Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Objective:To summarize the experience of blood glucose management and nursing for a newborn with repeated hypoglycemia in Beckwith-Wiedemann syndrome.Method:A multidisciplinary team was formed, and formulated an individualized care plan for a large infant with Beckwith-Wiedemann syndrome. A dynamic blood glucose monitoring system was used to closely monitor blood glucose fluctuations for this children, dynamically adjusted the amount of infusion and milk according to the blood sugar situation, detected and dealed with hypoglycemia in time, strengthened the skin care of child and implemented psychological care for the family.Results:After refined treatment and care, the child′s vital signs were stable, and his blood sugar could maintain within a normal range before being discharged from the hospital.Conclusions:The use of dynamic blood glucose monitoring system under the guidance of a multidisciplinary team can effectively monitor and control the blood glucose fluctuations of children with BWS syndrome, which can provide a basis for further treatment of children.