Global research trends in the field of liver cirrhosis from 2011 to 2020: A visualised and bibliometric study

Gan, Peiling; Huang, Shu; Pan, Xiao; Xia, Huifang; Zeng, Xinyi; Ren, Wensen; Zhou, Xian; Lv, Mu-Han; Tang, Xiaowei

doi:10.3748/wjg.v28.i33.4909

Cited by 4 publications

(4 citation statements)

References 31 publications

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“…Articles with the highest burst of citations are potentially valuable for exploring research frontiers [11] . In this study, “coffee” was the largest cluster, and “ spinal surgery ” was active until the most recent publication year.…”

Section: Discussionmentioning

confidence: 99%

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Global research trends in postoperative ileus from 2011 to 2023: A Scientometric study

Zhou,

Yin,

Sun

et al. 2024

Preprint

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Objective To explore the current status, trends, and frontiers of POI research from 2011 to the present based on bibliometric analysis. Methods Publications published on POI research from 2011 to 2023 were retrieved on June 1, 2023, from the Web of Science Core Collection. CiteSpace 6.2.R2 and VOSviewer were used to conduct bibliometric visualization. Results In total, 778 POI records published from 2011 to 2023 were retrieved. Over the past few decades, the annual cumulative number of related articles has linearly increased, with China and the United States of America (USA) contributing prominently. All the publications were from 59 countries and territories. China and the University of Bonn were the top contributing country and institution, respectively. Neurogastroenterology & Motility was the most prolific journal. The Journal of Gastrointestinal Surgery had the highest number of citations. Wehner Sven was the most productive author. Burst keywords (e.g., colon, prolonged ileus, acupuncture, paralytic ileus, pathophysiology, rectal cancer, gastrointestinal function, risk) and a series of reference citation bursts provided evidence for the research frontiers in recent years. Conclusions This study demonstrates trends in the published literature on POI and provides new insights for researchers. It emphasizes the importance of multidisciplinary cooperation in the development of this field.

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Section: Discussionmentioning

confidence: 99%

“…Scientometrics reveals the current status and trends of research in a particular field through quantitative analysis of the literature. This helps researchers identify research priorities and development frontiers, which is beneficial for future research [11], [12] .…”

Section: Introductionmentioning

confidence: 99%

Global research trends in postoperative ileus from 2011 to 2023: A Scientometric study

Zhou,

Yin,

Sun

et al. 2024

Preprint

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“…CiteSpace was used to determine the citation burst of keywords and to map the timeline of references by keywords. Burst detection can detect the citation burst of a specific keyword (or document) within a certain field, at least within a short period of time during a given time frame [ 18 ]. Keywords with a burst of occurrences are indicators of hot spot research topics in a field.…”

Section: Methodsmentioning

confidence: 99%

Research Trends and Evolution in Radiogenomics (2005-2023): Bibliometric Analysis

Wang,

Peng,

Wang

et al. 2024

Interact J Med Res

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Background: Radiogenomics has emerged as a field that links medical imaging (radiomics) with molecular pathology (genomics), including proteomics and metabolomics. It utilizes radiomics to improve disease diagnosis, predict prognosis, evaluate treatment efficacy, and analyze outcomes. This study aims to examine the current research status and highlight the dynamic changes in hotspots within radiogenomics research.Objective: Radiogenomics has emerged as a field that links medical imaging (radiomics) with molecular pathology (genomics), including proteomics and metabolomics. It utilizes radiomics to improve disease diagnosis, predict prognosis, evaluate treatment efficacy, and analyze outcomes. This study aims to examine the current research status and highlight the dynamic changes in hotspots within radiogenomics research.Methods: Visual software Vosviewer and Citespace were employed to analyze the data in this study. Vosviewer was used to assess the frequency of keywords, while Citespace was utilized to analyze countries, institutions, journals, co-citations, reference clusters, timeline views, and keyword bursts. The number of published documents was counted using Excel. Results:The United States and China are the leading contributors to this field, with the largest number of co-citations and published articles, respectively. The co-occurrence map of keywords indicates a recent prevalence of machine learning, deep learning, and artificial intelligence. The reference co-citations resulted in 11 clusters, including radiomics, glioma, breast cancer, EGFR, radiotherapy, normal tissue toxicity, glioblastoma, rectal cancer, immunotherapy, oncologic imaging, and deep learning. Conclusions:The United States and China are the leading contributors to this field, with the largest number of co-citations and published articles, respectively. The co-occurrence map of keywords indicates a recent prevalence of machine learning, deep learning, and artificial intelligence. The reference co-citations resulted in 11 clusters, including radiomics, glioma, breast cancer, EGFR, radiotherapy, normal tissue toxicity, glioblastoma, rectal cancer, immunotherapy, oncologic imaging, and deep learning.

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“…Besides, gut microbiota exert their actions at different levels of the gut-liver-brain axis, impacting disease progression via changing gut-liver-brain axis communication ( Fuenzalida et al, 2021 ). For example, liver cirrhosis is a common chronic progressive liver disease with high mortality caused by one or more factors, such as alcohol, metabolic disorders, drugs and so on ( Gan et al, 2022 ). Researchers found out that the gut microbiota is a key factor in the progression of chronic liver disease, while the gut microbiota (e.g., Enterococcus and Escherichia coli ) in patients with liver cirrhosis has significant changes compared to healthy individuals ( Hussain et al, 2020 ; Ren et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy

Shi

Li²,

Wang³

et al. 2023

Front. Neurosci.

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The interactions between the microbiota and the human host can affect the physiological functions of organs (such as the brain, liver, gut, etc.). Accumulating investigations indicate that the imbalance of microbial community is closely related to the occurrence and development of diseases. Thus, the identification of potential links between microbes and diseases can provide insight into the pathogenesis of diseases. In this study, we propose a deep learning framework (MDAGCAN) based on graph convolutional attention network to identify potential microbe-disease associations. In MDAGCAN, we first construct a heterogeneous network consisting of the known microbe-disease associations and multi-similarity fusion networks of microbes and diseases. Then, the node embeddings considering the neighbor information of the heterogeneous network are learned by applying graph convolutional layers and graph attention layers. Finally, a bilinear decoder using node embedding representations reconstructs the unknown microbe-disease association. Experiments show that our method achieves reliable performance with average AUCs of 0.9778 and 0.9454 ± 0.0038 in the frameworks of Leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. Furthermore, we apply MDAGCAN to predict latent microbes for two high-risk human diseases, i.e., liver cirrhosis and epilepsy, and results illustrate that 16 and 17 out of the top 20 predicted microbes are verified by published literatures, respectively. In conclusion, our method displays effective and reliable prediction performance and can be expected to predict unknown microbe-disease associations facilitating disease diagnosis and prevention.

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Global research trends in the field of liver cirrhosis from 2011 to 2020: A visualised and bibliometric study

Cited by 4 publications

References 31 publications

Global research trends in postoperative ileus from 2011 to 2023: A Scientometric study

Global research trends in postoperative ileus from 2011 to 2023: A Scientometric study

Research Trends and Evolution in Radiogenomics (2005-2023): Bibliometric Analysis

Identifying microbe-disease association based on graph convolutional attention network: Case study of liver cirrhosis and epilepsy

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