Simultaneous quantification of plasma immunoglobulin subclasses for assessment of maternal and fetal immune response during pregnancy.

Measurement of immunoglobulin subclasses is a useful tool for exploring humoral immune deficiency in the presence of total immunoglobulins within reference intervals. Conventional methods for immunoglobulin measurement are mostly immunoassays, which are of low throughput and laborious to run multiple immunoglobulin subclass tests. Liquid chromatography-mass spectrometry (LC-MS) has emerged as a promising technology for the measurement of protein biomarkers in biological matrices, owing to its high specificity, selectivity, multiplexing, and wide dynamic range. In fully taking these advantages, we developed here a LC-MS based methodology for simultaneous quantitation of the primary immunoglobulin isotypes (IgG, IgA, IgM) and their subclasses in human plasma. Method validation demonstrated that the proposed method showed good linearity with R > 0.99, high precision with coefficients of variation for inter- and intra-assay less than 15%, as well as high accuracy with relative errors less than 8.7%. The developed method was further applied to maternal and cord blood collected at delivery for assessment of maternal and fetal immune status. The immunoglobulin profiles and the features of transplacental transport of maternal immunoglobulin subclasses were comparable to the findings from previous reports, further demonstrating the reliability of this method. Therefore, our method provides a competitive approach to high-throughput detection of multiple immunoglobulin subclasses for biomonitoring or epidemiological studies.

Integrated Proteomics and Metabolomics Assessment Indicated Metabolic Alterations in Hypothalamus of Mice Exposed to Triclosan.

Triclosan (TCS) is a ubiquitous antimicrobial used in many daily consumer products. It has been reported to induce endocrine disrupting effects at low doses in mammals, disturbing sex hormone function and thyroid function. The hypothalamus plays a crucial role in the maintenance of neuroendocrine function and energy homeostasis. We speculated that the adverse effects of TCS might be related to the disturbance of metabolic processes in hypothalamus. The present study aimed at investigating the effects of TCS exposure on the protein and metabolite profiles in hypothalamus of mice. Male C57BL/6 mice were orally exposed to TCS at the dosage of 10 mg/kg/d for 13 weeks. The hypothalamus was isolated and processed for mass spectrometry (MS)-based proteomics and metabolomics analyses. The results showed that a 10.6% decrease (P = 0.066) in body weight gain was observed in the TCS exposure group compared with vehicle control group. Differential analysis defined 52 proteins and 57 metabolites that delineated TCS exposed mice from vehicle controls. Among the differential features, multiple proteins and metabolites were found to play vital roles in neuronal signaling and function. Bioinformatics analysis revealed that these differentially expressed proteins and metabolites were involved in four major biological processes, including glucose metabolism, purine metabolism, neurotransmitter release, and neural plasticity, suggesting the disturbance of homeostasis in energy metabolism, mitochondria function, neurotransmitter system, and neuronal function. Our results may provide insights into the neurotoxicity of TCS and extend our understanding of the biological effects induced by TCS exposure.

A novel approach to determine two optimal cut-points of a continuous predictor with a U-shaped relationship to hazard ratio in survival data: simulation and application.

BACKGROUND: In clinical and epidemiological researches, continuous predictors are often discretized into categorical variables for classification of patients. When the relationship between a continuous predictor and log relative hazards is U-shaped in survival data, there is a lack of a satisfying solution to find optimal cut-points to discretize the continuous predictor. In this study, we propose a novel approach named optimal equal-HR method to discretize a continuous variable that has a U-shaped relationship with log relative hazards in survival data. METHODS: The main idea of the optimal equal-HR method is to find two optimal cut-points that have equal log relative hazard values and result in Cox models with minimum AIC value. An R package 'CutpointsOEHR' has been developed for easy implementation of the optimal equal-HR method. A Monte Carlo simulation study was carried out to investigate the performance of the optimal equal-HR method. In the simulation process, different censoring proportions, baseline hazard functions and asymmetry levels of U-shaped relationships were chosen. To compare the optimal equal-HR method with other common approaches, the predictive performance of Cox models with variables discretized by different cut-points was assessed. RESULTS: Simulation results showed that in asymmetric U-shape scenarios the optimal equal-HR method had better performance than the median split method, the upper and lower quantiles method, and the minimum p-value method regarding discrimination ability and overall performance of Cox models. The optimal equal-HR method was applied to a real dataset of small cell lung cancer. The real data example demonstrated that the optimal equal-HR method could provide clinical meaningful cut-points and had good predictive performance in Cox models. CONCLUSIONS: In general, the optimal equal-HR method is recommended to discretize a continuous predictor with right-censored outcomes if the predictor has an asymmetric U-shaped relationship with log relative hazards based on Cox regression models.