Modulation of the gap junction protein Connexin36 in neurons in a mouse model of transient focalcerebral ischemia

Intercellular communication is achieved at specialized regions of the plasma membrane by¦gap junctions. Gap junctions are transmembrane channels allowing direct contacts between¦the cytoplasms of neighboring cells. Each cell participates with one hemichannel, or¦connexon, made of six protein subunits named connexins. Thanks to these junctions, cells¦potentially share a pool of small molecules and metabolites, such as nucleotides, amino acids¦and second messengers.¦In an ischemic (i.e. non-perfused) territory of the brain, irreversible damage progresses over¦time from the centre of the most severe flow reduction to the periphery with less disturbed¦perfusion. Functionally impaired tissue can survive and recover if sufficient reperfusion is reestablished¦within a limited time period, which depends on various factors and mechanisms¦modulating the signaling pathways leading to cell death.¦Observations were made indicating the presence of electrical coupling between neurons which¦resist better to an ischemic insult. This electrical coupling is likely to be mediated by¦Connexin36 (Cx36), a neuron specific connexin isoform. It was demonstrated in the past that¦global ischemia induces a selective upregulation of Cx36 expression in regions with neurons¦that survive the insult whereas others undergo apoptosis and die. These observations raise the¦possibility that the neuronal gap junction Cx36 might play a role in the destiny of neurons¦after cerebral ischemia.¦The aim of this work was to characterize the regulation of Connexin36 in a mouse model of¦transient focal cerebral ischemia by immunofluorescence and Western blot analysis. Our¦immunofluorescence results suggest a specific increase in Cx36 in the penumbral region of¦the ischemic hemisphere.

Optimization of spatial resolution for peripheral magnetic resonance angiography.

RATIONALE AND OBJECTIVES: To determine optimum spatial resolution when imaging peripheral arteries with magnetic resonance angiography (MRA). MATERIALS AND METHODS: Eight vessel diameters ranging from 1.0 to 8.0 mm were simulated in a vascular phantom. A total of 40 three-dimensional flash MRA sequences were acquired with incremental variations of fields of view, matrix size, and slice thickness. The accurately known eight diameters were combined pairwise to generate 22 "exact" degrees of stenosis ranging from 42% to 87%. Then, the diameters were measured in the MRA images by three independent observers and with quantitative angiography (QA) software and used to compute the degrees of stenosis corresponding to the 22 "exact" ones. The accuracy and reproducibility of vessel diameter measurements and stenosis calculations were assessed for vessel size ranging from 6 to 8 mm (iliac artery), 4 to 5 mm (femoro-popliteal arteries), and 1 to 3 mm (infrapopliteal arteries). Maximum pixel dimension and slice thickness to obtain a mean error in stenosis evaluation of less than 10% were determined by linear regression analysis. RESULTS: Mean errors on stenosis quantification were 8.8% +/- 6.3% for 6- to 8-mm vessels, 15.5% +/- 8.2% for 4- to 5-mm vessels, and 18.9% +/- 7.5% for 1- to 3-mm vessels. Mean errors on stenosis calculation were 12.3% +/- 8.2% for observers and 11.4% +/- 15.1% for QA software (P = .0342). To evaluate stenosis with a mean error of less than 10%, maximum pixel surface, the pixel size in the phase direction, and the slice thickness should be less than 1.56 mm2, 1.34 mm, 1.70 mm, respectively (voxel size 2.65 mm3) for 6- to 8-mm vessels; 1.31 mm2, 1.10 mm, 1.34 mm (voxel size 1.76 mm3), for 4- to 5-mm vessels; and 1.17 mm2, 0.90 mm, 0.9 mm (voxel size 1.05 mm3) for 1- to 3-mm vessels. CONCLUSION: Higher spatial resolution than currently used should be selected for imaging peripheral vessels.

Iterative reconstruction methods in two different MDCT scanners: Physical metrics and 4-alternative forced-choice detectability experiments - A phantom approach.

This paper characterizes and evaluates the potential of three commercial CT iterative reconstruction methods (ASIR?, VEO? and iDose(4 ()?())) for dose reduction and image quality improvement. We measured CT number accuracy, standard deviation (SD), noise power spectrum (NPS) and modulation transfer function (MTF) metrics on Catphan phantom images while five human observers performed four-alternative forced-choice (4AFC) experiments to assess the detectability of low- and high-contrast objects embedded in two pediatric phantoms. Results show that 40% and 100% ASIR as well as iDose(4) levels 3 and 6 do not affect CT number and strongly decrease image noise with relative SD constant in a large range of dose. However, while ASIR produces a shift of the NPS curve apex, less change is observed with iDose(4) with respect to FBP methods. With second-generation iterative reconstruction VEO, physical metrics are even further improved: SD decreased to 70.4% at 0.5 mGy and spatial resolution improved to 37% (MTF(50%)). 4AFC experiments show that few improvements in detection task performance are obtained with ASIR and iDose(4), whereas VEO makes excellent detections possible even at an ultra-low-dose (0.3 mGy), leading to a potential dose reduction of a factor 3 to 7 (67%-86%). In spite of its longer reconstruction time and the fact that clinical studies are still required to complete these results, VEO clearly confirms the tremendous potential of iterative reconstructions for dose reduction in CT and appears to be an important tool for patient follow-up, especially for pediatric patients where cumulative lifetime dose still remains high.

In vitro chloroquine resistance modulation study on fresh isolates of Brazilian Plasmodium falciparum: intrinsic antimalarial activity of phenothiazine drugs

Phenothiazine drugs - fluphenazine, chlorpromazine, methotrimeprazine and trifluoperazine - were evaluated as modulating agents against Brazilian chloroquine-resistant fresh isolates of Plasmodium falciparum. Aiming to simulate therapeutic schedules, chloroquine was employed at the concentration used for sensitive falciparum malaria treatment and anti-psychotic therapeutic concentrations of the phenothiazine drugs were adopted in two-fold serial dilutions. The in vitro microtechnique for drug susceptibility was employed. Unlike earlier reported data, the phenothiazine modulating effect was not observed. However, all the drugs demonstrated intrinsic antiplasmodial activity in concentrations lower than those described in the literature. In addition, IC50 estimates have been shown to be inferior to the usual anti-psychotic therapeutic concentrations. Statistical analysis also suggested an increase in the parasitaemia rate or, even, a predominant antiparasitic effect of phenothiazine over chloroquine when used in combination.

Temporal and spatial distribution of Leishmania mexicana infections in a population of Neotoma micropus

A 19-month mark-release-recapture study of Neotoma micropus with sequential screening for Leishmania mexicana was conducted in Bexar County, Texas, USA. The overall prevalence rate was 14.7% and the seasonal prevalence rates ranged from 3.8 to 26.7%. Nine incident cases were detected, giving an incidence rate of 15.5/100 rats/year. Follow-up of 101 individuals captured two or more times ranged from 14 to 462 days. Persistence of L. mexicana infections averaged 190 days and ranged from 104 to 379 days. Data on dispersal, density, dispersion, and weight are presented, and the role of N. micropus as a reservoir host for L. mexicana is discussed.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Cardiac Lineage Protein-1 (CLP-1) Regulates Cardiac Remodeling via Transcriptional Modulation of Diverse Hypertrophic and Fibrotic Responses and Angiotensin II-transforming Growth Factor β (TGF-β1) Signaling Axis.

It is well known that the renin-angiotensin system contributes to left ventricular hypertrophy and fibrosis, a major determinant of myocardial stiffness. TGF-β1 and renin-angiotensin system signaling alters the fibroblast phenotype by promoting its differentiation into morphologically distinct pathological myofibroblasts, which potentiates collagen synthesis and fibrosis and causes enhanced extracellular matrix deposition. However, the atrial natriuretic peptide, which is induced during left ventricular hypertrophy, plays an anti-fibrogenic and anti-hypertrophic role by blocking, among others, the TGF-β-induced nuclear localization of Smads. It is not clear how the hypertrophic and fibrotic responses are transcriptionally regulated. CLP-1, the mouse homolog of human hexamethylene bis-acetamide inducible-1 (HEXIM-1), regulates the pTEFb activity via direct association with pTEFb causing inhibition of the Cdk9-mediated serine 2 phosphorylation in the carboxyl-terminal domain of RNA polymerase II. It was recently reported that the serine kinase activity of Cdk9 not only targets RNA polymerase II but also the conserved serine residues of the polylinker region in Smad3, suggesting that CLP-1-mediated changes in pTEFb activity may trigger Cdk9-dependent Smad3 signaling that can modulate collagen expression and fibrosis. In this study, we evaluated the role of CLP-1 in vivo in induction of left ventricular hypertrophy in angiotensinogen-overexpressing transgenic mice harboring CLP-1 heterozygosity. We observed that introduction of CLP-1 haplodeficiency in the transgenic α-myosin heavy chain-angiotensinogen mice causes prominent changes in hypertrophic and fibrotic responses accompanied by augmentation of Smad3/Stat3 signaling. Together, our findings underscore the critical role of CLP-1 in remodeling of the genetic response during hypertrophy and fibrosis.

Down-modulation of lymphoproliferation and interferon-gamma production by beta-glucan derived from Saccharomyces cerevisiae

beta-glucan, one of the major cell wall components of Saccharomyces cerevisiae, has been found to enhance immune functions. This study investigated in vivo and in vitro effects of beta-glucan on lymphoproliferation and interferon-gamma (IFN-gamma) production by splenic cells from C57BL/6 female mice. All experiments were performed with particulate beta-glucan derived from S. cerevisiae. Data demonstrated that both, i.p administration of particulate beta-glucan (20 or 100 µg/animal) and in vitro stimulation of splenic cells (20 or 100 µg/ml of culture) decreased lymphoproliferation and IFN-gamma production induced by concanavalin A. These results suggest that beta-glucan can trigger a down-modulatory effect regulating a deleterious immune system hyperactivity in the presence of a strong stimulus.

Spatial analysis of the distribution of leprosy in the State of Ceará, Northeast Brazil

The aim of this study was to describe spatial patterns of the distribution of leprosy and to investigate spatial clustering of incidence rates in the state of Ceará, Northeast Brazil. The average incidence rate of leprosy for the period of 1991 to 1999 was calculated for each municipality of Ceará. Maps were used to describe the spatial distribution of the disease, and spatial statistics were applied to explore large- and small-scale variations of incidence rates. Three regions were identified in which the incidence of leprosy was particularly high. A spatial gradient in the incidence rates was identified, with a tendency of high rates to be concentrated on the North-South axis in the middle region of the state. Moran's I statistic indicated that a significant spatial autocorrelation also existed. The spatial distribution of leprosy in Ceará is heterogeneous. The reasons for spatial clustering of disease rates are not known, but might be related to an heterogeneous distribution of other factors such as crowding, social inequality, and environmental characteristics which by themselves determine the transmission of Mycobacterium leprae.

Fine-scale spatial genetic structure and gene dispersal in Silene latifolia.

Plants are sessile organisms, often characterized by limited dispersal. Seeds and pollen are the critical stages for gene flow. Here we investigate spatial genetic structure, gene dispersal and the relative contribution of pollen vs seed in the movement of genes in a stable metapopulation of the white campion Silene latifolia within its native range. This short-lived perennial plant is dioecious, has gravity-dispersed seeds and moth-mediated pollination. Direct measures of pollen dispersal suggested that large populations receive more pollen than small isolated populations and that most gene flow occurs within tens of meters. However, these studies were performed in the newly colonized range (North America) where the specialist pollinator is absent. In the native range (Europe), gene dispersal could fall on a different spatial scale. We genotyped 258 individuals from large and small (15) subpopulations along a 60 km, elongated metapopulation in Europe using six highly variable microsatellite markers, two X-linked and four autosomal. We found substantial genetic differentiation among subpopulations (global F(ST)=0.11) and a general pattern of isolation by distance over the whole sampled area. Spatial autocorrelation revealed high relatedness among neighboring individuals over hundreds of meters. Estimates of gene dispersal revealed gene flow at the scale of tens of meters (5-30 m), similar to the newly colonized range. Contrary to expectations, estimates of dispersal based on X and autosomal markers showed very similar ranges, suggesting similar levels of pollen and seed dispersal. This may be explained by stochastic events of extensive seed dispersal in this area and limited pollen dispersal.