Determination of urinary arsenic, mercury, and selenium in steel production workers

The fumes and dust of trace elements and their compounds are very toxic and have been related to an increase in the incidence of diseases. Occupational exposure to toxic metals and metalloids can be determined by means of workplace air measurements and biological monitoring. The aim of our investigation was to determine the concentrations of As, Hg, and Se in urine samples under routine clinical laboratory conditions. To assess the reliability of these methods, critical factors such as detection limit(s), calibration range(s), cost, accuracy, and precision were studied. The method was employed for the quantitative determination of arsenic, mercury, and selenium in urine samples from steel production and quality control workers and healthy unexposed controls. After pretreatment with acids, the samples were digested by means of a microwave oven. Arsenic was determined by hydride atomic absorption spectrometry and mercury was determined by cold vapor atomic absorption spectrometry, whereas selenium was determined by a graphite furnace atomic absorption spectrometry. The results indicate those urinary arsenic, mercury, and selenium levels of the exposed workers are significantly higher than those of the controls. The possibility that these elements are involved in the etiology of diseases is discussed and recommendations are made to improve workplace ventilation and industrial hygiene practices.     

Determination of Interspin Distance Distributions by cw-ESR Is a Single Linear Inverse Problem

Cw-ESR distance measurement method is extremely valuable for studying the dynamics-function relationship of biomolecules. However, extracting distance distributions from experiments has been a highly technique-demanding procedure. It has never been conclusively identified, to our knowledge, that the problems involved in the analysis are ill posed and are best solved using Tikhonov regularization. We treat the problems from a novel point of view. First of all, we identify the equations involved and uncover that they are actually two linear first-kind Fredholm integral equations. They can be combined into one single linear inverse problem and solved in a Tikhonov regularization procedure. The improvement with our new treatment is significant. Our approach is a direct and reliable mathematical method capable of providing an unambiguous solution to the ill-posed problem. It need not perform nonlinear least-squares fitting to infer a solution from noise-contaminated data and, accordingly, substantially reduces the computation time and the difficulty of analysis. Numerical tests and experimental data of polyproline II peptides with variant spin-labeled sites are provided to demonstrate our approach. The high resolution of the distance distributions obtainable with our new approach enables a detailed insight into the flexibility of dynamic structure and the identification of conformational species in solution state.     

A Single Compartment Quantal Response Model with an Inspection Process

In a single compartment quantal response model, besides the input and release processes, an inspection process, assumed to be independent of the input and release processes, is considered. Each time when a release occurs, we assume the amount of release is randomly proportional to the amount present and the proportional rates form a sequence of independent and identically distributed random variables with support on [0, 1]. The input policy we consider is a modification of (s, S) input policy in the inventory model. More precisely, let 0 ≦s₂ ≦s₁ ≦s ≦ S, if after a release, the amount of the drug in subject's body is less than a level s₂ which is small enough, then there will be an input immediately with probability 1 — p and no more inputs thereafter with probability p, also there will be an input immediately if the dose level is in the interval [s₂, s₁). If the dose level is in the interval [s₁, s) there will be no input unless the inspector arrives. On the other hand, if the dose level is greater than or equal to s, then there will be no input. We consider a stochastic model as described above, and obtain the expressions for some quantities of interest. A Monte Carlo study has also been carried out to demonstrate some behaviors of our quantal response process.