Jinjuan Wang’s research while affiliated with Shandong University of Traditional Chinese Medicine and other places

Background In recent years, the incidence of Kawasaki disease among the pediatric population has experienced a significant increase. With complications mainly affecting the cardiovascular system, Kawasaki disease has received widespread attention from scholars worldwide. Numerous articles on Kawasaki disease in children have been published far. However, there is a lack of studies that use visualization methods to perform a bibliometric analysis of the relevant literature. This study aims to obtain overall information on the output characteristics of publications on childhood Kawasaki disease between 2012 and 2022 through bibliometric analysis, identify research hotspots and frontiers, and provide new ideas and references for future clinical and scientific research. Methods Literature meeting the inclusion criteria was screened from the Web of Science Core Collection, PubMed, and Scopus databases. Visual analysis of the literature by country, institution, journal, author, keywords, and references was performed using Citespace (6.1.R6), VOSviewer (1.6.18), and the online bibliometric website ( https://bibliometric.com/ ). Results A total of 4,867 eligible publications were included. The number of annual publications is generally rising, rapidly increasing since 2019. Among countries and institutions, China and KAOHSIUNG CHANG GUNG MEMORIAL HOSPITAL have the highest output of articles. With 104 publications, Ho-Chang Kuo has a high impact in the field of KD. The most cited author is Jane W. Newburger. The most prolific journal is FRONTIERS IN PEDIATRICS. CIRCULATION is the most frequently co-cited journal. The most popular keyword in frequency and centrality is “immunoglobulin”. The reference with the highest burst intensity was Verdoni L, LANCET, 2020. Conclusion Kawasaki disease in children remains a hot topic among pediatricians worldwide and is receiving increasing attention. We innovated the “national-institutional-journal” model, which promotes further international cooperation in this field. The hot topics in the field of pediatric KD are “KD pathogenesis”, “immunoglobulin resistance and complementary therapy”, and “cardiovascular complications”. Frontiers include disease-related (“multisystem inflammatory syndrome”, “coronavirus disease 2019”, “hypotension”), treatment-related (“procalcitonin”, “ anakinra”), and pathogenesis (“polymerase chain reaction”).

Bronchiolitis obliterans (BO) is a rare and irreversible chronic respiratory disease. The diagnosis of BO is challenging, and there still needs to be specific therapies and uniform treatment guidelines available. Research on BO has grown steadily over the past 20 years, and with the continued interest of researchers in this area, a bibliometric study of BO becomes necessary. This topic aims to assess the current state of research in BO over the last 2 decades and to identify research hotspots and emerging directions. Information on BO-related articles were obtained from the Science Citation Index Expand of the Web of Science Core Collection (WOSCC [SCI-E]) database. Citespace (6.1.R6), VOSviewer (1.6.18), and the online bibliometrics website (https://bibliometric.com/) were used for bibliometric analysis mainly to include country/region, institution, author, journal, keywords, and references and to construct visual knowledge network diagrams. A total of 4153 publications from the WOSCC [SCI-E] database were included in this study. Most publications come from the United States, Japan, and Germany, which collaborate relatively more frequently. Research institutions in the United States, especially the University of Washington, published the largest number of BO-related articles. Regarding authors, Vos, R is the most productive author, while Verleden, GM is the most influential in BO. In addition, JOURNAL OF HEART AND LUNG TRANSPLANTATION is the journal with the most published articles. The most cited article is Estenne M, 2002. Based on the clustering analysis of keywords and references, the diagnosis of bronchiolitis obliterans syndrome (BOS), treatment of BOS, and risk factors of BO are the current research hotspots and future research trends. We analyzed the publication trends in BO by bibliometrics and mapped the knowledge network of major contributing countries/regions, institutions, authors, and journals. Current research hotspots were found based on the main keywords and references. The outcome may help researchers identify potential collaborators, collaborating institutions, and hot fronts in BO to enhance collaboration on critical issues and improve the diagnosis and treatment of BO.

This article is objected to explore the value of machine learning algorithm in predicting the risk of renal damage in children with IgA vasculitis by constructing a predictive model and analyzing the related risk factors of IgA vasculitis Nephritis in children. Case data of 288 hospitalized children with IgA vasculitis from November 2018 to October 2021 were collected. The data included 42 indicators such as demographic characteristics, clinical symptoms and laboratory tests, etc. Univariate feature selection was used for feature extraction, and logistic regression, support vector machine (SVM), decision tree and random forest (RF) algorithms were used separately for classification prediction. Lastly, the performance of four algorithms is compared using accuracy rate, recall rate and AUC. The accuracy rate, recall rate and AUC of the established RF model were 0.83, 0.86 and 0.91 respectively, which were higher than 0.74, 0.80 and 0.89 of the logistic regression model; higher than 0.70, 0.80 and 0.89 of SVM model; higher than 0.74, 0.80 and 0.81 of the decision tree model. The top 10 important features provided by RF model are: Persistent purpura ≥4 weeks, Cr, Clinic time, ALB, WBC, TC, Relapse, TG, Recurrent purpura and EB-DNA. The model based on RF algorithm has better performance in the prediction of children with IgA vasculitis renal damage, indicated by better classification accuracy, better classification effect and better generalization performance.

Background This article is objected to explore the value of machine learning algorithm in predicting the risk of renal damage in children with Henoch-Schönlein Purpura, and to construct a predictive model of Henoch-Schönlein Purpura Nephritis in children and analyze the related risk factors of Henoch-Schönlein Purpura Nephritis in children. Methods Case data of 288 hospitalized children with Henoch-Schönlein Purpura from November 2018 to October 2021 were collected. The data included 42 indicators such as demographic characteristics, clinical symptoms and laboratory tests, etc. Univariate feature selection was used for feature extraction, and Logistic regression, support vector machine, decision tree and random forest algorithm were used respectively for classification prediction. Last, the performance of four algorithms are compared using accuracy rate and recall rate. Results The accuracy rate, recall rate and AUC of the established random forest model were 0.83, 0.86 and 0.91 respectively, which were higher than 0.74, 0.80 and 0.89 of the Logistic regression model; higher than 0.70, 0.80 and 0.89 of support vector machine model; higher than 0.74, 0.80 and 0.81 of the decision tree model. The top 10 important features provided by random forest model are Persistent purpura≥4weeks, Cr, Clinic time, ALB, WBC, TC, TG, Relapse, TG, Recurrent purpura and EB-DNA. Conclusion The model based on random forest algorithm has better performance in the prediction of children with allergic purpura renal damage, indicated by better classification accuracy, better classification effect and better generalization performance.