bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between advanced lung cancer inflammation index and unstable asthma: a population-based study from the NHANES 2007–2018
Authors: Jin Zhou , Sun Wen , Huang Junjun , Wang Guangfa 
Published: November 2024

BackgroundAsthma exacerbation is associated with obesity and systemic inflammatory diseases, and advanced lung cancer inflammation index (ALI) is a novel biomarker of nutritional inflammation. The purpose of this study was to investigate the potential relationship between ALI and unstable asthma.MethodsThis cross-sectional study utilized data from the 2007–2018 National Health and Nutrition Examination Survey (NHANES). Asthma was assessed through self-reported questionnaires. Multifactorial logistic regression, subgroup analyses, interaction assessments, smoothed curve fitting, and threshold effect analysis models were conducted to investigate the association between ALI and unstable asthma.ResultsThe study included 1,822 subjects with current asthma, and we found a linear positive association between ALI and unstable asthma, with higher levels of ALI significantly associated with an increased risk of asthma exacerbations in fully corrected models. However, the associations were not entirely consistent across subgroups. In subgroup analyses by body mass index (BMI) and race, unstable asthma and ALI were independently significant in the BMI (25–29.9) range and the Non-Hispanic White group. Interaction analysis suggested that BMI moderated the relationship between ALI and unstable asthma. Furthermore, smoothed curve fitting showed an inverted U-shaped relationship between log ALI and unstable asthma in subjects with a BMI &lt;25 and male individuals, with inflection points observed at 1.53 and 2.13, respectively.ConclusionWe found a linear positive association between ALI and unstable asthma, which remained constant in the fully adjusted model. These findings suggest that higher levels of ALI were significantly associated with an increased risk of asthma exacerbation, particularly in asthmatic populations with BMI in the 25–29.9 range. However, more prospective studies are required to confirm our findings.
Comparison of multiple machine learning models for predicting prognosis of pancreatic ductal adenocarcinoma based on contrast-enhanced CT radiomics and clinical features
Authors: Huang Yue , Zhang Han , Ding Qingzhu , Chen Dehua , Zhang Xiang , Weng Shangeng , Liu Guozhong 
Published: November 2024

ObjectiveThe aim of this study was to evaluate the prognostic potential of combining clinical features and radiomics with multiple machine learning (ML) algorithms in pancreatic ductal adenocarcinoma (PDAC).MethodsA total of 116 patients with PDAC who met the eligibility criteria were randomly assigned to a training or validation cohort. Seven ML algorithms, including Supervised Principal Components, stepwise Cox, Random Survival Forest, CoxBoost, Least absolute shrinkage and selection operation (Lasso), Ridge, and Elastic network, were integrated into 43 algorithm combinations. Forty-three radiomics models were constructed separately using radiomics features extracted from arterial phase (AP), venous phase (VP), and combined arterial and venous phase (AP+VP) images. The concordance index (C-index) of each model was calculated. The model with the highest mean C-index was identified as the best model for calculating the radiomics score (Radscore). Univariate and multivariate Cox analyses were used to identify independent prognostic indicators and create a clinical model for prognosis prediction. The multivariable Cox regression was used to combine Radscore with clinical features to create a combined model. The efficacy of the model was evaluated using the C-index, calibration curves, and decision curve analysis (DCA).ResultsThe model based on the Lasso+StepCox[both] algorithm constructed using AP+VP radiomics features showed the best predictive ability among the 114 radiomics models. The C-indices of the model in the training and validation cohorts were 0.742 and 0.722, respectively. Based on the results of the univariate and multivariate Cox regression analyses, sex, Tumor-Node-Metastasis (TNM) stage, and systemic inflammation response index were included to build the clinical model. The combined model, incorporating three clinical factors and AP+VP-Radscore, achieved the highest C-indices of 0.764 and 0.746 in the training and validation cohorts, respectively. In terms of preoperative prognosis prediction for PDAC, the calibration curve and DCA showed that the combined model had a good consistency and greatest net benefit.ConclusionA combined model of clinical features and AP+VP-Radscore screened using multiple ML algorithms has an excellent ability to predict the prognosis of PDAC and may provide a noninvasive and effective method for clinical decision-making.
Case report: Cochlear implantation for deafness caused by endolymphatic sac tumors in patients with von Hippel–Lindau syndrome
Authors: Rosiak Oskar , Pietrzak Nikodem , Jankowska Katarzyna , Kucharski Andrzej , Konopka Wiesław , Szymański Marcin 
Published: November 2024

IntroductionEndolymphatic sac tumors (ELSTs) are rare neuroectodermal neoplasms that originate in the endolymphatic sac and duct. They exhibit no specific age or gender predilection, although they are more prevalent in patients with von Hippel–Lindau syndrome.Material and methodsThe manuscript preparation adhered to the CARE guidelines for standardizing clinical cases and the PRISMA guidelines for scientific reviews. Three studies that met the inclusion criteria were selected from an analysis of 15 articles, supplemented by two additional studies identified through citation review.ResultsThe presented case describes a 16-year-old boy diagnosed with unilateral sensorineural hearing loss secondary to bilateral ELST. Surgical excision of the right ear tumor with simultaneous cochlear implantation was performed, followed by left ear surgery. Hearing restoration was achieved in the implanted right ear, with normal hearing in the left ear, and no postoperative vestibular symptoms were observed.ConclusionPrompt surgical intervention remains the cornerstone of ELST treatment. While cochlear implantation is sporadically reported in scientific literature, it offers a potential option for hearing improvement in patients suffering from deafness caused by the disease.
An event-related potential study of P300 in preschool children with attention deficit hyperactivity disorder
Authors: Tao Mengjiao , Sun Jing , Liu Siqi , Zhu Yike , Ren Yongying , Liu Ziqi , Wang Xiaoyue , Yang Wenmin , Li Guannan , Wang Xin , Zheng Wei , Zhang Jianzhao , Yang Jian 
Published: November 2024

ObjectiveTo study the characteristics of event related potential P300 in preschool children with attention deficit hyperactivity disorder (ADHD) aged 4–6 years (4≤ age &lt;6), and to explore the differences in cognitive function compared with healthy children. To explore a new method for the study of cognitive function in preschool children with ADHD.MethodsA total of 73 preschool children aged 4–6 years were selected from the outpatient clinic of Neurology Department, the ADHD-specialized Clinic and Health Care Department of the Children's Hospital Affiliated to the Capital Institute of Pediatrics from March 2021 to May 2024. They were divided into the ADHD group (45 cases) and healthy children group (28 cases). Event related potential P300 was measured in all children and the amplitude and latency of the wave were compared between the two groups separately.ResultsThe latency of P300 at all the recording electrodes (Fz, Cz, Pz, Oz, C3, C4) in the ADHD group was significantly longer than controls (p &lt; 0.05). The wave amplitudes of children with ADHD were significantly higher than controls at Pz and Oz points.ConclusionThe differences between two groups in P300 test show that preschool children with ADHD present longer latency at central line of the brain and bilateral central lobes compared with healthy children, and higher amplitude at the central parietal lobe and central occipital lobe. It may suggest that attention cognition has already impaired in preschool children with ADHD.
Integrated analysis of single-cell and bulk transcriptome reveals hypoxia-induced immunosuppressive microenvironment to predict immunotherapy response in high-grade serous ovarian cancer
Authors: Chen Qingshan , Zhang Yue , Wang Chao , Ding Hui , Chi Liqun 
Published: November 2024

BackgroundHypoxia is significantly associated with cancer progression and treatment outcomes. Nevertheless, the precise molecular mechanisms underlying the hypoxia-induced immunosuppressive microenvironment in high-grade serous ovarian cancer (HGSOC) are still not fully understood.MethodsBy analyzing five independent transcriptomic datasets, we investigated the effect of hypoxia on prognosis and tumor microenvironment (TME) in HGSOC. The hypoxia levels and the intercellular communication signaling pathways were studied by using single-cell analysis. Furthermore, the Hypoxia-TME classifier was developed and then validated in the multiple HGSOC datasets. In addition, we also investigated the prognostic significance, genetic variations, signaling pathways, and the potential for immunotherapy benefits in different Hypoxia-TME subgroups.ResultsHypoxia was identified as a crucial risk factor in HGSOC, and strongly correlated with an immunosuppressive microenvironment characterized by alterations in the composition and distribution of immune cells. Single-cell analysis elucidated the heterogeneity inherent within the TME in HGSOC, and demonstrated an association between the hypoxic TME and fibroblasts as well as macrophages. CellChat analysis identified SPP1-CD44 and CXCL12-CXCR4 as the principal signaling axes through which macrophages and fibroblasts interact with T cells, respectively. Moreover, a personalized Hypoxia-TME classifier was constructed and validated through the integration of the hypoxia (18 genes) and TME (7 immune cells) scores. It was observed that patients in the Hypoxialow/TMEhigh subgroup displayed a significantly better prognosis than other subgroups. Different subgroups exhibited unique genomic alterations and variations in signaling pathway differences, including TGF-β and Wnt/β-catenin pathways, which are closely associated with various biological functions. Finally, our results indicated that patients in the Hypoxialow/TMEhigh subgroup exhibit a better response to immunotherapy, suggesting the potential utility of the Hypoxia-TME classifier as a new biomarker in HGSOC.ConclusionOur study revealed hypoxia-induced immunosuppressive microenvironment, and developed Hypoxia-TME classifier to distinguish the prognosis, immune characteristics, and potential benefits of immunotherapy in HGSOC.
Inhibition of CISD2 enhances sensitivity to doxorubicin in diffuse large B-cell lymphoma by regulating ferroptosis and ferritinophagy
Authors: Zhang Chaofeng , Zhan Siting , He Yanjun , Pan Zhiqun , You Zhongyi , Zhu Xiongpeng , Lin Qi 
Published: November 2024

BackgroundCDGSH iron-sulfur domain 2 (CISD2), an iron-sulfur protein with a [2Fe-2S] cluster, plays a pivotal role in the progression of various cancers, including Diffuse Large B-cell Lymphoma (DLBCL). However, the mechanisms by which CISD2 regulates the occurrence and development of DLBCL remain to be fully elucidated.MethodsThe potential role of CISD2 as a predictive marker in DLBCL patients treated with the R-CHOP regimen was investigated through bioinformatics analysis and clinical cohort studies. DLBCL cell lines (SUDHL-4 and HBL-1) were employed in this research. Adenoviral (AV) plasmids were used to either silence or overexpress CISD2 in these DLBCL cell lines. Additionally, the induction of ferroptosis in DLBCL cell lines was assessed. Various parameters, including cell proliferation, intracellular free iron levels, lipid peroxides, reactive oxygen species (ROS), and mitochondrial membrane potential (MMP), were measured. Furthermore, the expression of proteins associated with ferroptosis and ferritinophagy was analyzed. Drug-resistant DLBCL cell lines were developed by gradually increasing doxorubicin (DOX) concentration over 6 months. The biological role of CISD2 in these drug-resistant DLBCL cell lines was subsequently assessed.ResultsElevated CISD2 levels were found to be associated with decreased sensitivity of DLBCL patients to the R-CHOP regimen, as indicated by bioinformatics and clinical cohort analysis. Silencing CISD2 significantly reduced cell proliferation, increased iron accumulation, depleted glutathione (GSH), and elevated malondialdehyde (MDA) levels, alongside the accumulation of ROS and increased MMP. Additionally, BECN1 and NCOA4 expressions were upregulated, while p62, FTH1, and GPX4 expressions were downregulated. Conversely, overexpression of CISD2 reversed these effects. Treatment of DLBCL cell lines with Erastin led to decreased CISD2 levels. Notably, in drug-resistant DLBCL cell lines, CISD2 knockdown promoted ferroptosis and ferritinophagy, restoring sensitivity to DOX and enhancing the efficacy of Erastin treatment.ConclusionOur findings suggest that CISD2 may play a role in the drug resistance observed in DLBCL patients. Inhibition of CISD2 could enhance ferroptosis and ferritinophagy, potentially improving the sensitivity of DLBCL cells to DOX treatment.
Predicting adverse drug event using machine learning based on electronic health records: a systematic review and meta-analysis
Authors: Hu Qiaozhi , Chen Yuxian , Zou Dan , He Zhiyao , Xu Ting 
Published: November 2024

IntroductionAdverse drug events (ADEs) pose a significant challenge in current clinical practice. Machine learning (ML) has been increasingly used to predict specific ADEs using electronic health record (EHR) data. This systematic review provides a comprehensive overview of the application of ML in predicting specific ADEs based on EHR data.MethodsA systematic search of PubMed, Web of Science, Embase, and IEEE Xplore was conducted to identify relevant articles published from the inception to 20 May 2024. Studies that developed ML models for predicting specific ADEs or ADEs associated with particular drugs were included using EHR data.ResultsA total of 59 studies met the inclusion criteria, covering 15 drugs and 15 ADEs. In total, 38 machine learning algorithms were reported, with random forest (RF) being the most frequently used, followed by support vector machine (SVM), eXtreme gradient boosting (XGBoost), decision tree (DT), and light gradient boosting machine (LightGBM). The performance of the ML models was generally strong, with an average area under the curve (AUC) of 76.68% ± 10.73, accuracy of 76.00% ± 11.26, precision of 60.13% ± 24.81, sensitivity of 62.35% ± 20.19, specificity of 75.13% ± 16.60, and an F1 score of 52.60% ± 21.10. The combined sensitivity, specificity, diagnostic odds ratio (DOR), and AUC from the summary receiver operating characteristic (SROC) curve using a random effects model were 0.65 (95% CI: 0.65–0.66), 0.89 (95% CI: 0.89–0.90), 12.11 (95% CI: 8.17–17.95), and 0.8069, respectively. The risk factors associated with different drugs and ADEs varied.DiscussionFuture research should focus on improving standardization, conducting multicenter studies that incorporate diverse data types, and evaluating the impact of artificial intelligence predictive models in real-world clinical settings.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024565842, identifier CRD42024565842.
A diffusion tensor imaging-based multidimensional study of brain structural changes after long-term high-altitude exposure and their relationships with cognitive function
Authors: Liu Ning , Feng Li , Chai Shuangwei , Li Hailong , He Yuanyuan , Guo Yongyue , Hu Xin , Li Hengyan , Li Xiangwei , Zhou Zan , Li Xiaomei , Huang Yonghong , He Wanlin , Huang Xiaoqi , Wu Yunhong , Meng Jinli 
Published: November 2024

BackgroundBrain structure changes after long-term adaptation to the high-altitude environment; however, related studies are few, results are in consistent, and long-term effects on cognitive function and pathophysiological mechanisms are unclear. Therefore, diffusion tensor imaging (DTI) was used to investigate the damage to white matter fiber tracts and correlations between brain structural abnormalities and cognitive function.MethodsForty healthy Han people living on the high-altitude and 40 healthy Han people living on the plains were enrolled in this study and underwent magnetic resonance imaging, emotional state assessment, and cognitive function tests. The sex, age, education level, and social status of the two groups were not different. The tract-based spatial statistics (TBSS) method was used to analyze the DTI parameters of the white matter fiber tracts of the two groups. Moreover, the partial correlation method (age and sex as covariates) was used to analyze the correlations between the intergroup differences in the DTI parameters and a series of clinical indicators of emotional state and cognitive function. Two-sample t tests, Mann-Whitney U test, generalized linear model, or chi-square tests were used for statistical analysis.ResultsCompared with those individuals in the plain group, the scores on the PSQI, SDS, SAS, PHQ-9, and GAD-7 of individuals in the high-altitude group were higher, while the scores on the DST-Backwards, MoCA, and MMSE in the high-altitude group were lower. The fractional anisotropy (FA) value of the body of the corpus callosum in the high-altitude group was lower than that in the plain group. The FA value of the body of the corpus callosum in the plain group was negatively correlated with the Logical Memory, while no significant correlation was found in the high-altitude group.ConclusionThis study revealed that long-term exposure to a high-altitude environment could lead to a series of changes in sleep, emotion, and cognitive function and irreversible damage to the white matter microstructure of the body of the corpus callosum, which is the related brain region responsible for logical memory. The absence of logical memory impairment in the healthy Han Chinese population living on the high-altitude in this study may be due to the existence of adaptive compensation after long-term high-altitude exposure.
Psychosocial predictors of attitude toward premarital sexual practice among university students, Ethiopia
Authors: Yedemie Yordanos Yibeltal 
Published: November 2024

ObjectivesPremarital sexual practice during adolescence may lead to different sexual and reproductive health problems, including HIV/AIDS. The objective of this study was to assess psychosocial predictors of attitudes toward premarital sex among Bahir Dar University students.MethodsA correlational design was used. A multi-stage sampling technique was used to select 375 respondents for the study, although the analysis was based on the 361 questionnaires that were returned from the field and found useful for analysis. Data was collected using a self-developed and modified instrument on standardized scales. The internal consistency of the Perception of Peer Pressure Scale was 0.83, while the self-esteem measuring scale had a reliability of 0.77. The internal consistency of the sexual attitude inventory scale was 0.97. The data were quantitatively analyzed using (inferential statistics) logistic regression, one sample t test, and an independent sample t test.ResultsThe findings of the study showed that all the psychological variables investigated were found to significantly predict premarital sexual relationships among the respondents. Male students with the experience of premarital sex have a higher level of self-esteem and exposure to peer pressure than their female counterparts. A study suggests that male students with the experience of premarital sex have a higher level of self-esteem and exposure to peer pressure than their female counterparts. Self-esteem negatively predicted attitudes toward premarital sex.ConclusionScaling up the level of school counseling, especially in the area of sexual adjustment and modification of the psychological variables investigated, was recommended.
Do scientific attitude and intelligence affect motivation towards STEM? Structural equation modelling
Authors: Dere Zeynep 
Published: November 2024

IntroductionThe aim of this study is to reveal the effect of scientific attitude and intelligence on STEM (science, technology, engineering and mathematics) motivation using Structural Equation Modelling (SEM). It is possible to say that there is a reciprocal relationship between attitudes towards science, intelligence and motivation towards STEM. Motivation is closely and positively related to STEM, scientific attitude, intelligence and organizational development. When female students are supported and motivated positively, it is possible for them to be successful in STEM. It is possible to say that female students are the female leadership candidates of the future. Female leader candidates can play a role in future organizational development. Female leader candidates who are supported and motivated by their environment can take their place in organizational development. There is a gap in the research literature on this subject in Türkiye. This study fills an important gap in terms of sampling, research method and data analysis.MethodIn the study, personal information form, Attitude Towards Scientific Research Scale, Multiple Intelligences Self-Perception Scale and STEM Value-Expectation Rating Scale were applied to 159 female undergraduate students who are disadvantaged in STEM field and studying at Ege University Ödemiş Faculty of Health Sciences.ResultsIt is possible to say that most female students have positive attitudes towards scientific research. According to the research results, as the reluctance to help researchers and negative attitudes towards research increase, motivation towards STEM decreases. As positive attitudes towards research and researchers increase, motivation towards STEM increases. In addition, increases in verbal–linguistic intelligence, logical-mathematical intelligence, spatial intelligence, interpersonal intelligence, intrapersonal intelligence and natural intelligence lead to an increase in positive attitudes towards research and thus STEM motivation.ConclusionWhen the value expectations of future female leadership candidates for the STEM field were evaluated, it was found that 81.13% of the students had a medium level of value expectation evaluation level for the STEM field. This situation suggests that female students are not positively motivated for STEM in the family, school and peer environment.