Molecular fusion events in carcinogenic organisms: a bioinformatics study for the detection of fused proteins between viruses, bacteria and eukaryotes.

Molecular fusion events have a prominent role in the initial steps of carcinogenesis. In this study, a bioinformatics analysis was performed between four organisms that are known to induce cancer development in humans: two viruses, Human Herpesvirus 4, and Human T-cell leukaemia virus, one bacterium, Helicobacter Pylori, and one trematode, Schistosoma mansoni. The annotated proteomes from these organisms were analysed using the SAFE software to identify protein fusion events, which may provide insight into protein function similarities and possible merging events during the course of evolution. Based on the results, five fused proteins with very similar functions were detected, whereas proteins with different functions that might act in the same molecular complex or biochemical pathway were not found. Thus, this study analysed the above four well-known cancer-related organisms with de novo bioinformatics programs and provided useful information on protein fusion events, hopefully leading to deeper understanding of carcinogenenesis.

The medical cyborg concept.

Medical technology has made significant advances in the 21st century and, at present, medicine makes use of information technology, telecommunications, and state-of-the-art engineering to provide the best possible healthcare services. Electronic sensors provide health practitioners with the ability to constantly monitor their patients' health, to streamlines a number of medical processes, and to increase patients' access to health services. Mobile phones also empower patients and play a major role in their health's monitoring. The use of cybernetics technology can now help patients overcome even serious disabilities, enabling many disabled patients to live their lives similarly to their non-disabled fellow men through the use of artificial organs and implants. All these advances have paved the way for a more personalized type of healthcare that provides individualized solutions to each patient. Once a number of hurdles are overcome, medical technology will bring forth a new era of more precise and enabling medicine.

Analyzing tree-shape anatomical structures using topological descriptors of branching and ensemble of classifiers.

The analysis of anatomical tree-shape structures visualized in medical images provides insight into the relationship between tree topology and pathology of the corresponding organs. In this paper, we propose three methods to extract descriptive features of the branching topology; the asymmetry index, the encoding of branching patterns using a node labeling scheme and an extension of the Sholl analysis. Based on these descriptors, we present classification schemes for tree topologies with respect to the underlying pathology. Moreover, we present a classifier ensemble approach which combines the predictions of the individual classifiers to optimize the classification accuracy. We applied the proposed methodology to a dataset of x-ray galactograms, medical images which visualize the breast ductal tree, in order to recognize images with radiological findings regarding breast cancer. The experimental results demonstrate the effectiveness of the proposed framework compared to state-of-the-art techniques suggesting that the proposed descriptors provide more valuable information regarding the topological patterns of ductal trees and indicating the potential of facilitating early breast cancer diagnosis.