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PubMedDate not supplied

Modelling of 2,3,7,8-tetrachlorodibenzo-p-dioxin levels in a cohort of workers with exposure to phenoxy herbicides and chlorophenols.

Several cohort studies of herbicide manufacturing workers have been conducted over the last years. Most of these studies used simple proxies of exposure in the analysis such as a crude grouping in exposure categories based on job titles, presence in certain production areas over a period of time or during an accident, and duration of exposure. Current serum 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) levels available for a subset of workers can be used to back-extrapolate TCDD levels at the end of exposure using first order kinetic models, and relate TCDD levels to job history using regression models. The regression model obtained can be used to estimate TCDD levels for all cohort members. In this paper, the effect of changes in model assumptions on estimated TCDD levels is explored. TCDD levels are back-extrapolated assuming different values for TCDD half-life. A range of regression models with different sets of exposure determinants is used to relate back-extrapolated TCDD levels to determinants of exposure. These models were used to predict TCDD levels in the epidemiological analysis of data from a Dutch cohort study. The results show that the predicted serum TCDD level is strongly dependent on the assumed half-life. However, the ranking of all individuals on the exposure axis (from low to high) is not affected by changes in the half-life. Predicted serum TCDD levels seem not sensitive to changes in assumption regarding TCDD half-life. Predicted TCDD levels were positively associated with increased (cause specific) mortality.

Adult↗
PubMedDate not supplied

Producing 13C NMR, infrared absorption, and electron ionization mass spectrometric data models of the monodechlorination of chlorobenzenes, chlorophenols, and chloroanilines.

We have developed four spectroscopic data-activity relationship (SDAR) models of monodechlorination of 32 chlorinated benzene compounds in anaerobic estuarine sediment. The SDAR models were based on combinations of 13C nuclear magnetic resonance (NMR), infrared absorption (IR), and electron ionization mass spectrometric (EI MS) data. The SDAR models segregated the 32 compounds into 17 readily monodechlorinated compounds and 15 not readily monodechlorinated compounds. The SDAR model based on 13C NMR, IR, and EI MS data gave a leave-one-out cross-validation of 93.8%. The SDAR model based on a composite of 13C NMR and IR data gave a leave-one-out cross-validation of 90.6%. The SDAR model based on a composite of IR and EI MS data gave a leave-one-out cross-validation of 84.4%. The SDAR model based on a composite of 13C NMR and EI MS data gave a leave-one-out cross-validation of 84.4%. These reliable SDAR models provide a rapid and simple way to predict whether a chlorinated benzene compound will readily go through monodechlorination. The FDA has filed a patent application on methods of using any combination of spectral data (NMR, MS, UV-vis, IR, and fluorescence, phosphorescence) to model a chemical, physical, or biological endpoint.

Carbon Isotopes↗
PubMed2006

Electron spin resonance spin-trapping detection of radical intermediates in N-doped TiO2-assisted photodegradation of 4-chlorophenol.

The electron spin resonance (ESR) spin-trapping technique using 5,5-dimethyl-1-pyrroline-N-oxide as the spin-trap reagent has been applied to detect free radical intermediates generated during in situ ultraviolet or visible irradiation of aqueous 4-chlorphenol (4-CP)/N-doped TiO(2) suspensions. ESR measurements gave the first direct evidence that the active species ((*)OH and O(2*-)) are responsible for the photodecomposition of 4-CP over N-doped TiO(2) under visible-light irradiation, strongly suggesting that the photocatalytic reaction of organic compounds in powdered N-doped TiO(2) systems proceed via surface intermediates of oxygen reduction or water oxidation, not via direct reaction with holes trapped at the N-induced midgap level. These results have important implications for the evaluation of the oxidative powder of TiO(2-x)N(x) catalysts.

Journal Article↗
PubMed2004

A novel Na-O-Ni-O-Ni bridging complex of Cl-HXTA, where Cl-HXTA is 2,6-bis[[bis(carboxylatomethyl)amino]methyl]-4-chlorophenolate.

In the structure of the title compound, heptaaqua-1kappa3O,2kappa2O,3kappa2O-(mu3-2,6-bis[[bis(carboxylatomethyl)amino]methyl]-4-chlorophenolato-1kappaO;2kappa4O,O',N,O1;3kappa4O1N',O",O"')dinickel(II)sodium(I) pentahydrate, [NaNi2(C16H14ClN2O9)(H2O)7].5H2O or [Ni2(Cl-HXTA)(H2O)4[Na(H2O)3]].5H2O, the trinuclear complex unit consists of two distorted NiNO5 octahedra bridged by a phenolate O atom and an NaO4 tetrahedron bridged to one of the Ni octahedra by a carboxylate O atom. There are four intramolecular hydrogen bonds forming four six-membered rings in the complex and the complex molecules are connected to each other by a very complicated hydrogen-bond network.

Journal Article↗
PubMed1995

Specificity of Reductive Dehalogenation of Substituted ortho-Chlorophenols by Desulfitobacterium dehalogenans JW/IU-DC1.

Volume 61, no. 1, abstract, lines 4 and 5: "2,6-dichloro-4-R-phenols, where . . ." should read "2,6-dichloro-4-R-phenols (2,6-DCl-4-RPs, where R is -H, -F, -Cl, -NO(inf2), -CO(inf2)(sup-), or -COOCH(inf3)) . . ." Line 6: ". . . bromophenols (2-BrP, 2,6-DBrP, and 2-Br-4ClP)" should read ". . . the bromophenols 2-BrP, 2,6-DBrP, and 2-Br-4-ClP." [This corrects the article on p. 346 in vol. 61.].

Journal Article↗