuracy and reliability of injury risk assessments by tailoring prevention strategies to individual athlete profiles and processing real-time data. A literature review was conducted through searches in PubMed, Google Scholar, Science Direct, and Web of Science, focusing on studies from 2014 to 2024 and using keywords such as ‘artificial intelligence’, ‘machine learning’, ‘sports injury’, and ‘risk prediction’. While AI’s predictive power supports both team and individual sports, its effectiveness varies based on the unique data requirements and injury risks of each, with team sports presenting additional complexity in data integration and injury tracking across multiple players. This review also addresses critical issues such as data quality, ethical concerns, privacy, and the need for transparency in AI applications. By shifting the focus from reactive to proactive injury management, AI technologies contribute to enhanced athlete safety, optimized performance, and reduced human error in medical decisions. As AI continues to evolve, its potential to revolutionize sports injury prediction and prevention promises further advancements in athlete health and performance while addressing current challenges. Full article

The last decade has seen a significant growth in the adoption of low-cost air quality monitoring systems (LCAQMSs), mostly driven by the need to overcome the spatial density limitations of traditional regulatory grade networks. However, urban air quality monitoring scenarios have proved extremely challenging for their operative deployment. In fact, these scenarios need pervasive, accurate, personalized monitoring solutions along with powerful data management technologies and targeted communications tools; otherwise, these scenarios can lead to a lack of stakeholder trust, awareness, and, consequently, environmental inequalities. The AirHeritage project, funded by the EU’s Urban Innovative Action (UIA) program, addressed these issues by integrating intelligent LCAQMSs with conventional monitoring systems and engaging the local community in multi-year measurement strategies. Its implementation allowed us to explore the benefits and limitations of citizen science approaches, the logistic and functional impacts of IoT infrastructures and calibration methodologies, and the integration of AI and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements and reporting. Similar research or operative projects have been implemented in the recent past, often focusing on a limited set of the involved challenges. Unfortunately, detailed reports as well as recorded and/or cured data are often not publicly available, thus limiting the development of the field. This work openly reports on the lessons learned and experiences from the AirHeritage project, including device accuracy variance, field recording assessments, and high-resolution mapping outcomes, aiming to guide future implementations in similar contexts and support repeatability as well as further research by delivering an open datalake. By sharing these insights along with the gathered datalake, we aim to inform stakeholders, including researchers, citizens, public authorities, and agencies, about effective strategies for deploying and utilizing LCAQMSs to enhance air quality monitoring and public awareness on this challenging urban environment issue. Full article