Pathological excretion patterns of urinary proteins in miners highly exposed to dinitrotoluene

A cohort of 161 underground miners who had been highly exposed to dinitrotoluene (DNT) in the copper-mining industry of the former German Democratic Republic was reinvestigated for signs of subclinical renal damage. The study included a screening of urinary proteins excreted by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and quantitations of the specific urinary proteins α 1-microglobulin and glutathione-S-transferase α (GST α) as biomarkers for damage of the proximal tubule and glutathione-S-transferase π (GST π) for damage of the distal tubule. The exposures were categorized semiquantitatively (low, medium, high, and very high), according to the type and duration of professional contact with DNT. A straight dose-dependence of pathological protein excretion patterns with the semiquantitative ranking of DNT exposure was seen. Most of the previously reported cancer cases of the urinary tract, especially those in the higher exposed groups, were confined to pathological urinary protein excretion patterns. The damage from DNT was directed toward the tubular system. In many cases, the appearance of Tamm-Horsfall protein, a 105-kD protein marker, was noted. Data on the biomarkers α 1-microglobulin, GST α, and GST π consistently demonstrated a dose-dependent increase in tubular damage, which confirmed the results of screening by SDS-PAGE and clearly indicated a nephrotoxic effect of DNT under the given conditions of exposure. Within the cluster of cancer patients observed among the DNT-exposed workers, only in exceptional cases were normal biomarker excretions found.

The use of a Riesz fractional differential-based approach for texture enhancement in image processing

Texture enhancement is an important component of image processing that finds extensive application in science and engineering. The quality of medical images, quantified using the imaging texture, plays a significant role in the routine diagnosis performed by medical practitioners. Most image texture enhancement is performed using classical integral order differential mask operators. Recently, first order fractional differential operators were used to enhance images. Experimentation with these methods led to the conclusion that fractional differential operators not only maintain the low frequency contour features in the smooth areas of the image, but they also nonlinearly enhance edges and textures corresponding to high frequency image components. However, whilst these methods perform well in particular cases, they are not routinely useful across all applications. To this end, we apply the second order Riesz fractional differential operator to improve upon existing approaches of texture enhancement. Compared with the classical integral order differential mask operators and other first order fractional differential operators, we find that our new algorithms provide higher signal to noise values and superior image quality.