RESUMEN
BACKGROUND: Approximately 10-20% of Kawasaki disease (KD) patients suffer from intravenous immunoglobulin (IVIG) resistance, placing them at higher risk of developing coronary artery aneurysms. Therefore, we aimed to construct an IVIG resistance prediction tool for children with KD in Shanghai, China. METHODS: Retrospective analysis was conducted on data from 1271 patients diagnosed with KD and the patients were randomly divided into a training set and a validation set in a 2:1 ratio. Machine learning algorithms were employed to identify important predictors associated with IVIG resistance and to build a predictive model. The best-performing model was used to construct a dynamic nomogram. Moreover, receiver operating characteristic curves, calibration plots, and decision-curve analysis were utilized to measure the discriminatory power, accuracy, and clinical utility of the nomogram. RESULTS: Six variables were identified as important predictors, including C-reactive protein, neutrophil ratio, procalcitonin, CD3 ratio, CD19 count, and IgM level. A dynamic nomogram constructed with these factors was available at https://hktk.shinyapps.io/dynnomapp/. The nomogram demonstrated good diagnostic performance in the training and validation sets (area under the receiver operating characteristic curve = 0.816 and 0.800, respectively). Moreover, the calibration curves and decision curves analysis indicated that the nomogram showed good consistency between predicted and actual outcomes and had good clinical benefits. CONCLUSION: A web-based dynamic nomogram for IVIG resistance was constructed with good predictive performance, which can be used as a practical approach for early screening to assist physicians in personalizing the treatment of KD patients in Shanghai.
Asunto(s)
Resistencia a Medicamentos , Inmunoglobulinas Intravenosas , Aprendizaje Automático , Síndrome Mucocutáneo Linfonodular , Nomogramas , Humanos , Síndrome Mucocutáneo Linfonodular/tratamiento farmacológico , Síndrome Mucocutáneo Linfonodular/inmunología , Síndrome Mucocutáneo Linfonodular/diagnóstico , Inmunoglobulinas Intravenosas/uso terapéutico , Masculino , Femenino , Estudios Retrospectivos , Preescolar , Lactante , China , Niño , Proteína C-Reactiva/análisisRESUMEN
Background and Objectives: This study aimed to investigate the diagnostic value of immunological biomarkers in children with asthmatic bronchitis and asthma and to develop a machine learning (ML) model for rapid differential diagnosis of these two diseases. Materials and Methods: Immunological biomarkers in peripheral blood were detected using flow cytometry and immunoturbidimetry. The importance of characteristic variables was ranked and screened using random forest and extra trees algorithms. Models were constructed and tested using the Scikit-learn ML library. K-fold cross-validation and Brier scores were used to evaluate and screen models. Results: Children with asthmatic bronchitis and asthma exhibit distinct degrees of immune dysregulation characterized by divergent patterns of humoral and cellular immune responses. CD8+ T cells and B cells were more dominant in differentiating the two diseases among many immunological biomarkers. Random forest showed a comprehensive high performance compared with other models in learning and training the dataset of immunological biomarkers. Conclusions: This study developed a prediction model for early differential diagnosis of asthmatic bronchitis and asthma using immunological biomarkers. Evaluation of the immune status of patients may provide additional clinical information for those children transforming from asthmatic bronchitis to asthma under recurrent attacks.
Asunto(s)
Asma , Bronquitis , Humanos , Niño , Linfocitos T CD8-positivos , Asma/diagnóstico , Bronquitis/complicaciones , Bronquitis/diagnóstico , Diagnóstico Diferencial , BiomarcadoresRESUMEN
Pediatric urolithiasis is a common urologic disease with high morbidity and recurrence rates. Recent studies have shown that metabolic dysfunction plays a vital role in the pathogenesis of urolithiasis, especially in children, but the specific mechanism is still unclear. Metabolomics is an ideal technology for exploring the mechanism of metabolic disorders in urolithiasis. In the present study, a serum metabolomics based on ultra-performance liquid chromatography mass spectrometry was performed. A total of 50 children subjects were recruited for the study, including 30 patients with kidney stones and 20 normal controls (NCs). Principal component analysis and orthogonal partial least-squares determinant analysis were carried, and 40 metabolites were found to be significantly altered in patients with kidney stones, mainly involving retinol metabolism, steroid hormone biosynthesis, and porphyrin and chlorophyll metabolism. The kidney stone group appeared to have a lower serum level of bilirubin, but a relative higher level of retinal, all-transretinoic acid, progesterone, and prostaglandin E2 compared with those of the NC group. All the findings suggest that patients with urolithiasis have several metabolic characteristics, which are related to stone formation or compensation. These metabolites and pathways are very likely associated with development of kidney stones and should be considered as potential novel targets for treatment and prevention.
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Urolitiasis/metabolismo , Biomarcadores/sangre , Biomarcadores/metabolismo , Estudios de Casos y Controles , Niño , Preescolar , Cromatografía Líquida de Alta Presión/métodos , Femenino , Humanos , Lactante , Masculino , Metabolómica/métodos , Análisis de Componente Principal , Espectrometría de Masas en Tándem/métodos , Urolitiasis/sangreRESUMEN
CD4(+) T cell polarization plays a critical role in a number of immune disorders; the pathogenesis is unclear. Chromobox homolog 7 (Cbx7) is involved in the gene transcription of several cell types. This study aims to investigate the mechanism by which Cbx7 modulates the CD4(+) T cell polarization. Expression of Cbx7 was assessed by quantitative RT-PCR and Western blotting. Apoptosis of CD4(+) T cell was analyzed by flow cytometry. The FasL promoter methylation was evaluated by the methylation specific PCR. The results showed that CD4(+) CD25(-) T cells express Cbx7 that was increased significantly after activation by exposing to anti-CD3/CD28 Ab, but suppressed by exposing to specific antigens. More apoptotic cells were detected in CD4(+) T cells with the Cbx7 gene knockdown. Exposure to insulin-like growth factor-1 up regulated the expression of Cbx7 in CD4(+) T cells. After antigen-specific TCR activation, Cbx7-deficient CD4(+) T cells expressed more FasL and showed the FasL gene promoter hyper demethylation than wild CD4(+) T cells. In addition, CD4(+) T cells with overexpression of Cbx7 showed lower levels of FasL gene promoter demethylation. We conclude that CD4(+) T cells express Cbx7; the latter prevents FasL expression and the activation-induced CD4(+) T cell apoptosis.