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Diagnostic role of galanin in distinguishing endometrial atypical hyperplasia and carcinoma from benign endometrial conditions
Survival outcomes and prognostic factors in children and adults with medulloblastoma from a Latin America country: A retrospective cohort
Background Few studies have evaluated the real-world outcomes of patients with medulloblastoma with contradictory results. Therefore, we aimed to compare the characteristics, survival outcomes, and prognostic factors between children and adults with medulloblastoma. Methods We conducted a retrospective cohort study in a single academic center between 2000 and 2016. Patients were categorized into children and adolescents (≤19 years) and adults (>19 years). Overall survival (OS) and disease-free survival (DFS) were estimated with the Kaplan-Meier method. Prognostic factors were determined using Cox models. Results In total, 173 patients were included (79 adults and 94 children). No differences were observed in clinical characteristics according to age groups. At 5 years, DFS was 36.88% in children and 50.99% in adults (p = 0.476). Prognostic factors of DFS in adults were radiotherapy (adjusted Hazard ratio [aHR]: 0.22; 95% confidence interval [CI]: 0.07–0.67) and performance status 2–4 (aHR: 3.60; 95% CI: 1.48–8.77); while in children was radiotherapy (aHR: 0.33; 95% CI: 0.11–0.96) and chemotherapy (aHR: 0.34; 95% CI: 0.14–0.84). The 5-year OS rate was 64.25% in children and 60.87% in adults (p = 0.447). In adults, prognostic factors of OS were histologic type (aHR: 6.11, 95% CI: 1.19–31.48, for anaplastic) and radiotherapy (aHR: 0.22, 95% CI: 0.07–0.71), while in children and adolescents, lower performance status (aHR: 2.43, 95% CI: 1.09–5.39, for performance status 2–4) was the only prognostic factor. Subgroup analyses revealed a trend toward improved DFS and significantly better OS among adults who completed chemotherapy, highlighting the importance of treatment adherence. Conclusions Both populations demonstrated similar survival rates that were comparable to or lower than those reported in previous studies. In adults, performance status and radiotherapy were prognostic factors for DFS, while histologic type and radiotherapy for OS. In children, chemotherapy and radiotherapy were prognostic factors for DFS, and performance status was the only OS prognostic factor. These findings underscore the importance of optimizing treatment adherence and completing standard therapies, as well as improving staging practices, to enhance outcomes in real-world settings.
Resilience in neurodivergence: professional perspectives mapped to the World Health Organisations’ International Classification of Functioning
Abstract Individuals with childhood-onset divergent neurological development, such as autism or Attention-Deficit Hyperactivity Disorder (ADHD), may live a good life according to objectively or subjectively determined standards. Yet, most research has focused on deficits and risks for negative outcomes. This international study forms part of a larger project examining the factors contributing to well-being, mental health, and functioning outcomes in neurodivergent populations using the World Health Organization (WHO) International Classification of Functioning (ICF). Following ICF research branch methodology, 198 professionals were surveyed on the factors that they believe are important for risk and resilience in neurodivergent populations and linked responses to ICF nomenclature using a standardized linking process. A range of bio-psycho-social factors perceived to be important for risk and resilience in neurodivergent populations were identified, including temperament and personality, emotional functions, the structure of the brain, financial status, recreation and leisure, and the immediate family. Most factors identified were environmental or related to activity and participation. Findings add to the limited literature on resilience in neurodivergent populations. We identify risk- and resilience-inducing factors that operate in the context of neurodivergence across the lifespan. These serve as candidates for future investigation and provide targets for intervention and social participation support.
Stylometry can reveal artificial intelligence authorship, but humans struggle: A comparison of human and seven large language models in Japanese
The purpose of this study was to estimate the artificial intelligence (AI) detection potential using stylometric analysis in Study 1 and examine the AI detection abilities of humans in Study 2. In Study 1, we compared 100 human-written public comments with 350 texts generated by seven large language models (LLMs) (ChatGPT [GPT-4o and o1], Claude3.5, Gemini, Microsoft Copilot, Llama3.1, and Perplexity) using multidimensional scaling (MDS) to visualize differences by focusing on three stylometric features (phrase patterns, part-of-speech bigrams, and unigrams of function words). In general, each stylometric feature can distinguish between LLM-generated and human-written texts. In particular, three integrated stylometric features achieved perfect discrimination on MDS dimensions. Interestingly, only Llama3.1 exhibited distinct characteristics compared with the other six LLMs. The random forest classifier also achieved 99.8% accuracy. In Study 2, we performed an online survey to assess the Japanese participants’ AI detection abilities by presenting LLM-generated and human-written texts, as used in Study 1. 403 participants tackled “AI or Human” judgment task and estimated their own confidence, revealing that overall human AI-detection ability was limited. Moreover, in our materials, more advanced ChatGPT(o1), plausibly reflecting relatively greater fluency and polish, tends to mislead the participants to believe “human-written” texts compared with ChatGPT(GPT-4o) and improves their confidence for their own judgments. Furthermore, an additional comment from the survey suggested that participants primarily relied on superficial impressions based on phraseology, expression, the ends of words, conjunctions, and punctuation marks in judgments. These findings have important implications for various scenarios, including public policy, education, and marketing, where the rapid and reliable detection of AI-generated content is increasing.
Machine learning-driven development of a behaviour-based student classification system (SCS-B) for enhanced educational analytics
Enrollment patterns among medicaid beneficiaries with sickle cell disease: Multistate findings from the sickle cell data collection program
Background Majority of individuals living with sickle cell disease (SCD) in the United States are enrolled in Medicaid. The objective of the study was to determine the patterns of Medicaid enrollment among individuals with SCD. Method We determined the enrollment pattern among SCD Medicaid beneficiaries categorizing them in three groups: continuously enrolled, had exit and no return, had gaps in duration of enrollment during 2017–2019, leveraging the data from the Sickle Cell Data Collection Program in four states. We compared characteristics of individuals with gaps and those continuously enrolled using chi square tests. Results Among 5883 children and 9260 adults, 70.5% and 61.8% respectively, were continuously enrolled. Gaps were observed in 12.5% of children and 12.9% of adults. A significantly smaller proportion of adults with gaps as compared to those who had continuous enrollment were disabled (CA:30.6% vs 65.3%; GA:23.7% vs 77.6%; MI:40.1% vs 69.5%; WI:39.8% vs 77.0%). Of all observed gaps, 60% were among adults. Enrollment patterns and gap duration varied by state. Conclusion Approximately 12% of individuals with SCD have gaps in enrollment during our 3-year study period. Individuals with disabilities were more likely to have continuous enrollment. Future work is needed to determine reasons for observed gaps and their impact on SCD health outcomes.
Using Sentinel 2A and Landsat 8 imagery to assess changes in forest carbon storage
Abstract Estimating carbon storage using high-resolution imagery of dominant species and types is often constrained by the availability of data. Herein, we developed a carbon storage estimation model for dominant species and types using high-resolution Sentinel 2A imagery and compared the two approaches using lower-resolution Landsat 8 imagery for whole-forest estimation. Approach 1 employs a traditional method using in-situ carbon storage measurements with Landsat 8 vegetation indices, whereas Approach 2 uses Sentinel 2A carbon storage estimates as a reference. Using Random Forest, Decision Tree, and Multiple Linear Regression models, we compared both approaches and found that Approach 2 estimates matched the Sentinel 2A results for different species and types more accurately, including Populus , Salix , Pinus tabuliformis , and shrub types. At the same time, our research results show that machine learning models effectively estimated carbon storage using Sentinel 2A imagery and dominant species classification. For the whole forest assessment with Landsat 8 imagery, Approach 2 yielded superior accuracy over Approach 1. This method enabled the calculation of historical carbon storage, showing that the Ordos Forest carbon storage increased by 27 Mt (89%) from 2013 to 2023, demonstrating the feasibility of long-term carbon monitoring using lower-resolution imagery.
Influencing factors of sports tourism safety accidents in Tibet, China: fsQCA analysis based on the SCM
A comprehensive analysis of the systemic causes of safety accidents in sports tourism on the Qinghai-Tibet Plateau is significant for high-quality development. Utilizing 32 verified accident cases (2010–2025) in the Tibet Autonomous Region of China, this study extracted six critical factors through content analysis: organizational professionalism, rescue capacity, management systems, natural environment, tourist vulnerability, and tourist behavior. The Swiss Cheese Model (SCM) analyzed latent/active failures through case reports and regional environmental data, while the fuzzy-set Qualitative Comparative Analysis (fsQCA) deciphered nonlinear configuration paths across six factors. The results reveal that sports tourism accidents in Tibet arise from the coupling of multiple factors. Specifically, the combination of six influencing factors constitutes the causal paths for severe and general accidents. Among these factors, environmental factors and tourist characteristics are key contributors to accidents. Based on these findings, it is essential to establish a sports tourism risk prevention system for Tibet, which should comprise four layers: natural defense, behavioral defense, managerial defense, and rescue defense. This study deepens the understanding of sports tourism safety accidents on the Qinghai-Tibet Plateau. It integrates the SCM and the fsQCA method, contributing to sports tourism safety research. The proposed risk prevention system provides useful references for local safety management. Future research can focus on the dynamic changes of influencing factors and test the research framework and risk prevention system’s applicability in other similar areas.
Hydrogen fuel production from CO2/CH4 mixture using a novel type of an atmospheric pressure microwave (2.45 GHz) plasma source
Understanding the role of psychological distance in preventing the spread of kauri dieback
Background Kauri dieback is a soil-borne pathogen of the family Phytophthora which is lethal to kauri trees. Despite its risks, residents of New Zealand often do not follow imposed mitigation strategies. In this study we explored the potential impact of three factors on psychological distance to kauri dieback: pro-environmental worldviews, trust in government and physical distance from kauri forests. We also investigated the extent to which previously validated psychological distance measures predicted kauri forest visitors’ compliance with boot-cleaning and trail-usage guidelines (behaviours linked to the spread of kauri dieback). Methods A survey assessing beliefs and behaviours related to kauri dieback was completed by a sample of 451 New Zealand residents who had visited a kauri forest in the past four years. Two path analyses were conducted to determine whether the effects of environmental worldview (NEP score), trust in government, and physical distance on boot cleaning and track use compliance behaviours were mediated by psychological distance. Results Direct effects indicated that higher NEP score and closer physical distance significantly reduced psychological distance, but trust in government did not. Closer psychological distance also significantly improved self-reported track use and boot cleaning behaviours. Indirect effects indicated that psychological distance significantly mediated the effects of worldview, trust and physical distance on boot cleaning and track usage. Several significant direct effects of the exogenous predictors on the compliance behaviours were present after controlling for the mediator, indicative of partial mediation. Conclusions Psychological distance is a reliable predictor of respondents’ boot-cleaning and track-use compliance. Interventions to decrease psychological distance may be beneficial for increasing compliance, although the effects were modest and other potential determinants of compliance also require investigation.
Lichen colonization and associated biodeterioration processes on ancient bricks of the Gonbad-e Qābus tower, UNESCO World Heritage Site, Iran
Smaller infarct size with ticagrelor vs. clopidogrel in STEMI patients: Insights from cardiac magnetic resonance
Background Ticagrelor has many protective cardiovascular properties beyond potent antiplatelet action. This study aimed to compare the effects of ticagrelor versus clopidogrel on infarcted mass, quantified by cardiac magnetic resonance (CMR), in patients with ST-segment elevation acute myocardial infarction (STEMI). Methods Adult patients of both sexes with STEMI under a pharmaco-invasive strategy were included (n = 225). Patients were treated by thrombolysis within six hours of symptom onset and underwent angiography with percutaneous coronary interventions, when needed, within the first 24 hours. Prior to the invasive procedures, patients were randomly assigned to receive either ticagrelor or clopidogrel using a centralized computerized system. Patients were followed on a weekly basis to optimize their medical therapy. Results After 30 days, CMR was performed and a smaller percentage of left ventricular infarcted mass was found with ticagrelor (p = 0.012), despite similar angiographic findings at baseline (Syntax score, Gensini score, culprit artery, TIMI flow, and myocardial blush). At 30 days, left ventricular ejection fraction (LVEF) was comparable between groups. Still, the K-means algorithm displayed more homogeneous responses for smaller infarcted mass and better LVEF among those patients treated with ticagrelor. Standard lipid panel and most inflammatory parameters were similar at baseline and after 30 days. However, lower high-sensitivity troponin T and high-sensitivity C-reactive protein levels were found in samples collected from patients treated with ticagrelor on the first day of STEMI. Conclusion In patients with STEMI under a pharmaco-invasive strategy, therapy with ticagrelor was associated with a smaller infarct size than clopidogrel. Trial registration Clinicaltrials.gov ( NCT02428374 ).
AI-enhanced bilingual banking assistant
The prevalence of overweight and obesity and the assessment of associated risk factors among school-aged adolescents in Kandahar City, Afghanistan
Background Adolescent obesity is a growing global public health issue, contributing to the early onset of non-communicable diseases like type 2 diabetes and cardiovascular disorders. In South Asia, including Afghanistan, urbanization and lifestyle changes have triggered a nutritional shift marked by unhealthy diets and reduced physical activity. Yet, research on adolescent obesity in Afghanistan, particularly in urban areas like Kandahar, is scarce. Kandahar’s rapid development and cultural diversity necessitate an assessment of obesity prevalence and associated risk factors among school-going adolescents to inform health policies. Methods This cross-sectional analytical study was conducted among 384 male adolescents (aged 10–19 years) in Kandahar City between February and July 2023. Height and weight were measured to calculate BMI, which was classified using CDC BMI-for-age percentiles. Results The prevalence of overweight/obesity was 13.3%. Multivariate analysis identified parental obesity (+10.4; p = 0.002), screen time ≥30 minutes/day (+8.8; p = 0.011), and consumption of school canteen food (7.8; p = 0.037) as significant predictors of higher BMI percentiles. Conclusion Targeted interventions involving Family-centered education, promotion of active lifestyles, and regulation of school nutrition are critical to address adolescent obesity in this setting. Findings are limited to male adolescents due to cultural constraints and may not be generalizable to females.
Digital impression accuracy for bone-level and tissue-level implants using scan bodies of different heights
Lidar IMU fusion navigation system for AGVs in smart factories
Automated Guided Vehicles (AGVs) are vital to smart factories, enabling autonomous and efficient material transport. However, precise navigation is challenging because LiDAR provides high-dimensional, dynamic spatial data, while Inertial Measurement Unit (IMU) signals are often intermittent, leading to inconsistencies and navigation drift. This work proposes the Screened Inertial Data Fusion Method (SIDFM), a novel framework that systematically screens LiDAR data using a minimal differential function and fuses it with IMU intervals through linear regression learning. The SIDFM approach ensures that only consistent LiDAR points are integrated with IMU data, reducing mismatches and improving motion estimation. SIDFM was validated using a benchmark AGV dataset and compared against baseline LiDAR-IMU fusion methods under varying acceleration conditions. Results show that SIDFM reduces navigation errors by 12.09% at low acceleration and 11.43% at high acceleration while also significantly decreasing positioning errors. These improvements enhance the stability, precision, and safety of AGVs in dynamic manufacturing environments. The findings establish SIDFM as an effective and practical solution for robust AGV navigation, with potential applications in smart factories, warehouses, and autonomous mobility systems that demand both efficiency and reliability.
Multimodal prototypical network for interpretable sentiment classification
Abstract Recent advances in sentiment analysis have primarily focused on fusing multimodal information from video data, including visual, acoustic, and textual features, across temporal sequences. While great effort has been made to integrate or fuse information across modalities, less is known about the extent to which temporal segments contribute to model decisions. In addition, current interpretable methods, such as prototype networks, are primarily designed for uni-modal analysis and fail to handle the complex interactions between multiple modalities and temporal dependencies inherent in video data. To address the challenges, we propose M ulti M odal P rototypical Net works (MMPNet), which extends prototype-based interpretability to multimodal sentiment classification. Specifically, MMPNet can identify contributions of time-level features and leverage them to explain why a particular prediction was made, while also helping to find the relative importance of modality-level features. Experimental results show that MMPNet outperforms existing methods by 2.9% and 1.6% in accuracy on CMU-MOSI and CMU-MOSEI respectively, and achieves better interpretability.