Diffusive sampling of methyl isocyanate using 4-nitro-7-piperazinobenzo-2-oxa-1,3-diazole (NBDPZ) as derivatizing agent

A diffusive sampling method for the determination of methyl isocyanate (MIC) in air is introduced. MIC is collected using a glass fiber filter impregnated with 4-nitro-7-piperazinobenzo-2-oxa-1,3-diazole (NBDPZ). The urea derivative formed is desorbed from the filter with acetonitrile and analyzed by means of high-performance liquid chromatography (HPLC) using fluorescence detection (FLD) with lambdaex = 471 nm and lambdaex = 540 nm. Additionally, a method was developed using tandem mass spectrometric (MS-MS) detection, which was performed as selected reaction monitoring (SRM) on the transition [MIC-NBDPZ + H]+ (m/z 307) to [NBDPZ + H]+ (m/z 250). The diffusive sampler was tested with MIC concentrations between 1 and 35 microg m(-3). The sampling periods varied from 15 min to 8 h, and the relative humidity (RH) was set from 20% up to 80%. The sampling rate for all 15 min experiments was determined to be 15.0 mL min(-1) (using HPLC-FLD) with a relative standard deviation of 9.9% for 56 experiments. At 80% RH, only 15 min sampling gave acceptable results. Further experiments revealed that humidity did not affect the MIC derivative but the reagent on the filter prior to and during sampling. The sampling rate for all experiments (including long term sampling) performed at 20% RH was found to be 15.0 mL min(-1) with a relative standard deviation of 6.3% (N = 42). The limit of quantification was 3 microg m(-3) (LC-MS-MS: 1.3 microg m(-3)) for 15 min sampling periods and 0.2 microg m(-3) (LC-MS-MS: 0.15 microg m(-3)) for 8 h sampling runs applying fluorescence detection.     

Uncommon chromosomal mosaicism in chorionic villi

Three cases of unusual chromosomal mosaicism are reported for which the cytogenetic data show inconsistent findings between CVS and AC or fetal tissue, and which cannot be explained simply by non-disjunction. For case 1, in CVS the karyotype was 46,XY, whereas lymphocytes and fibroblasts revealed 69,XXY. DNA fingerprinting indicated one paternal and two maternal chromosome sets, the latter most probably due to omission of maternal meiosis II. For case 2, in CVS mos 46,XX/47,XX,+mar de novo was observed. Amniotic fluid cells had the karyotype 46,XX. The origin of the marker chromosome might be explained by at least two events of unknown order (a somatic chromosome/chromatid deletion and non-disjunction of the homologous chromosome). In case 3 (CVS: mos 46,XY/46,XY,19q+ de novo; amniotic fluid cells, lymphocytes, and fibroblasts: 46,XY), the surplus of chromosome material in 19q+ might be explained on the basis of a somatic translocation. The idea of a chimera is less convincing, as the mosaic finding is restricted to one tissue. Furthermore, there was no hint of a vanishing twin. Hitherto, no case of structural chromosome mosaicism in CVS has been reconfirmed in fetal tissues.     

PREDICTING FECAL COLIFORM BACTERIA LEVELS IN THE CHARLES RIVER,MASSACHUSETTS, USA1

In Massachusetts, the Charles River Watershed Association conducts a regular water quality monitoring and public notification program in the Charles River Basin during the recreational season to inform users of the river's health. This program has relied on laboratory analyses of river samples for fecal coliform bacteria levels, however, results are not available until at least 24 hours after sampling. To avoid the need for laboratory analyses, ordinary least squares (OLS) and logistic regression models were developed to predict fecal coliform bacteria concentrations and the probabilities of exceeding the Massachusetts secondary contact recreation standard for bacteria based on meteorological conditions and streamflow. The OLS models resulted in adjusted R²s ranging from 50 to 60 percent. An uncertainty analysis reveals that of the total variability of fecal coliform bacteria concentrations, 45 percent is explained by the OLS regression model, 15 percent is explained by both measurement and space sampling error, and 40 percent is explained by time sampling error. Higher accuracy in future bacteria forecasting models would likely result from reductions in laboratory measurement errors and improved sampling designs.