The ESR1 and GPX1 gene expression level in human malignant and non-malignant breast tissues.

Background: The aim of this study was to establish whether the gene expression of estrogen receptor alpha (encoded by ESR1) correlates with the expression of glutathione peroxidase 1 (encoded by GPX1) in the tumor and adjacent tumor-free breast tissue, and whether this correlation is affected by breast cancer. Such relationships may give further insights into breast cancer pathology with respect to the status of estrogen receptor.

Methods: We used the quantitative real-time PCR technique to analyze differences in the expression levels of the ESR1 and GPX1 genes in paired malignant and non-malignant tissues from breast cancer patients.

Expression of MMP and TIMP mRNA in peripheral blood leukocytes of patients with invasive ductal carcinoma of the breast.

Purpose: An imbalance between matrix metalloproteinases (MMPs) and tissue inhibitors of MMPs (TIMPs) appears critical for tumor progression and metastasis. This study aimed to determine whether gene expression of MMP1, MMP2, MMP9, TIMP1 and TIMP3 and the MMP/TIMP expression ratio in peripheral blood leukocytes (PBLs) and the MMP1 and TIMP1 contents or MMP1/TIMP1 ratio in plasma were associated with clinicopathological characteristics in invasive ductal carcinoma (IDC) of the breast.

Materials And Methods: Blood samples were collected from women newly diagnosed with IDC who had not received prior treatment (n = 102).

Lipid peroxidation and glutathione peroxidase activity relationship in breast cancer depends on functional polymorphism of GPX1.

Background: Since targeting oxidative stress markers has been recently recognized as a novel therapeutic target in cancer, it is interesting to investigate whether genetic susceptibility may modify oxidative stress response in cancer. The aim of this study was to elucidate whether genetic polymorphism in the antioxidant enzymes is associated with lipid peroxidation in breast cancer.

Methods: We conducted a study among Polish women, including 136 breast cancer cases and 183 healthy controls.

A time-variant processing approach for the analysis of alpha and gamma MEG oscillations during flicker stimulus generated entrainment.

Repetitive flicker stimulation (photic driving) offers the possibility to study the properties and coupling characteristics of stimulation-sensitive neuronal oscillators by means of the MEG/EEG analysis. With flicker frequencies in the region of the individual alpha band frequency, the dynamics of the entrainment process of the alpha oscillation, as well as the dynamics of the accompanying gamma oscillations and the coupling between the oscillations, are investigated by means of an appropriate combination of time-variant analysis methods. The Hilbert and the Gabor transformation reveal time-variant properties (frequency entrainment, phase locking, and n:m synchronization) of the entrainment process in the whole frequency range.

Identification of neural activity based on fMRI data: a simulation study.

This study proposes a technique for determining effective connectivity among brain regions which operates at the level of neuronal dynamics. We propose an alternative time-variant dynamic causal model (TV-DCM) where neuronal dynamic activity evolves based on generalized dynamic neural networks (GDNNs). The identification of brain architecture connectivity is carried out based on a least squares criterion and on a global search technique.

Promoter methylation of cancer-related genes in gastric carcinoma.

Unlabelled: Genetic changes associated with gastric cancer are not completely known, but epigenetic mechanisms involved in this disease seem to play an important role in its pathophysiology. One of these mechanisms, an aberrant methylation in the promoter regions of genes involved in cancer induction and promotion, may be of particular importance in gastric cancer.

Aim: To analyze the methylation status of eight genes: Apaf-1, Casp8, CDH1, MDR1, GSTP1, BRCA1, hMLH1, Fas in gastric cancer patients.

Learning continuous trajectories in recurrent neural networks with time-dependent weights.

This paper is concerned with a general learning (with arbitrary criterion and state-dependent constraints) of continuous trajectories by means of recurrent neural networks with time-varying weights. The learning process is transformed into an optimal control framework, where the weights to be found are treated as controls. A new learning algorithm based on a variational formulation of Pontryagin's maximum principle is proposed.

Initial state training procedure improves dynamic recurrent networks with time-dependent weights.

The problem of learning multiple continuous trajectories by means of recurrent neural networks with (in general) time-varying weights is addressed. The learning process is transformed into an optimal control framework where both the weights and the initial network state to be found are treated as controls. For such a task, a learning algorithm is proposed which is based on a variational formulation of Pontryagin's maximum principle.

Training trajectories by continuous recurrent multilayer networks.

This paper addresses the problem of training trajectories by means of continuous recurrent neural networks whose feedforward parts are multilayer perceptrons. Such networks can approximate a general nonlinear dynamic system with arbitrary accuracy. The learning process is transformed into an optimal control framework where the weights are the controls to be determined.

Application of generalized dynamic neural networks to biomedical data.

This paper reviews the application of continuous recurrent neural networks with time-varying weights to pattern recognition tasks in medicine. A general learning algorithm based on Pontryagin's maximum principle is recapitulated, and possibilities of improving the generalization capabilities of these networks are given. The effectiveness of the methods is demonstrated by three different real-world examples taken from the fields of anesthesiology, orthopedics, and radiology.

Classification of equinus in ambulatory children with cerebral palsy-discrimination between dynamic tightness and fixed contracture.

In this study a generalised dynamic neural network (GDNN) was designed to process gait analysis parameters to evaluate equinus deformity in ambulatory children with cerebral palsy. The aim was to differentiate dynamic calf muscle tightness from fixed muscle contracture. Patients underwent clinical examination and had instrumented gait analysis before evaluating their equinus under anaesthesia and muscle relaxation at the time of surgery to improve gait.

Improving generalization capabilities of dynamic neural networks.

This work addresses the problem of improving the generalization capabilities of continuous recurrent neural networks. The learning task is transformed into an optimal control framework in which the weights and the initial network state are treated as unknown controls. A new learning algorithm based on a variational formulation of Pontrayagin's maximum principle is proposed.

Prediction of movement following noxious stimulation during 1 minimum alveolar anesthetic concentration isoflurane/nitrous oxide anesthesia by means of middle latency auditory evoked responses.

This paper investigates the applicability of generalized dynamic neural networks for the design of a two-valued anesthetic depth indicator during isoflurane/nitrous oxide anesthesia. The indicator construction is based on the processing of middle latency auditory evoked responses (MLAER) in combination with the observation of the patient's movement reaction to skin incision. The framework of generalized dynamic neural networks does not require any data preprocessing, visual data inspection or subjective feature extraction.

Identification of hemifield single trial PVEP on the basis of generalized dynamic neural network classifiers.

This paper is concerned with the application of generalized dynamic neural networks for the identification of hemifield pattern-reversal visual evoked potentials. The identification process is performed by different networks with time-varying weights using signals from different electrode positions as external inputs. Since dynamic neural networks are able to process time-varying signals, the identification of the stimulated hemiretinae is performed without feature extraction.

Adaptable preprocessing units and neural classification for the segmentation of EEG signals.

In this contribution, a methodology for the simultaneous adaptation of preprocessing units (PPUs) for feature extraction and of neural classifiers that can be used for time series classification is presented. The approach is based upon an extension of the backpropagation algorithm for the correction of the preprocessing parameters. In comparison with purely neural systems, the reduced input dimensionality improves the generalization capability and reduces the numerical effort.

Common Optimization of Adaptive Preprocessing Units and a Neural Network during the Learning Period. Application in EEG Pattern Recognition.

In this study, a proposition of simultaneous training of the neural network (multilayer perceptron) and adaptive preprocessing unit is presented. This cooperation enables the network to affect the preprocessing and as a consequence to vary the locations of pattern vectors in a feature space. Thus, during the learning process the network tries to find a good separation of classes of patterns, which results in convergence of the whole learning process.

Structure optimization of neural networks with the A*-algorithm.

A method for the construction of optimal structures for feedforward neural networks is introduced. On the basis of a construction of a graph of network structures and an evaluation value which is assigned to each of them, an heuristic search algorithm can be installed on this graph. The application of the A*-algorithm ensures, in theory, both the optimality of the solution and the optimality of the search.

Application of optimized pattern recognition units in EEG analysis: common optimization of preprocessing and weights of neural networks as well as structure optimization.

The main goal of this study is to demonstrate the possibility of training the Neural Network (multilayer perceptron) classifier and preprocessing units simultaneously, i.e., that properties of preprocessing are chosen automatically during the training phase.

Structure optimization of neural networks with A* -algorithm application in EEG pattern analysis.

The A* - Algorithm for heuristic search is applied to construct a Neural Network structure (NS) that optimally fits the structure of data to be learned. In this way, the user of Neural Networks (NN) is able to avoid the empirical testing of different structures. The method given here is applied to the recognition of different patterns derived from the EEG of an epileptic patient.