Shen-Fu decoction is a traditional Chinese medicine prescription with a 3:2 ratio of Radix Ginseng and Fuzi ( Radix Aconiti lateralis praeparata ). Ginsenosides and alkaloids are considered to be the main active components of Shen-Fu decoction. However, no analytical methods have been used to quantitatively analyse both components in Shen-Fu decoction simultaneously.

Background: Lung cancer (LC) remains the deadliest form of cancer globally. While surgery remains the optimal treatment strategy for individuals with early-stage LC, what the metabolic consequences are of such surgical intervention remains uncertain. Methods: Negative enrichment-fluorescence in situ hybridization (NE-FISH) was used in an effort to detect circulating tumor cells (CTCs) in pre- and post-surgery peripheral blood samples from 51 LC patients. In addition, targeted metabolomics analyses, multivariate statistical analyses, and pathway analyses were used to explore surgery-associated metabolic changes. Results: LC patients had significantly higher CTC counts relative to healthy controls with 66.67% of LC patients having at least 1 detected CTC before surgery. CTC counts were associated with clinical outcomes following surgery. In a targeted metabolomics analysis, we detected 34 amino acids, 147 lipids, and 24 fatty acids. When comparing LC patients before and after surgery to control patients, metabolic shifts were detected via PLS-DA and pathway analysis. Further surgery-associated metabolic changes were identified when comparing LA (LC patients after surgery) and LB (LC patients before surgery) groups. We identified SM 42:4, Ser, Sar, Gln, and LPC 18:0 for inclusion in a biomarker panel for early-stage LC detection based upon an AUC of 0.965 (95% CI 0.900–1.000). This analysis revealed that SM 42:2, SM 35:1, PC (16:0/14:0), PC (14:0/16:1), Cer (d18:1/24:1), and SM 38:3 may offer diagnostic and prognostic benefits in LC. Conclusions: These findings suggest that CTC detection and plasma metabolite profiling may be an effective means of diagnosing early-stage LC and identifying patients at risk for disease recurrence.

Introduction: Dyslipidemia is a hallmark of T2DM, and as such, analyses of lipid metabolic profiles in affected patients have the potential to permit the development of an integrated lipid metabolite-based biomarker model that can facilitate early patient diagnosis and treatment.Methods: Untargeted and targeted lipidomics approaches were used to analyze serum samples from newly diagnosed 93 Chinese participants in discovery cohort and 440 in validation cohort via UHPLC-MS and UHPLC-MS/MS first. The acid sphingomyelinase protein expression was analyzed by Western blot.Results and Discussion: Through these analyses, we developed a novel integrated biomarker signature composed of LPC 22:6, PC(16:0/20:4), PE(22:6/16:0), Cer(d18:1/24:0)/SM(d18:1/19:0), Cer(d18:1/24:0)/SM(d18:0/16:0), TG(18:1/18:2/18:2), TG(16:0/16:0/20:3), and TG(18:0/16:0/18:2). The area under the curve (AUC) values for this integrated biomarker signature for prediabetes and T2DM patients were 0.841 (cutoff: 0.565) and 0.894 (cutoff: 0.633), respectively. Furthermore, theresults of western blot analysis of frozen adipose tissue from 3 week (prediabetes) and 12 week (T2DM) Goto–Kakizaki (GK) rats also confirmed that acid sphingomyelinase is responsible for significant disruptions in ceramide and sphingomyelin homeostasis. Network analyses of the biomarkers associated with this biosignature suggested that the most profoundly affected lipid metabolism pathways in the context of diabetes include de novo ceramide synthesis, sphingomyelin metabolism, and additional pathways associated with phosphatidylcholine synthesis. Together, these results offer new biological insights regarding the role of serum lipids in the context of insidious T2DM development, and may offer new avenues for future diagnostic and/or therapeutic research.