bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gender disparity in academic performance in higher education institutions: a case of Wollo University, Ethiopia
Authors: Eshetu Amogne Asfaw , Alemu Simret , Mulat Aster , Abdu Aminat , Gobezie Bezawit , Muluken Enatnesh , Anagaw Asemach , Mulugeta Elsabet , Hailemariam Ehite , Aragaw Sewasew 
Published: November 2024

IntroductionGender disparity in academic achievement in developing countries is a pervasive issue that requires attention. Examining the gender gap provides empirical evidence for decision-makers. This study explored gender-based disparities in academic achievement in higher education institutions.MethodsWe employed an ex-post facto research design and analyzed the cumulative grade point average (CGPA) and national exit examination results of 1,323 students to attain the study objective. Data analysis was conducted using the Chi-square test, independent samples t-test, correlation, and simple linear regression tests.ResultsThe results revealed a statistically significant underrepresentation of female students in the top-achieving groups (χ2 (1) = 7.929, p &lt; 0.05), whereas their proportion in the lower-achieving groups (χ2 (1) = 19.18, p &lt; 0.05) was higher than expected. Additionally, a significantly more significant proportion of female students scored below the 50% minimum passing mark on the national exit examination (χ2 (1) = 23.857, p &lt; 0.05). Furthermore, the analysis demonstrated that male students attained statistically and significantly higher marks on both their CGPA [t (1231) = 6.432, p &lt; 0.001] and national exit examination [t (1231) =10.855, p &lt; 0.001] at a 95% level of significance. The correlation test results revealed that students who performed well academically in their CGPA were more likely to score higher on the national exit examination [r (1323) =0.525, p &lt; 0.01].DiscussionThe findings suggest that further research is necessary to explore the underlying factors contributing to the observed gender disparities in academic achievement at the tertiary level. Moreover, additional efforts have to be undertaken by concerned bodies to support and mentor female students at higher institutions.
Two machine learning-derived nomogram for predicting the occurrence and severity of acute graft-versus-host disease: a retrospective study based on serum biomarkers
Authors: He Qiang , Li Xin , Fang Yuan , Kong Fansheng , Yu Zhe , Xie Linna 
Published: November 2024

BackgroundAcute graft-versus-host disease (aGVHD) is a common complication after allogeneic hematopoietic cell transplantation (allo-HSCT), with high morbidity and mortality. Although glucocorticoids are the standard treatment, only half of patients achieve complete remission. Thus, there is an urgent need to screen biomarkers for the diagnosis of aGVHD to assist in the identification of individuals at risk of aGVHD. This study was to construct prediction models for the occurrence and severity of aGVHD using two machine learning algorithms based on serum biochemical data.MethodsClinical data of 120 patients with hematological diseases who received allo-HSCT were retrospectively analyzed. Seventy-six patients developed aGVHD, including 56 grade I/II and 20 grade III/IV. First, 15 serum biochemical indicators were considered as potential risk factors, and the differences in the levels of indicators between non-aGVHD and aGVHD were observed, followed by evaluation of the diagnostic property. Subsequently, to develop the prediction models for the occurrence and severity of aGVHD, LASSO and random forest (RF) analyses were performed with experimental indicators. Finally, Venn diagram analysis was utilized to obtain shared biomarkers in the two algorithms to construct the nomogram. The model performance was measured by calibration curves. Internal and external validations were performed based on risk score models and ROC curve analyses.ResultsTotal 12 of 15 indicators exhibited significant differences between the aGVHD and non-aGVHD groups, with AUC values &gt; 0.75. In machine learning analysis, eight features (LAG-3, TLR-2, PD-L1, IP-10, elafin, REG-3α, ST2, TIM3) and seven variables (LAG-3, TLR-2, PD-1, Flt_3, IL-9, elafin, TIM3) were selected to distinguish aGVHD vs. non-aGVHD as well as grade I/II vs. III/IV, respectively. Further, the corresponding nomogram models were established and calibration curves showed that prediction was in good agreement with the actual probability. Biomarker-based risk score model was constructed, which obtained AUC value &gt;0.89 in internal and external datasets.ConclusionClinical variables screened through learning algorithm can predict the risk and severity of aGVHD. Our findings may help clinicians develop more personalized and reasonable management strategies.
Branched-chain amino acid catabolism promotes M2 macrophage polarization
Authors: Lu Manxi , Luo Da , Zhang Zixuan , Ouyang Feng , Shi Yihong , Hu Changyong , Su Hang , Li Yining , Zhang Jiayi , Gui Qian , Yang Tian-Shu 
Published: November 2024

IntroductionDuring an immune response, macrophages undergo systematic metabolic rewiring tailored to support their functions. Branched-chain amino acid (BCAA) metabolism has been reported to modulate macrophage function; however, its role in macrophage alternative activation remain unclear. We aimed to investigate the role of BCAA metabolism in macrophage alternative activation.MethodThe metabolomics of BMDM-derived M0 and M2 macrophages were analyzed using LC-MS. BCAAs were supplemented and genes involved in BCAA catabolism were inhibited during M2 macrophage polarization. The expression of M2 marker genes was assessed through RT-qPCR, immunofluorescence, and flow cytometry.Results and discussionMetabolomic analysis identified increased BCAA metabolism as one of the most significantly rewired pathways upon alternative activation. M2 macrophages had significantly lower BCAA levels compared to controls. BCAA supplementation promoted M2 macrophage polarization both in vitro and in vivo and increased oxidative phosphorylation in M2 macrophages. Blocking BCAA entry into mitochondria by knockdown of SLC25A44 inhibited M2 macrophage polarization. Furthermore, M2 macrophages polarization was suppressed by knockdown of Branched-chain amino-acid transaminase 2 (BCAT2) and branched chain keto acid dehydrogenase E1 subunit alpha (BCKDHA), both of which are key enzymes involved in BCAA oxidation. Overall, our findings suggest that BCAA catabolism plays an important role in polarization toward M2 macrophages.
Case report: Successful remission with upadacitinib in a young patient with anti-TNF-refractory intestinal Behçet’s disease
Authors: Sha Sumei , Xu Bin , Wang Kairuo , Qiao Chenyang , Shi Haitao , Jiang Jiong , Quan Xiaojing , Liu Xin 
Published: November 2024

BackgroundThe limited therapeutic options and inconsistent treatment efficacy of Intestinal Behçet’s disease complicate its management, with the absence of standardized guidelines further exacerbating the challenges faced by clinicians.Case PresentationIn this case report, we present a patient with refractory intestinal Behçet’s disease who experienced treatment failure with prior biologic agents. This case sheds light on managing this complex scenario with Janus kinase (JAK) inhibitors, the patient achieved mucosal healing after switching to Upadacitinib.ConclusionThis case underscores the potential of Upadacitinib as an effective alternative for managing difficult cases of intestinal Behçet’s disease.
Analysis of factors influencing bronchiectasis patients with active pulmonary tuberculosis and development of a nomogram prediction model
Authors: Yang Yitian , Du Lianfang , Ye Weilong , Liao Weifeng , Zheng Zhenzhen , Lin Xiaoxi , Chen Feiju , Pan Jingjing , Chen Bainian , Chen Riken , Yao Weimin 
Published: November 2024

BackgroundTo identify the risk factors for bronchiectasis patients with active pulmonary tuberculosis (APTB) and to develop a predictive nomogram model for estimating the risk of APTB in bronchiectasis patients.MethodsA retrospective cohort study was conducted on 16,750 bronchiectasis patients hospitalized at the Affiliated Hospital of Guangdong Medical University and the Second Affiliated Hospital of Guangdong Medical University between January 2019 and December 2023. The 390 patients with APTB were classified as the case group, while 818 patients were randomly sampled by computer at a 1:20 ratio from the 16,360 patients with other infections to serve as the control group. Relevant indicators potentially leading to APTB in bronchiectasis patients were collected. Patients were categorized into APTB and inactive pulmonary tuberculosis (IPTB) groups based on the presence of tuberculosis. The general characteristics of both groups were compared. Variables were screened using the least absolute shrinkage and selection operator (LASSO) analysis, followed by multivariate logistic regression analysis. A nomogram model was established based on the analysis results. The model’s predictive performance was evaluated using calibration curves, C-index, and ROC curves, and internal validation was performed using the bootstrap method.ResultsLASSO analysis identified 28 potential risk factors. Multivariate analysis showed that age, gender, TC, ALB, MCV, FIB, PDW, LYM, hemoptysis, and hypertension are independent risk factors for bronchiectasis patients with APTB (p &lt; 0.05). The nomogram demonstrated strong calibration and discrimination, with a C-index of 0.745 (95% CI: 0.715–0.775) and an AUC of 0.744 for the ROC curve. Internal validation using the bootstrap method produced a C-index of 0.738, further confirming the model’s robustness.ConclusionThe nomogram model, developed using common clinical serological characteristics, holds significant clinical value for assessing the risk of APTB in bronchiectasis patients.
The impact of appendicular skeletal muscle index and trunk muscle index on stress urinary incontinence risk in female adults: a retrospective study
Authors: Wang Junwei , Zhang Cunming , Zhang Aiwei 
Published: November 2024

ObjectiveThis study sought to examine the effect of the appendicular skeletal muscle index (ASMI) and trunk muscle index (TMI) on the likelihood of stress urinary incontinence (SUI) among female adults.MethodsThis investigation utilized data from the National Health and Nutrition Examination Survey from 2001 to 2006 and 2011–2018. To evaluate the impact of ASMI and TMI on the likelihood of SUI, the study utilized restricted cubic splines (RCS) and weighted multivariable logistic regression models. Subgroup and interaction analyses were conducted to investigate how other covariates influenced their relationship.ResultsIn total, 11,168 female adults participated in the analysis. Multivariable logistic regression analysis revealed that high TMI was associated with a decreased likelihood of SUI (OR = 0.34; 95% CI: 0.16–0.75; p = 0.013). ASMI was not correlated with the likelihood of SUI. RCS analysis demonstrated a linear correlation between TMI and SUI risk, showing a decreasing trend in SUI risk as TMI increases (p for overall &lt;0.001, p for nonlinearity = 0.73).ConclusionOur study results showed that there was no association between ASMI and the risk of SUI, while a high TMI reduced the risk of SUI. This suggested that the ratio of muscle mass and BMI in different body regions has varying effects on SUI.
Deep learning radiomics based on contrast enhanced MRI for preoperatively predicting early recurrence in hepatocellular carcinoma after curative resection
Authors: Zhao Ying , Wang Sen , Wang Yue , Li Jun , Liu Jinghong , Liu Yuhui , Ji Haitong , Su Wenhan , Zhang Qinhe , Song Qingwei , Yao Yu , Liu Ailian 
Published: November 2024

PurposeTo explore the role of deep learning (DL) and radiomics-based integrated approach based on contrast enhanced magnetic resonance imaging (CEMRI) for predicting early recurrence (ER) in hepatocellular carcinoma (HCC) patients after curative resection.MethodsTotal 165 HCC patients (ER, n = 96 vs. non-early recurrence (NER), n = 69) were retrospectively collected and divided into a training cohort (n = 132) and a validation cohort (n = 33). From pretreatment CEMR images, a total of 3111 radiomics features were extracted, and radiomics models were constructed using five machine learning classifiers (logistic regression, support vector machine, k-nearest neighbor, extreme gradient Boosting, and multilayer perceptron). DL models were established via three variations of ResNet architecture. The clinical-radiological (CR), radiomics combined with clinical-radiological (RCR), and deep learning combined with RCR (DLRCR) models were constructed. Model discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively. The best-performing model was compared with the widely used staging systems and preoperative prognostic indexes.ResultsThe RCR model (area under the curve (AUC): 0.841 and 0.811) and the optimal radiomics model (AUC: 0.839 and 0.804) achieved better performance than the CR model (AUC: 0.662 and 0.752) in the training and validation cohorts, respectively. The optimal DL model (AUC: 0.870 and 0.826) outperformed the radiomics model in the both cohorts. The DL, radiomics, and CR predictors (aspartate aminotransferase (AST) and tumor diameter) were combined to construct the DLRCR model. The DLRCR model presented the best performance over any model, yielding an AUC, an accuracy, a sensitivity, a specificity of 0.917, 0.886, 0.889, and 0.882 in the training cohort and of 0.844, 0.818, 0.800, and 0.846 in the validation cohort, respectively. The DLRCR model achieved better clinical utility compared to the clinical staging systems and prognostic indexes.ConclusionBoth radiomics and DL models derived from CEMRI can predict HCC recurrence, and DL and radiomics-based integrated approach can provide a more effective tool for the precise prediction of ER for HCC patients undergoing resection.
The impact of skin-to-skin contact upon stress in preterm infants in a neonatal intensive care unit
Authors: Pavlyshyn Halyna , Sarapuk Iryna , Saturska Uliana 
Published: November 2024

IntroductionNeonatal stress significantly affects the early adaptation, maturation and long-term development of preterm infants.The objective of the studyTo investigate the effect of skin-to-skin contact (SSC) on stress level in preterm infants.Materials and methodsThe research was a prospective study. Stress indicators (cortisol, melatonin) were measured before the SSC began (pre-intervention level) and after this intervention (post-intervention).ResultsThe study included 150 preterm infants in the NICU with gestational age (GA) ≤36 weeks. Pre-intervention salivary cortisol level was higher in extremely and very preterm neonates compared to moderate and late preterm newborns (p = 0.028), in children with low Apgar scores (p = 0.041), in those who were on mechanical ventilation (p = 0.005), and suffered neonatal sepsis (p = 0.005). Pre-intervention melatonin level was lower in children with low Apgar scores (p = 0.032). Salivary cortisol levels were significantly decreased after SSC in preterm infants [pre-intervention: 0.294 (0.111; 0.854) μg/dL vs. post-intervention: 0.127 (0.070; 0.229) μg/dL, p &lt; 0.001], and urinary melatonin levels were significantly increased after SSC [pre-intervention: 4.01 (2.48; 6.34) ng/mL vs. post-intervention: 5.48 (3.39; 9.17) ng/mL, p &lt; 0.001]. A greater reduction in cortisol levels after skin-to-skin contact was revealed in infants with a lower gestational age (p = 0.022), in boys compared to girls (p = 0.012), in infants with respiratory distress syndrome (p = 0.048), in those who had mechanical ventilation compared to non-ventilated neonates (p = 0.008), and in infants with seizures (p = 0.036). The melatonin levels increased more intensively in infants with low Apgar scores (p = 0.002), and in those with late-onset sepsis (p = 0.006).ConclusionThe reduction in cortisol levels and the increase in melatonin levels provided strong evidence that SSC ameliorated the NICU-related stress in preterm infants. We found higher indicators of stress and more dramatic responses to SSC in reducing indicators of stress in infants with lower GA than in infants with higher GA, indicating that SSC may be even more important for lower GA infants. The infants who need SSC the most should not be denied the care they need to reduce the stress they experience from being born too soon and continuing their gestational development in the stressful environment of the NICU.
Using single-sample networks and genetic algorithms to identify radiation-responsive genes in rice affected by heavy ions of the galactic cosmic radiation with different LET values
Authors: Zhang Yan , Wang Wei , Zhang Meng , Zhang Binquan , Gao Shuai , Hao Meng , Zhou Dazhuang , Zhao Lei , Reitz Guenther , Sun Yeqing 
Published: November 2024

IntroductionHeavy ions of the galactic cosmic radiation dominate the radiation risks and biological effects for plants under spaceflight conditions. However, the biological effects and sensitive genes caused by heavy ions with different linear energy transfer (LET) values have not been thoroughly studied.MethodsTo comprehensively analyze the biological effects of heavy ions with different LET values on rice under spaceflight conditions, we utilized the Shijian-10 recoverable satellite (SJ-10) to transport the dehydrated rice seeds on a 12.5-day mission in a 252 km low Earth orbit (LEO), and obtained rice plants hit by individual heavy ions with LET values ranging from 18 keV/μm to 213 keV/μm. The transcriptome and methylation sequencing were conducted on above plants, and a bioinformatics pipeline based on single-sample networks (SSNs) and genetic algorithms (GA) was developed to analyze the multi-omics expression profiles in this work. Note that SSNs can depict the gene interaction patterns within a single sample. The LET regression models were constructed from both gene expression and interaction pattern perspectives respectively, and the radiation response genes that played significant roles in the models were identified. We designed a gene selection algorithm based on GA to enhance the performance of LET regression models.ResultsThe experimental results demonstrate that all our models exhibit excellent regression performance (R2 values close to 1), which indicates that both gene expressions and interaction patterns can reflect the molecular changes caused by heavy ions with different LET values. LET-related genes (genes exhibiting strong correlation with LET values) and radiation-responsive genes were identified, primarily involved in DNA damage and repair, oxidative stress, photosynthesis, nucleic acid metabolism, energy metabolism, amino acid/protein metabolism, and lipid metabolism, etc. DNA methylation plays a crucial role in responding to heavy ions stressors and regulates the aforementioned processes.DiscussionTo the best of our knowledge, this is the first study to report the multi-omics changes in plants after exposure to heavy ions with different LET values under spaceflight conditions.
Defense mechanisms in individuals with depressive and anxiety symptoms: a network analysis
Authors: Di Giuseppe Mariagrazia , Lo Buglio Gabriele , Cerasti Erika , Boldrini Tommaso , Conversano Ciro , Lingiardi Vittorio , Tanzilli Annalisa 
Published: November 2024

BackgroundDefense mechanisms play a crucial role in depression and anxiety. The current study aimed at estimating the network structure of defense mechanisms in individuals with symptoms of depression and anxiety to understand the most central defenses and relevant connections. Moreover, we aimed at examining the associations between defense mechanisms and symptoms.MethodsWe employed the Symptom Checklist-90 to recruit 655 individuals with depressive and anxiety symptoms during the first wave of the COVID-2019 Pandemic in Italy. Defense mechanisms were assessed with the DMRS-SR-30.ResultsResults showed a main component in the network graph featuring 16 defense mechanisms. Self-assertion was the most central node in the network, displaying positive and negative connections with an array of mature and immature defenses, respectively. Among immature defenses, passive aggression was the most interconnected node. Some mature defenses (i.e., humor, affiliation, and sublimation) were not connected to other nodes. A range of defense mechanisms were associated with anxiety and depressive symptoms.ConclusionsThis is the first research effort supporting the conceptualization of defense mechanisms as a complex system. Results suggest that defense mechanisms of the same cluster (e.g., mature defenses) play different roles in the network. Central defenses (i.e., self-assertion and passive aggression) detected in this study may be promising intervention targets.
Does higher endorsement with collaboration lead to better performance on collaborative problem solving? An explanatory item response approach to cross-cultural comparisons
Authors: Lu Chang , Bulut Okan 
Published: November 2024

IntroductionCollaborative problem solving (CPS) is an essential competency in the 21st century. However, the understanding of how cultural background shapes individuals’ collaboration awareness and its relationship with collaborative problem-solving skills are underexplored by psychometrics.MethodsThis study employs Explanatory Item Response Modeling to examine the impact of cultural orientations on students’ endorsement with collaboration in PISA 2015.ResultsResults show that students endorsed valuing teamwork more than valuing relationships. Western countries with individualist cultures generally demonstrated higher endorsement with collaboration than Eastern countries with collectivist cultures. China has the highest collaboration endorsement, followed by the US, Canada, Korea, and Japan. In contrast, Japan has the highest CPS assessment scores, followed by Korea, Canada, the US, and China.DiscussionFindings revealed that students from collectivist cultures do not have higher endorsements of collaboration compared with students from individualist cultures. Also, higher endorsement of collaboration does not necessarily lead to better success in the CPS assessment. Further research is needed to understand the gap between students’ attitudes towards collaboration and their achievement based on cultural values and schools’ CPS training.