Determination of heparanase levels in urine (A, B) and plasma (C, D) of individuals from the study groups.

<p>Shown are average (±SE; A, C) and median (B, D) values quantified by an ELISA method, as described under ‘<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017312#s2" target="_blank">Materials and Methods</a>’.</p

Immunofluorescent staining. Heparanase transfected 293 cells were left untreated as control (Con) or stimulated with insulin (250 and 50 pM) for 2 h.

<p>Cells were then fixed, stained with monoclonal anti-heparanase antibody (upper panel, green) and examined by confocal microscopy. Merged images with nuclear counterstaining (red) are shown in the lower panels. Note more diffused heparanase-positive vesicles in response to insulin stimulation.</p

Demographic description of enrolled individuals.

<p>Demographic description of enrolled individuals.</p

Heparanase levels in the urine and plasma of healthy volunteers (control), type 2 diabetic patients (T2DM) and T2DM patients who underwent kidney transplantation.

<p>Heparanase levels in the urine and plasma of healthy volunteers (control), type 2 diabetic patients (T2DM) and T2DM patients who underwent kidney transplantation.</p

Association between blood glucose and heparanase levels.

<p><b>A, B</b>. Urine and plasma heparanase levels presented according to gender. Shown are average (±SE) values of heparanase levels in urine (A) and plasma (B) quantified by ELISA in males (M) and females (F) of healthy volunteers (control), T2DM patients and T2DM patients who underwent kidney transplantation. <b>C, D</b>. Association between urine and plasma heparanase and blood glucose levels. Heparanase levels in plasma and urine were correlated with blood glucose levels using the non-parametric Spearman's rank test. Heparanase levels in the urine were found to correlate with blood glucose (<b>C</b>; r = 0.52, p = 0.0001); blood glucose was also associated with plasma heparanase (<b>D</b>; r = 0.38, p = 0.003).</p

Insulin cooperates with glucose to stimulate secretion of enzymatically active heparanase.

<p><b>A</b>. Immunoblotting. Heparanase-transfected 293 cells were cultured under normal (0.45%) or high (3%) glucose conditions in serum free medium for 20 h. Cells were left untreated (0) or incubated with the indicated concentration of insulin for 2 h. Cell conditioned medium (1 ml) was then collected, and TCA-precipitates were subjected to immunoblotting applying anti-heparanase (upper panel) and anti-cathepsin D (second panel) antibodies. Densitometry analysis of the active 50 kDa heparanase is shown in the lower panel. <b>B</b>. Heparanase enzymatic activity. Corresponding medium samples of untreated cells (control; ♦) or cells treated with insulin (50 pM) under low (▪) or high (▴) glucose were applied onto 35 mm dishes coated with ³⁵S-labeled ECM for 20 h. The medium was then collected and sulfate labeled HS degradation fragments were analyzed by gel filtration, as described under "<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0017312#s2" target="_blank">Materials and Methods</a>". <b>C.</b> Kinetics. Heparanase transfected 293 cells were grown under serum-free conditions and left untreated (Con) or stimulated with insulin under low (0.45%; Ins) or high (3%; Ins+glu) glucose levels. At the time indicated, conditioned medium was collected and subjected to immunoblotting applying anti-heparanase antibody.</p

Diagnosis and Classification of 17 Diseases from 1404 Subjects <i>via</i> Pattern Analysis of Exhaled Molecules

We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, <i>i.e.</i>, gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development