Neural Mechanisms And Delayed Gastric Emptying Of Liquid Induced Through Acute Myocardial Infarction In Rats.

Background: In pathological situations, such as acute myocardial infarction, disorders of motility of the proximal gut can trigger symptoms like nausea and vomiting. Acute myocardial infarction delays gastric emptying (GE) of liquid in rats. Objective: Investigate the involvement of the vagus nerve, α 1-adrenoceptors, central nervous system GABAB receptors and also participation of paraventricular nucleus (PVN) of the hypothalamus in GE and gastric compliance (GC) in infarcted rats. Methods: Wistar rats, N = 8-15 in each group, were divided as INF group and sham (SH) group and subdivided. The infarction was performed through ligation of the left anterior descending coronary artery. GC was estimated with pressure-volume curves. Vagotomy was performed by sectioning the dorsal and ventral branches. To verify the action of GABAB receptors, baclofen was injected via icv (intracerebroventricular). Intravenous prazosin was used to produce chemical sympathectomy. The lesion in the PVN of the hypothalamus was performed using a 1mA/10s electrical current and GE was determined by measuring the percentage of gastric retention (% GR) of a saline meal. Results: No significant differences were observed regarding GC between groups; vagotomy significantly reduced % GR in INF group; icv treatment with baclofen significantly reduced %GR. GABAB receptors were not conclusively involved in delaying GE; intravenous treatment with prazosin significantly reduced GR% in INF group. PVN lesion abolished the effect of myocardial infarction on GE. Conclusion: Gastric emptying of liquids induced through acute myocardial infarction in rats showed the involvement of the vagus nerve, alpha1- adrenergic receptors and PVN.Fundamento: Distúrbios da motilidade do intestino proximal no infarto agudo do miocárdio podem desencadear sintomas digestivos como náuseas e vômitos. O infarto do miocárdio ocasiona retardo do esvaziamento gástrico (EG) de líquido em ratos. Objetivo: Investigar se existe a influência do nervo vago (VGX), adrenoreceptores α-1, receptores GABAB do sistema nervoso central e participação do núcleo paraventricular (NPV) do hipotálamo no esvaziamento gástrico (EG) e complacência gástrica (CG) em ratos infartados. Métodos: Ratos Wistar (n = 8-15) foram divididos em: grupo infarto (INF), sham (SH) e subdivididos. O infarto foi realizado por ligadura da artéria coronária descendente anterior. A complacência gástrica foi estimada com curvas pressão-volume. Realizada vagotomia por secção dos ramos dorsal e ventral. Para verificar a ação dos receptores GABAB foi injetado baclofeno por via intra ventrículo-cerebral. Simpatectomia química foi realizada com prazosina intravenosa (iv), e na lesão do núcleo paraventricular do hipotálamo foi utilizada corrente elétrica de 1mA/10s, com esvaziamento gástrico determinado por medição da retenção gástrica (% RG) de uma refeição salina. Resultados: Não houve diferença significativa na CG. A vagotomia (VGX) reduziu significativamente a %RG; no grupo INF, o tratamento intra ventrículo-cerebral (ivc) com baclofeno reduziu significativamente a % RG; não houve conclusivamente envolvimento dos receptores GABAB em retardar o EG; o tratamento intravenoso com prazosina reduziu significativamente a %RG no grupo INF. A lesão do NPV aboliu o efeito do infarto do miocárdio no EG. Conclusão: O nervo vago, receptores α-adrenérgicos e núcleo paraventricular estão envolvidos no retardo do esvaziamento gástrico no infarto agudo do miocárdio em ratos.

Plasticidade neural: relações com o comportamento e abordagens experimentais

Behavioral adaptiveness to different situations as well as behavioral individuality result from the interrelations between environmental sitmuli and the responses of an organism.These kind of interrelationships also shape the neural circuits as well as characterize the plasticity and the neural individuality of the organism. Studies on neural plasticity may analyze changes in neural circuitry after environmental manipulations or changes in behavior after lesions in the nervous system. Issues on neural plasticity and recovery of function refer both to physiology and behavior as well as to the subjacent mechanisms related to morphology, biochemistry and genetics. They may be approached at the systemic, behavioral, cellular and molecular levels. This work intends to characterize these kinds of studies pointing to their relations with the analyis of behavior and learning.The analysis of how the environmental-organismic interrelationships affect the neural substrates of behavior is pointed as a very stimulating area for investigation.

Central chemoreceptors and neural mechanisms of cardiorespiratory control

The arterial partial pressure (P CO2) of carbon dioxide is virtually constant because of the close match between the metabolic production of this gas and its excretion via breathing. Blood gas homeostasis does not rely solely on changes in lung ventilation, but also to a considerable extent on circulatory adjustments that regulate the transport of CO2 from its sites of production to the lungs. The neural mechanisms that coordinate circulatory and ventilatory changes to achieve blood gas homeostasis are the subject of this review. Emphasis will be placed on the control of sympathetic outflow by central chemoreceptors. High levels of CO2 exert an excitatory effect on sympathetic outflow that is mediated by specialized chemoreceptors such as the neurons located in the retrotrapezoid region. In addition, high CO2 causes an aversive awareness in conscious animals, activating wake-promoting pathways such as the noradrenergic neurons. These neuronal groups, which may also be directly activated by brain acidification, have projections that contribute to the CO2-induced rise in breathing and sympathetic outflow. However, since the level of activity of the retrotrapezoid nucleus is regulated by converging inputs from wake-promoting systems, behavior-specific inputs from higher centers and by chemical drive, the main focus of the present manuscript is to review the contribution of central chemoreceptors to the control of autonomic and respiratory mechanisms.

Engagement of multifocal neural circuits during recall of autobiographical happy events

Happy emotional states have not been extensively explored in functional magnetic resonance imaging studies using autobiographic recall paradigms. We investigated the brain circuitry engaged during induction of happiness by standardized script-driven autobiographical recall in 11 healthy subjects (6 males), aged 32.4 ± 7.2 years, without physical or psychiatric disorders, selected according to their ability to vividly recall personal experiences. Blood oxygen level-dependent (BOLD) changes were recorded during auditory presentation of personal scripts of happiness, neutral content and negative emotional content (irritability). The same uniform structure was used for the cueing narratives of both emotionally salient and neutral conditions, in order to decrease the variability of findings. In the happiness relative to the neutral condition, there was an increased BOLD signal in the left dorsal prefrontal cortex and anterior insula, thalamus bilaterally, left hypothalamus, left anterior cingulate gyrus, and midportions of the left middle temporal gyrus (P < 0.05, corrected for multiple comparisons). Relative to the irritability condition, the happiness condition showed increased activity in the left insula, thalamus and hypothalamus, and in anterior and midportions of the inferior and middle temporal gyri bilaterally (P < 0.05, corrected), varying in size between 13 and 64 voxels. Findings of happiness-related increased activity in prefrontal and subcortical regions extend the results of previous functional imaging studies of autobiographical recall. The BOLD signal changes identified reflect general aspects of emotional processing, emotional control, and the processing of sensory and bodily signals associated with internally generated feelings of happiness. These results reinforce the notion that happiness induction engages a wide network of brain regions.

Neural networks and statistical analysis for classification of corneal videokeratography maps based on Zernike coefficients: a quantitative comparison

PURPOSE: The main goal of this study was to develop and compare two different techniques for classification of specific types of corneal shapes when Zernike coefficients are used as inputs. A feed-forward artificial Neural Network (NN) and discriminant analysis (DA) techniques were used. METHODS: The inputs both for the NN and DA were the first 15 standard Zernike coefficients for 80 previously classified corneal elevation data files from an Eyesys System 2000 Videokeratograph (VK), installed at the Departamento de Oftalmologia of the Escola Paulista de Medicina, São Paulo. The NN had 5 output neurons which were associated with 5 typical corneal shapes: keratoconus, with-the-rule astigmatism, against-the-rule astigmatism, "regular" or "normal" shape and post-PRK. RESULTS: The NN and DA responses were statistically analyzed in terms of precision ([true positive+true negative]/total number of cases). Mean overall results for all cases for the NN and DA techniques were, respectively, 94% and 84.8%. CONCLUSION: Although we used a relatively small database, results obtained in the present study indicate that Zernike polynomials as descriptors of corneal shape may be a reliable parameter as input data for diagnostic automation of VK maps, using either NN or DA.

Learning processes and the neural analysis of conditioning

Classical and operant conditioning principles, such as the behavioral discrepancy-derived assumption that reinforcement always selects antecedent stimulus and response relations, have been studied at the neural level, mainly by observing the strengthening of neuronal responses or synaptic connections. A review of the literature on the neural basis of behavior provided extensive scientific data that indicate a synthesis between the two conditioning processes based mainly on stimulus control in learning tasks. The resulting analysis revealed the following aspects. Dopamine acts as a behavioral discrepancy signal in the midbrain pathway of positive reinforcement, leading toward the nucleus accumbens. Dopamine modulates both types of conditioning in the Aplysia mollusk and in mammals. In vivo and in vitro mollusk preparations show convergence of both types of conditioning in the same motor neuron. Frontal cortical neurons are involved in behavioral discrimination in reversal and extinction procedures, and these neurons preferentially deliver glutamate through conditioned stimulus or discriminative stimulus pathways. Discriminative neural responses can reliably precede operant movements and can also be common to stimuli that share complex symbolic relations. The present article discusses convergent and divergent points between conditioning paradigms at the neural level of analysis to advance our knowledge on reinforcement.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Classification of breast masses using a committee machine of artificial neural networks

This work proposes a new approach using a committee machine of artificial neural networks to classify masses found in mammograms as benign or malignant. Three shape factors, three edge-sharpness measures, and 14 texture measures are used for the classification of 20 regions of interest (ROIs) related to malignant tumors and 37 ROIs related to benign masses. A group of multilayer perceptrons (MLPs) is employed as a committee machine of neural network classifiers. The classification results are reached by combining the responses of the individual classifiers. Experiments involving changes in the learning algorithm of the committee machine are conducted. The classification accuracy is evaluated using the area A. under the receiver operating characteristics (ROC) curve. The A, result for the committee machine is compared with the A, results obtained using MLPs and single-layer perceptrons (SLPs), as well as a linear discriminant analysis (LDA) classifier Tests are carried out using the student's t-distribution. The committee machine classifier outperforms the MLP SLP, and LDA classifiers in the following cases: with the shape measure of spiculation index, the A, values of the four methods are, in order 0.93, 0.84, 0.75, and 0.76; and with the edge-sharpness measure of acutance, the values are 0.79, 0.70, 0.69, and 0.74. Although the features with which improvement is obtained with the committee machines are not the same as those that provided the maximal value of A(z) (A(z) = 0.99 with some shape features, with or without the committee machine), they correspond to features that are not critically dependent on the accuracy of the boundaries of the masses, which is an important result. (c) 2008 SPIE and IS&T.

Activation of Neural and Pluripotent Stem Cell Signatures Correlates with Increased Malignancy in Human Glioma

The presence of stem cell characteristics in glioma cells raises the possibility that mechanisms promoting the maintenance and self-renewal of tissue specific stem cells have a similar function in tumor cells. Here we characterized human gliomas of various malignancy grades for the expression of stem cell regulatory proteins. We show that cells in high grade glioma co-express an array of markers defining neural stem cells (NSCs) and that these proteins can fulfill similar functions in tumor cells as in NSCs. However, in contrast to NSCs glioma cells co-express neural proteins together with pluripotent stem cell markers, including the transcription factors Oct4, Sox2, Nanog and Klf4. In line with this finding, in high grade gliomas mesodermal-and endodermal-specific transcription factors were detected together with neural proteins, a combination of lineage markers not normally present in the central nervous system. Persistent presence of pluripotent stem cell traits could only be detected in solid tumors, and observations based on in vitro studies and xenograft transplantations in mice imply that this presence is dependent on the combined activity of intrinsic and extrinsic regulatory cues. Together these results demonstrate a general deregulated expression of neural and pluripotent stem cell traits in malignant human gliomas, and indicate that stem cell regulatory factors may provide significant targets for therapeutic strategies.

Correlation of Modified Natural Rubber Properties by Artificial Neural Networks

Natural rubber (NR) is a raw material largely used by the modern industry; however, it is common that chemical modifications must be made to NR in order to improve properties such as hydrophobicity or mechanical resistance. This work deals with the correlation of properties of NR modified with dimethylaminoethylmethacrylate or methylmethacrylate as grafting agents. Dynamic-mechanical behavior and stress/strain relations are very important properties because they furnish essential characteristics of the material such as glass transition temperature and rupture point. These properties are concerned with different physical principles; for this reason, normally they are not related to each other. This work showed that they can be correlated by artificial neural networks (ANN). So, from one type of assay, the properties that as a rule only could be obtained from the other can be extracted by ANN correlation. POLYM. ENG. SCI., 49:499-505, 2009. (c) 2009 Society of Plastics Engineers

Optimization of the AZO dyes decoloration process through neural networks: Determination of the H2O2 addition critical point

The concentration of hydrogen peroxide is an important parameter in the azo dyes decoloration process through the utilization of advanced oxidizing processes, particularly by oxidizing via UV/H2O2. It is pointed out that, from a specific concentration, the hydrogen peroxide works as a hydroxyl radical self-consumer and thus a decrease of the system`s oxidizing power happens. The determination of the process critical point (maximum amount of hydrogen peroxide to be added) was performed through a ""thorough mapping"" or discretization of the target region, founded on the maximization of an objective function objective (constant of reaction kinetics of pseudo-first order). The discretization of the operational region occurred through a feedforward backpropagation neural model. The neural model obtained presented remarkable coefficient of correlation between real and predicted values for the absorbance variable, above 0.98. In the present work, the neural model had, as phenomenological basis the Acid Brown 75 dye decoloration process. The hydrogen peroxide addition critical point, represented by a value of mass relation (F) between the hydrogen peroxide mass and the dye mass, was established in the interval 50 < F < 60. (C) 2007 Elsevier B.V. All rights reserved.

Harmonic identification using parallel neural networks in single-phase systems

In this paper, artificial neural networks are employed in a novel approach to identify harmonic components of single-phase nonlinear load currents, whose amplitude and phase angle are subject to unpredictable changes, even in steady-state. The first six harmonic current components are identified through the variation analysis of waveform characteristics. The effectiveness of this method is tested by applying it to the model of a single-phase active power filter, dedicated to the selective compensation of harmonic current drained by an AC controller. Simulation and experimental results are presented to validate the proposed approach. (C) 2010 Elsevier B. V. All rights reserved.

Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks

A hybrid system to automatically detect, locate and classify disturbances affecting power quality in an electrical power system is presented in this paper. The disturbances characterized are events from an actual power distribution system simulated by the ATP (Alternative Transients Program) software. The hybrid approach introduced consists of two stages. In the first stage, the wavelet transform (WT) is used to detect disturbances in the system and to locate the time of their occurrence. When such an event is flagged, the second stage is triggered and various artificial neural networks (ANNs) are applied to classify the data measured during the disturbance(s). A computational logic using WTs and ANNs together with a graphical user interface (GU) between the algorithm and its end user is then implemented. The results obtained so far are promising and suggest that this approach could lead to a useful application in an actual distribution system. (C) 2009 Elsevier Ltd. All rights reserved.

Neural network model for designing automotive devices using SMD LED

The advantages offered by the electronic component LED (Light Emitting Diode) have resulted in a quick and extensive application of this device in the replacement of incandescent lights. In this combined application, however, the relationship between the design variables and the desired effect or result is very complex and renders it difficult to model using conventional techniques. This paper consists of the development of a technique using artificial neural networks that makes it possible to obtain the luminous intensity values of brake lights using SMD (Surface Mounted Device) LEDs from design data. This technique can be utilized to design any automotive device that uses groups of SMD LEDs. The results of industrial applications using SMD LED are presented to validate the proposed technique.

An approach based on neural networks for estimation and generalization of crossflow filtration processes

The crossflow filtration process differs of the conventional filtration by presenting the circulation flow tangentially to the filtration surface. The conventional mathematical models used to represent the process have some limitations in relation to the identification and generalization of the system behaviour. In this paper, a system based on artificial neural networks is developed to overcome the problems usually found in the conventional mathematical models. More specifically, the developed system uses an artificial neural network that simulates the behaviour of the crossflow filtration process in a robust way. Imprecisions and uncertainties associated with the measurements made on the system are automatically incorporated in the neural approach. Simulation results are presented to justify the validity of the proposed approach. (C) 2007 Elsevier B.V. All rights reserved.