Multifunctional isotopic standards based steroidomics strategy: Exploration of cancer screening model

Carbon Nanofibers-Based Nanoconfined Liquid Phase Filtration for the Rapid Removal of Chlorinated Pesticides from Ginseng Extracts

A rapid nanoconfined liquid phase filtration system (NLPF) based on solvent-confined carbon nanofibers/carbon fiber materials (CNFs/CFs) was proposed to effectively remove chlorinated pesticides from ginsenosides-containing ginseng extracts. A series of major parameters that may affect the separation performance of the CNFs-NLPF method were extensively investigated, including the water solubility of nanoconfined solvents, filtration rate, ethanol content of the ginseng extracts, and reusability of the material for repeated adsorption. The developed method showed a high removal efficiency of pesticides (85.5-97.5%), high retainment rate of ginsenosides (95.4-98.9%), and consistent reproducibility (RSD < 11.8%). Furthermore, the feasibility of the CNFs-NLPF technique to be scaled-up for industrial application was systematically explored by analyzing large-volume ginseng extract (1 L), which also verified its excellent modifiable characteristic. This filtration method exhibits promising potential as a practical tool for removing pesticide residues and other organic pollutants in food samples to assure food quality and safeguard human health.

Development and validation of a sensitive LC-MS/MS method for simultaneous quantification of thirteen steroid hormones in human serum and its application to the study of type 2 diabetes mellitus

Endogenous steroid hormones with similar structure, poor content and high efficacy are difficult and vital to be quantitatively detected. In this study, a validated method was established for the simultaneous quantification of thirteen steroids in human serum, and applied to the study of type 2 diabetes mellitus (T2DM). An ultra-high performance liquid chromatography coupled with triple quadrupole mass spectrometry (UPLC-MS/MS) was developed for the simultaneous determination of thirteen steroid hormones in human serum, including androstenedione, corticosterone (B), cortisol (F), cortisone, 18-hydroxycortisol (18OHF), 11-deoxycorticosterone, 11-deoxycortisol, pregnenolone, progesterone, 17-hydroxyprogesterone, testosterone, androstanolone and estradiol. Under the optimum conditions, method was achieved with a BEH Shield RP18 column within 18 min. The lower limits of quantitation for steroids were 0.08-7.81 ng/mL. The intra- and inter-day precision for all the analytes were less than 15 %, and the accuracy ranged from -14.19 % to 12.89 % at three quality control levels. The proposed method, indicating high steady and sensitivity, was successfully applied to the quantification of thirteen steroids levels in serum from patients with T2DM and healthy individuals. The serum concentrations of 18OHF and F were significantly increased in the patients compared with the healthy individuals, while B was significantly decreased. The fold change was 1.98, 1.25 and 0.79 respectively. The ratio of 18OHF to B (18OHF/B) exhibited a 2.51-fold increase in T2DM patients and presented a more significant change. 18OHF/B was identified as a prospective serum marker, which deserves further attention.

"Modeling-Prediction" Strategy for Deep Profiling of Lysophosphatidic Acids by Liquid Chromatography-Mass Spectrometry: Exploration Biomarkers of Breast Cancer

Lysophosphatidic acids (LPAs) are important bioactive phospholipids consisting of various species involved in a wide array of physiological and pathological processes. However, LPAs were rarely identified in untargeted lipidomics studies because of the incompatibility with analytical methods. Moreover, in targeted studies, the coverages of LPAs remained unsatisfactorily low due to the limitation of reference standards. Herein, a "modeling-prediction" workflow for deep profiling of LPAs by liquid chromatography-mass spectrometry was developed. Multiple linear regression models of qualitative and quantitative parameters were established according to features of fatty acyl tails of the commercial standards to predict the corresponding parameters for unknown LPAs. Then 72 multiple reaction monitoring (MRM) transitions were monitored simultaneously and species of LPA 14:0, LPA 16:1, LPA 18:3, LPA 20:3 and LPA 20:5 were firstly characterized and quantified in plasma. Finally, the workflow was applied to explore the changes of LPAs in plasma of breast cancer patients compared with healthy volunteers. Multi-LPAs indexes with strong diagnostic ability for breast cancer were identified successfully using Student's t- test, orthogona partial least-squares discrimination analysis (OPLS-DA) and logistic regression- receiver operating characteristic (ROC) curve analysis. The proposed workflow with high sensitivity, high accuracy, high coverage and reliable identification would be a powerful complement to untargeted lipidomics and shed a light on the analysis of other lipids.

Multiplatform Investigation of Plasma and Tissue Lipid Signatures of Breast Cancer Using Mass Spectrometry Tools

Plasma and tissue from breast cancer patients are valuable for diagnostic/prognostic purposes and are accessible by multiple mass spectrometry (MS) tools. Liquid chromatography-mass spectrometry (LC-MS) and ambient mass spectrometry imaging (MSI) were shown to be robust and reproducible technologies for breast cancer diagnosis. Here, we investigated whether there is a correspondence between lipid cancer features observed by desorption electrospray ionization (DESI)-MSI in tissue and those detected by LC-MS in plasma samples. The study included 28 tissues and 20 plasma samples from 24 women with ductal breast carcinomas of both special and no special type (NST) along with 22 plasma samples from healthy women. The comparison of plasma and tissue lipid signatures revealed that each one of the studied matrices (i.e., blood or tumor) has its own specific molecular signature and the full interposition of their discriminant ions is not possible. This comparison also revealed that the molecular indicators of tissue injury, characteristic of the breast cancer tissue profile obtained by DESI-MSI, do not persist as cancer discriminators in peripheral blood even though some of them could be found in plasma samples.