Proteomic analysis of low- to high-grade astrocytomas reveals an alteration of the expression level of raf kinase inhibitor protein and nucleophosmin

Proteomic approaches have been useful for the identification of aberrantly expressed proteins in complex diseases such as cancer. These proteins are not only potential disease biomarkers, but also targets for therapy. The aim of this study was to identify differentially expressed proteins in diffuse astrocytoma grade II, anaplastic astrocytoma grade III and glioblastoma multiforme grade IV in human tumor samples and in non-neoplastic brain tissue as control using 2-DE and MS. Tumor and control brain tissue dissection was guided by histological hematoxylin/eosin tissue sections to provide more than 90% of tumor cells and astrocytes. Six proteins were detected as up-regulated in higher grade astrocytomas and the most important finding was nucleophosmin (NPM) (p < 0.05), whereas four proteins were down-regulated, among them raf kinase inhibitor protein (RKIP) (p < 0.05). We report here for the first time the alteration of NPM and RKIP expression in brain cancer. Our focus on these proteins was due to the fact that they are involved in the PI3K/AKT/mTOR and RAS/RAF/MAPK pathways, known for their contribution to the development and progression of gliomas. The proteomic data for NPM and RKIP were confirmed by Western blot, quantitative real-time PCR and immunohistochemistry. Due to the participation of NPM and RKIP in uncontrolled proliferation and evasion of apoptosis, these proteins are likely targets for drug development.

Morphological Study of Myoneural Interaction between the Levator Labii Superioris Muscle and its Motor Nerve in Rats

The progress of science in search of new techniques of the nerve regeneration and the functional repair in reinnervated muscle has been the target of many researchers around the world. Consequently, nerves and muscles in different body segments asked for more enlightenment of their morphology, their interrelation with other anatomic structures and their peculiarities. One of the most significant areas that need deeper studies is the region of the head and neck, since they are often affected by important pathologies. In order to offer the researcher`s community a morphological myoneural interaction model, this study elected the levator labii superioris muscle and its motor nerve, the buccal branch of the facial nerve (VII pair) not only for its special characteristics, but also its value on the facial expression. The rat was chosen for this investigation for being easy to obtain, to keep, to manipulate and to compare this experiment with many others studies previously published. The techniques used were Mesoscopic (dissection), histoenzymologic and morphometric ones. In the results the muscle proved to have a predominance of fast twich fibers (FG and FOG) and superficial location, with a proximal bone and a distal cutaneous insertion. Its motor nerve, the buccal branch of the facial nerve (VII pair), breaks through the muscle belly into its deep face, and comprised a heterogeneous group of myelinic nerve fibers disposed in a regular form in all fascicle. Near the motor point, the nerve showed to be composed of two fascicles with different sizes. Due to the small nerve dimensions, the nerve fibers have a smaller diameter if compared to the motor nerve of pectineus muscle of the cat. Further studies with neural tracers have already had a start in order to provide more information about the distribution and the architecture of these fibers.

A Survey on Graphical Methods for Classification Predictive Performance Evaluation

Predictive performance evaluation is a fundamental issue in design, development, and deployment of classification systems. As predictive performance evaluation is a multidimensional problem, single scalar summaries such as error rate, although quite convenient due to its simplicity, can seldom evaluate all the aspects that a complete and reliable evaluation must consider. Due to this, various graphical performance evaluation methods are increasingly drawing the attention of machine learning, data mining, and pattern recognition communities. The main advantage of these types of methods resides in their ability to depict the trade-offs between evaluation aspects in a multidimensional space rather than reducing these aspects to an arbitrarily chosen (and often biased) single scalar measure. Furthermore, to appropriately select a suitable graphical method for a given task, it is crucial to identify its strengths and weaknesses. This paper surveys various graphical methods often used for predictive performance evaluation. By presenting these methods in the same framework, we hope this paper may shed some light on deciding which methods are more suitable to use in different situations.

Chaotic phase synchronization and desynchronization in an oscillator network for object selection

Object selection refers to the mechanism of extracting objects of interest while ignoring other objects and background in a given visual scene. It is a fundamental issue for many computer vision and image analysis techniques and it is still a challenging task to artificial Visual systems. Chaotic phase synchronization takes place in cases involving almost identical dynamical systems and it means that the phase difference between the systems is kept bounded over the time, while their amplitudes remain chaotic and may be uncorrelated. Instead of complete synchronization, phase synchronization is believed to be a mechanism for neural integration in brain. In this paper, an object selection model is proposed. Oscillators in the network representing the salient object in a given scene are phase synchronized, while no phase synchronization occurs for background objects. In this way, the salient object can be extracted. In this model, a shift mechanism is also introduced to change attention from one object to another. Computer simulations show that the model produces some results similar to those observed in natural vision systems.