Introduction: Kawasaki disease (KD) is an acute febrile illness primarily affecting children and characterized by systemic inflammation and vasculitis that can lead to coronary artery complications. The aim of this study was to gain a comprehensive understanding of immune dysregulation in KD.Methods: To this end, we employed integration of single-cell RNA sequencing (scRNA-Seq) and bulk RNA sequencing (bulk RNA-Seq) data. Furthermore, we conducted flow cytometry analysis for a cohort of 82 KD patients.Results: Our analysis revealed significant heterogeneity within immune cell populations in KD patients, with distinct clusters of T cells, B cells, and natural killer (NK) cells. Importantly, CD4⁺ naïve T cells in KD patients were found to predominantly differentiate into Treg cells and Th2 cells, potentially playing a role in the excessive inflammation and vascular damage characteristic of the disease. Dysregulated signaling pathways were also identified, including the mTOR signaling pathway, cardiomyopathy pathway, COVID-19 signaling pathway, and pathways involved in bacterial or viral infection.Discussion: These findings provide insights into the immunopathogenesis of KD, emphasizing the importance of immune cell dysregulation and dysregulated signaling pathways. Integration of scRNA-Seq and bulk RNA-Seq data offers a comprehensive view of the molecular and cellular alterations in KD and highlights potential therapeutic targets for further investigation. Validation and functional studies are warranted to elucidate the roles of the identified immune cell types and pathways in KD pathogenesis and to develop targeted interventions to improve patient outcomes.

Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) are common X-inherited neuromuscular diseases. The genetic diagnosis has been used as the diagnostic choice for DMD/BMD. The study subjects consisted of 37 patients from Southwest China. Peripheral blood was collected for the extraction of genomic DNA. DMD mutation was sequenced using the next-generation sequencing approach. The detected mutation was validated using the multiplex ligation-dependent probe amplification or Sanger sequencing methods. Variation annotation and pathogenicity prediction were performed using the online databases. Pathogenic mutations were identified 3 splicing site, 7 single nucleotide, 1 indel, 23 deletion, and 3 duplication mutations. Novel DMD variants were discovered, including two novel splicing variations (c.1890 + 1G>T; c.1923 + 1G>A), one missense mutation (c.1946G>T), one nonsense mutation (c.7441G>T), one indel mutation (INDEL EX20), and one duplication mutation (DUP EX75-78). The current study provides mutation information of DMD for the genetic diagnosis of DMD/BMD.

Background: Kawasaki disease (KD) is an acute systemic vasculitis predominantly affecting children under 5 years of age. Its pathogenesis remains incompletely understood, and the lack of specific diagnostic biomarkers during the acute phase poses substantial challenges to clinical diagnosis. Such diagnostic uncertainty often results in missed or delayed cases, leading to lost therapeutic opportunities and the subsequent development of coronary artery lesions (CAL). The present study aimed to establish a machine learning–based diagnostic model to optimize the KD diagnostic workflow, enable early identification, and reduce the incidence of CAL. Methods: We retrospectively analyzed Pre-treatment clinical data from 4,469 patients admitted to Kunming Children’s Hospital between January 2017 and December 2023, including 2,345 patients diagnosed with Kawasaki disease (case group) and 2,124 febrile non-KD patients (control group). A Light Gradient Boosting Machine (LightGBM) algorithm was employed to develop a diagnostic model for KD. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), as well as accuracy, sensitivity, and specificity calculated from the confusion matrix at a classification threshold of 0.5. SHapley Additive exPlanations (SHAP) analysis was performed to identify key predictive features and interpret the model’s decision-making mechanism. On the basis of SHAP-derived feature importance and clinical availability, six core variables were selected to establish a simplified diagnostic model, which was further deployed as an offline application. Results: The full-feature LightGBM model achieved an outstanding area under the receiver operating characteristic curve (AUC) of 0.9956. At a classification threshold of 0.5, the confusion matrix yielded an accuracy of 0.9653, a sensitivity of 0.9596, and a specificity of 0.9717. SHAP analysis revealed that C-reactive protein (CRP), activated partial thromboplastin time (APTT), total calcium (Ca ²⁺ ), erythrocyte sedimentation rate (ESR), serum chloride (Cl ⁻ ), and several other variables exhibited strong predictive value. To achieve an optimal balance between predictive performance and clinical applicability, six core variables were selected: white blood cell count (WBC), platelet count (PLT), CRP, APTT, thrombin time (TT), and albumin (ALB). The simplified LightGBM model constructed using these parameters achieved an AUC of 0.9792; at the 0.5 classification threshold, it demonstrated an accuracy of 0.9340, a sensitivity of 0.9277, and a specificity of 0.9410, indicating robust discriminative capacity. Finally, an offline executable application was developed to support clinicians—especially those in primary care settings—in achieving rapid and accurate early diagnosis of KD. Conclusion: Machine learning approaches enable effective simplification of the diagnostic process for KD. The predictive model constructed on the basis of WBC, PLT, CRP, APTT, TT, and ALB shows high diagnostic efficacy and favorable clinical applicability.