bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cross-sectional and longitudinal associations between self-esteem and BMI depends on baseline BMI category in a population-based study
Authors: Robert Margaux , Allès Benjamin , Gisch Ulrike A. , Shankland Rebecca , Hercberg Serge , Touvier Mathilde , Leys Christophe , Péneau Sandrine 
Published: January 2024

Abstract
Background
Some studies have reported associations between self-esteem and weight status, but longitudinal data on adults remain scarce. The aim of this population-based study was to analyze the cross-sectional and longitudinal association between self-esteem and body mass index (BMI) and to investigate whether baseline BMI has an impact on this association.

Methods
In 2016, 29,735 participants aged ≥ 18 years in the NutriNet-Santé cohort completed the Rosenberg Self-Esteem Scale. BMI was self-reported yearly over a 4-year period. Association between self-esteem and BMI was assessed using mixed models and logistic regressions. Analyses were stratified by BMI (categorical) at baseline and adjusted on sociodemographic and lifestyle characteristics.

Results
At baseline, higher self-esteem was associated with higher BMI in normal weight individuals(p = 0.32), and with lower BMI in obese class II and III individuals (p = 0.13). In addition, higher baseline self-esteem was associated with BMI increase over time in normal weight individuals (p = 0.15). Among normal weight individuals, those with higher self-esteem were less likely to show a decrease in their BMI (p = 0.005), while no association was observed with BMI increase (p = 0.81).

Discussion
Our findings suggest that the association between self-esteem and BMI depends on the initial category of BMI, with a negligible effect of self-esteem.
Higher insoluble fiber intake is associated with a lower risk of prostate cancer: results from the PLCO cohort
Authors: Shen Yang , Yuan Qinbo , Shi Minhong , Luo Banxin 
Published: January 2024

AbstractStudies regarding the relationship between fiber intake and prostate cancer (PCa) have conflicting results. Therefore, this study examined the relationship between fiber intake and the risk of PCa by using data from Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. A total of 54,336 participants in the United States, consisting of 6,414 patients with PCa, were included in this study. Multivariate Cox regression models were applied to estimate adjusted hazard ratios (aHRs) and corresponding 95% confidence intervals (CIs). Compared with individuals in the lowest quartile, individuals in the highest quartile of insoluble fiber intake had a significantly lower risk of PCa (aHR, 0.87; 95% CI, 0.78–0.98). By contrast, no significant associations were detected between total fiber intake (aHR, 0.90; 95% CI, 0.80–1.01) or soluble fiber intake (aHR, 0.90; 95% CI, 0.80–1.02). Subgroup analyses showed that insoluble fiber was related to a decreased risk of PCa in subjects with the following characteristics: age > 65 years, nonsmoking or former smokers, education level ≤ high school, non-Hispanic white ethnicity, or without a family history of PCa. In addition, significant combined effects of insoluble fiber intake, age and family history of PCa on the risk of PCa were observed, but no combined effects of smoking status and insoluble fiber intake were observed. In addition, total fiber, insoluble fiber, and soluble fiber intake had no influence on the mortality of PCa patients. These results show that all 3 measures of fiber suggest a protective association, but insoluble fiber may have a stronger association with the risk of PCa. Future studies are warranted to further investigate these relationships.
Effects and mechanisms of Porphyromonas gingivalis outer membrane vesicles induced cardiovascular injury
Authors: Guo Jianbin , Lin Kaijin , Wang Siyi , He Xiaozhen , Huang Zhen , Zheng Minqian 
Published: January 2024

AbstractBackgroundThe outer membrane vesicles (OMVs) derived fromPorphyromonas gingivalis(P. gingivalis) have long been acknowledged for their crucial role in the initiation of periodontitis. However, the implications ofP. gingivalisOMVs in the context of cardiovascular disease (CVD) remain incompletely understood. This study aimed to clarify both the impact and the underlying mechanisms through whichP.gingivalisOMVs contribute to the propagation of distal cardiovascular inflammation and trauma.MethodsIn this study, various concentrations (0, 1.25, 2.5, and 4.5 µg/µL) ofP. gingivalisOMVs were microinjected into the common cardinal vein of zebrafish larvae at 48 h post-fertilization (hpf) to assess changes in cardiovascular injury and inflammatory response. Zebrafish larvae from both the PBS and the 2.5 µg/µL injection cohorts were harvested at 30 h post-injection (hpi) for transcriptional analysis. Real-time quantitative PCR (RT-qPCR) was employed to evaluate relative gene expression.ResultsThese findings demonstrated thatP. gingivalisOMVs induced pericardial enlargement in zebrafish larvae, caused vascular damage, increased neutrophil counts, and activated inflammatory pathways. Transcriptomic analysis further revealed the involvement of the immune response and the extracellular matrix (ECM)-receptor interaction signaling pathway in this process.ConclusionThis study illuminated potential mechanisms through whichP. gingivalisOMVs contribute to CVD. It accentuated their involvement in distal cardiovascular inflammation and emphasizes the need for further research to comprehensively grasp the connection between periodontitis and CVD.
Development and application of Chinese medical ontology for diabetes mellitus
Authors: Hu Jie , Huang Zixian , Ge Xuewen , Shen Yulin , Xu Yihan , Zhang Zirui , Zhou Guangyin , Wang Junjie , Lu Shan , Yu Yun , Wan Cheng , Zhang Xin , Huang Ruochen , Liu Yun , Cheng Gong 
Published: January 2024

Abstract
Objective
To develop a Chinese Diabetes Mellitus Ontology (CDMO) and explore methods for constructing high-quality Chinese biomedical ontologies.

Materials and methods
We used various data sources, including Chinese clinical practice guidelines, expert consensus, literature, and hospital information system database schema, to build the CDMO. We combined top-down and bottom-up strategies and integrated text mining and cross-lingual ontology mapping. The ontology was validated by clinical experts and ontology development tools, and its application was validated through clinical decision support and Chinese natural language medical question answering.

Results
The current CDMO consists of 3,752 classes, 182 fine-grained object properties with hierarchical relationships, 108 annotation properties, and over 12,000 mappings to other well-known medical ontologies in English. Based on the CDMO and clinical practice guidelines, we developed 200 rules for diabetes diagnosis, treatment, diet, and medication recommendations using the Semantic Web Rule Language. By injecting ontology knowledge, CDMO enhances the performance of the T5 model on a real-world Chinese medical question answering dataset related to diabetes.

Conclusion
CDMO has fine-grained semantic relationships and extensive annotation information, providing a foundation for medical artificial intelligence applications in Chinese contexts, including the construction of medical knowledge graphs, clinical decision support systems, and automated medical question answering. Furthermore, the development process incorporated natural language processing and cross-lingual ontology mapping to improve the quality of the ontology and improved development efficiency. This workflow offers a methodological reference for the efficient development of other high-quality Chinese as well as non-English medical ontologies.
Daly/Cost comparison in the management of peripheral arterial disease at 17 Belgian hospitals
Authors: Rondelet Benoît , Dehanne Fabian , Van Den Bulcke Julie , Martins Dimitri , Belhaj Asmae , Libert Benoît , Leclercq Pol , Pirson Magali 
Published: January 2024

Abstract
Objective
Peripheral arterial disease (PAD) is a manifestation of atherosclerosis that affects the lower extremities and afflicts more than 200 million people worldwide. Because of limited resources, the need to provide quality care associated with cost control is essential for health policies. Our study concerns an interhospital comparison among seventeen Belgian hospitals that integrates the weighting of quality indicators and the costs of care, from the hospital perspective, for a patient with this pathology in 2018.

Methods
The disability-adjusted life years (DALYs) were calculated by adding the number of years of life lost due to premature death and the number of years of life lost due to disability for each in-hospital stay. The DALY impact was interpreted according to patient safety indicators. We compared the hospitals using the adjusted values ​​of costs and DALYs for their case mix index, obtained by relating the observed value to the predicted value obtained by linear regression.

Results
We studied 2,437 patients and recorded a total of 560.1 DALYs in hospitals. The in-hospital cost average [standard deviation (SD)] was €8,673 (€10,893). Our model identified the hospitals whose observed values were higher than predicted; six needed to reduce the costs and impacts of DALYs, six needed to improve one of the two factors, and four seemed to have good results. The average cost (SD) for the worst performing hospitals amounted to €27,803 (€28,358).

Conclusions
Studying the costs of treatment according to patient safety indicators permits us to evaluate the entire chain of care using a comparable unit of measurement.
Pulse wave-based evaluation of the blood-supply capability of patients with heart failure via machine learning
Authors: Wang Sirui , Ono Ryohei , Wu Dandan , Aoki Kaoruko , Kato Hirotoshi , Iwahana Togo , Okada Sho , Kobayashi Yoshio , Liu Hao 
Published: January 2024

AbstractPulse wave, as a message carrier in the cardiovascular system (CVS), enables inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Heart failure (HF) is a major CVD, typically requiring expensive and time-consuming treatments for health monitoring and disease deterioration; it would be an effective and patient-friendly tool to facilitate rapid and precise non-invasive evaluation of the heart’s blood-supply capability by means of powerful feature-abstraction capability of machine learning (ML) based on pulse wave, which remains untouched yet. Here we present an ML-based methodology, which is verified to accurately evaluate the blood-supply capability of patients with HF based on clinical data of 237 patients, enabling fast prediction of five representative cardiovascular function parameters comprising left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left ventricular end-systolic diameter (LVDs), left atrial dimension (LAD), and peripheral oxygen saturation (SpO2). Two ML networks were employed and optimized based on high-quality pulse wave datasets, and they were validated consistently through statistical analysis based on the summary independent-samplest-test (p > 0.05), the Bland–Altman analysis with clinical measurements, and the error-function analysis. It is proven that evaluation of the SpO2, LAD, and LVDd performance can be achieved with the maximum error < 15%. While our findings thus demonstrate the potential of pulse wave-based, non-invasive evaluation of the blood-supply capability of patients with HF, they also set the stage for further refinements in health monitoring and deterioration prevention applications.
Multimodal diagnosis model of Alzheimer’s disease based on improved Transformer
Authors: Tang Yan , Xiong Xing , Tong Gan , Yang Yuan , Zhang Hao 
Published: January 2024

AbstractPurposeRecent technological advancements in data acquisition tools allowed neuroscientists to acquire different modality data to diagnosis Alzheimer’s disease (AD). However, how to fuse these enormous amount different modality data to improve recognizing rate and find significance brain regions is still challenging.MethodsThe algorithm used multimodal medical images [structural magnetic resonance imaging (sMRI) and positron emission tomography (PET)] as experimental data. Deep feature representations of sMRI and PET images are extracted by 3D convolution neural network (3DCNN). An improved Transformer is then used to progressively learn global correlation information among features. Finally, the information from different modalities is fused for identification. A model-based visualization method is used to explain the decisions of the model and identify brain regions related to AD.ResultsThe model attained a noteworthy classification accuracy of 98.1% for Alzheimer’s disease (AD) using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Upon examining the visualization results, distinct brain regions associated with AD diagnosis were observed across different image modalities. Notably, the left parahippocampal region emerged consistently as a prominent and significant brain area.ConclusionsA large number of comparative experiments have been carried out for the model, and the experimental results verify the reliability of the model. In addition, the model adopts a visualization analysis method based on the characteristics of the model, which improves the interpretability of the model. Some disease-related brain regions were found in the visualization results, which provides reliable information for AD clinical research.
A district-level geospatial analysis of anaemia prevalence among rural men in India, 2019-21
Authors: Singh Aditya , Ram Sumit , Chandra Rakesh , Tanti Arabindo , Singh Shivani , Kundu Ananya 
Published: January 2024

AbstractBackgroundDespite its considerable impact on health and productivity, anemia among men has received limited attention. In a country as diverse as India, characterized by extensive geographic variations, there is a pressing need to investigate the nuanced spatial patterns of anemia prevalence among men. The identification of specific hotspots holds critical implications for policymaking, especially in rural areas, where a substantial portion of India’s population resides.MethodsThe study conducted an analysis on a sample of 61,481 rural men from 707 districts of India, utilizing data from the National Family Health Survey-5 (2019-21). Various analytical techniques, including Moran’s I, univariate LISA (Local Indicators of Spatial Association), bivariate LISA, and spatial regression models such as SLM (Spatial Lag Model), and SEM (Spatial Error Model) were employed to examine the geographic patterns and spatial correlates of anaemia prevalence in the study population.ResultsIn rural India, three out of every ten men were found to be anemic. The univariate Moran’s I value for anaemia was 0.66, indicating a substantial degree of spatial autocorrelation in anaemia prevalence across the districts in India. Cluster and outlier analysis identified five prominent ‘hotspots’ of anaemia prevalence across 97 districts, primarily concentrated in the eastern region (encompassing West Bengal, Jharkhand, and Odisha), the Dandakaranya region, the Madhya Pradesh-Maharashtra border, lower Assam, and select districts in Jammu and Kashmir. The results of SLM revealed significant positive association between anaemia prevalence at the district-level and several key factors including a higher proportion of Scheduled Tribes, men in the 49–54 years age group, men with limited or no formal education, individuals of the Muslim faith, economically disadvantaged men, and those who reported alcohol consumption.ConclusionsSubstantial spatial heterogeneity in anaemia prevalence among men in rural India suggests the need for region-specific targeted interventions to reduce the burden of anaemia among men in rural India and enhance the overall health of this population.
New immunotherapy approaches for colorectal cancer: focusing on CAR-T cell, BiTE, and oncolytic viruses
Authors: Kamrani Amin , Nasiri Hadi , Hassanzadeh Ali , Ahmadian Heris Javad , Mohammadinasab Reza , Sadeghvand Shahram , Sadeghi Mohammadreza , Valedkarimi Zahra , Hosseinzadeh Ramin , Shomali Navid , Akbari Morteza 
Published: January 2024

AbstractColorectal cancer is one of the most common causes of mortality worldwide. There are several potential risk factors responsible for the initiation and progression of colorectal cancer, including age, family history, a history of inflammatory bowel disease, and lifestyle factors such as physical activity and diet. For decades, there has been a vast amount of study on treatment approaches for colorectal cancer, which has led to conventional therapies such as chemotherapy, surgery, etc. Considering the high prevalence and incidence rate, scholars believe there is an urgent need for an alternative, more efficacious treatment with fewer adverse effects than the abovementioned treatments. Immunotherapy has emerged as a potential treatment alternative in a few years and has become one of the fastest-evolving therapeutic methods. Immunotherapy works by activating or enhancing the immune system’s power to identify and attack cancerous cells. This review summarizes the most crucial new immunotherapy methods under investigation for colorectal cancer treatment, including Immune checkpoint inhibitors, CAR-T cell therapy, BiTEs, Tumor-infiltrating lymphocytes, and Oncolytic virus therapy. Furthermore, this study discusses the application of combination therapy, precision medicine, biomarker discovery, overcoming resistance, and immune-related adverse effects.
Faecalibacterium prausnitzii as a potential Antiatherosclerotic microbe
Authors: Yang Hai-Tao , Jiang Zhi-hui , Yang Yi , Wu Ting-Ting , Zheng Ying-Ying , Ma Yi-Tong , Xie Xiang 
Published: January 2024

Abstract
Background
The gut microbiota plays a crucial role in coronary artery disease (CAD) development, but limited attention has been given to the role of the microbiota in preventing this disease. This study aimed to identify key biomarkers using metagenomics and untargeted metabolomics and verify their associations with atherosclerosis.

Methods
A total of 371 participants, including individuals with various CAD types and CAD-free controls, were enrolled. Subsequently, significant markers were identified in the stool samples through gut metagenomic sequencing and untargeted metabolomics. In vivo and in vitro experiments were performed to investigate the mechanisms underlying the association between these markers and atherosclerosis.

Results
Faecal omics sequencing revealed that individuals with a substantial presence of Faecalibacterium prausnitzii had the lowest incidence of CAD across diverse CAD groups and control subjects. A random forest model confirmed the significant relationship between F. prausnitzii and CAD incidence. Notably, F. prausnitzii emerged as a robust, independent CAD predictor. Furthermore, our findings indicated the potential of the gut microbiota and gut metabolites to predict CAD occurrence and progression, potentially impacting amino acid and vitamin metabolism. F. prausnitzii mitigated inflammation and exhibited an antiatherosclerotic effect on ApoE−/− mice after gavage. This effect was attributed to reduced intestinal LPS synthesis and reinforced mechanical and mucosal barriers, leading to decreased plasma LPS levels and an antiatherosclerotic outcome.

Conclusions
Sequencing of the samples revealed a previously unknown link between specific gut microbiota and atherosclerosis. Treatment with F. prausnitzii may help prevent CAD by inhibiting atherosclerosis.
Establishment and analysis of a novel diagnostic model for systemic juvenile idiopathic arthritis based on machine learning
Authors: Ding Pan , Du Yi , Jiang Xinyue , Chen Huajian , Huang Li 
Published: January 2024

Abstract
Background
Systemic juvenile idiopathic arthritis (SJIA) is a form of childhood arthritis with clinical features such as fever, lymphadenopathy, arthritis, rash, and serositis. It seriously affects the growth and development of children and has a high rate of disability and mortality. SJIA may result from genetic, infectious, or autoimmune factors since the precise source of the disease is unknown. Our study aims to develop a genetic-based diagnostic model to explore the identification of SJIA at the genetic level.

Methods
The gene expression dataset of peripheral blood mononuclear cell (PBMC) samples from SJIA was collected from the Gene Expression Omnibus (GEO) database. Then, three GEO datasets (GSE11907-GPL96, GSE8650-GPL96 and GSE13501) were merged and used as a training dataset, which included 125 SJIA samples and 92 health samples. GSE7753 was used as a validation dataset. The limma method was used to screen differentially expressed genes (DEGs). Feature selection was performed using Lasso, random forest (RF)-recursive feature elimination (RFE) and RF classifier.

Results
We finally identified 4 key genes (ALDH1A1, CEACAM1, YBX3 and SLC6A8) that were essential to distinguish SJIA from healthy samples. And we combined the 4 key genes and performed a grid search as well as 10-fold cross-validation with 5 repetitions to finally identify the RF model with optimal mtry. The mean area under the curve (AUC) value for 5-fold cross-validation was greater than 0.95. The model’s performance was then assessed once more using the validation dataset, and an AUC value of 0.990 was obtained. All of the above AUC values demonstrated the strong robustness of the SJIA diagnostic model.

Conclusions
We successfully developed a new SJIA diagnostic model that can be used for a novel aid in the identification of SJIA. In addition, the identification of 4 key genes that may serve as potential biomarkers for SJIA provides new insights to further understand the mechanisms of SJIA.
N6-methyladenosine modification positively regulate Japanese encephalitis virus replication
Authors: Yao Min , Cheng Zhirong , Li Xueyun , Li Yuexiang , Ye Wei , Zhang Hui , Liu He , Zhang Liang , Lei Yingfeng , Zhang Fanglin , Lv Xin 
Published: January 2024

AbstractN6-methyladenosine (m6A) is present in diverse viral RNA and plays important regulatory roles in virus replication and host antiviral innate immunity. However, the role of m6A in regulating JEV replication has not been investigated. Here, we show that the JEV genome contains m6A modification upon infection of mouse neuroblast cells (neuro2a). JEV infection results in a decrease in the expression of m6A writer METTL3 in mouse brain tissue. METTL3 knockdown by siRNA leads to a substantial decrease in JEV replication and the production of progeny viruses at 48 hpi. Mechanically, JEV triggered a considerable increase in the innate immune response of METTL3 knockdown neuro2a cells compared to the control cells. Our study has revealed the distinctive m6A signatures of both the virus and host in neuro2a cells infected with JEV, illustrating the positive role of m6A modification in JEV infection. Our study further enhances understanding of the role of m6A modification in Flaviviridae viruses.