bluuebunny/crossref_metadata_2025_split huggingface dataset:
A nomogram with Nottingham prognostic index for predicting locoregional recurrence in breast cancer patients
Authors: Zheng Jianqing , Zeng Bingwei , Huang Bifen , Wu Min , Xiao Lihua , Li Jiancheng 
Published: September 2024

BackgroundThe Nottingham prognostic index (NPI) has been shown to negatively impact survival in breast cancer (BC). However, its ability to predict the locoregional recurrence (LRR) of BC remains still unclear. This study aims to determine whether a higher NPI serves as a significant predictor of LRR in BC.MethodsIn total, 238 patients with BC were included in this analysis, and relevant clinicopathological features were collected. Correlation analysis was performed between NPI scores and clinicopathological characteristics. The optimal nomogram model was determined by Akaike information criterion. The accuracy of the model’s predictions was evaluated using receiver operating characteristic curves (ROC curves), calibration curves and goodness of fit tests. The clinical application value was assessed through decision curve analysis.ResultsSix significant variables were identified, including age, body mass index (BMI), TNM stage, NPI, vascular invasion, perineural invasion (P&lt;0.05). Two prediction models, namely a TNM-stage-based model and an NPI-based model, were constructed. The area under the curve (AUC) for the TNM-stage- and NPI-based models were 0.843 (0.785,0.901) and 0.830 (0.766,0.893) in training set and 0.649 (0.520,0.778) and 0.728 (0.610,0.846) in validation set, respectively. Both models exhibited good calibration and goodness of fit. The F-measures were 0.761vs 0.756 and 0.556 vs 0.696, respectively. Clinical decision curve analysis showed that both models provided clinical benefits in evaluating risk judgments based on the nomogram model.Conclusionsa higher NPI is an independent risk factor for predicting LRR in BC. The nomogram model based on NPI demonstrates good discrimination and calibration, offering potential clinical benefits. Therefore, it merits widespread adoption and application.
Assessing microvascular invasion in HBV-related hepatocellular carcinoma: an online interactive nomogram integrating inflammatory markers, radiomics, and convolutional neural networks
Authors: Zhong Yun , Chen Lingfeng , Ding Fadian , Ou Wenshi , Zhang Xiang , Weng Shangeng 
Published: September 2024

ObjectiveThe early recurrence of hepatocellular carcinoma (HCC) correlates with decreased overall survival. Microvascular invasion (MVI) stands out as a prominent hazard influencing post-resection survival status and metastasis in patients with HBV-related HCC. The study focused on developing a web-based nomogram for preoperative prediction of MVI in HBV-HCC.Materials and methods173 HBV-HCC patients from 2017 to 2022 with complete preoperative clinical data and Gadopentetate dimeglumine-enhanced magnetic resonance images were randomly divided into two groups for the purpose of model training and validation, using a ratio of 7:3. MRI signatures were extracted by pyradiomics and the deep neural network, 3D ResNet. Clinical factors, blood-cell-inflammation markers, and MRI signatures selected by LASSO were incorporated into the predictive nomogram. The evaluation of the predictive accuracy involved assessing the area under the receiver operating characteristic (ROC) curve (AUC), the concordance index (C-index), along with analyses of calibration and decision curves.ResultsInflammation marker, neutrophil-to-lymphocyte ratio (NLR), was positively correlated with independent MRI radiomics risk factors for MVI. The performance of prediction model combined serum AFP, AST, NLR, 15 radiomics features and 7 deep features was better than clinical and radiomics models. The combined model achieved C-index values of 0.926 and 0.917, with AUCs of 0.911 and 0.907, respectively.ConclusionNLR showed a positive correlation with MRI radiomics and deep learning features. The nomogram, incorporating NLR and MRI features, accurately predicted individualized MVI risk preoperatively.
Advanced CNN models in gastric cancer diagnosis: enhancing endoscopic image analysis with deep transfer learning
Authors: Bhardwaj Priya , Kim SeongKi , Koul Apeksha , Kumar Yogesh , Changela Ankur , Shafi Jana , Ijaz Muhammad Fazal 
Published: September 2024

IntroductionThe rapid advancement of science and technology has significantly expanded the capabilities of artificial intelligence, enhancing diagnostic accuracy for gastric cancer.MethodsThis research aims to utilize endoscopic images to identify various gastric disorders using an advanced Convolutional Neural Network (CNN) model. The Kvasir dataset, comprising images of normal Z-line, normal pylorus, ulcerative colitis, stool, and polyps, was used. Images were pre-processed and graphically analyzed to understand pixel intensity patterns, followed by feature extraction using adaptive thresholding and contour analysis for morphological values. Five deep transfer learning models—NASNetMobile, EfficientNetB5, EfficientNetB6, InceptionV3, DenseNet169—and a hybrid model combining EfficientNetB6 and DenseNet169 were evaluated using various performance metrics.Results &amp; discussionFor the complete images of gastric cancer, EfficientNetB6 computed the top performance with 99.88% accuracy on a loss of 0.049. Additionally, InceptionV3 achieved the highest testing accuracy of 97.94% for detecting normal pylorus, while EfficientNetB6 excelled in detecting ulcerative colitis and normal Z-line with accuracies of 98.8% and 97.85%, respectively. EfficientNetB5 performed best for polyps and stool with accuracies of 98.40% and 96.86%, respectively.The study demonstrates that deep transfer learning techniques can effectively predict and classify different types of gastric cancer at early stages, aiding experts in diagnosis and detection.
High-precision HD spectroscopy near 1.53 μm
Authors: Gotti Riccardo , Wójtewicz Szymon , Marangoni Marco , Gatti Davide , Lamperti Marco 
Published: September 2024

In this study, we report highly precise and accurate measurements of the P(5), P(6), and O(3) transitions of the 2–0 band of HD occurring at approximately 1.5 μm. HD spectra were acquired in the pressure range of 100–900 Torr using a cavity ring-down spectrometer linked to an optical frequency comb. The line-shape analysis was performed using the Hartmann–Tran profile in the so-called β-corrected version. For the P(5) transition, we improved the accuracy of previous line center frequency determinations by more than two orders of magnitude. Moreover, for the P(5) and P(6) line centers, the total standard uncertainty is below 3 MHz, similar to those provided by quantum electrodynamics predictions, making them useful for direct comparisons that might foster future improvements of the theoretical model.
Effects of perceived variety-support on middle school students’ learning engagement in physical education: the mediating role of motivation
Authors: Miao Miao , Chai Hongqin , Xue Rui , Wang Qi 
Published: September 2024

IntroductionHigh engagement in physical education (PE) could effectively develop students’ motor competence and promote physical activity, which was significantly important for students’ physical and mental health. Researches had shown that motivation was an important factor in explaining students’ learning engagement, and variety-support as the fourth independent psychological need was a potential factor influencing students’ learning motivation. However, there was a lack of empirical research evidence on the effect of perceived variety-support on middle school students’ learning engagement in PE and the influencing mechanisms. This study aimed to investigate the direct effect of perceived variety-support on learning engagement in PE and the mediating effect of motivation in PE on the relationship.MethodsA cross-sectional study was conducted and 587 middle school students from Liaoning province filled the paper-and-pencil questionnaires adopting perceived variety-support in PE scale (PVSPES), utrecht work engagement scale-student (UWES-S), and perceived locus of causality in PE scale, which had been proved to have good reliability and validity (294 boys and 293 girls, Mage=13.47 ± 0.94).ResultsThe results showed three variables were significantly positively correlated with each other (r = 0.323-0.562 p &lt; 0.01) and perceived variety-support in PE could not only directly promote middle school students’ learning engagement in PE but also indirectly through the mediating effect of motivation in PE.DiscussionTherefore, in order to better promote students’ participation in PE class, we should pay more attention to satisfy students’ varied PE learning needs and stimulate students’ autonomous learning motivation.
Effect of community attitudes on suicide mortality in South Korea: a nationwide ecological study
Authors: Hong Minseok , Kim Hyesoo , Park C. Hyung Keun , Lee Hyunju , Rhee Sang Jin , Min Sooyeon , Kim Min Ji , Yang Jeong Hun , Song Yoojin , Son Kyunghoon , Ahn Yong Min 
Published: September 2024

BackgroundAttitudes toward suicide are essential in suicide prevention, as suicide is socio-culturally nuanced. Although the relationship between individual attitudes and suicidal behavior has been extensively studied, the effect of community attitudes—aggregated by region—on suicide mortality remains ambiguous. This study explored the association between community attitudes and real-world suicide mortality.MethodsData on attitudes toward suicide from the 2018 Korea National Suicide Survey (N = 1500) and individual mortality data from the MicroData Integrated System were obtained. Confirmatory factor analysis supported a factor structure with three factors: “Permissiveness,” “Unjustified behavior,” and “Readiness to help/Preventability.” Thirty regional units in South Korea aggregated the data for ecological analysis. We used negative binomial models to examine the association at the regional level, and stratified analysis by gender and age group was conducted.Results“Permissiveness” was associated with reduced suicide rates in a univariate model (P &lt; 0.001). Adjusting for gender, age, and additional sociodemographics did not alter the association. Additionally, this relationship was observed in males and individuals under 60 years of age after stratification. However, “Unjustified Behavior” and “Readiness to help/Preventability” exhibited no significant association with suicide in any model or stratum.ConclusionThe observed inverse association between permissive community attitudes and suicide contradicts the findings of previous research that links permissive individual attitudes to increased suicidal behavior. Our findings suggest that attitudes may operate differently at the individual and group levels. Although the cross-sectional design and single-country focus of this study warrant further investigation, our findings indicate that attitudes are significant contextual factors in the process of suicide, which could lead to novel approaches in suicide prevention.
Differential item functioning in the autism behavior checklist in children with autism spectrum disorder based on a machine learning approach
Authors: Peng Kanglong , Chen Meng , Zhou Libing , Weng Xiaofang 
Published: September 2024

AimOur study utilized the Rasch analysis to examine the psychometric properties of the Autism Behavior Checklist (ABC) in children with autism spectrum disorder (ASD).MethodsA total of 3,319 children (44.77 ± 23.52 months) were included. The Rasch model (RM) was utilized to test the reliability and validity of the ABC. The GPCMlasso model was used to test the differential item functioning (DIF).ResultThe response pattern of this sample showed acceptable fitness to the RM. The analysis supported the unidimensionality assumption of the ABC. Disordered category functions and DIF were found in all items in the ABC. The participants responded to the ABC items differently depending not only on autistic traits but also on age groups, gender, and symptom classifications.ConclusionThe Rasch analysis produces reliable evidence to support that the ABC can precisely depict clinical ASD symptoms. Differences in population characteristics may cause unnecessary assessment bias and lead to overestimated or underestimated symptom severity. Hence, special consideration for population characteristics is needed in making an ASD diagnosis.
Factors influencing user’s health information discernment abilities in online health communities: based on SEM and fsQCA
Authors: Wei CaiPing , Cai Yufeng , Liu Jianwei , Guo Yi , Wu Xusheng , He Xiaofeng , Hu DeHua 
Published: September 2024

IntroductionOnline health communities have become the main source for people to obtain health information. However, the existence of poor-quality health information, misinformation, and rumors in online health communities increases the challenges in governing information quality. It not only affects users’ health decisions but also undermines social stability. It is of great significance to explore the factors that affect users’ ability to discern information in online health communities.MethodsThis study integrated the Stimulus-Organism-Response Theory, Information Ecology Theory and the Mindsponge Theory to constructed a model of factors influencing users’ health information discernment abilities in online health communities. A questionnaire was designed based on the variables in the model, and data was collected. Utilizing Structural Equation Modeling (SEM) in conjunction with fuzzy-set Qualitative Comparative Analysis (fsQCA), the study analyzed the complex causal relationships among stimulus factors, user perception, and the health information discernment abilities.ResultsThe results revealed that the dimensions of information, information environment, information technology, and information people all positively influenced health information discernment abilities. Four distinct configurations were identified as triggers for users’ health information discernment abilities. The core conditions included information source, informational support, technological security, technological facilitation, and perceived risk. It was also observed that information quality and emotional support can act as substitutes for one another, as can informational support and emotional support.DiscussionThis study provides a new perspective to study the influencing factors of health information discernment abilities of online health community users. It can provide experiences and references for online health community information services, information resource construction and the development of users’ health information discernment abilities.
Machine learning approaches to identify the link between heavy metal exposure and ischemic stroke using the US NHANES data from 2003 to 2018
Authors: Zibibula Yierpan , Tayier Gulifeire , Maimaiti Aierpati , Liu Tianze , Lu Jinshuai 
Published: September 2024

PurposeThere is limited understanding of the link between exposure to heavy metals and ischemic stroke (IS). This research aimed to develop efficient and interpretable machine learning (ML) models to associate the relationship between exposure to heavy metals and IS.MethodsThe data of this research were obtained from the National Health and Nutrition Examination Survey (US NHANES, 2003–2018) database. Seven ML models were used to identify IS caused by exposure to heavy metals. To assess the strength of the models, we employed 10-fold cross-validation, the area under the curve (AUC), F1 scores, Brier scores, Matthews correlation coefficient (MCC), precision-recall (PR) curves, and decision curve analysis (DCA) curves. Following these tests, the best-performing model was selected. Finally, the DALEX package was used for feature explanation and decision-making visualization.ResultsA total of 15,575 participants were involved in this study. The best-performing ML models, which included logistic regression (LR) (AUC: 0.796) and XGBoost (AUC: 0.789), were selected. The DALEX package revealed that age, total mercury in blood, poverty-to-income ratio (PIR), and cadmium were the most significant contributors to IS in the logistic regression and XGBoost models.ConclusionThe logistic regression and XGBoost models showed high efficiency, accuracy, and robustness in identifying associations between heavy metal exposure and IS in NHANES 2003–2018 participants.
Diapause, pollen ball incidence, and overwintering energetics in the alfalfa leafcutting bee, Megachile rotundata
Authors: Singh Preetpal , Rajamohan Arun , Waybright Sarah A. , Dillon Michael E. , Ferrenberg Scott M. , Rinehart Joseph P. , Bowsher Julia H. 
Published: September 2024

IntroductionWarming summer temperatures have the potential to harm managed pollinators, impacting both summer performance and overwintering success. The alfalfa leafcutting bee, Megachile rotundata, is a solitary bee used for commercial pollination of alfalfa. M. rotundata undergoes facultative diapause in the prepupal stage. Prepupae that diapause early in the season are exposed to warm temperatures for a longer period of time than the individuals that start diapause closer to fall, which may reduce lipid reserves required for overwintering survival. Warm temperatures may also contribute to pollen ball incidence, which is when a provision is present but there is no sign of a larva in the brood cell. Our goal was to identify factors that regulate diapause and pollen ball incidence and examine effects of pre-wintering field conditions on post-overwintering energy reserves in M. rotundata.MethodsNest boxes were installed near Fargo, ND, Laramie, WY, and Las Cruces, NM, which exposed bees to different photoperiods and thermal regimes. Three nest boxes were placed at each site. We monitored nesting conditions and diapause and pollen ball incidence throughout the season. Lipids, sugars, and glycogen reserves were measured in adults after overwintering.Results and DiscussionOur models indicate that most of the variation in diapause incidence was explained by nest, with individuals within a nest tending to have the same diapause outcome. This suggests that the environmental conditions experienced by the mother, or genetic predisposition, influences offspring diapause. We also found evidence that high cavity temperatures can cause diapause aversion. In addition, our study is the first to link high nesting cavity temperatures to increased pollen ball incidence. Exposure to stressful temperatures during development and early diapause resulted in an increase in adult lipid reserves after overwintering. Adult sugar and glycogen reserves were not affected by exposure to warm temperatures during development and early diapause. In conclusion, maternal effects and temperature were important factors for diapause and pollen ball incidence in M. rotundata with macronutrient reserves similar for early and late season bees.
Meaningful patient and public engagement in dissemination—embedding co-production in dementia research
Authors: de Wolf-Linder Susanne , Kramer Iris , Hersperger Martina , Schubert Maria , Bächi Sonja , Stolz Monika , Wolverson Emma , Ramsenthaler Christina 
Published: September 2024

BackgroundPatient and Public Involvement and Engagement (PPIE) is still underutilised in both dementia research and corresponding dissemination activities.AimTo describe the methods, format, and lessons learned in co-creating and co-producing a dissemination strategy for a research project focused on establishing patient-centred outcome measures into routine palliative community care for persons living with dementia (PLWD) and their informal carers.Materials and methodsA participatory, hybrid-format workshop was conducted to co-create the dissemination strategy with a PPIE group. A video presentation of findings and a list of prompts shared prior to the workshop were used to elicit views on dissemination strategies and knowledge translation. The workshop was followed up with a survey to consolidate the dissemination strategy. Workshop minutes and survey responses were analysed using qualitative thematic analysis.Results22 participants from our diverse PPIE group attended the workshop. Two major themes emerged: (a) Knowledge translation: building bridges between research and practise, and (b) Collaboration and dissemination: everyone's voice is needed. Participants suggested critical changes to dissemination methods and materials. Successful knowledge translation depends on a strong evidence base. For this, materials need to be tailored to specific audiences. Everyone's voice needs to be integrated through co-production in dissemination activities by PPIE members to influence societal change. Tailored dissemination activities within a dissemination strategy were co-created spanning all phases of the research cycle.DiscussionInforming and educating the public and policymakers about the needs of PLWD relies on disseminating and fostering knowledge translation throughout all phases of the research cycle.
Effects of inspiratory muscle training on lung function parameter in swimmers: a systematic review and meta-analysis
Authors: Carvajal-Tello Nathali , Ortega José Guillermo , Caballero-Lozada Andrés Fabricio , Devia-Quiñonez María Juliana , González-Calzada Isabella , Rojas-Hernández Daniela , Segura-Ordoñez Alejandro 
Published: September 2024

BackgroundThis systematic review and meta-analysis aimed to assess the impact of inspiratory muscle training (IMT) on lung function parameters (MIP, MEP, FEV1, and FVC) between both elite and non-elite swimmers.MethodsWe searched for controlled clinical trials (CCT) and prospective longitudinal studies (PLS) in elite and non-elite swimmers following an inspiratory muscle training (IMT) protocol with a standardized device, published between 2012 and 2023. The databases used in the search were PubMed, Science Direct, Scopus, Springer, Cochrane Central Register of Controlled Trials, and Google Scholar. The primary outcome assessed was the impact of IMT on lung function parameters, including MIP, MEP, FEV1, and FVC.ResultsWe selected 13 articles involving 277 subjects aged 11–21 years, with 61.4% being male, and 84.6% being elite swimmers. The most commonly used IMT device was the PowerBreathe®, prescribed for 3–12 weeks, 1–2 sessions per day, 3–6 times per week, with 30 repetitions, starting at 50% of MIP and progressing up to 80%. The meta-analysis showed that IMT was associated with a higher MIP (MD = 29.35 cmH2O, 95% CI: 13.04–45.65 cmH2O, p &lt; 0.01) without affecting FEV1 and FVC.ConclusionThe swimmers that used IMT improved muscle strength, specifically MIP, without changes in MEP, FEV1, and FVC.
Discovery of Coumarins from Zanthoxylum dimorphophyllum var. spinifoliumas and Their Potential against Rheumatoid Arthritis
Authors: Du Caixia , Li Xingyu , Chen Junlei , Luo Lili , Yuan Chunmao , Yang Jue , Hao Xiaojiang , Gu Wei 
Published: September 2024

In the present study, a series of coumarins, including eight undescribed bis-isoprenylated ones Spinifoliumin A-H, were isolated and identified from the aerial parts of Zanthoxylum dimorphophyllum var. spinifolium (ZDS), a plant revered in traditional Chinese medicine, particularly for treating rheumatoid arthritis (RA). The structures of the compounds were elucidated using 1D and 2D NMR spectroscopy, complemented by ECD, [Rh2(OCOCF3)4]-induced ECD, Mo2(OAc)4 induced ECD, IR, and HR-ESI-MS mass spectrometry. A network pharmacology approach allowed for predicting their anti-RA mechanisms and identifying the MAPK and PI3K-Akt signaling pathways, with EGFR as a critical gene target. A CCK-8 method was used to evaluate the inhibition activities on HFLS-RA cells of these compounds. The results demonstrated that Spinifoliumin A, B, and D-H are effective at preventing the abnormal proliferation of LPS-induced HFLS-RA cells. The results showed that compounds Spinifoliumin A, D, and G can significantly suppress the levels of IL-1β, IL-6, and TNF-α. Moreover, molecular docking methods were utilized to confirm the high affinity between Spinifoliumin A, D, and G and EGFR, SRC, and JUN, which were consistent with the results of network pharmacology. This study provides basic scientific evidence to support ZDS’s traditional use and potential clinical application.