Neural regulation of the stress response: glucocorticoid feedback mechanisms

The mammalian stress response is an integrated physiological and psychological reaction to real or perceived adversity. Glucocorticoids are an important component of this response, acting to redistribute energy resources to both optimize survival in the face of challenge and to restore homeostasis after the immediate challenge has subsided. Release of glucocorticoids is mediated by the hypothalamo-pituitary-adrenal (HPA) axis, driven by a neural signal originating in the paraventricular nucleus (PVN). Stress levels of glucocorticoids bind to glucocorticoid receptors in multiple body compartments, including the brain, and consequently have wide-reaching actions. For this reason, glucocorticoids serve a vital function in negative feedback inhibition of their own secretion. Negative feedback inhibition is mediated by a diverse collection of mechanisms, including fast, non-genomic feedback at the level of the PVN, stress-shut-off at the level of the limbic system, and attenuation of ascending excitatory input through destabilization of mRNAs encoding neuropeptide drivers of the HPA axis. In addition, there is evidence that glucocorticoids participate in stress activation via feed-forward mechanisms at the level of the amygdala. Feedback deficits are associated with numerous disease states, underscoring the necessity for adequate control of glucocorticoid homeostasis. Thus, rather than having a single, defined feedback ‘switch’, control of the stress response requires a wide-reaching feedback ‘network’ that coordinates HPA activity to suit the overall needs of multiple body systems.

What ethologically based models have taught us about the neural systems underlying fear and anxiety

Classical Pavlovian fear conditioning to painful stimuli has provided the generally accepted view of a core system centered in the central amygdala to organize fear responses. Ethologically based models using other sources of threat likely to be expected in a natural environment, such as predators or aggressive dominant conspecifics, have challenged this concept of a unitary core circuit for fear processing. We discuss here what the ethologically based models have told us about the neural systems organizing fear responses. We explored the concept that parallel paths process different classes of threats, and that these different paths influence distinct regions in the periaqueductal gray - a critical element for the organization of all kinds of fear responses. Despite this parallel processing of different kinds of threats, we have discussed an interesting emerging view that common cortical-hippocampal-amygdalar paths seem to be engaged in fear conditioning to painful stimuli, to predators and, perhaps, to aggressive dominant conspecifics as well. Overall, the aim of this review is to bring into focus a more global and comprehensive view of the systems organizing fear responses.

Mapping and signaling of neural pathways involved in the regulation of hydromineral homeostasis

Several forebrain and brainstem neurochemical circuitries interact with peripheral neural and humoral signals to collaboratively maintain both the volume and osmolality of extracellular fluids. Although much progress has been made over the past decades in the understanding of complex mechanisms underlying neuroendocrine control of hydromineral homeostasis, several issues still remain to be clarified. The use of techniques such as molecular biology, neuronal tracing, electrophysiology, immunohistochemistry, and microinfusions has significantly improved our ability to identify neuronal phenotypes and their signals, including those related to neuron-glia interactions. Accordingly, neurons have been shown to produce and release a large number of chemical mediators (neurotransmitters, neurohormones and neuromodulators) into the interstitial space, which include not only classic neurotransmitters, such as acetylcholine, amines (noradrenaline, serotonin) and amino acids (glutamate, GABA), but also gaseous (nitric oxide, carbon monoxide and hydrogen sulfide) and lipid-derived (endocannabinoids) mediators. This efferent response, initiated within the neuronal environment, recruits several peripheral effectors, such as hormones (glucocorticoids, angiotensin II, estrogen), which in turn modulate central nervous system responsiveness to systemic challenges. Therefore, in this review, we shall evaluate in an integrated manner the physiological control of body fluid homeostasis from the molecular aspects to the systemic and integrated responses.

Relationship of macrophage migration inhibitory factor levels in PBMCs, lesional skin and serum with disease severity and activity in vitiligo vulgaris

Melanocyte loss in vitiligo vulgaris is believed to be an autoimmune process. Macrophage migration inhibitory factor (MIF) is involved in many autoimmune skin diseases. We determined the possible role of MIF in the pathogenesis of vitiligo vulgaris, and describe the relationship between MIF expressions and disease severity and activity. Serum MIF concentrations and mRNA levels in PBMCs were measured in 44 vitiligo vulgaris patients and 32 normal controls, using ELISA and real-time RT-PCR. Skin biopsies from 15 patients and 6 controls were analyzed by real-time RT-PCR. Values are reported as median (25th-75th percentile). Serum MIF concentrations were significantly increased in patients [35.81 (10.98-43.66) ng/mL] compared to controls [7.69 (6.01-9.03) ng/mL]. MIF mRNA levels were significantly higher in PBMCs from patients [7.17 (3.59-8.87)] than controls [1.67 (1.23-2.42)]. There was also a significant difference in MIF mRNA levels in PBMCs between progressive and stable patients [7.86 (5.85-9.13)vs 4.33 (2.23-8.39)] and in serum MIF concentrations [40.47 (27.71-46.79) vs 26.80 (10.55-36.07) ng/mL]. In addition, the vitiligo area severity index scores of patients correlated positively with changes of both serum MIF concentrations (r = 0.488) and MIF mRNA levels in PBMCs (r = 0.426). MIF mRNA levels were significantly higher in lesional than in normal skin [2.43 (2.13-7.59)vs 1.18 (0.94-1.83)] and in patients in the progressive stage than in the stable stage [7.52 (2.43-8.84)vs 2.13 (1.98-2.64)]. These correlations suggest that MIF participates in the pathogenesis of vitiligo vulgaris and may be useful as an index of disease severity and activity.

Involvement of Src tyrosine kinase and protein kinase C in the expression of macrophage migration inhibitory factor induced by H2O2 in HL-1 mouse cardiac muscle cells

Macrophage migration inhibitory factor (MIF), a pleiotropic cytokine, plays an important role in the pathogenesis of atrial fibrillation; however, the upstream regulation of MIF in atrial myocytes remains unclear. In the present study, we investigated whether and how MIF is regulated in response to the renin-angiotensin system and oxidative stress in atrium myocytes (HL-1 cells). MIF protein and mRNA levels in HL-1 cells were assayed using immunofluorescence, real-time PCR, and Western blot. The result indicated that MIF was expressed in the cytoplasm of HL-1 cells. Hydrogen peroxide (H2O2), but not angiotensin II, stimulated MIF expression in HL-1 cells. H2O2-induced MIF protein and gene levels increased in a dose-dependent manner and were completely abolished in the presence of catalase. H2O2-induced MIF production was completely inhibited by tyrosine kinase inhibitors genistein and PP1, as well as by protein kinase C (PKC) inhibitor GF109203X, suggesting that redox-sensitive MIF production is mediated through tyrosine kinase and PKC-dependent mechanisms in HL-1 cells. These results suggest that MIF is upregulated by HL-1 cells in response to redox stress, probably by the activation of Src and PKC.

Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China

The mortality rate of older patients with intertrochanteric fractures has been increasing with the aging of populations in China. The purpose of this study was: 1) to develop an artificial neural network (ANN) using clinical information to predict the 1-year mortality of elderly patients with intertrochanteric fractures, and 2) to compare the ANN's predictive ability with that of logistic regression models. The ANN model was tested against actual outcomes of an intertrochanteric femoral fracture database in China. The ANN model was generated with eight clinical inputs and a single output. ANN's performance was compared with a logistic regression model created with the same inputs in terms of accuracy, sensitivity, specificity, and discriminability. The study population was composed of 2150 patients (679 males and 1471 females): 1432 in the training group and 718 new patients in the testing group. The ANN model that had eight neurons in the hidden layer had the highest accuracies among the four ANN models: 92.46 and 85.79% in both training and testing datasets, respectively. The areas under the receiver operating characteristic curves of the automatically selected ANN model for both datasets were 0.901 (95%CI=0.814-0.988) and 0.869 (95%CI=0.748-0.990), higher than the 0.745 (95%CI=0.612-0.879) and 0.728 (95%CI=0.595-0.862) of the logistic regression model. The ANN model can be used for predicting 1-year mortality in elderly patients with intertrochanteric fractures. It outperformed a logistic regression on multiple performance measures when given the same variables.

RNAi-mediated knockdown of FANCF suppresses cell proliferation, migration, invasion, and drug resistance potential of breast cancer cells

Fanconi anemia complementation group F protein (FANCF) is a key factor, which maintains the function of FA/BRCA, a DNA damage response pathway. However, the functional role of FANCF in breast cancer has not been elucidated. We performed a specific FANCF-shRNA knockdown of endogenous FANCF in vitro. Cell viability was measured with a CCK-8 assay. DNA damage was assessed with an alkaline comet assay. Apoptosis, cell cycle, and drug accumulation were measured by flow cytometry. The expression levels of protein were determined by Western blot using specific antibodies. Based on these results, we used cell migration and invasion assays to demonstrate a crucial role for FANCF in those processes. FANCF shRNA effectively inhibited expression of FANCF. We found that proliferation of FANCF knockdown breast cancer cells (MCF-7 and MDA-MB-435S) was significantly inhibited, with cell cycle arrest in the S phase, induction of apoptosis, and DNA fragmentation. Inhibition of FANCF also resulted in decreased cell migration and invasion. In addition, FANCF knockdown enhanced sensitivity to doxorubicin in breast cancer cells. These results suggest that FANCF may be a potential target for molecular, therapeutic intervention in breast cancer.

Microglia and neurons in the hippocampus of migratory sandpipers

The semipalmated sandpiper Calidris pusilla and the spotted sandpiper Actitis macularia are long- and short-distance migrants, respectively. C. pusilla breeds in the sub-arctic and mid-arctic tundra of Canada and Alaska and winters on the north and east coasts of South America. A. macularia breeds in a broad distribution across most of North America from the treeline to the southern United States. It winters in the southern United States, and Central and South America. The autumn migration route of C. pusilla includes a non-stop flight over the Atlantic Ocean, whereas autumn route of A. macularia is largely over land. Because of this difference in their migratory paths and the visuo-spatial recognition tasks involved, we hypothesized that hippocampal volume and neuronal and glial numbers would differ between these two species. A. macularia did not differ from C. pusilla in the total number of hippocampal neurons, but the species had a larger hippocampal formation and more hippocampal microglia. It remains to be investigated whether these differences indicate interspecies differences or neural specializations associated with different strategies of orientation and navigation.

HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS

This work presents the results of a Hybrid Neural Network (HNN) technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.

An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.