In general, bioassay-guided fractionation and isolation of bioactive constituents from botanical materials frequently ended up with the reward of a single compound. However, botanical materials typically exert their therapeutic actions through multi-pathway effects due to the intrinsic complex nature of chemical constituents. In addition, the content of bioactive compounds in botanical materials is largely dependent on humidity, temperature, soil, especially geographical origins, from which rapid and accurate identification of plant materials is pressingly needed. These long-standing obstacles collectively impede the deep exploitation and application of these versatile natural sources. To address the challenges, a new paradigm integrating biogravimetric analyses and machine learning-driven origin classification (BAMLOC) was developed. The biogravimetric analyses are based on absolute qHNMR quantification and in vivo zebrafish model-assisted activity index calculation, by which bioactive substance groups jointly responsible for the bioactivities in all fractions are pinpointed before any isolation effort. To differentiate origin-different botanical materials varying in the content of bioactive substance groups, principal component analysis, linear discriminant analysis, and hierarchical cluster analysis in conjunction with supervised support vector machine are employed to classify and predict production areas based on the detection of volatile organic compounds by E-nose and GC-MS. Expanding BAMLOC to Codonopsis Radix enables the identification of polyacetylenes and pyrrolidine alkaloids as the bioactive substance group for immune restoration effect and accurately determines the origins of plants. This study advances the toolbox for the discovery of bioactive compounds from complex mixtures and lays a more definitive foundation for the in-depth utilization of botanical materials.

Objective To explore and validate the mechanism of Prepared Radix Polygoni Multiflori in the treatment of NAFLD based on network pharmacology and animal non-alcoholic fatty liver disease(NAFLD)model experiments.Methods Consult the literature to compare the differences between Radix Polygoni Multiflori and Prepared Radix Polygoni Multiflori(PRPM).Herb database and SwissADME database were used to screen the active ingredients of Prepared Radix Polygoni Multiflori,SwissTargetPrediction database was used to predict its targets,OMIM,DISGENET and GEENCARDS databases were used to screen the NAFLD-related targets,conduct GO functional enrichment analysis and KEGG pathway enrichment analysis.The active ingredient-target-KEGG signaling pathway-NAFLD network was mapped later.The mice with NAFLD were treated with Prepared Radix Polygoni Multiflori by gavage for 8 weeks;serum triglyceride level and alanine aminotransferase(ALT)activity were measured;the liver lesions were observed by HE staining;the potential mechanism of action of Radix Polygoni Multiflori in the treatment of NAFLD was verified by Western blot.Results The differences between Radix Polygoni Multiflori and PRPM were consulted.Six pharmacological components and 32 potential action targets of Radix Polygoni Multiflori for the treatment of NAFLD were screened by network pharmacology,GO and KEGG pathways were enriched to lipid and atherosclerosis-related pathways,AMPK signaling pathway,etc.;HE staining verified that Prepared Radix Polygoni Multiflori has the function of improving NAFLD and is associated with the alteration of FASN,ACC,SCD protein of AMPK signaling pathway.Conclusion Radix Polygoni Multiflori has the potential to improve NAFLD by regulating FASN,ACC and SCD.

Objective:To explore the predictive value of quantitative electroencephalogram (qEEG) in the poor outcome of children with non-traumatic disturbance of consciousness (DoC) in the pediatric intensive care unit (PICU).Methods:A prospective study was conducted. From January 2019 to May 2019, a total of 62 patients aged from 1 month to 11 years with non-traumatic DoC in the PICU of the First Affiliated Hospital of Bengbu Medical College were enrolled. Bedside monitoring with NicoletOne monitor was performed within 24 hours after admission, and qEEG parameters, including amplitude-integrated electroencephalogram (aEEG), relative alpha variability (RAV), relative band power (RBP), and spectral entropy (SE) were recorded. The state of consciousness was assessed with modified pediatric Glasgow coma scale (MPGCS) before monitoring. According to the pediatric cerebral performance category score at 1 year after discharge, the enrolled subjects were divided into good and poor outcome groups. The association between these variables and the poor outcome was analyzed by univariate and multivariate logistic regression analysis, and the predictive performance was analyzed by receiver operator characteristic (ROC) curve.Results:There were 39 males and 23 females, with the age of 12.0 (5.8, 24.0) months. Fifty patients (81%) were in the good outcome group and 12 patients (19%) in the poor outcome group. The univariate Logistic regression analysis showed that age ( OR=1.037, 95% CI 1.001-1.074, P=0.041), severe abnormal aEEG ( OR=128.000, 95% CI 10.274-1 594.656, P<0.01), RAV ( OR=0.877, 95% CI 0.810-0.949, P=0.001), SE ( OR=0.892, 95% CI 0.814-0.978, P=0.015), and MPGCS score ( OR=0.511, 95% CI 0.349-0.747, P=0.001) were significantly associated with the poor outcome. However, the multivariate Logistic regression analysis showed that only severe abnormal aEEG ( OR=315.692, 95% CI 6.091-16 362.298, P=0.004) and RAV ( OR=0.808, 95% CI 0.664-0.983, P=0.033) were significantly associated with the poor outcome. The area under the curve (AUC) of the aEEG and RAV in predicting the poor outcome were 0.848 (95% CI 0.735-0.927, P<0.01) and 0.847 (95% CI 0.733-0.926, P<0.01), respectively. The optimal cut-off value was severe abnormal for the aEEG and 38% for the RAV, with sensitivity of 67% and 83%, specificity of 98% and 84%, positive predictive value of 89% and 55%, negative predictive value of 92% and 95%, and Youden index of 0.647 and 0.673, respectively. The AUC of the novel combined index of aEEG and RAV for predicting the poor outcome was 0.974 (95% CI 0.898-0.998, P<0.01). Conclusions:The aEEG and RAV are reliable predictors for the poor outcome of children with non-traumatic DoC, and the novel combined index of aEEG and RAV can improve the predictive performance. The qEEG can be used as a routine method for outcome assessment due to its good objectivity.