bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between “Life's Essential 8” cardiovascular health and apparent treatment-resistant hypertension among US adults from the NHANES, 2005 to 2018
Authors: Xu Zhong-jiao , Shen Ru-ming , Hu Wu-ming , Shen Jia-yi , Wu Xiao-yan , Lv Ling-chun 
Published: December 2024

BackgroundThe association between healthy lifestyle and American Heart Association (AHA) Life's Essential 8 (LE8) score and apparent treatment-resistant hypertension(aTRH)remains uncertain. We aimed to explore the association between healthy lifestyle and higher LE8 score and apparent treatment-resistant hypertension in the general population.MethodsUsing NHANES data from 2005 to 2018, we included and analyzed information on 7,474 participants eligible for this study. The association between LE8 and aTRH was explored using logistic regression models, and the association between LE8 and antihypertension drugs uncontrolled hypertension was further explored using logistic regression models.ResultsParticipants with higher LE8 scores tended to be non-Hispanic white and married or living with a partner; have low income and higher education; and be without Chronic kidney disease (CKD)(all p-values &lt;0.001). Compared to subjects with low CVH, participants with moderate and high CVH exhibited lower risks of 47% and 76%, respectively. After adjusting for covariates, there was no evidence of a nonlinear association between LE8 and aTRH (p for nonlinearity = 0.456). Physical activity (PA), body mass index (BMI), and blood glucose were associated with aTRH (all p-values &lt; 0.05), while diet, nicotine exposure, sleep, and blood lipids were not significantly associated with aTRH. Compared to the low LE8 group, the ORs for the high LE8 group were 0.46 (95% CI, 0.28 to 0.76) and 0.07 (95% CI, 0.02 to 0.20) for uncontrolled hypertension with 1–2 and 3–4 antihypertensive drugs, respectively. In the sensitivity analysis, subgroup analyses were performed on all covariates, and the results remained stable.ConclusionIn our study, we found a significant association between higher LE8 scores and a lower risk of aTRH. Our findings suggest that implementing various healthy lifestyle practices and managing known cardiovascular risk factors could be a feasible comprehensive preventive approach to aTRH.
Predicting student self-efficacy in Muslim societies using machine learning algorithms
Authors: Ba-Aoum Mohammed , Alrezq Mohammed , Datta Jyotishka , Triantis Konstantinos P. 
Published: December 2024

IntroductionSelf-efficacy is a critical determinant of students' academic success and overall life outcomes. Despite its recognized importance, research on predictors of self-efficacy using machine learning models remains limited, particularly within Muslim societies. This study addresses this gap by leveraging advanced machine learning techniques to analyze key factors influencing students' self-efficacy.MethodsAn empirical dataset collected was used to examine self-efficacy among secondary school students in Muslim societies. Four machine learning algorithms-Decision Tree, Random Forest, XGBoost, and Neural Network-were employed to predict self-efficacy using two demographic variables and 10 socio-emotional, cognitive, and regulatory factors. The predictors included culturally relevant variables such as religious/spiritual beliefs and collectivist-individualist orientation. Model performance was assessed using root mean square error (RMSE) and r-squared (R2) metrics to ensure reliability and validity.ResultsThe results showed that Random Forest outperformed the other models in accuracy, as measured by R2 and RMSE metrics. Among the predictors, self-regulation, problem-solving, and a sense of belonging emerged as the most significant factors, contributing to more than half of the model's predictive power. Other variables such as gratitude, forgiveness, empathy, and meaning-making displayed moderate predictive value, while gender, emotion regulation, and collectivist-individualist orientation had minimal impact. Notably, religious/spiritual beliefs and regional factors showed negligible influence on self-efficacy predictions.DiscussionThis study enhances the understanding of factors influencing self-efficacy among students in Muslim societies and offers a data-driven foundation for developing targeted educational interventions. The findings highlight the utility of machine learning in education research, demonstrating its ability to uncover insights for equitable and effective decision-making. By emphasizing the importance of regulatory and socio-emotional factors, this research provides actionable insights to elevate student performance and well-being in diverse cultural contexts.
The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: evidence from Wuhan-area hospitals
Authors: Liu Yang , Liang Renzhao , Zhang Chengzhi 
Published: December 2024

ObjectiveThe COVID-19 pandemic has placed unprecedented strain on healthcare systems, mainly due to the highly variable and challenging to predict patient length of stay (LOS). This study aims to identify the primary factors impacting LOS for patients before and during the COVID-19 pandemic.MethodsThis study collected electronic medical record data from Zhongnan Hospital of Wuhan University. We employed six machine learning algorithms to predict the probability of LOS.ResultsAfter implementing variable selection, we identified 35 variables affecting the LOS for COVID-19 patients to establish the model. The top three predictive factors were out-of-pocket amount, medical insurance, and admission deplanement. The experiments conducted showed that XGBoost (XGB) achieved the best performance. The MAE, RMSE, and MAPE errors before and during the COVID-19 pandemic are lower than 3% on average for household registration in Wuhan and non-household registration in Wuhan.ConclusionsResearch finds machine learning is reasonable in predicting LOS before and during the COVID-19 pandemic. This study offers valuable guidance to hospital administrators for planning resource allocation strategies that can effectively meet the demand. Consequently, these insights contribute to improved quality of care and wiser utilization of scarce resources.
Predicting nuclear power plant operational parameters using clustering and mutual information for feature selection and Transformer neural network optimized by TPE
Authors: Tuo Yanjie , Liu Xiaojing 
Published: December 2024

IntroductionIn the domain of nuclear power plant operations, accurately and rapidly predicting future states is crucial for ensuring safety and efficiency. Data-driven methods are becoming increasingly important for nuclear power plant parameter forecasting. While Transformer neural networks have emerged as powerful tools due to their self-attention mechanisms and ability to capture long-range dependencies, their application in the nuclear energy field remains limited and their capabilities largely untested. Additionally, Transformer models are highly sensitive to data complexity, presenting challenges for model development and computational efficiency.MethodsThis study proposes a feature selection method that integrates clustering and mutual information techniques to reduce the dimensionality of training data before applying Transformer models. By identifying key physical quantities from large datasets, we refine the data used for training a Transformer model, which is then optimized using the Tree-structured Parzen Estimator algorithm.ResultsApplying this method to a dataset for predicting a shutdown condition of a nuclear power plant, we demonstrate the effectiveness of the proposed “feature selection + Transformer” approach: (1) The Transformer model achieved high accuracy in predicting nuclear power plant parameters, with key physical quantities such as temperature, pressure, and water level attaining a normalized root mean squared error below 0.009, indicating that the RMSE is below 0.9% of the range of the original data, reflecting a very small prediction error. (2) The feature selection method effectively reduced input data dimensionality with minimal impact on model accuracy.DiscussionThe results demonstrate that the proposed clustering and mutual information-based method provides an effective feature selection strategy that encapsulates operational information of the plant.
Spatial heterogeneity of polycyclic aromatic hydrocarbons pollution in surface soil of China and its response to regional energy consumption
Authors: Chen Kun , Shen He , Zang Shuying , Sun Li 
Published: December 2024

The regional variation of energy structure in China is a significant contributor to the geographic distribution of polycyclic aromatic hydrocarbons (PAHs) in soils, posing a substantial risk to food safety and human health. However, the precise mechanisms and extent to which energy consumption influences the spatial variability of PAHs remain largely unknown. To address this research gap, PAHs data in surface soils across China screened from 155 literatures between 2003 and 2022, coupled with anthropogenic indicators were used to investigate the concentrations, sources, and spatial heterogeneity of the effects of energy consumption on PAHs. Total PAH concentrations exhibited a range of four orders of magnitude from 1.05 to 6,778 μg kg−1, indicating weak to heavy contamination and exhibiting strong spatial variability influenced by human factors. Furthermore, 3–4-ring PAHs were dominant, accounting for an average of 25.07% and 38.55%, respectively, reflecting the significant role of coal in China energy consumption and the associated coal-smoke pollution as a primary factor in ecological environment destruction. The spatial distributions of mean total PAH concentrations descended in the order of Northeast China &gt; North China &gt; Central China &gt; East China &gt; Northwest China &gt; Southwest China &gt; South China, with different PAH composition profiles among regions affected by energy consumption. The geographical weighted regression (GWR) model was used to explore the spatial heterogeneity of energy factors affecting on PAH concentrations. Natural gas, oil, and coal were identified as energy sources with a significant positive correlation to PAH pollution. This indicated that combustion and traffic sources were the primary contributors to PAHs in surface soil in China, with coal, natural gas combustion, and oil consumption identified as the principal energy factors influencing PAH concentrations. The results of this study provide a foundation for future efforts to prevent and control soil pollution.
Effect of social play deprivation on stress sensitivity in response to social and non-social challenges
Authors: Achterberg E. J. Marijke , Lozeman-van ‘t Klooster Jose G. , Pimentel Tara C. , Aleksić Anastasija , Hendriks Judith C. M. J. , Nijhof Sanne , van der Ent C. Kors , Lesscher Heidi M. B. 
Published: December 2024

IntroductionSocial play behaviour facilitates the development of social, emotional and cognitive capacities, including resilience. Deprivation of social play in rats leads to alterations in anxiety, stress and social behaviour. The aim of this study was to elucidate the effects of social play deprivation, specifically on the responsivity to social and non-social challenges. We hypothesised that play deprivation leads to impairments in stress resilience later in life, particularly in social contexts. MethodsTo test this, play-deprived rats were compared with undeprived control rats for their responsivity to (1) a novel environment, (2) facing an aggressive male rat in its territory (resident-intruder test) and (3) an encounter with two unfamiliar conspecifics in a neutral, familiar environment (stranger encounter test). ResultsCorticosterone concentrations in response to these different stressors were comparable between play-deprived and control rats. Behavioural analysis revealed that play-deprived rats responded similar to undeprived controls when confronted with an aggressive rat in its territory. In the stranger encounter test, the unfamiliar strangers directed more play behaviour towards play-deprived animals than towards undeprived control animals. DiscussionTogether our results indicate that, except for subtle differences in play behaviour in a social challenging condition, play deprivation did not result in altered corticosterone responses to the different stressors. Our data add to the existing knowledge about the impact of social play for the development of resilient and social behaviour later in life.
Systemic inflammatory regulators and age-related macular degeneration: a bidirectional Mendelian randomization study
Authors: Liu Xi , Cao Yu , Wang Ying , Kang Lihua , Zhang Guowei , Zhang Junfang , Qin Bai , Yang Ling , Luo Jiawei , Li Pengfei , Geng Wenjing , Ji Min , Guan Huaijin 
Published: December 2024

IntroductionWe investigated the relationship between systematic regulators of inflammation and the risk of age-related macular degeneration (AMD), both wet and dry forms, by using bidirectional, two-sample Mendelian randomization (MR).MethodsWe performed bidirectional two-sample Mendelian randomization analysis using genome-wide study (GWAS) data for 91 plasma proteins from 14,824 individuals of European descent across 11 study groups. Next, we utilized data from the FinnGen consortium to study AMD using the inverse- variance-weighted approach for Mendelian randomization. Additional analyses involved MR-Egger, Weighted median, Weighted mode, MR-PRESSO, and MR- Steiger filtering techniques.ResultsWe identified 16 cytokines associated AMD outcomes and post FDR correction, higher levels of fibroblast growth factor 19 and leukemia inhibitory factor receptor were associated with decreased risk for AMD, while higher levels of tumour necrosis factor ligand superfamily member 14 were associated with increased risk for AMD. Additionally, higher levels of interleukin-10 receptor subunit alpha were associated with decreased risk for wet AMD, higher levels of leukemia inhibitory factor receptor were associated with decreased risk for dry AMD, and higher levels of signaling lymphocytic activation molecule were associated with increased risk for dry AMD. Genetic susceptibility to AMD was associated with elevated levels of TNF-related activation-induced cytokines (TNFSF11), and genetic susceptibility to wet AMD was associated with elevated levels of TNFSF11, interleukin-18 receptor 1 (IL18R1), and CUB domain-containing protein 1 (CDCP1).DiscussionThis research enhances our understanding of systemic inflammation in AMD, providing insights into etiology, diagnosis, and treatment of AMD and its forms.
Husband involvement in postnatal care services utilization and associated factors in Bishoftu Town, Central Ethiopia; community-based cross-sectional study
Authors: Gesisa Hana Israel , Oyato Befekadu Tesfaye , Sileshi Warkitu , Abasimel Husen Zakir , Hussien Dursa 
Published: December 2024

BackgroundBoth mothers and newborns go through a critical time in their lives during the postnatal period, and the majority of deaths of mothers and neonates happen during this period. Postnatal care is the care given to women and their neonates, starting from delivery to 42 postnatal days. Although the significance of postnatal care in maternal and child health is acknowledged, the influence of husbands in either facilitating or impeding access to these services has been largely unexplored. So it's important to find out husbands’ involvement in postnatal care service utilization in Bishoftu town.ObjectivesTo assess husband involvement in postnatal care services utilization and associated factors in Bishoftu town, Central Ethiopia.MethodsA community-based cross-sectional study was conducted in Bishoftu town, central Ethiopia, between April 1 and 27, 2022, among 624 fathers. After being selected through simple random sampling, 610 husbands were interviewed face-to-face. The data was then entered into Epi-Data version 3.1 and analyzed using SPSS version 26. Binary logistic regression was employed for analysis and variables with a p-value ≤0.2 in the bivariate analysis were entered into multivariable logistic regression analysis. In multivariable analysis, statistical significance was declared at p-value &lt;0.05.ResultsThis study reveals that overall husband involvement in postnatal care utilization was found to be 34.1% with a 95% CI (30.3%–38.0%). Residing in urban areas (AOR = 2.3, 95% CI 1.39–3.82), having good knowledge of neonatal (AOR = 3.1, 95% CI 2.04–4.7) and maternal danger signs and complications during the postnatal period (AOR = 2.44; 95% CI: 1.64, 3.63), having history of child illness (AOR = 2.18; 95% CI: 1.4–3.3), and accompanying wife in antenatal care services (AOR = 2.73, 95% CI 1.82–4.07) were the factors found to determine husband's involvement in postnatal care service utilization.ConclusionThe level of husband involvement in their spouse's postnatal care service utilization was relatively low in this study. Encouraging husbands to participate in prenatal care services, availing healthcare facilities to rural communities, and increasing community awareness of maternal and neonatal warning signs might boost husbands’ involvement in postnatal care services utilization.
Assessment of body composition and prediction of infectious pancreatic necrosis via non-contrast CT radiomics and deep learning
Authors: Huang Bingyao , Gao Yi , Wu Lina 
Published: December 2024

AimThe current study aims to delineate subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), the sacrospinalis muscle, and all abdominal musculature at the L3–L5 vertebral level from non-contrast computed tomography (CT) imagery using deep learning algorithms. Subsequently, radiomic features are collected from these segmented images and subjected to medical interpretation.Materials and methodsThis retrospective analysis includes a cohort of 315 patients diagnosed with acute necrotizing pancreatitis (ANP) who had undergone comprehensive whole-abdomen CT scans. The no new net (nnU-Net) architecture was adopted for the imagery segmentation, while Python scripts were employed to derive radiomic features from the segmented non-contrast CT images. In light of the intrinsic medical relevance of specific features, two categories were selected for analysis: first-order statistics and morphological characteristics. A correlation analysis was conducted, and statistically significant features were subjected to medical scrutiny.ResultsWith respect to VAT, skewness (p = 0.004) and uniformity (p = 0.036) emerged as statistically significant; for SAT, significant features included skewness (p = 0.023), maximum two-dimensional (2D) diameter slice (p = 0.020), and maximum three-dimensional (3D) diameter (p = 0.044); for the abdominal muscles, statistically significant metrics were the interquartile range (IQR; p = 0.023), mean absolute deviation (p = 0.039), robust mean absolute deviation (p = 0.015), elongation (p = 0.025), sphericity (p = 0.010), and surface volume ratio (p = 0.014); and for the sacrospinalis muscle, significant indices comprised the IQR (p = 0.018), mean absolute deviation (p = 0.049), robust mean absolute deviation (p = 0.025), skewness (p = 0.008), maximum 2D diameter slice (p = 0.008), maximum 3D diameter (p = 0.005), sphericity (p = 0.011), and surface volume ratio (p = 0.005).ConclusionDiminished localized deposition of VAT and SAT, homogeneity in the VAT and SAT density, augmented SAT volume, and a dispersed and heterogeneous distribution of abdominal muscle density are identified as risk factors for infectious pancreatic necrosis (IPN).
Temperature affects major fatty acid biosynthesis in noug (Guizotia abyssinica) self-compatible lines
Authors: Gebeyehu Adane , Hammenhag Cecilia , Tesfaye Kassahun , Ortiz Rodomiro , Geleta Mulatu 
Published: December 2024

IntroductionNoug (Guizotia abyssinica) is an economically important edible oilseed crop in Ethiopia with a large variation in seed set, seed oil content, and fatty acid composition among populations. Although noug is generally strictly self-incompatible (SI), self-compatible (SC) lines were recently developed. This study was conducted to investigate the levels of variation in seed setting and oleic acid content among the self-compatible lines.MethodsThe starting materials for the seed setting study were 200 genotypes selected from 100 inbred lines and having, on average, 57 seeds per capitulum, which is higher than that of the SI populations. The SC genotypes were analyzed for their oleic acid content using the half-seed technique.ResultsThe analysis of 20 SC lines revealed a high variation in oleic acid content with 70% of the SC lines having 20% or more oleic acid after they were grown under 25°C/21°C day/night temperatures (high-temperature treatment). The oleic acid content increased from 8.2% before to 22.5%, on average, after the high-temperature experiment in the greenhouse. In contrast, the percentage of oleic acid in these lines grown at 21°C/18°C day/night temperatures decreased from 8.2% to 4.4% on average. There was a highly significant positive correlation between oleic acid content and temperature in SC lines.ConclusionThe study suggests a significant contribution of genotype to the variation in seed setting and environmental factors (mostly temperature) to the oleic acid content. The noug SC-lines showed highly significant variation in seed setting and oleic acid content, which could be used for improving the crop’s seed yield and oil quality.
Nomogram prediction model for length of hospital stay following laparoscopic appendectomy in pediatric patients: a retrospective study
Authors: Liu Ming , Yang Ping , Gou Yunpeng , Chen Qiang , Xu Dong 
Published: December 2024

ObjectiveThe aim of this research was to develop and internally validate a nomogram for forecasting the length of hospital stay following laparoscopic appendectomy in pediatric patients diagnosed with appendicitis.MethodsWe developed a prediction model based on a training dataset of 415 pediatric patients with appendicitis, and hospitalization data were collected retrospectively from January 2021 and December 2022. The primary outcome measure in this study was hospital length of stay (LOS), with prolonged LOS defined as admission for a duration equal to or exceeding the 75th percentile of LOS, including the discharge day. Risk factor analysis was conducted through univariate and multivariate logistic regression analyses. Based on the regression coefficients, a nomogram prediction model was developed. The discriminative performance of the predicting model was evaluated using the C-index, and an adjusted C-index was computed through bootstrapping validation. Calibration curves were generated to assess the accuracy of the nomogram. Decision curve analysis was conducted to determine the clinical utility of the predicting model.ResultsPredictors contained in the prediction nomogram included Age, neutrophil-to-lymphocyte ratio, C-reactive protein level, operative time, appendiceal fecalith, and drainage tube. The C-index of the prediction nomogram was determined to be 0.873 (95% CI: 0.838–0.908), with a corrected C-index of 0.8625 obtained through bootstrapping validation (1,000 resamples), indicating the model's favorable discrimination. Calibration curves illustrated a strong agreement between predicted and observed outcomes. According to the decision curve analysis of the nomogram, the predictive model demonstrates a net benefit at threshold probabilities exceeding 2%.ConclusionThis nomogram, incorporating variables such as Age, neutrophil-to-lymphocyte ratio, C-reactive protein level, operative time, appendiceal fecalith, and drainage tube, offers a convenient method for assessing the duration of hospitalization in pediatric patients with appendicitis.