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We examine the spatial structure of the melanoma in tissue sections. The pigs melanoma was examined in 10 tissue samples obtained from animals of age between 4 and 22 weeks. The tissue sections were measured by light microscopy and Laser Ablation Ion Inductively Coupled Plasma Mass Spectrometry to obtain spatial metal (Cu, Zn) distribution. The exploratory analysis of the tissue sections indicates there is clearly a pattern in the spatial structure. Different projections of the spatial structure of the melanoma are obtained by the different measurement methods. The spectral clustering on the data was utilized to describe the structure in the data. According to the clustering results, there are distinct clusters of observations in the histological data. The spatial elemental distribution of the metals Cu and Zn cannot be clustered—the data form one compact cluster. The clustering of the histological images produces clusters which are related to the annotation of the biological samples in broader terms—the differences between fibrous and cancerous tissue.

We describe a new procedure for the parallel mapping of selected metals in histologically characterized tissue samples. Mapping is achieved via image registration of digital data obtained from two neighbouring cryosections by scanning the first as a histological sample and subjecting the second to laser ablation inductively coupled plasma mass spectrometry. This computer supported procedure enables determination of the distribution and content of metals of interest directly in the chosen histological zones and represents a substantial improvement over the standard approach, which determines these values in tissue homogenates or whole tissue sections. The potential of the described procedure was demonstrated in a pilot study that analysed Zn and Cu levels in successive development stages of pig melanoma tissue using MeLiM (Melanoma-bearing-Libechov-Minipig) model. We anticipate that the procedure could be useful for a complex understanding of the role that the spatial distribution of metals plays within tissues affected by pathological states including cancer.

Papillomaviruses, small non-enveloped DNA viruses, are considered as the cause of a number of cancers, such as a cervical, skin, anal, and penile cancers. Roe deer papillomavirus infection can be easily characterized by typical warts on the skin. The aim of this study was to characterize differences of amino acid and protein composition in healthy and tumour tissue of roe deer using methods including ion-exchange chromatography, SDS-PAGE, two-dimensional gel electrophoresis and MALDI-TOF MS. The obtained data were statistically evaluated. Cluster analysis of all variables showed biggest differences in proline in control and papilloma samples. Further, we aimed at electrophoretic analysis of the samples. Discriminant analysis projections to latent structures (Projection to the latent structure Discrimination analysis, syn. Discrimination Partial least squares analysis, PLS DA) was used to evaluate the obtained results. Examining model PLS DA, there was found that the greatest effect on whether the individual is classified as healthy, or with papilloma, is the latent variable that best correlates with the molecular mass from 108.8 to 128.1 kDa and from 231.2 to 290.4 kDa in the original signal. Finally, using mass spectrometry three proteins, pyridoxal kinase, myoblast determination protein and leucine zipper transcription factor-like protein 1, most likely related to cancer were found.

Detailed study of the period before schizophrenic relapse when early warning signs (EWS) are present is crucial to effective pre-emptive strategies. To investigate the temporal properties of EWS self-reported weekly via a telemedicine system. EWS history was obtained for 61 relapses resulting in hospitalization involving 51 patients with schizophrenia. Up to 20 weeks of EWS history per case were evaluated using a non-parametric bootstrap test and generalized mixed-effects model to test the significance and homogeneity of the findings. A statistically significant increase in EWS sum score was detectable 5 weeks before hospitalization. However, analysis of EWS dynamics revealed a gradual, monotonic increase in EWS score across during the 8 weeks before a relapse. The findings-in contrast to earlier studies-suggest that relapse is preceded by a lengthy period during which pathophysiological processes unfold; these changes are reflected in subjective EWS.

Presented exploratory pilot study was aimed at evaluation of proteins present in urinary specimens collected from prostate cancer suffering subjects after radical prostatectomy, divided into two experimental cohorts: positive (n=15) and negative (n=15) surgical margins (PSM/NSM). The presence of PSM suggests inadequate cancer clearance and the possible need for additional treatment. Proper identification of these risk-patients is therefore of a paramount importance. Total protein profiles were firstly identified by using SDS-PAGE and compared by using partial least square discrimination analysis (PLS-DA), which revealed differences in molecular weights of 80-99 and 150-235 kDa between the experimental groups. For further identification of proteins, comparative proteomic technologies were employed. Two-dimensional gel electrophoresis with subsequent identification of protein spots by using MALDI-TOF mass fingerprinting revealed differential expression of proteins between NSM/PSM cohorts. Moreover, in PSM group, three uniquely identified proteins (cyclin-dependent kinase 6, galectin-3-binding protein and L-lactate dehydrogenase C chain) were found, which show tight connection with prostate cancer and presence of all of them was previously linked to certain aspects of prostate cancer. These proteins may be associated with the molecular mechanisms of prostate cancer development; hence, their identification may be helpful for the assessment of disease progression risk after radical prostatectomy, but also for possible early diagnosis.

In this paper we would like to suggest a modification of data vases visualization that can help understanding complex data where each subject is described by a vector of synchronized signals taken in the same time intervals. The advantages of our new approach were verified when visualizing set of Brdicka curves and answering an interesting question raised in proteomics about the Brdicka curves proportionality.

In this study, we determined serum levels of metallothioneins (MTs) and zinc in children with solid tumours (neuroblastoma, Hodgkin lymphoma, medulloblastoma, osteosarcoma, Ewing sarcoma and nephroblastoma) by differential pulse voltammetry Brdicka reaction and ELISA. Zn(II) level in patients sera was 40% compared to controls, contrariwise, MT level was 4.2 × higher in patients. No significant differences among single diagnoses were found both for Zn(II) and MT. When determined Zn(II)/MT ratio, in controls its value was 24.6, but it was 2.6 in patients. After Western-blotting with anti-MT and anti-Zn chicken antibodies, variable intensities of the bands within the samples were observed. The brightness curve obtained for each sample both for MT- and Zn blots was further analysed to produce a list of band positions together with some complementary information related to the intensity of the observed bands by the optimised algorithm. We constructed from those profiles decision trees that enable to distinguish different groups of tumours. The blood samples were heat-treated, in which we supposed mainly MT, but samples contained other thermostable Zn-containing proteins that were helpful for identification of embryonal tumours with 88% accuracy and for identification of sarcomas with 78% accuracy. In MT blots the accuracies were 53 and 45%, respectively. Simultaneous analysis of MT and Zn blots did not increased accuracy of identification neither in embryonal tumours (80%) nor in sarcomas. Those results are promising not only from diagnostic point of view but particularly in the area of studying of individual MT isoforms and their aggregates in malignant tumours.

BACKGROUND: Proteomics and metalloproteomics are rapidly developing interdisciplinary fields providing enormous amounts of data to be classified, evaluated and interpreted. Approaches offered by bioinformatics and also by biostatistical data analysis and treatment are therefore of extreme interest. Numerous methods are now available as commercial or open source tools for data processing and modelling ready to support the analysis of various datasets. The analysis of scientific data remains a big challenge, because each new task sets its specific requirements and constraints that call for the design of a targeted data pre-processing approach. METHODOLOGY/PRINCIPAL FINDINGS: This study proposes a mathematical approach for evaluating and classifying datasets obtained by electrochemical analysis of metallothionein in rat 9 tissues (brain, heart, kidney, eye, spleen, gonad, blood, liver and femoral muscle). Tissue extracts were heated and then analysed using the differential pulse voltammetry Brdicka reaction. The voltammograms were subsequently processed. Classification models were designed making separate use of two groups of attributes, namely attributes describing local extremes, and derived attributes resulting from the level=5 wavelet transform. CONCLUSIONS/SIGNIFICANCE: On the basis of our results, we were able to construct a decision tree that makes it possible to distinguish among electrochemical analysis data resulting from measurements of all the considered tissues. In other words, we found a way to classify an unknown rat tissue based on electrochemical analysis of the metallothionein in this tissue.

This paper is devoted to analysis of voltammograms resulting from Brdicka reaction - the graphs that are currently used for determination of content of metallothioneins (MT) in tissue samples most often. We describe our search for typical patterns in the considered curves that would make it possible to distinguish among voltammograms produced by samples taken from different body parts. We suggest a rather compact representation of information contained in the considered graphs that is based on Haar's Simple Wavelet transformation. The resulting representation is successfully tested for classification of real data obtained from 8 rats and their 9 body parts. The preliminary experiments confirm that the suggested derived attributes of Brdicka curves seem to be good candidates for becoming numerical biomarkers exhibiting an important advantage: the process leading to their calculation can be fully automated.