bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations between physical activity and heart disease among middle-aged and older Chinese adults
Authors: Liu Yujia , Yuan Chaoqun , Chen Tong , Zhou Linlin , Zhu Gengyin , Chen Yu 
Published: January 2025

ObjectiveTo quantify the risk of heart diseases (HD) and determine the relationship between physical activity (PA) dimensions and HD among Chinese middle-aged and older individuals.MethodsUsing data from the China Health and Retirement Longitudinal Study (CHARLS, 2018), 16,927 participants were included in this study. Multivariate logistic regression was performed to determine the association between HD risk and PA dimensions, including volume, intensity, frequency, and duration. Restricted cubic spline analysis was conducted to assess the dose-response relationship between PA and HD risk.ResultsCompared to the least active participants, a low risk of HD was significantly associated with a higher PA volume. With regards to frequency, a lower HD risk was associated with performing vigorous PA except for 3–5 days/week. The frequency and duration of light as well as moderate PA had no significant associations with HD risk after adjusted by using covariates. A non-linear association was also noted, with increased PA being associated with decreased HD risk, with steeper reductions in HD risk at low activity levels than at high activity levels. There was a non-linear association between PA and HD risk in participants in male and aged older than 65 years.ConclusionsAn inverse non-linear dose-response association was detected between the total volume of PA and HD risk. As PA increased to 4,000 METs-min/week, HD risk in the overall population decreased by approximately 26%, while further increases in PA did not produce any further marked reduction in the risk. A vigorous intensity of PA was associated with a reduced risk of HD and is strongly recommended.
Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model
Authors: Lin Qingqing , Zhao Wenxiang , Zhang Hailin , Chen Wenhao , Lian Sheng , Ruan Qinyun , Qu Zhaoyang , Lin Yimin , Chai Dajun , Lin Xiaoyan 
Published: January 2025

Background
Early prediction of heart failure (HF) after acute myocardial infarction (AMI) is essential for personalized treatment. We aimed to use interpretable machine learning (ML) methods to develop a risk prediction model for HF in AMI patients.MethodsWe retrospectively included patients initially with AMI who received percutaneous coronary intervention (PCI) in our hospital from November 2016 to February 2020. The primary endpoint was the occurrence of HF within 3 years after operation. For developing a predictive model for HF risk in AMI patients, the least absolute shrinkage and selection operator (LASSO) Regression was used to feature selection, and four ML algorithms including Random Forest (RF), Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), and Logistic Regression (LR) were employed to develop the model on the training set. The performance evaluation of the prediction model was carried out on the training set and the testing set, utilizing metrics including AUC (Area under the receiver operating characteristic curve), calibration plot, and decision curve analysis (DCA). In addition, we used the Shapley Additive Explanations (SHAP) value to determine the importance of the selected features and interpret the optimal model.ResultsA total of 1220 AMI patients were included and 244 (20%) patients developed HF during follow-up. Among the four evaluated ML models, the XGBoost model exhibited exceptional accuracy, with an AUC value of 0.922. The SHAP method showed that left ventricular ejection fraction (LVEF), left ventricular end-systolic diameter (LVDs) and lactate dehydrogenase (LDH) were identified as the three most important characteristics to predict HF risk in AMI patients. Individual risk assessment was performed using SHAP plots and waterfall plot analysis.ConclusionsOur research demonstrates the potential of ML methods in the early prediction of HF risk in AMI patients. Furthermore, it enhances the interpretability of the XGBoost model through SHAP analysis to guide clinical decision-making.
Conditional diffusion-generated super-resolution for myocardial perfusion MRI
Authors: Sun Changyu , Goyal Neha , Wang Yu , Tharp Darla L. , Kumar Senthil , Altes Talissa A. 
Published: January 2025

IntroductionMyocardial perfusion MRI is important for diagnosing coronary artery disease, but current clinical methods face challenges in balancing spatial resolution, temporal resolution, and slice coverage. Achieving broader slice coverage and higher temporal resolution is essential for accurately detecting abnormalities across different slice locations but remains difficult due to constraints in acquisition speed and heart rate variability. While techniques like parallel imaging and compressed sensing have significantly advanced perfusion imaging, they still suffer from noise amplification, residual artifacts, and potential temporal blurring due to the rapid transit of dynamic contrast vs. the temporal constraints of the reconstruction.MethodsThis study introduces a conditional diffusion-based generative model for myocardial perfusion MRI super resolution, addressing the trade-offs between spatiotemporal resolution and slice coverage. We adapted Denoising Diffusion Probabilistic Models (DDPM) to enhance low-resolution perfusion images into high-resolution outputs without requiring temporal regularization. The forward diffusion process introduces Gaussian noise incrementally, while the reverse process employs a U-Net architecture to progressively denoise the images, conditioned on the low-resolution input image.ResultsWe trained and validated the model on a retrospective dataset of dynamic contrast-enhanced (DCE) perfusion MRI, consisting of both stress and rest images from 47 patients with heart disease. Our results showed significant image quality improvements, with a 5.1% reduction in nRMSE, a 1.1% increase in PSNR, and a 2.2% boost in SSIM compared to GAN-based super-resolution method (P &lt; 0.05 for all metrics using paired t-test) in retrospective study. For the 9 prospective subjects, we achieved a total nominal acceleration of 8.5-fold across 5–6 slices through a combination of low-resolution acquisition and GRAPPA. PerfGen outperformed GAN-based approach in sharpness (4.36 ± 0.38 vs. 4.89 ± 0.22) and overall image quality (4.14 ± 0.28 vs. 4.89 ± 0.22), as assessed by two experts in a blinded evaluation (P &lt; 0.05) in prospective study.DiscussionThis work demonstrates the capability of diffusion-based generative models in generating high-resolution myocardial perfusion MRI from conditional low-resolution images. This approach has shown the potentials to accelerate myocardial perfusion MRI while enhancing slice coverage and temporal resolution, offering a promising alternative to existing methods.
The plight of Ukrainian refugees staying in Sweden under EU:s mass refugee directive: a brief trauma-focused, participatory, online intervention as a pilot feasibility study
Authors: Ekblad Solvig , Gramatik Oksana , Suprun Yuliia 
Published: January 2025

BackgroundUkrainians staying in Sweden under the EU mass refugee directive may face challenges due to traumas caused by the invasion that started on February 24, 2022. Part of an European Social Fund (ESF) project, our study showed that a brief trauma-focused group intervention onsite helped to increase health and mental-health literacy. The intervention has not yet been adapted online.MethodsThis pilot study during six months aimed to investigate the feasibility, acceptability and outcome in perceived trust, anxiety/stress, and perceived health after this brief trauma-focused group intervention online. A second aim was to observe perceived acceptability of the group intervention with different ways of online intervention. Local coaches, interpreters, the authors, and local experts participated. A mixed-methods design with participatory methodology and evaluation were used. Data was collected with a short questionnaire in Ukrainian. Additionally, at the end of each set, we orally asked about perceived trust and integrity. There were six sets of five group sessions per set, a total of 30 sessions. Each group met online five times for 2 h, a total of 10 h excluding pre- and post-assessment. Breathing exercises sought to reduce stress among the participants.ResultsThe group intervention had both strengths and limitations. Baseline data were obtained from 136 participants, mostly females (75.7%). Answers to pre- and post-questionnaires showed that perceived anxiety/stress was significantly reduced (N = 91, chi-2 20.648, df = 6, p &lt; .02). Perceived health significantly improved between pre- (mean 63.6) and post (77.2) (N = 77, t = −8.08, df = 66, p &lt; .001). Older participants were vulnerable with higher stress and lower mean perceived health after the intervention. Four out of ten needed individual psychosocial support online.The participants' open questions were analyzed with qualitative content analysis, giving five general categories and 25 sub-categories and the theme “Strong efforts to cope with Swedish system”.ConclusionTrust and reduced anxiety level changed after the intervention and a combination online of small, closed group meetings with the possibility of personal acquaintance, trust and individual follow-up psychosocial support for those in need to be paid attention to for future refugee support services, particular an online format.
What do I need from myself as a student but also from others to reduce the impact of stress on academic performance? Self-efficacy and social support
Authors: Pǎduraru Ancuţa Elena , Soponaru Camelia , Dîrţu Cǎtǎlin , Gavrilovici Ovidiu , Bucuţǎ Mihaela Dana 
Published: January 2025

IntroductionThe literature emphasizes the link between academic stress and academic performance and the fact that the most vulnerable students are first-year students, but we still need to understand the underlying mechanism for forming targeted intervention strategies and the protective factors. The main objective of the present study was to test the mediating effect of self-efficacy and social support on the relationship between academic stress and academic performance. We also aimed to identify the main sources of stress and their significant sources of support.MethodsData were collected via an online survey platform in January 2024, with 436 students, with a mean age of 19.99 ± 3.72, responding affirmatively to our invitation. Thus, in this cross-sectional study, the sample was a convenience sample.ResultsThe highest source of stress was the pressure to perform, followed by time restraints, perceptions of workload and examinations, and self-perceptions. The highest level of support is obtained from significant others, followed by family support and, last, friends' support. Both self-efficacy and social support partially mediate the effect of academic stress on academic performance.ConclusionsOur results suggest that one way to improve students' performance, even when they experience academic stress, is to increase their levels of social support, especially from family, as well as their self-efficacy. These results can be used by services that provide psychological support to students in order to design and implement intervention programs.
Interpretative phenomenological analysis of guidance advocates’ online counseling experiences
Authors: Tangco-Siason Amabel , Paulino Rex S. 
Published: January 2025

IntroductionIn order to document and understand the lived experiences of guidance advocates navigating transactional distance in education, this phenomenological study examined the experiences of advocates in the Philippines.MethodsThis study examined how clients and guidance advocates interact in online learning environments, with a particular emphasis on the experiences of eight individuals who underwent online counseling (OC). These individuals provided firsthand experiences that elaborated the phenomenon.ResultsThe study, which has its roots in Transactional Distance Theory (TDT), finds interesting connections between the variables and the experiences of the individuals. Among these variables are: (i) discourse that mirrors the exchanges between a counselor and a client; (ii) framework, which includes OC dynamics and changing communication requirements; and (iii) learner autonomy, which stands for the involvement of clients in online learning.DiscussionA community-based framework for developing students is formed by these variables. The data shared by participants reveal complex issues as they struggle with the limitations and realities of applying standards in multimodal counseling sessions. This change emphasizes how important it is for students to have a strong support system. The study, which focused on guidance advocate-client relations, took inspiration from the eight participants’ OC experiences. The mechanisms that promote a reciprocal, technology-mediated kind of communication are central to this emerging structure.
Household level fuelwood use and carbon dioxide emissions in Delanta district, Northeastern Ethiopia
Authors: Sintayehu Eshetu Abebaw 
Published: January 2025

IntroductionEthiopian rural households primarily rely on fuelwood as their main energy Q7 source, yet the country lacks precise data on fuelwood harvesting and its economic significance. Consequently, there is limited understanding of CO2 emissions resulting from fuelwood use.MethodsThis study aimed to estimate the annual amount of fuelwood collected and the associated CO2 and carbon (C) emissions. Using simple random sampling for household selection, data were analyzed with Excel and Stata.ResultsThe results reveal that fuelwood dependency is a major driver of deforestation and CO2 emissions, with households consuming approximately 2,725 kg of firewood and 26 kg of charcoal annually. Each household also extracts an average of 3,909.3 kg of firewood and 516.5 kg of charcoal annually for sale. Among sampled households, fuelwood constitutes 904,261 kg of energy, with 51% used for household consumption and 96% allocated for income generation. The inefficient burning of this fuel results in significant emissions, adding 974,000 kg of CO2 or 265,600 kg of carbon annually to the household carbon footprint. On average, each household emits 7,740 kg of CO2 and 1,960 kg of carbon per year.DiscussionThe study emphasizes that, while fuelwood plays a critical role in household energy and income, its unsustainable use accelerates emissions and deforestation. To mitigate these effects, the adoption of alternative energy sources like electricity and forest conservation through local plantations is essential for climate resilience.
Identification of key genes underlying radiosensitivity and radioresistance in endometrial cancer through integrated bioinformatics analysis
Authors: Wan Chunhui , Zhang Lei , Yu Ting , Lu Hui , Xiao Han , Du Juan 
Published: January 2025

IntroductionRadiation therapy is crucial in the treatment of endometrial cancer (UCEC). Patients exhibit significant variability in radiosensitivity, affecting therapeutic effect. Scarcity of studies exploring the gene-radiosensitivity relationship based on clinical data. Underlying molecular mechanisms of radiosensitivity and radioresistance require further investigation.MethodsStudy aimed to reveal molecular mechanisms underlying radiosensitivity and radioresistance in UCEC patients. Included 12 radiosensitive and 20 radioresistant UCEC samples. Conducted differential expression analysis to screen for significantly different genes between groups. Applied Lasso regression and randomized survival forest model to identify key genes. Performed functional annotation, correlation analysis, and survival analysis on key genes.ResultsKey genes positively correlated with UCEC tumorigenesis-related genes in the radioresistant group. Reduction in the proportion of Macrophages.M0 observed in the radioresistant group, associated with poor prognosis. GO and GSVA analyses revealed biological processes and signaling pathways involved in key genes. High expression of MARCKS, MACC1, and GRB10 correlated with poorer survival rates. High expression of NINJ2 correlated with higher survival rates and higher sensitivity to radiation therapy.DiscussionStudy contributes to a deeper understanding of UCEC radiosensitivity. Provides theoretical support for the development of personalized radiotherapy regimens in clinical practice. Potential to improve prognosis and quality of life of patients.
Prevalence and characteristics of gastrointestinal disorders, medication use, and diagnostic interventions in pediatric patients with cystic fibrosis: a nested case–control analysis from the TriNetX EMR-derived global research network real-world dataset
Authors: Trocchia Carolena , Mosha Maua , Hamner Bailey , Goldenberg Neil , Khalaf Racha 
Published: January 2025

BackgroundGastrointestinal (GI) disease in pediatric patients with cystic fibrosis (CF) is a growing concern in the era of improved lung disease; however, the true prevalence of GI diagnoses, medical therapies, and frequency of diagnostic and therapeutic interventions in this population have yet to be explored on a multisystem scale. The aim of the present study was to describe, among pediatric patients with CF (PwCF) compared to a large cohort of healthy controls, the prevalence and types of 1) GI disorders; 2) GI medication use; and 3) GI procedural interventions. MethodsThis was a multicenter case-control analysis using the TriNetX electronic medical record (EMR) global anonymized data platform. Patients were included if they had an ICD9/10 diagnosis code and were between the age of zero to ≤ 21 years between January 1st, 2010, and January 1st, 2020. Those with a history of solid organ transplants were excluded.ResultsThe cohort was comprised of 7,649 patients with a diagnosis of CF (cases) and 22,516,240 patients without CF (controls). The prevalence of any GI disorder was greater in pediatric PwCF compared to those without CF (73% versus &lt;1%). The prevalence of diseases of the biliary tract and pancreas (43% vs. &lt;1%; p&lt;.0001), hepatic disease (8.8% vs. &lt;1%; p&lt;.0001), disease of the esophagus to duodenum (34% vs. &lt;1%; p&lt;.0001), and intestinal disease (43% vs. &lt;1%; p&lt;.0001) were each significantly greater in PwCF (p&lt;0.0001). Furthermore, the frequencies of esophagogastroduodenoscopy (EGD), colonoscopy and endoscopic retrograde cholangiopancreatography (ERCP) were significantly higher among PwCF than controls (9.5% vs. &lt;1%; 2.8%% vs. &lt;1%; and 0.3% vs &lt;.001% respectively; p&lt;0.0001 for all comparisons). ConclusionThe prevalence of GI diagnoses, use of GI medications, and frequency of GI procedures are all higher among pediatric patients PwCF. Prospective multicenter studies are warranted to substantiate these findings, further investigate risk factors for GI disorders, and to describe potential changes in GI disorders with novel CF disease-modifying therapies.
Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning
Authors: Pu Zuhui , Wang Tony Bowei , Lu Ying , Wu Zijing , Chen Yuxian , Luo Ziqi , Wang Xinyu , Mou Lisha 
Published: January 2025

IntroductionType 2 diabetes (T2D) is a complex metabolic disorder with significant global health implications. Understanding the molecular mechanisms underlying T2D is crucial for developing effective therapeutic strategies. This study employs single-cell RNA sequencing (scRNA-seq) and machine learning to explore the the pathogenesis of T2D, with a particular focus on immune cell infiltration.MethodsWe analyzed scRNA-seq data from islet cells of T2D and nondiabetic (ND) patients, identifying differentially expressed genes (DEGs), especially those related to metal ion transport (RMITRGs). We employed 12 machine learning algorithms to develop predictive models and assessed immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). Correlations between immune cells and key RMITRGs were investigated, and the interactions among these genes were explored through protein-protein interaction (PPI) network analysis. Additionally, we performed a detailed cell-cell communication analysis to identify significant signaling pathways in T2D.ResultsOur analysis identified 1953 DEGs between T2D and ND patients, with the Stepglm[backward] plus GBM model demonstrating high predictive accuracy and identifying 13 hub RMITRGs. Twelve protein structures were predicted using AlphaFold 3, revealing potential functional conformations. We observed a strong correlation between hub RMITRGs and immune cells, and PPI network analysis revealed key interactions. Cell-cell communication analysis highlighted 16 active signaling pathways, with CXCL, MIF, and COMPLEMENT linked to immune and inflammatory responses, and WNT, KIT, LIFR, and HGF pathways uniquely activated in T2D.ConclusionOur analysis identified genes crucial for T2D, emphasizing ion transport, signaling, and immune cell interactions. These findings suggest therapeutic potential to enhance T2D management. The identified pathways and genes provide valuable insights into the disease mechanisms and potential targets for intervention.
MUC2 expression modulates immune infiltration in colorectal cancer
Authors: Raynaud Christophe M. , Jabeen Ayesha , Ahmed Eiman I. , Hubrack Satanay , Sanchez Apryl , Sherif Shimaa , Al-Shaibi Ahmad A. , Lo Bernice , Roelands Jessica , Bedognetti Davide , Hendrickx Wouter 
Published: January 2025

IntroductionColorectal cancer (CRC) is a prevalent malignancy with significant morbidity and mortality worldwide. A deeper understanding of the interaction of cancer cells with other cells in the tumor microenvironment is crucial to devise effective therapeutic strategies. MUC2, a major component of the protective mucus layer in the gastrointestinal tract, has been implicated in CRC progression and immune response regulation.MethodIn this study, we sought to elucidate the relationship between MUC2 expression and immune infiltration within CRC using in vitro models involving two well-established cell lines, HT-29 and LS-174T. By employing CRISPR-mediated MUC2 knockout, we investigated the influence of MUC2 on tumor immune infiltration and its interplay with T cells and NK cells enriched peripheral blood mononuclear cells (PBMCs) in 3D spheroid cultures.ResultsWhile MUC2 was more abundant in LS-174T cell line compared to HT-29, its knockout resulted in increased immune infiltration solely in the HT-29 cell line, but not in the LS-174T cell line. We revealed that the removal of MUC2 protein was compensated in LS-174T by the expression of other gel-forming mucin proteins (MUC6, MUC5B) commonly expressed in the gastrointestinal epithelium, while this was not observed in HT-29 cell line.ConclusionOur study is the first to demonstrate that MUC2 functions as a physical barrier to immune infiltration in colorectal cancer (CRC) in vitro. In HT-29 cells, MUC2 knockout increased immune infiltration, while in LS-174T cells, compensatory expression of other mucins (MUC6, MUC5B) maintained the barrier. These findings reveal the complexity of mucin biology in CRC and suggest that targeting mucin pathways could be a novel therapeutic approach.