Environmental Applications of Electromembrane Extraction: A Review.

Electromembrane extraction (EME) is a miniaturized extraction technique that has been widely used in recent years for the analysis and removal of pollutants in the environment. It is based on electrokinetic migration across a supported liquid membrane (SLM) under the influence of an external electrical field between two aqueous compartments. Based on the features of the SLM and the electrical field, EME offers quick extraction, effective sample clean-up, and good selectivity, and limits the amount of organic solvent used per sample to a few microliters. In this paper, the basic devices (membrane materials and types of organic solvents) and influencing factors of EME are first introduced, and the applications of EME in the analysis and removal of environmental inorganic ions and organic pollutants are systematically reviewed. An outlook on the future development of EME for environmental applications is also given.

Development and Validation of a Modified QuEChERS Method for Simultaneous Analysis of 250 Flavor Constituents in Tobacco by Gas Chromatography Tandem Mass Spectrometry.

BACKGROUND: Flavor constituents play an important role in the flavor characteristics of tobacco leaves and cigarettes. Sensitive, selective, and high-throughput multi-analyte analytical methods are needed to satisfy the demand for analyzing trace-level flavor constituents in tobacco. However, trace analysis of multi-targets in a complex tobacco matrix is significantly challenging. OBJECTIVE: This study was undertaken to develop and validate a fast, selective, sensitive, and accurate GC-tandem mass spectrometry (GC-MS/MS) method for the simultaneous analysis of 250 flavor constituents in tobacco using a modified QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) extraction procedure and backflushing technique. METHODS: The samples were extracted with a mixture of acetonitrile and phosphate buffer. GC-MS/MS served as a reliable tool to quantify the flavor constituents due to its high sensitivity, selectivity, and good repeatability. RESULTS: Our evaluation showed that 243 flavor constituents presented good linearity. Average recoveries of 216 target compounds in tobacco ranged from 70 to 120% with RSDs less than 20% at three fortification levels. The limits of quantitation of 225 and 25 compounds were in the range of 2-50 and 51-112 µg/kg, respectively. This method was then successfully applied to the analysis of 15 commercial cigarette samples with different style characteristics. CONCLUSION: The modified QuEChERS method worked very well for a wide range of flavor constituents that have not been reported by QuEChERS pretreatment previously, and the use of concurrent backflushing offered significant increase in system robustness and sample throughput. The method greatly improved the detection performance and the range of the flavor constituents, and proved to be more accurate, sensitive, selective, convenient, and practical than the reported method, and thus, can be applied in routine analysis. HIGHLIGHTS: A validated QuEChERS-based GC-MS/MS method for multiple flavor constituents analysis in tobacco was reported for the first time. The use of concurrent backflushing markedly improved the instrument robustness and sample throughput.

Simultaneous analysis of multiple pesticide residues in tobacco by magnetic carbon composite-based QuEChERS method and liquid chromatography coupled to quadrupole time-of-flight mass spectrometry.

Magnetic carbon composite (Fe3O4@C) was synthesized and applied as a reversed-dispersive solid-phase extraction sorbent for the simultaneous analysis of 40 pesticide residues in tobacco by ultrahigh-performance liquid chromatography coupled to quadrupole time-of-fight mass spectrometry. Compared to the traditional QuEChERS method, the optimized Fe3O4@C simplified clean-up process and exhibited better clean-up capability than conventional sorbents. The pesticides were qualitatively identified by accurate mass of protonated molecules, fragment ions, isotopic peak clusters, and retention time, and quantitatively determined by matrix-matched external standard method. Good linearity of the proposed method was obtained with R value greater than 0.997 for all target pesticides at concentration levels of 2-200 µg/L. The limit of detection ranged from 0.14 to 2.67 µg/kg. The recoveries and relative standard deviations of all target pesticides at three spiked concentrations of 20, 50 and 200 µg/kg were in the ranges of 80.8%-113.3% and 0.6%-16.3%, respectively. Compared with the reported methods for the analysis of multiple pesticide residues in tobacco, the proposed method has the advantages of simple to operate, high clean-up ability and less time-consuming in clean-up process.

Osteoporosis and Osteopenia Among Patients With Type 2 Diabetes Aged ≥50: Role of Sex and Clinical Characteristics.

INTRODUCTION/BACKGROUND: Although some studies have explored the association of adiposity and life habits (such as smoking) with osteoporosis and osteopenia among type 2 diabetes mellitus (T2DM) patients, the association between diabetic clinical characteristics (especially hypoglycemic drug use) and osteoporosis/osteopenia remains unclear. This study aimed to investigate the relationship of clinical characteristics with osteoporosis and osteopenia among T2DM patients by sex. METHODS: A total of 1222 T2DM patients aged ≥50 were included in the present study. Information on demographic, anthropometric and clinical characteristics was collected from medical records. Bone mineral density was assessed by dual-energy X-ray absorptiometry densitometer. Multiple adjusted logistic regression analyses were performed to estimate the odds ratio (OR) and 95% confidence interval (CI) of osteoporosis and osteopenia related to clinical characteristics. RESULTS: Of all participants, the prevalence of osteoporosis and osteopenia was 9.2% and 41.3%, respectively, and they were higher in females (14.7% and 48.5%) than in males (2.8% and 33%). After adjustment for potential confounders, the results showed that overweight (OR = 0.59; 95% CI, 0.42-0.81) and obesity (OR = 0.35; 95% CI, 0.24-0.50) were related to decreased odds of osteoporosis and osteopenia in both male and female T2DM patients, poor glycemic control (OR = 1.63; 95% CI, 1.08-2.47) was associated with increased odds of osteoporosis and osteopenia in males, and metformin treatment (OR = 0.65; 95% CI, 0.43-0.99) was associated with decreased odds of osteoporosis and osteopenia in females. CONCLUSIONS: Better glycemic management and rational choice of antidiabetic medication might be promising to prevent osteoporosis in T2DM patients. Further longitudinal studies are warranted to explore the association between antidiabetic treatment and osteoporosis.

Automatic data analysis workflow for ultra-high performance liquid chromatography-high resolution mass spectrometry-based metabolomics.

Data analysis for ultra-performance liquid chromatography high-resolution mass spectrometry-based metabolomics is a challenging task. The present work provides an automatic data analysis workflow (AntDAS2) by developing three novel algorithms, as follows: (i) a density-based ion clustering algorithm is designed for extracted-ion chromatogram extraction from high-resolution mass spectrometry; (ii) a new maximal value-based peak detection method is proposed with the aid of automatic baseline correction and instrumental noise estimation; and (iii) the strategy that clusters high-resolution m/z peaks to simultaneously align multiple components by a modified dynamic programing is designed to efficiently correct time-shift problem across samples. Standard compounds and complex datasets are used to study the performance of AntDAS2. AntDAS2 is better than several state-of-the-art methods, namely, XCMS Online, Mzmine2, and MS-DIAL, to identify underlying components and improve pattern recognition capability. Meanwhile, AntDAS2 is more efficient than XCMS Online and Mzmine2. A MATLAB GUI of AntDAS2 is designed for convenient analysis and is available at the following webpage: http://software.tobaccodb.org/software/antdas2.

Automatic untargeted metabolic profiling analysis coupled with Chemometrics for improving metabolite identification quality to enhance geographical origin discrimination capability.

Untargeted metabolic profiling analysis is employed to screen metabolites for specific purposes, such as geographical origin discrimination. However, the data analysis remains a challenging task. In this work, a new automatic untargeted metabolic profiling analysis coupled with a chemometric strategy was developed to improve the metabolite identification results and to enhance the geographical origin discrimination capability. Automatic untargeted metabolic profiling analysis with chemometrics (AuMPAC) was used to screen the total ion chromatographic (TIC) peaks that showed significant differences among the various geographical regions. Then, a chemometric peak resolution strategy is employed for the screened TIC peaks. The retrieved components were further analyzed using ANOVA, and those that showed significant differences were used to build a geographical origin discrimination model by using two-way encoding partial least squares. To demonstrate its performance, a geographical origin discrimination of flaxseed samples from six geographical regions in China was conducted, and 18 TIC peaks were screened. A total of 19 significant different metabolites were obtained after the peak resolution. The accuracy of the geographical origin discrimination was up to 98%. A comparison of the AuMPAC, AMDIS, and XCMS indicated that AuMPACobtained the best geographical origin discrimination results. In conclusion, AuMPAC provided another method for data analysis.

The prevalence and determinants of hypothyroidism in hospitalized patients with type 2 diabetes mellitus.

The purpose of this study was to investigate the prevalence of hypothyroidism among hospitalized patients with type 2 diabetes mellitus and its related factors, and to assess the prevalence of macrovascular and microvascular diseases among type 2 diabetes mellitus inpatients with hypothyroidism and euthyroidism. A total of 1662 type 2 diabetes mellitus inpatients hospitalized at the Metabolic Diseases Hospital, Tianjin Medical University from 1 January 2008 to 1 March 2013 were included in this study. Information on demographic and anthropometric factors and additional variables related to hypothyroidism were collected from medical records. Prevalence rates were calculated and standardized using direct method based on the age-specific and sex-specific structure of all participants. Data were analyzed using binary logistic regression with adjustment for potential confounders. The prevalence of hypothyroidism among type 2 diabetes mellitus inpatients was 6.8 %, and 77.0 % of the patients with hypothyroidism had subclinical hypothyroidism. The prevalence of hypothyroidism increased with age, and was higher in women (10.8 %) than in men (3.4 %). Older age (odds ratio, 1.74; 95 % confidence interval, 1. 05 to 2.89), female gender (odds ratio, 2.02; 95 % confidence interval, 1.05 to 3.87), and positive thyroid peroxidase antibody (odds ratio, 4.99; 95 % confidence interval, 2.83 to 8.79) were associated with higher odds of hypothyroidism among type 2 diabetes mellitus inpatients. The type 2 diabetes mellitus inpatients with hypothyroidism had higher prevalence of cerebrovascular diseases than those with euthyroidism after adjustment for age and gender. The prevalence of hypothyroidism among type 2 diabetes mellitus inpatients was 6.8 %, and most patients had subclinical hypothyroidism. Older age, female gender, and positive thyroid peroxidase antibody could be indicators for detecting hypothyroidism in type 2 diabetes mellitus inpatients.