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Biomedicines
Special Issues
Updates on Kawasaki Disease
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Updates on Kawasaki Disease

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 3308

Special Issue Editor

Dr. Eisuke Suganuma

E-Mail Website
Guest Editor
Saitama Children's Medical Center, Saitama, Japan
Interests: children

Special Issue Information

Dear Colleagues,

Kawasaki disease, first described by Dr. Tomisaku Kawasaki in 1967, is a febrile systemic vasculitis that primarily affects infants and young children. The development of coronary artery aneurysms, the most serious complication, can lead to acute coronary events such as myocardial infarction. Thanks to the development of acute treatments, the incidence of coronary sequelae in Japan remains at 2–3%, but the development of treatments aimed at eliminating all instances of coronary artery aneurysms is desirable. In recent years, according to a report by Burns et al., data-driven analysis has revealed that Kawasaki disease can be divided into four clusters, each with individual characteristics such as age at onset, response to treatment, and risk of coronary artery aneurysm. Based on these findings, we recognize that individualizing treatment methods for each cluster will be important in preventing future coronary artery aneurysm complications. We hope that this Special Issue will lead to future advances in the acute management and individualized treatment of Kawasaki disease.

Dr. Eisuke Suganuma
Guest Editor

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Keywords

  • coronary artery aneurysm
  • cluster
  • individualized treatment

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Published Papers (3 papers)

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Research

13 pages, 3627 KB  
Article
TCR Repertoire Analysis Unveils the Link Between Kawasaki Disease and Viral Infection
by Zhimi Geng, Wei Zhou, Zhihao Fang, Yihua Jin, Guoqiang Qi, Lin Zhao, Chunhong Xie, Yujia Wang and Fangqi Gong
Biomedicines 2026, 14(3), 574; https://doi.org/10.3390/biomedicines14030574 - 3 Mar 2026
Viewed by 695
Abstract
Background: Kawasaki disease (KD) is a systemic vasculitis of unknown origin, though recent evidence implicates viral pathogens in its pathogenesis. Given the central role of T cell receptors (TCRs) in antigen recognition and immune response, this study investigated the association between KD [...] Read more.
Background: Kawasaki disease (KD) is a systemic vasculitis of unknown origin, though recent evidence implicates viral pathogens in its pathogenesis. Given the central role of T cell receptors (TCRs) in antigen recognition and immune response, this study investigated the association between KD and viral infection through comparative analysis of TCR repertoires. Methods: TCR repertoires from KD patients, healthy children, and individuals with viral infections were comparatively analyzed. TCR diversity and V(D)J usage were assessed using Shannon’s entropy, the Mann–Whitney U test, and Fisher’s exact test. Positional motif enrichment analysis within CDR3 regions was performed based on paratope hotspot classification. Results: Relatively reduced TCR clonal abundance and diversity were observed in KD patients compared to healthy controls. While substantial overlap in VJ gene segment usage was detected between KD and cytomegalovirus (CMV) infection, limited overlap in clonal TCRαβ chains was found between KD and viral infection groups. A predominant TCR combination, TRAV14DV4-J13-TRBV20-1-J2-5, enriched with characteristic amino acid motifs (EET, YNE, LAG, GQG, and AYE), was frequently identified in KD. Conclusions: These observations suggest potential differences in TCR repertoire features between KD patients and both healthy and virus-infected groups. However, the relationship between KD pathogenesis and the viruses examined requires further investigation with larger cohorts. Full article
(This article belongs to the Special Issue Updates on Kawasaki Disease)
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15 pages, 2026 KB  
Article
Changes in Serum Levels of NINJ1 and HMGB1 in Children with Kawasaki Disease and Their Clinical Significance
by Tong Tong, Ting Zhao, Jiawen Xu, Fei Liu, Linghao Cai, Xinrui Mao, Chunhong Xie, Yujia Wang and Fangqi Gong
Biomedicines 2026, 14(2), 402; https://doi.org/10.3390/biomedicines14020402 - 10 Feb 2026
Viewed by 601
Abstract
Purpose: Kawasaki disease (KD) is an acute systemic vasculitis that can result in coronary artery lesions (CALs). This study aims to explore the expression levels of serum Ninjurin-1 (NINJ1) and high-mobility group box 1 (HMGB1) in the acute phase of KD and [...] Read more.
Purpose: Kawasaki disease (KD) is an acute systemic vasculitis that can result in coronary artery lesions (CALs). This study aims to explore the expression levels of serum Ninjurin-1 (NINJ1) and high-mobility group box 1 (HMGB1) in the acute phase of KD and evaluate their clinical significance. Methods: A total of 180 children were enrolled, comprising 113 KD patients, 35 healthy controls (HCs), and 32 febrile controls whose clinical data were collected. Serum levels of NINJ1, HMGB1, Lactate Dehydrogenase (LDH), and routine inflammatory markers were compared across groups. Serum levels of NINJ1 and HMGB1 were measured via ELISA. Correlations were analyzed using Spearman tests. The diagnostic and predictive performance of biomarkers was assessed using Receiver Operating Characteristic (ROC) curve analyses. Results: Serum levels of NINJ1 and HMGB1 were significantly elevated in the KD group compared with both the HC and FC groups (all p < 0.001). NINJ1 levels were positively correlated with the z-scores of coronary arteries and were significantly higher in the CAL subgroup than in the non-CAL subgroup (p = 0.004). A strong positive correlation was observed between serum NINJ1 and HMGB1 levels in the KD group (p < 0.001). Conclusions: Elevated serum NINJ1 levels during the acute phase of KD were associated with the presence of CALs, while HMGB1 shows promise in differentiating KD from other febrile illnesses. These findings collectively suggest that the NINJ1-HMGB1 axis may offer novel insights into the mechanisms underlying KD vasculitis, supporting further investigation into its potential clinical relevance. Full article
(This article belongs to the Special Issue Updates on Kawasaki Disease)
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11 pages, 1984 KB  
Article
Limited Performance of Machine Learning Models Developed Based on Demographic and Laboratory Data Obtained Before Primary Treatment to Predict Coronary Aneurysms
by Mi-Jin Kim, Gi-Beom Kim, Dongha Yang, Yeon-Jin Jang and Jeong-Jin Yu
Biomedicines 2025, 13(5), 1073; https://doi.org/10.3390/biomedicines13051073 - 29 Apr 2025
Cited by 1 | Viewed by 1313
Abstract
Background/objectives: Kawasaki disease is the leading cause of acquired heart disease in children within developed countries. Although treatment with intravenous immunoglobulin (IVIG) significantly reduces the incidence of coronary artery aneurysm (CAA), the risk of it persists, affecting long-term patient outcomes. While intensified [...] Read more.
Background/objectives: Kawasaki disease is the leading cause of acquired heart disease in children within developed countries. Although treatment with intravenous immunoglobulin (IVIG) significantly reduces the incidence of coronary artery aneurysm (CAA), the risk of it persists, affecting long-term patient outcomes. While intensified primary treatment is recommended for patients at high risk of IVIG resistance or CAA development, a universally accepted predictive model for such resistance remains unestablished. This study aims to develop a machine learning model to predict the occurrence of CAAs prior to initiating IVIG therapy. Methods: Data from two nationwide epidemiological surveys conducted between 2012 and 2017 were analyzed, encompassing 17,189 patients with calculable coronary artery z-scores and Harada scores. Various supervised machine learning algorithms were applied to develop a model for predicting CAA. Afterward, unsupervised learning techniques were employed to explore the data’s inherent structure. Results: The Harada score’s receiver operating characteristic (ROC) analysis yielded an area under the curve (AUC) of 0.558. The highest AUC among the machine learning models was 0.661, achieved by the Light Gradient Boosting Machine. However, this model’s sensitivity was 0.615, and specificity was 0.647, indicating limited clinical applicability. Unsupervised learning revealed no distinct distribution patterns between patients with/without CAAs. Conclusions: Despite utilizing a large dataset to develop a machine learning-based prediction model for CAAs, the performance was unsatisfactory. Future studies should focus on enhancing predictive models by incorporating additional clinical data, such as acute-phase coronary artery diameter measurements, to improve accuracy and clinical utility. Full article
(This article belongs to the Special Issue Updates on Kawasaki Disease)
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