Abstract Objective: Reliably prediction models for coronary artery abnormalities (CAA) in children aged > 5 years with Kawasaki disease (KD) are still lacking. This study aimed to develop a nomogram model for predicting CAA at 4 to 8 weeks of illness in children with KD older than 5 years. Methods: A total of 644 eligible children were randomly assigned to a training cohort (n = 450) and a validation cohort (n = 194). The least absolute shrinkage and selection operator (LASSO) analysis was used for optimal predictors selection, and multivariate logistic regression was used to develop a nomogram model based on the selected predictors. Area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, Brier score, and decision curve analysis (DCA) were used to assess model performance. Results: Neutrophil to lymphocyte ratio, intravenous immunoglobulin resistance, and maximum baseline z-score ≥ 2.5 were identified by LASSO as significant predictors. The model incorporating these variables showed good discrimination and calibration capacities in both training and validation cohorts. The AUC of the training cohort and validation cohort were 0.854 and 0.850, respectively. The DCA confirmed the clinical usefulness of the nomogram model. Conclusions: A novel nomogram model was established to accurately assess the risk of CAA at 4–8 weeks of onset among KD children older than 5 years, which may aid clinical decisionmaking. Objective (CAA (KD lacking Methods 64 n 450 194. 194 . 194) (LASSO AUC, , (AUC) curves HosmerLemeshow Hosmer Lemeshow test score (DCA performance Results ratio resistance zscore z 25 2 2. cohorts 0854 0 854 0.85 0850 850 0.850 respectively Conclusions 48 4– decisionmaking 6 45 19 (AUC 085 85 0.8 1 08 0.

Abstract The nutritional profile, especially amino acid profile, determines the quality and commercial value of insect protein products. Multiple previous studies have used spectroscopy technologies and machine learning algorithms to predict essential amino acid content in various foods and feeds. However, these approaches were not applied for predicting essential amino acid content in insects before. In this study, 200 insect samples containing 9 commercial insect species were collected. Machine learning methods were applied to build the prediction models to predict amino acid content using Fourier-transform infrared spectroscopy (FTIR) raw spectra and first derivative. For all amino acids, partial least square regression, decision tree and radial basis artificial neural network exhibited high performances to predict essential amino acids. Model performances were improved for some amino acids using first derivative than using raw spectra. The highest performance (coefficient of determination: 0.97, root mean square error of prediction: 0.05 g/100 g and ratio of performance: 4.07) was achieved for phenylalanine prediction using radial basis artificial neural network modeling. The high model performance indicates the potential of applying FTIR and subsequent machine learning modeling for fast and non-destructive prediction of amino acid of insect products.