IFX could downregulate activated monocytes and upregulate T cells towards the normal range. IFX treatment thus contributes to the process of attenuating inflammation in KD.

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In Kawasaki disease (KD), the effect of plasma exchange (PE) on immune cells has not been fully elucidated. Therefore, we examined the changes in the number of CD14 + CD16 + activated monocytes, regulatory T (T reg ), and T-helper type 17 (Th17) cells in KD patients treated with PE. The percentage of total monocytes and subclasses of lymphocytes, including CD4 + and CD8 + T cells, and CD19 + B cells, showed no significant difference before and after PE. However, the percentage of CD14 + CD16 + monocytes in total leukocytes decreased significantly after PE (1.1% AE 1.5% vs. 2.1% AE 2.3%, P < 0.05). Furthermore, while the percentage of Th17 cells in CD4 + T cells did not change, the percentage of T reg cells in CD4 + T cells increased significantly after PE (11.1% AE 5.1% vs. 8.0% AE 4.4%, P < 0.05). Therefore, PE downregulates activated monocytes and upregulates T reg cells toward normal levels and thus attenuates inflammation in KD.

Krüpple-like factors (Klfs) are highly conserved zinc-finger transcription factors that regulate various developmental processes, such as haematopoiesis and cardiovascular development. In zebrafish, transient knockdown analysis of biklf/klf17 using antisense morpholino suggests the involvement of biklf/klf17 in primitive erythropoiesis and hatching gland development; however, the continuous physiological importance of klf17 remains uncharacterized under the genetic ablation of the klf17 gene among vertebrates. We established the klf17-disrupted zebrafish lines using the CRISPR/Cas9 technology and performed phenotypic analysis throughout early embryogenesis. We found that the klf17-deficient embryos exhibited abnormal lateral line neuromast deposition, whereas the production of primitive erythrocytes and haemoglobin production were observed in the klf17-deficient embryos. The expression of lateral line neuromast genes, klf17 and s100t, in the klf17-deficient embryos was detected in posterior lateral line neuromasts abnormally positioned at short intervals. Furthermore, the klf17-deficient embryos failed to hatch and died without hatching around 15 days post-fertilization (dpf), whereas the dechorionated klf17-deficient embryos and wild-type embryos were alive at 15 dpf. The klf17-deficient embryos abolished hatching gland cells and Ctsl1b protein expression, and eliminated the expression of polster and hatching gland marker genes, he1.1, ctsl1b and cd63. Thus, the klf17 gene plays important roles in posterior lateral line neuromast and hatching gland development.

Introduction In Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions. Objective To establish a simple scoring model predicting IVIG resistance in KD patients based on the machine learning model. Methods A retrospective cohort study of 1002 KD patients diagnosed at 12 facilities for 10 years, in which 22.7% were resistant to initial IVIG treatment. We performed machine learning with diverse models using 30 clinical variables at diagnosis in 801 and 201 cases for training and test datasets, respectively. SHAP was applied to identify the variables that influenced the prediction model. A scoring model was designed using the influential clinical variables based on the Shapley additive explanation results. Results Light gradient boosting machine model accurately predicted IVIG resistance (area under the receiver operating characteristic curve (AUC), 0.78; sensitivity, 0.50; specificity, 0.88). Next, using top three influential features (days of illness at initial therapy, serum levels of C-reactive protein, and total cholesterol), we designed a simple scoring system. In spite of its simplicity, it predicted IVIG resistance (AUC, 0.72; sensitivity, 0.49; specificity, 0.82) as accurately as machine learning models. Moreover, accuracy of our scoring system with three clinical features was almost identical to that of Gunma score with seven clinical features (AUC, 0.73; sensitivity, 0.53; specificity, 0.83), a well-known logistic regression scoring model. Conclusion A simple scoring system based on the findings in machine learning seems to be a useful tool to accurately predict IVIG resistance in KD patients. Key Points• In Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions.• Machine learning model predicted IVIG resistance in KD patients, and Shapley additive explanation (SHAP) was a useful approach for explaining the outcome of the machine learning model.• A simple scoring system using three clinical features (days of illness at initial therapy, serum levels of CRP, and total cholesterol at diagnosis) based on SHAP efficiently predicted IVIG resistance.