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.

The prevention and treatment of posthepatectomy liver failure (PHLF) is a difficult problem for hepatectomy. PHLF is characterized by many risk factors, limited treatment methods and high mortality. There are many factors that lead to the insufficient quantity or quality of postoperative remnant liver cells, such as the comorbid conditions of patients, underlying disease of the liver, excessive liver resection and so on, which are increase the incidence of PHLF. However, the therapeutic treatment of PHLF is limited at present, the key to reduce the morbidity and mortality of PHLF lies in the accurate evaluation and screening of high-risk patients. This paper reviewed the relevant literature on the definition, risk factors and clinical prevention and treatment of posthepatectomy liver failure.

Objective To investigate the perioperative risk factors for posthepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC).Methods Data of 322 cases of liver resection for HCC were retrospectively analyzed from Sep 2013 to Sep 2018.Logistic regression was used to analyze the risk factors for PHLF.The receiver operating characteristic (ROC) curve was used to analyze the predictive power of the ALBI score and the Child-Pugh score for PHLF.Results Child-Pugh score,ALBI score,intraoperative bleeding amount,ICG R15 and liver fibrosis,peritoneal effusion were independent factors affecting PHLF of HCC patients(P ＜ 0.05).ROC analysis of Child-Pugh and ALBI scores predicting PHLF showed that area under the ROC was respectively 0.621 (95％ CI:0.531-0.712) in the Child-Pugh score and 0.729 (95％ CI:0.645-0.812)in the ALBI score.The best critical value,sensitivity and specificity of PHLF that were predicted by ALBI score were-2.74,71.7％ and 71.4％,respectively.Conclusions The prognostic power of the ALBI score was greater than that of the Child-Pugh score in predicting PHLF.

Deflazacort (DFZ, a prodrug) is well absorbed and rapidly metabolized into the active metabolite 21-hydroxydeflazacort (21-OH DFZ) after oral administration. The aim of this study is to evaluate the pharmacokinetic properties of 21-OH DFZ in healthy Chinese volunteers after a single and multiple oral administration of DFZ tablets under fed condition. Twelve volunteers (six males and six females) were administered a single dose of 6 mg or 12 mg or 24 mg of DFZ in three different periods separately, according to the 3 x 3 Latin square design. Between each administration period there was a washout period of one week. The multiple-dose study of 12 mg dose DFZ per day for 7 consecutive days was started after a 1 w washout period when the single-dose study completed. The pharmacokinetic parameters of 21-OH DFZ after the single oral administration of 6 mg, 12 mg and 24 mg DFZ tablets were as follows: (37.7 +/- 11.6), (61.5 +/- 17.7) and (123 +/- 23) ng x mL(-1) for C(max); (1.90 +/- 0.32), (1.96 +/- 0.27) and (2.13 +/- 0.34) h for t1/2; (96.6 +/- 25.9), (190 +/- 44) and (422 +/- 107) ng x h x mL(-1) for AUC(0-14 h), respectively. After the multiple dose administration, the mean plasma concentration at steady-state C(av) was (7.00 +/- 1.66) ng x mL(-1) and the degree of plasma concentration fluctuation DF was 7.7 +/- 1.2. The results showed that the pharmacokinetic characteristics of 21-OH DFZ in healthy Chinese volunteers were linear over the dose range of 6 to 24 mg. No significant gender differences were found in the pharmacokinetics of 21-OH DFZ in healthy Chinese volunteers. After the multiple dose administration of 12 mg DFZ for 7 d, no accumulation of 21-OH DFZ in healthy Chinese volunteers was observed.