Neutrophil percentage-to-albumin ratio is a potential marker of intravenous immunoglobulin resistance in Kawasaki disease.

Intravenous immunoglobulin (IVIG) resistance in Kawasaki disease (KD) was associated with coronary artery lesions. Neutrophil percentage-to-albumin ratio (NPAR) is an index of mortality in several inflammatory diseases. This study focused on the association of NPAR with IVIG- resistance in KD. Clinical and laboratory data of 438 children with KD before IVIG treatment were retrospectively analyzed. Notably, high NPAR was associated with older age, high WBC, NP, ALT, total bilirubin and CRP, as well as with high the incidence of IVIG-resistance, and with low hemoglobin (Hb), PLT, ALB and sodium levels. NPAR (OR: 2.366, 95% CI: 1.46-3.897, p = 0.001) and Hb (OR: 0.967, 95% CI: 0.944-0.989, p = 0.004) were independent risk factors for IVIG-resistance. NPAR showed linear relation with IVIG-resistance (p for nonlinear = 0.711) and the nonlinear correlation was found between IVIG-resistance and Hb (p for nonlinear = 0.002). The predictive performance of NPAR was superior to Beijing model (z = 2.193, p = 0.028), and not inferior to Chongqing model (z = 0.983, p = 0.326) and the combination of NPAR and Hb (z = 1.912, p = 0.056). These findings revealed that NPAR is a reliable predictor of IVIG-resistance.

Construction and validation of predictive models for intravenous immunoglobulin-resistant Kawasaki disease using an interpretable machine learning approach.

BACKGROUND: Intravenous immunoglobulin (IVIG)-resistant Kawasaki disease is associated with coronary artery lesion development. PURPOSE: This study aimed to explore the factors associated with IVIG-resistance and construct and validate an interpretable machine learning (ML) prediction model in clinical practice. METHODS: Between December 2014 and November 2022, 602 patients were screened and risk factors for IVIG-resistance investigated. Five ML models are used to establish an optimal prediction model. The SHapley Additive exPlanations (SHAP) method was used to interpret the ML model. RESULTS: Na+, hemoglobin (Hb), C-reactive protein (CRP), and globulin were independent risk factors for IVIG-resistance. A nonlinear relationship was identified between globulin level and IVIG-resistance. The XGBoost model exhibited excellent performance, with an area under the receiver operating characteristic curve of 0.821, accuracy of 0.748, sensitivity of 0.889, and specificity of 0.683 in the testing set. The XGBoost model was interpreted globally and locally using the SHAP method. CONCLUSION: Na+, Hb, CRP, and globulin levels were independently associated with IVIG-resistance. Our findings demonstrate that ML models can reliably predict IVIG-resistance. Moreover, use of the SHAP method to interpret the established XGBoost model's findings would provide evidence of IVIG-resistance and guide the individualized treatment of Kawasaki disease.

Improved AlexNet with Inception-V4 for Plant Disease Diagnosis.

Timely disease detection and pest treatment are key issues in modern agricultural production, especially in large-scale crop agriculture. However, it is very time and effort-consuming to identify plant diseases manually. This paper proposes a deep learning model for agricultural crop disease identification based on AlexNet and Inception-V4. AlexNet and Inception-V4 are combined and modified to achieve an efficient but good performance. Experimental results on the expanded PlantVillage dataset show that the proposed model outperforms the compared methods: AlexNet, VGG11, Zenit, and VGG16, in terms of accuracy and F1 scores. The proposed model obtains the highest accuracy for corn, tomato, grape, and apple: 94.5%, 94.8%, 92.3%, and 96.5%, respectively. Also, the highest F1 scores for corn, tomato, grape, and apple: 0.938, 0.910, 0.945, and 0.924, respectively, are obtained. The results indicate that the proposed method has promising generalization ability in crop disease identification.