Rapid Determination of Trace Multiresidues of 18 Sulfonamides in Chicken Eggs Using a Modified QuEChERS Method Coupled with Ultrahigh Performance Liquid Chromatography.

A modified QuEChERS method was used and an ultrahigh performance liquid chromatography (UHPLC) method was developed for the rapid determination of 18 kinds of sulfonamide residues in chicken eggs. Sample preparation and cleanup conditions were carefully evaluated, and factors such as the adsorbent type and adsorption condition were key parameters in improving the cleanup. The modified QuEChERS method removed matrix interferences, and the sensitivity of the method increased about 5% for recovery and efficiency of the method. Under the optimized UHPLC method with UV detection, all 18 sulfonamide residues were simultaneously separated and rapidly identified within 15 min. The qualitative and quantitative method limits of the 18 sulfonamide residues were 2.06 to 4.12 and 6.86 to 13.7 µg·kg-1, respectively. A close linear relationship (R2 = 0.990 to 0.999) was observed within the concentration range of 0.10 to 2.25 µg·ml-1. Recovery was satisfactory (71 to 102%) for all the sulfonamides in three standard spiked levels, with relative standard deviations of <9.7%. After the modified sample pretreatment, the speed of sample pretreatment, purification, and analysis efficiency were all significantly increased. This method is suitable for the rapid detection of multisulfonamide residues in chicken eggs and other animal-derived foods.

Blood glucose fluctuation aggravates lower extremity vascular disease in type 2 diabetes.

OBJECTIVES: Lower-extremity vascular diseases are important complication of diabetes. In the present study, we investigated the influence of blood glucose fluctuation in type 2 diabetes-associated lower-extremity vascular diseases, and explore the possible mechanism. PATIENTS AND METHODS: Patients with type 2 diabetes was assigned to Group B (without lower-extremity vascular disease) and group C (with lower-extremity vascular disease). Healthy subjects (Group A) served as normal controls. All patients received dynamic blood glucose monitoring for 72 h. The mean amplitude of glycemic excursion (MAGE) and the largest amplitude of glycemic excursion (LAGE) were estimated. The levels of von Willebrand factor (vWF), ischemia-modified albumin (IMA), glycosylated hemoglobin (HbA1c), and biochemical indices were examined, and the lower-extremity vascular diseases were scored in patients from group C. RESULTS: Groups B and C have higher systolic blood pressure (SBP), total cholesterol (TC) level, high-density lipoprotein cholesterol (HDL-C) level, HbA1c level, and vWF level and lower IMA level than those in Group A (p < 0.05). Elevated MAGE and LAGE were observed in groups B and C as compared with Group A. Correlation analysis revealed that the score of lower-extremity vascular diseases was associated with MAGE, LAGE, SBP, LDL-C, vWF, HbA1c, and IMA (p < 0.05). Stepwise multiple-linear regression analysis revealed that lower-extremity vascular diseases were involved with MAGE, IMA, and vWF. CONCLUSIONS: Enhanced fluctuation in patients with type 2 diabetes may promote the occurrence and development of lower-extremity vascular diseases through aggravating vascular endothelial injury.

Determining times to maximum urine excretion of 1-aminopyrene after diesel exhaust exposure.

Biomonitoring of exposures to toxins is an important tool for monitoring public health and safety. Using this tool, exposures are typically measured by the collection of biological specimens such as blood and urine samples. Urine sampling represents a more convenient and less-invasive alternative to blood sampling; however, less work has been published on methodologies for characterizing the time course of excretion and the determination of the time of maximum excretion from urine samples. This paper compares two methods of characterizing the urine excretion profile and estimating the time of maximum excretion: Non-compartmental analysis versus a non-linear pharmacokinetic (PK) modeling. We examine these methodologies using both simulated data and observed data taken from a recent experiment examining a biomarker of diesel exhaust (DE), urinary 1-aminopyrene (1-AP). In the experiment, a series of spot urine samples were collected in a group of healthy volunteers for 24 h after a controlled DE exposure. Simulated data showed that the use of non-linear modeling techniques to estimate PK parameters was more likely to estimate the true time of maximum excretion compared with the non-compartmental approach. Our analysis of observed concentrations of 1-AP led to a hypothesis that there are two subgroups of subjects in terms of the timing of their 1-AP excretion. Results showed that approximately 63% of the subjects had a median time of maximum excretion of 5.37 h, whereas 30% of the subjects may have had maximum excretion times longer than 24 h.