Non-small cell lung cancer (NSCLC) remains a leading cause of cancer death globally. More accurate and reliable diagnostic methods/biomarkers are urgently needed. Joint application of metabolomics and transcriptomics technologies possesses the high efficiency of identifying key metabolic pathways and functional genes in lung cancer patients. In this study, we performed an untargeted metabolomics analysis of 142 NSCLC patients and 159 healthy controls; 35 identified metabolites were significantly different between NSCLC patients and healthy controls, of which 6 metabolites (hypoxanthine, inosine, L-tryptophan, indoleacrylic acid, acyl-carnitine C10:1, and lysoPC(18:2)) were chosen as combinational potential biomarkers for NSCLC. The area under the curve (AUC) value, sensitivity (SE), and specificity (SP) of these six biomarkers were 0.99, 0.98, and 0.99, respectively. Potential diagnostic implications of the metabolic characteristics in NSCLC was studied. The metabolomics results were further verified by transcriptomics analysis of 1027 NSCLC patients and 108 adjacent peritumoral tissues from TCGA database. This analysis identified 2202 genes with significantly different expressions in cancer cells compared to normal controls, which in turn defined pathways implicated in the metabolism of the compounds revealed by metabolomics analysis. We built a fully connected network of metabolites and genes, which shows a good correspondence between the transcriptome analysis and the metabolites selected for diagnosis. In conclusion, this work provides evidence that the metabolic biomarkers identified may be used for NSCLC diagnosis and screening. Comprehensive analysis of metabolomics and transcriptomics data offered a validated and comprehensive understanding of metabolism in NSCLC.

Obesity and its common association with type 2 diabetes, dyslipidemia, and cardiovascular diseases are worldwide epidemics. Currently, to prevent or treat obesity and associated metabolic disorders, herbal dietary supplements or medicines have attracted more and more attention owing to their relative effectiveness with fewer significant side effects. We investigate the therapeutic effects and underlying mechanisms of Plantago asiatica L. seed extract (PSE) on obesity and associated metabolic disorders in high-fat (HF) diet-induced mice. Our results displayed that PSE did not modify food intake or body weight but decreased abdominal white adipose tissue ratio, white/brown adipocyte size, serum total cholesterol, triglyceride (TG), low density lipoprotein cholesterol, free fatty acid, and hepatic TG concentrations when compared with the HF group. The levels of fasting blood glucose and glucose tolerance were improved in the PSE group when compared with the HF group. Furthermore, PSE upregulated mRNA expressions of peroxisome proliferator activated receptors (PPARs) and target genes related to fatty acid metabolism and energy expenditure in liver and adipose tissue of obese mice when compared with the HF group. PSE treatment effectively improved lipid and glucose metabolism in HF diet-induced obese mice. These effects might be attributed to the upregulation of PPAR signaling

Background: Quality evaluation of multi-species resourced herb medicine (MSRHM) is a main problem for quality control of herb medicine. Current quality evaluation methodology lost consideration of species discrepancy. New quality evaluation strategy for MSRHM is in urgent need. Qinjiao, a representative MSRHM, originated from Gentiana macrophylla Pall., Gentiana straminea Maxim., Gentiana crassicaulis Duthie ex Burk. or Gentiana dahurica Fisch., has been used as an important herb medicine over 2000 years for expelling wind-dampness and relieving impediment pain. However, quality evaluation among species has never been revealed. The current work proposes an integrated quality evaluation strategy for MSRHM of Qinjiao, which may promote innovation of quality control of MSRHM. Methods: In this work, 58 batches of Qinjiao covering 4 species were collected. Genetic comparative analysis based on ITS2 sequence was conducted. Metabolomics analysis based on TOF–MS and NMR spectrum were carried out. Compounds underlying species differences were identified and their discrepancies among species were investigated by ANOVA analysis and multivariate analysis. Results: Four species of Qinjiao can be authenticated by ITS2 sequence comparation. Metabolomics analysis by TOF/MS and NMR revealed chemical discrepancies among species of Qinjiao. Maximum discrepancy was present between Gentiana crassicaulis Duthie ex Burk. and Gentiana dahurica Fisch. Chemical difference among species were tentative explored. For TOF–MS profiling, 28 constituents were tentative identified, 17 of which were further confirmed by standards. For ¹H-NMR profiling, signals from 5 compounds were assigned. Contents discrepancies were investigated by ANOVA analysis. It seems that (seco)iridoids like loganic acid, gentiopicroside or swertiamarin were richer in specie of Gentiana crassicaulis Duthie ex Burk., while flavonoid (morroniside) and triterpenoids (roburic aicd, ursolic acid, oleanolic acid, β-sitosterone) were richer in specie of Gentiana dahurica Fisch. The current research demonstrates that metabolite profiling based on both UPLC/Q-TOF MS and ¹H-NMR coupled with ITS2 sequence comparation can be a powerful tool for quality investigation of MSRHM of Qinjiao. Conclusions: A comprehensive quality evaluation strategy for MSRHM was proposed by integrating UPLC-Q-TOF–MS, NMR based metabolic analysis and ITS2 sequence genetic comparation. The proposed quality evaluation strategy shall promote innovation of quality control of traditional Chinese medicine.

Background: Several studies have investigated the population pharmacokinetics (popPK) of valproic acid (VPA) in children with epilepsy. However, the predictive performance of these models in the extrapolation to other clinical environments has not been studied. Hence, this study evaluated the predictive abilities of pediatric popPK models of VPA and identified the potential effects of protein binding modeling strategies.Methods: A dataset of 255 trough concentrations in 202 children with epilepsy was analyzed to assess the predictive performance of qualified models, following literature review. The evaluation of external predictive ability was conducted by prediction- and simulation-based diagnostics as well as Bayesian forecasting. Furthermore, five popPK models with different protein binding modeling strategies were developed to investigate the discrepancy among the one-binding site model, Langmuir equation, dose-dependent maximum effect model, linear non-saturable binding equation and the simple exponent model on model predictive ability.Results: Ten popPK models were identified in the literature. Co-medication, body weight, daily dose, and age were the four most commonly involved covariates influencing VPA clearance. The model proposed by Serrano et al. showed the best performance with a median prediction error (MDPE) of 1.40%, median absolute prediction error (MAPE) of 17.38%, and percentages of PE within 20% (F₂₀, 55.69%) and 30% (F₃₀, 76.47%). However, all models performed inadequately in terms of the simulation-based normalized prediction distribution error, indicating unsatisfactory normality. Bayesian forecasting enhanced predictive performance, as prior observations were available. More prior observations are needed for model predictability to reach a stable state. The linear non-saturable binding equation had a higher predictive value than other protein binding models.Conclusion: The predictive abilities of most popPK models of VPA in children with epilepsy were unsatisfactory. The linear non-saturable binding equation is more suitable for modeling non-linearity. Moreover, Bayesian forecasting with prior observations improved model fitness.