Permeation profiles of potential urine-borne biomarkers of human presence over brick and concrete

Headspace solid phase micro-extraction gas chromatography-mass spectrometry (SPME-GC-MS) analysis was performed over an in-house made filling chamber loaded with brick or concrete, mimicking a potential entrapment scene of building collapse following natural or man-made disasters. Permeation profiles of 22 volatile species, released by human urine samples, were quantitatively monitored over the selected debris materials for a time period of 24 hours (LODs ranged from 0.05-0.8 ppb, R(2) varied from 0.991-0.999 and RSDs 3-9%). Ketones were the most abundant constituents of urine vapor with eleven representatives followed by five aldehydes, two furans, two sulphur-containing compounds, one nitrile and one heterocyclic compound. The majority of the detected compounds were found below 10 ppb, with the exception of some ketones including acetone, 2-butanone and 2-pentanone. The influence of debris materials on the permeation profiles of analytes under study depended on their fundamental physicochemical properties. Less volatile and more soluble compounds in urine (ketones and aldehydes) were found to be present for longer time periods in the surroundings of the urine samples than the more volatile and poorly soluble ones (furans, sulphur-containing compounds). More specifically, ketones exhibited longer residence times in the filling chamber and strongly interacted with the debris materials as their molecular masses were increased; their profiles were found to be significantly modified in the presence of concrete. In general, concrete demonstrated a stronger interaction with urine species than brick, affecting the observed concentrations and residence times of released volatiles in the chamber.     

人体邻苯二甲酸酯暴露的尿液生物标志物分析方法

邻苯二甲酸酯（phthalates，PAEs）是一类典型的环境内分泌干扰物。近年来，由于PAEs产量和使用量的增加，其对人体健康的危害尤其是生殖发育毒性受到了人们高度关注。由于PAEs在环境、食物（材）中广泛存在，导致人体不可避免地长期暴露于PAEs化合物，因此很有必要开展人体PAEs暴露评估。对人体尿液样品中的PAEs代谢物进行筛选和定量是评价PAEs暴露的重要手段，而建立它们准确、可靠的分析方法是重要前提。目前，邻苯二甲酸单酯和次级代谢物分别是短链和长链PAEs暴露最为常用的生物标志物。离线或在线固相萃取与高效液相色谱-串联质谱联用已成为测定PAEs代谢物的首选方法。本文主要综述了人体PAEs暴露的尿液生物标志物的分析方法，并讨论了这些方法在实际应用中的优点、局限性及挑战。     

TMT-based proteomics analysis to screen potential biomarkers of Achyranthis Bidentatae Radix for osteoporosis in rats

This study aimed to explore the possible mechanism of Achyranthis Bidentatae Radix for the treatment of osteoporosis using tandem mass tag-based proteomics technique combined with mass spectrometry. Proteomics techniques combined with bioinformatics were used to analyze the biological functions of differentially expressed proteins. In addition, western blotting was performed to verify the expression of related proteins. A total of 3,752 proteins were identifiable by proteomic analysis. Furthermore, 93 differentially expressed proteins were identified, of which 61 were upregulated and 32 were downregulated. Differentially expressed proteins were primarily associated with oxidative phosphorylation (p = 4.8 × 10⁻⁴) pathways and involved in transmembrane transport (p = 3.5 × 10⁻³), exocytic process (p = 1.2 × 10⁻²), cellular developmental process (p = 1.3 × 10⁻²), adenosine triphosphate metabolic process (p = 1.0 × 10⁻²) and other biological processes. Western blotting analysis showed that MT-CYB and NDUFA9 were differentially expressed in the bone microenvironment of rats with osteoporosis. We speculated that they were potential biomarkers linked to osteoporosis. This study employed proteomics to explore the potential therapeutic targets of Achyranthis Bidentatae Radix to treat osteoporosis. This revealed that mitochondria are a new target for the treatment of glucocorticoid-induced osteoporosis.     

Investigation of endogenous corticosteroids profiles in human urine based on liquid chromatography tandem mass spectrometry

The accurate and precise measurement of endogenous corticosteroids in urine is a powerful tool to understand the biochemical state in several diseases. In this study, a rapid, accurate, and sensitive method based on liquid chromatography-tandem mass spectrometry (LC–MS/MS) for the quantification of 67 endogenous gluco- and mineralo-corticosteroids and progestins has been developed and validated. Sample preparation, chromatographic separation, and mass spectrometric detection were optimized. Urine samples (0.5 mL) were hydrolyzed with β-glucuronidase and the released analytes were extracted by liquid–liquid extraction. The chromatographic separation was performed in 20 min after redisolution of the extract. MS behavior of endogenous corticosteroids was evaluated in order to select the most specific precursor ion ([M+H]⁺, [M+NH₄]⁺, or [M+H-nH₂O]⁺) for the detection. MS/MS determination was performed under selected reaction monitoring mode using electrospray ionization in positive mode. The method was shown to be linear (r > 0.99) in the range of endogenous concentrations for all studied metabolites. Limits of detection (LOD) below 1 ng mL⁻¹ were typically obtained for analytes with a 3-oxo-4-ene structure whereas LODs below 15 ng mL⁻¹ were common for the rest of analytes. Recoveries were higher than 80% and intra-assay precisions below 20%, evaluated at three concentration levels, were found for most steroids. No significant or moderate matrix effect, ranging from 54 to 155%, was observed for most of the analytes. The applicability of the method was confirmed by analyzing 24 h urine samples collected from twenty healthy volunteers and comparing the results with previously established normal ranges. The wide coverage of corticosteroid metabolism, together with short analysis time, low sample volume, simple sample preparation, and satisfactory quantitative results make this method useful for clinical purposes.     

Plasma phospholipid metabolic profiling and biomarkers of rats following radiation exposure based on liquid chromatography–mass spectrometry technique

Lipidomics, a prominent area of metabolomics, utilizes novel analytical methodologies to study the extensive classes of lipid molecules, changes in lipid metabolism and lipid‐mediated signaling processes. In this paper, the phospholipid metabolic profiles changes and potential biomarker identification in the rats plasma after γ‐irradiation exposure were investigated by coupling high performance liquid chromatography–mass spectrometry technology to multivariate statistical analysis. Orthogonal partial least‐squares to latent structures discriminate analysis (OPLS‐DA) was used to find the potential plasma phospholipids biomarkers of rats for radiation exposure. According to the corresponding tandem mass spectrometric results, potential biomarkers were identified. After exposure to γ‐rays, phosphatidylethanolamine and phosphatidylserine showed a marked increase, and phosphatidylcholine, sphingomyelin and lysophosphatidylcholine followed the same trend, although their rise was not significant. The results suggested that radiated rats had a phospholipid metabolic abnormality, which could be an alternative way to assess the radiaton exposure. The biomarkers may involve a radiation‐induced apoptosis pathway and represent a promising target for discovery new radioprotective drugs and radiosensitizers. Copyright © 2009 John Wiley & Sons, Ltd.     

Application of chemometrics to 1H NMR spectroscopic data to investigate a relationship between human serum metabolic profiles and hypertension

The application of chemometric methods to 1H NMR spectroscopic data has been documented for pathophysiological processes. In this study we show the application of 1H NMR-based metabonomics to investigate a relationship between serum metabolic profiles and hypertension. Although hypertension can be defined using blood pressure measurements, the underlying aetiology and metabolic effects are not so readily identified. Serum profiles for patients with low/normal systolic blood pressure (SBP < or = 130 mm Hg; n = 28), borderline SBP (131-149 mm Hg; n = 19) and high SBP (> or = 150 mm Hg; n = 17) were acquired using 1H NMR spectroscopy. Orthogonal signal correction followed by principal components analysis were applied to these NMR data in order to facilitate interpretation, and the resulting chemometric models were validated using Soft Independent Modelling of Class Analogy. Using 1H NMR-based metabonomics, it was possible to distinguish low/ normal SBP serum samples from borderline and high SBP samples. Borderline and high SBP samples, however, were indiscriminate from each other. Our preliminary results showed that there was a relationship between serum metabolic profiles and blood pressure which, in part, was due to lipoprotein particle composition differences between the samples. Furthermore, our results indicated that serum pathology associated with blood pressure is apparent at SBP values > 130 mm Hg, which the WHO and ISH currently define as the limit between normal and high-normal.     

Application of pathways activity profiling to urine metabolomics for screening Qi‐tonifying biomarkers and metabolic pathways of honey‐processed Astragalus

Honey‐processed Astragalus, a widely used Qi‐tonifying and immunomodulating herb in traditional Chinese medicine, has strengthened the tonic effects and achieved fewer side effects compared with astragali radix in clinical application. Here, we focus on Qi‐tonifying biomarkers and pathways of honey‐processed Astragalus using urine metabolomics that provide the basis for building the linkage between metabolites in rat urine and its symptoms. The spleen Qi deficiency model group, normal group, astragali radix group, and honey‐processed Astragalus group were implemented to evaluate Qi‐tonifying effects. Twelve potential biomarkers were screened by multivariate statistical analysis by using ultra high performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry. Furthermore, pathways activity profiling showed unique pathways that are primarily involved in tryptophan metabolism, tricarboxylic acids cycle, and methionine metabolism. The results demonstrated that metabolomics coupled with pathway activity profiling were promising tools. It might serve as a novel methodological clue to systematically dissect the underlying Qi‐tonifying mechanism of honey‐processed Astragalus.     

Prediction of 5-hydroxytryptamine transporter inhibitors based on machine learning

In patients with depression, the use of 5-HT reuptake inhibitors can improve the condition. Machine learning methods can be used in ligand-based activity prediction processes. In order to predict SERT inhibitors, the SERT inhibitor data from the ChEMBL database was screened and pre-processed. Then 4 machine learning methods (LR, SVM, RF, and KNN) and 4 molecular fingerprints (CDK, Graph, MACCS, and PubChem) were used to build 16 prediction models. The top 5 models of accuracy (Q) in the cross-validation of training set were used to build three different ensemble learning models. In the test1 set, the VOT_CLF3 model had the largest SP (0.871), Q (0.869), AUC (0.919), and MCC (0.728). In the unbalanced test2 set, VOT_CLF3 had the largest SE (0.857), SP (0.867), Q (0.865) and MCC (0.639). VOT_CLF3 was recommended for the virtual screening process of SERT inhibitors. In addition, 12 molecular structural alerts that frequently appear in SERT inhibitors were found (P < 0.05), which provided important reference value for the design work of SERT inhibitors.     

Application of NMR metabolomics to search for human disease biomarkers

Since antiquity, humans have used body fluids like saliva, urine and sweat for the diagnosis of diseases. The amount, color and smell of body fluids are still used in many traditional medical practices to evaluate an illness and make a diagnosis. The development and application of analytical methods for the detailed analysis of body fluids has led to the discovery of numerous disease biomarkers. Recently, mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR), and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The goal of these efforts is to identify metabolites that are uniquely correlated with a specific human disease in order to accurately diagnose and treat the malady. In this review we will discuss recent developments in sample preparation, experimental techniques, the identification and quantification of metabolites, and the chemometric tools used to search for biomarkers of human diseases using NMR.     

Pilot study of capillary electrophoresis coupled to mass spectrometry as a tool to define potential prostate cancer biomarkers in urine

We describe the use of capillary electrophoresis (CE) coupled with mass spectrometry (MS) to identify single polypeptides and patterns of polypeptides specific for prostate cancer (CaP) in human urine. Using improved sample preparation methods that enable enhanced comparability between different samples, we examined samples from 47 patients who underwent prostate biopsy. Of this group, 21 patients had benign pathology and 26 with CaP, and these were used to define potential biomarkers, which allow discrimination between these two states. In addition, CE-MS data from these 47 urine samples were compared to that of 41 young men (control) without known or suspected clinical CaP to further confirm the polypeptides indicative for CaP. Upon crossvalidation of the same samples, several polypeptides were selected that enabled correct classification of the CaP patients with 92% sensitivity and 96% specificity. We then examined an additional 474 samples from patients with renal disease enrolled in other studies and found that 14 (3%) had polypeptides suggestive of CaP possibly indicating that they harbor clinical CaP. In conclusion, this early pilot study suggests that CE-MS of urine warrants further investigation as a tool that can identify putative biomarkers for CaP.     

基于气相色谱-质谱联用技术的乙型肝炎血清代谢标志物的探究

探索了乙型肝炎患者和健康人血清代谢组的差异,寻找与疾病相关的潜在标志物。收集乙肝患者30例、健康对照35例,以气相色谱-质谱联用技术作为研究平台,应用主成分分析、正交偏最小二乘法-判别分析进行模式识别,然后通过变量重要性因子、非参数检验,结合数据库检索筛选鉴定有差异的代谢物。确认10个代谢物存在显著差异,其中柠檬酸、乌头酸、谷氨酰胺、N,N-二甲基甘氨酸、丙二酸与乙型肝炎患者组的相关性较好,受试者工作特征曲线下面积为0.975,具有较好的特异度和敏感度。因此这5个代谢物能够作为潜在的区分乙型肝炎患者和正常人的血清小分子标志物,有助于进一步了解病理机制,确定治疗目标。     

Proteomic profiling of human sera for discovery of potential biomarkers to monitor abstinence from alcohol abuse

Although numerous biomarkers or biomarker candidates have been discovered to detect levels of drinking and intervals of time after last drinking episode, only a few biomarkers have been applied to monitor abstinence in a longer interval (≥6 wks) from alcohol abuse. Considering sample sources, sensitivity, and specificity, new biomarkers from blood with better accuracy are needed. To address this, serum proteomic profiles were compared between pre‐ and post‐ treatment samples from subjects seeking treatment for alcohol abuse and dependence in an intensive 6wk daily outpatient program using high‐abundance plasma protein immunodepletion and LC‐MS/MS techniques. Protein identification, quantification, candidate biomarker selection, and prioritization analyses were carried out. Among the 246 quantified serum proteins, abundance of 13 and 45 proteins in female and male subjects were significantly changed (p ≤ 0.05), respectively. Of these biomarker candidate proteins, 2 (female) and 8 (male) proteins were listed in category 1, with high area under the receiver operating characteristic curve, sensitivity, specificity, and fold change. In summary, several new biomarker candidates have been identified to monitor abstinence from alcohol abuse.     

Effect of container material, storage time and temperature on determinations of cadmium levels in human urine

The effect of container material (polyethylene, polypropylene, polystyrene, borosilicate glass and flint glass), storage time (0-86 days), and temperature (22 degrees and 4 degrees ) on the cadmium content found by graphite-furnace atomic-absorption spectrometry for spiked and unspiked urine samples has been studied. No loss of cadmium occurred for at least 10 and 28 days when unspiked and spiked samples respectively, were stored in polyethylene containers at 22 degrees . For storage up to 3-4 days at 22 degrees , polypropylene, polystyrene, borosilicate glass and flint glass containers were also found suitable. Storage at 4 degrees was not effective in preventing loss of cadmium.     

Application of machine learning to improve the results of high-throughput docking against the HIV-1 protease

We have previously reported that the application of a Laplacian-modified naive Bayesian (NB) classifier may be used to improve the ranking of known inhibitors from a random database of compounds after High-Throughput Docking (HTD). The method relies upon the frequency of substructural features among the active and inactive compounds from 2D fingerprint information of the compounds. Here we present an investigation of the role of extended connectivity fingerprints in training the NB classifier against HTD studies on the HIV-1 protease using three docking programs: Glide, FlexX, and GOLD. The results show that the performance of the NB classifier is due to the presence of a large number of features common to the set of known active compounds rather than a single structural or substructural scaffold. We demonstrate that the Laplacian-modified naive Bayesian classifier trained with data from high-throughput docking is superior at identifying active compounds from a target database in comparison to conventional two-dimensional substructure search methods alone.     

Effects of the ambient fine particulate matter (PM2.5) exposure on urinary metabolic profiles in rats using UPLC-Q-TOF-MS

The pathogenesis of PM2.5 was evaluated on rats in model groups using a metabonomic method by UPLC-Q-TOF-MS and 17 potential endogenous metablites were identified. The primary metabolism pathways involved pentose and glucuronate interconversions, starch and sucrose metabolism, tryptophan metabolism, tyrosine metabolism, phenylalanine metabolism, purine metabolism, acetaminophen metabolism pathway, retinol metabolism and valproic acid metabolism pathway.     

Application of pattern recognition and feature extraction techniques to volatile constituent metabolic profiles obtained by capillary gas chromatography

The applicability of threshold logic units, a form of nonparametric pattern recognition, to the processing of metabolic profile data obtained by high-efficiency glass capillary column gas chromatography has been investigated. The test data included profiles of the volatile constituents of urine from normal individuals and from individuals with diabetes mellitus. A feature extraction algorithm allowed for dimensionality reduction and indicated the constituents most important in the normal versus pathological distinction. With an optimum number of dimensions, a normal versus pathological prediction rate of 93.75% was achieved. Gas chromatography-mass spectrometry was utilized to identify important profile constituents.