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At least 37 records · Page 2Linked to original sources
PubMed1997

Mould-devouring mites differ in guanine excretion from dust-eating Acari, a possible error source in mite allergen exposure studies.

BACKGROUND AND OBJECTIVE: Measurement of guanine in dust proved a good assessment of mite allergen exposure. METHODS: Exposure to mite allergens may lead to atopic inflictions. In a semi-natural test system the development of Dermatophagoides pteronyssinus (Trouessart) and Glycyphagus domesticus (De Geer), and the presence of their guanine excretion, was examined in a dust-soiled and mouldy environment. Mites were counted after heat-escape, and guanine was detected by means of capillary zone electrophoresis. For each species, 50 mites randomly taken, were inoculated on soiled test-surfaces of 10 x 10 cm. Rough wooden board, gypsum board, tufted carpet, and a self-made mattress representing wall surfaces and home-textiles, respectively, were used. Eight weeks after inoculation with mites only, the surfaces were all mould ridden, and mite and guanine measurements were taken. The Spearman rank correlation test and the Mann-Whitney U-test were used in statistical analysis. The confidence limit was set at 1%. RESULTS: Among the various test-surfaces, no differences were found regarding total mite numbers and amount of guanine present (P > 0.01). For the dust-eating mite D. pteronyssinus, total mite numbers correlated with the amount of guanine present (P = 0.002) on all inoculated surfaces, indicating feeding on the protein-rich dust. For the mould devouring mite G. domesticus, however, no such correlation was found (P = 0.72). Apparently, they mainly consumed fungal carbohydrates during this experiment. CONCLUSION: The allergological relevance of storage mites has been under discussion for the last 25 years. In humid homes, these mites will feed almost exclusively on fungi and may produce allergenic or irritating substances different from those arising on protein-rich laboratory media used in allergen extract production or present in carpets, bedding and furniture.

Acari↗
PubMed1999

Sources of error in lung densitometry with CT.

RATIONALE AND OBJECTIVES: To determine and analyze the most important error sources in lung CT densitometry in vivo. METHODS: The authors examined the influences of CT acquisition errors, physiologic changes, and image segmentation errors on lung densitometry. Among others, spatial dependency and long-term reproducibility of the density measurements of blood and air were examined over a period of 4 years in a group of 28 patients with pulmonary emphysema. These results were related to the measured lung densities in this group. RESULTS: The density measurement of blood and air is strongly dependent on the position in the thorax. Despite full-scanner calibrations, x-ray tube replacement can induce a significant increase in measured blood density. CONCLUSIONS: A change in a lung density parameter over time can actually be the result of tube replacement or changing blood density. A simple postprocessing technique can correct for these changes.

Absorptiometry, Photon↗
PubMed2004

Effects of long-term storage on Fusarium toxin concentrations in wheat--sources of error of the analytical results.

The concentrations of the Fusarium toxins deoxynivalenol (DON) and zearalenone (ZON) of a heavily contaminated wheat grain batch were followed over a period of 1 year by taking samples 15 times every 28 days. The air temperature and relative humidity at the top of the wheat batch ranged between 7 and 22 degrees C and 44 and 55%, respectively, and corresponded to a variation in the moisture content of the wheat grain between 11.5 and 12.3%. None of these fluctuations were related to ZON and DON concentrations, which varied between 0.46 and 0.66 and 15.0 and 19.5 mg/kg DM. Therefore, the data were used to analyse the error sources for the analytical results. It was found that the variance proportions due to sampling and sample preparation plus analysis were not similar for DON and ZON. The variance proportion due to sampling was found to be 0.62 for ZON, which corresponded to a variance proportion of 0.38 due to sample preparation plus analysis. In contrast, the latter variance proportion for DON was estimated to be 1.0 and consequently completely superimposed the sampling error. It is concluded that long-term storage of contaminated wheat grain does not affect the concentrations of DON and ZON considering the measured fluctuations in ambient temperature, relative humidity and moisture content of the grain. Therefore, no degradation of DON and ZON occurred during the storage of wheat for a period of one year under ambient conditions. The effects of sampling and sample preparation plus analysis on the final analytical results are different for DON and ZON and require further consideration.

Animal Feed↗
PubMed2002

Lesion type misidentification: EEG potential sampling and source reconstruction errors.

Accurate EEG source reconstruction needs an appropriate volume conductor head model including, in the presence of a morphological brain lesion, a lesion compartment. Lesion electrical properties (conductivity) can not be measured in vivo and need to be retrieved from literature on the base of lesion type identification, performed by means of diagnostic imaging. However, different pathologies can appear similar at bioimages inspection, leading to uncertain diagnosis and in turn to wrong lesion conductivity assumption. Besides many factors, source reconstruction accuracy depends on lesion conductivity value and on electrodes placing (scalp potential sampling). We investigated the relation between electrodes sampling of the EEG and source reconstruction accuracy in case of uncertain lesion type identification. We adopted an eccentric-spheres head model (including the lesion) and a dipole source. We simulated several pathological conditions considering different electrode montages on the scalp: a "clinical like" sampling with 64 electrodes and an extended sampling with 128 electrodes. Source reconstruction was performed assuming wrong lesion type identification (i.e. introducing an error in the model parametrical setting). We found large errors for both source localization (LE) and source intensity estimations. Maximum LE could be reduced (from 30 to 27 mm) by the extended sampling. Conversely, the mean values of LE mostly increased using more electrodes. The benefit of enlarging the sampling was clear for the intensity estimation. In conclusion, only the most inaccurate source reconstruction could be improved changing electrodes placing; the inherent source reconstruction error due to model parameter setting could not be avoided.

Brain Diseases↗