IL28B polymorphisms, IP-10 and viral load predict virological response to therapy in chronic hepatitis C.

Aliment Pharmacol Ther 2011; 33: 1162-1172 SUMMARY: Background  Hepatitis C virus (HCV) is a major cause of chronic liver disease, cirrhosis and hepatocellular carcinoma and the identification of the predictors of response to antiviral therapy is an important clinical issue. Aim  To determine the independent contribution of factors including IL28B polymorphisms, IFN-gamma inducible protein-10 (IP-10) levels and the homeostasis model assessment of insulin resistance (HOMA-IR) score in predicting response to therapy in chronic hepatitis C (CHC). Methods  Multivariate analysis of factors predicting rapid (RVR) and sustained (SVR) virological response in 280 consecutive, treatment-naive CHC patients treated with peginterferon alpha and ribavirin in a prospective multicentre study. Results  Independent predictors of RVR were HCV RNA <400 000 IU/mL (OR 11.37; 95% CI 3.03-42.6), rs12980275 AA (OR 7.09; 1.97-25.56) and IP-10 (OR 0.04; 0.003-0.56) in HCV genotype 1 patients and lower baseline γ-glutamyl-transferase levels (OR = 0.02; 0.0009-0.31) in HCV genotype 3 patients. Independent predictors of SVR were rs12980275 AA (OR 9.68; 3.44-27.18), age <40 years (OR = 4.79; 1.50-15.34) and HCV RNA <400 000 IU/mL (OR 2.74; 1.03-7.27) in HCV genotype 1 patients and rs12980275 AA (OR = 6.26; 1.98-19.74) and age <40 years (OR 5.37; 1.54-18.75) in the 88 HCV genotype 1 patients without a RVR. RVR was by itself predictive of SVR in HCV genotype 1 patients (OR 33.0; 4.06-268.32) and the only independent predictor of SVR in HCV genotype 2 (OR 9.0, 1.72-46.99) or genotype 3 patients (OR 7.8, 1.43-42.67). Conclusions  In HCV genotype 1 patients, IL28B polymorphisms, HCV RNA load and IP-10 independently predict RVR. The combination of IL28B polymorphisms, HCV RNA level and age may yield more accurate pre-treatment prediction of SVR. HOMA-IR score is not associated with viral response.

Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables

Questions Soil properties have been widely shown to influence plant growth and distribution. However, the degree to which edaphic variables can improve models based on topo-climatic variables is still unclear. In this study, we tested the roles of seven edaphic variables, namely (1) pH; (2) the content of nitrogen and of (3) phosphorus; (4) silt; (5) sand; (6) clay and (7) carbon-to-nitrogen ratio, as predictors of species distribution models in an edaphically heterogeneous landscape. We also tested how the respective influence of these variables in the models is linked to different ecological and functional species characteristics. Location The Western Alps, Switzerland. Methods With four different modelling techniques, we built models for 115 plant species using topo-climatic variables alone and then topo-climatic variables plus each of the seven edaphic variables, one at a time. We evaluated the contribution of each edaphic variable by assessing the change in predictive power of the model. In a second step, we evaluated the importance of the two edaphic variables that yielded the largest increase in predictive power in one final set of models for each species. Third, we explored the change in predictive power and the importance of variables across plant functional groups. Finally, we assessed the influence of the edaphic predictors on the prediction of community composition by stacking the models for all species and comparing the predicted communities with the observed community. Results Among the set of edaphic variables studied, pH and nitrogen content showed the highest contributions to improvement of the predictive power of the models, as well as the predictions of community composition. When considering all topo-climatic and edaphic variables together, pH was the second most important variable after degree-days. The changes in model results caused by edaphic predictors were dependent on species characteristics. The predictions for the species that have a low specific leaf area, and acidophilic preferences, tolerating low soil pH and high humus content, showed the largest improvement by the addition of pH and nitrogen in the model. Conclusions pH was an important predictor variable for explaining species distribution and community composition of the mountain plants considered in our study. pH allowed more precise predictions for acidophilic species. This variable should not be neglected in the construction of species distribution models in areas with contrasting edaphic conditions.

Development and validation of a clinical prediction rule for chest wall syndrome in primary care

Le "Chest wall syndrome" (CWS) est défini comme étant une source bénigne de douleurs thoraciques, localisées sur la paroi thoracique antérieure et provoquées par une affection musculosquelettique. Le CWS représente la cause la plus fréquente de douleurs thoraciques en médecine de premier recours. Le but de cette étude est de développer et valider un score de prédiction clinique pour le CWS. Une revue de la littérature a d'abord été effectuée, d'une part pour savoir si un tel score existait déjà, et d'autre part pour retrouver les variables décrites comme étant prédictives d'un CWS. Le travail d'analyse statistique a été effectué avec les données issues d'une cohorte clinique multicentrique de patients qui avaient consulté en médecine de premier recours en Suisse romande avec une douleur thoracique (59 cabinets, 672 patients). Un diagnostic définitif avait été posé à 12 mois de suivi. Les variables pertinentes ont été sélectionnées par analyses bivariées, et le score de prédiction clinique a été développé par régression logistique multivariée. Une validation externe de ce score a été faite en utilisant les données d'une cohorte allemande (n= 1212). Les analyses bivariées ont permis d'identifier 6 variables caractérisant le CWS : douleur thoracique (ni rétrosternale ni oppressive), douleur en lancées, douleur bien localisée, absence d'antécédent de maladie coronarienne, absence d'inquiétude du médecin et douleur reproductible à la palpation. Cette dernière variable compte pour 2 points dans le score, les autres comptent pour 1 point chacune; le score total s'étend donc de 0 à 7 points. Dans la cohorte de dérivation, l'aire sous la courbe sensibilité/spécificité (courbe ROC) est de 0.80 (95% de l'intervalle de confiance : 0.76-0.83). Avec un seuil diagnostic de > 6 points, le score présente 89% de spécificité et 45% de sensibilité. Parmi tous les patients qui présentaient un CWS (n = 284), 71% (n = 201) avaient une douleur reproductible à la palpation et 45% (n= 127) sont correctement diagnostiqués par le score. Pour une partie (n = 43) de ces patients souffrant de CWS et correctement classifiés, 65 investigations complémentaires (30 électrocardiogrammes, 16 radiographies du thorax, 10 analyses de laboratoire, 8 consultations spécialisées, et une tomodensitométrie thoracique) avaient été réalisées pour parvenir au diagnostic. Parmi les faux positifs (n = 41), on compte trois angors stables (1.8% de tous les positifs). Les résultats de la validation externe sont les suivants : une aire sous la courbe ROC de 0.76 (95% de l'intervalle de confiance : 0.73-0.79) avec une sensibilité de 22% et une spécificité de 93%. Ce score de prédiction clinique pour le CWS constitue un complément utile à son diagnostic, habituellement obtenu par exclusion. En effet, pour les 127 patients présentant un CWS et correctement classifiés par notre score, 65 investigations complémentaires auraient pu être évitées. Par ailleurs, la présence d'une douleur thoracique reproductible à la palpation, bien qu'étant sa plus importante caractéristique, n'est pas pathognomonique du CWS.

Uncertainties in the prediction of spatial variability of soil CO2 emissions and related properties

The soil CO2 emission has high spatial variability because it depends strongly on soil properties. The purpose of this study was to (i) characterize the spatial variability of soil respiration and related properties, (ii) evaluate the accuracy of results of the ordinary kriging method and sequential Gaussian simulation, and (iii) evaluate the uncertainty in predicting the spatial variability of soil CO2 emission and other properties using sequential Gaussian simulations. The study was conducted in a sugarcane area, using a regular sampling grid with 141 points, where soil CO2 emission, soil temperature, air-filled pore space, soil organic matter and soil bulk density were evaluated. All variables showed spatial dependence structure. The soil CO2 emission was positively correlated with organic matter (r = 0.25, p < 0.05) and air-filled pore space (r = 0.27, p < 0.01) and negatively with soil bulk density (r = -0.41, p < 0.01). However, when the estimated spatial values were considered, the air-filled pore space was the variable mainly responsible for the spatial characteristics of soil respiration, with a correlation of 0.26 (p < 0.01). For all variables, individual simulations represented the cumulative distribution functions and variograms better than ordinary kriging and E-type estimates. The greatest uncertainties in predicting soil CO2 emission were associated with areas with the highest estimated values, which produced estimates from 0.18 to 1.85 t CO2 ha-1, according to the different scenarios considered. The knowledge of the uncertainties generated by the different scenarios can be used in inventories of greenhouse gases, to provide conservative estimates of the potential emission of these gases.

Chlamydiales and the innate immune response: friend or foe?

Pathogenicity of Chlamydia and Chlamydia-related bacteria could be partially mediated by an enhanced activation of the innate immune response. The study of this host pathogen interaction has proved challenging due to the restricted in vitro growth of these strict intracellular bacteria and the lack of genetic tools to manipulate their genomes. Despite these difficulties, the interactions of Chlamydiales with the innate immune cells and their effectors have been studied thoroughly. This review aims to point out the role of pattern recognition receptors and signal molecules (cytokines, reactive oxygen species) of the innate immune response in the pathogenesis of chlamydial infection. Besides inducing clearance of the bacteria, some of these effectors may be used by the Chlamydia to establish chronic infections or to spread. Thus, the induced innate immune response seems to be variable depending on the species and/or the serovar, making the pattern more complex. It remains crucial to determine the common players of the innate immune response in order to help define new treatment strategies and to develop effective vaccines. The excellent growth in phagocytic cells of some Chlamydia-related organisms such as Waddlia chondrophila supports their use as model organisms to study conserved features important for interactions between the innate immunity and Chlamydia.

Bacterial bioassay for rapid and accurate analysis of arsenic in highly variable groundwater samples.

In this study, we report the first ever large-scale environmental validation of a microbial reporter-based test to measure arsenic concentrations in natural water resources. A bioluminescence-producing arsenic-inducible bacterium based on Escherichia coli was used as the reporter organism. Specific protocols were developed with the goal to avoid the negative influence of iron in groundwater on arsenic availability to the bioreporter cells. A total of 194 groundwater samples were collected in the Red River and Mekong River Delta regions of Vietnam and were analyzed both by atomic absorption spectroscopy (AAS) and by the arsenic bioreporter protocol. The bacterial cells performed well at and above arsenic concentrations in groundwater of 7 microg/L, with an almost linearly proportional increase of the bioluminescence signal between 10 and 100 microg As/L (r2 = 0.997). Comparisons between AAS and arsenic bioreporter determinations gave an overall average of 8.0% false negative and 2.4% false positive identifications for the bioreporter prediction at the WHO recommended acceptable arsenic concentration of 10 microg/L, which is far betterthan the performance of chemical field test kits. Because of the ease of the measurement protocol and the low application cost, the microbiological arsenic test has a great potential in large screening campaigns in Asia and in other areas suffering from arsenic pollution in groundwater resources.

Elderly age is not a negative predictive factor for virological response to therapy with pegylated interferon-α and ribavirin in chronic hepatitis C virus patients.

BACKGROUND & AIMS: Age is frequently discussed as negative host factor to achieve a sustained virological response (SVR) to antiviral therapy of chronic hepatitis C. However, elderly patients often show advanced fibrosis/cirrhosis as known negative predictive factor. The aim of this study was to assess age as an independent predictive factor during antiviral therapy. METHODS: Overall, 516 hepatitis C patients were treated with pegylated interferon-α and ribavirin, thereof 66 patients ≥60 years. We analysed the impact of host factors (age, gender, fibrosis, haemoglobin, previous hepatitis C treatment) and viral factors (genotype, viral load) on SVR per therapy course by performing a generalized estimating equations (GEE) regression modelling, a matched pair analysis and a classification tree analysis. RESULTS: Overall, SVR per therapy course was 42.9 and 26.1%, respectively, in young and elderly patients with hepatitis C virus (HCV) genotypes 1/4/6. The corresponding figures for HCV genotypes 2/3 were 74.4 and 84%. In the GEE model, age had no significant influence on achieving SVR. In matched pair analysis, SVR was not different in young and elderly patients (54.2 and 55.9% respectively; P = 0.795 in binominal test). In classification tree analysis, age was not a relevant splitting variable. CONCLUSIONS: Age is not a significant predictive factor for achieving SVR, when relevant confounders are taken into account. As life expectancy in Western Europe at age 60 is more than 20 years, it is reasonable to treat chronic hepatitis C in selected elderly patients with relevant fibrosis or cirrhosis but without major concomitant diseases, as SVR improves survival and reduces carcinogenesis.

Soil phosphorus availability and soybean response to phosphorus starter fertilizer

Phosphorus fixation in tropical soils may decrease under no-till. In this case, P fertilizer could be surface-spread, which would improve farm operations by decreasing the time spend in reloading the planter with fertilizers. In the long term, less soluble P sources could be viable. In this experiment, the effect of surface-broadcast P fertilization with both soluble and reactive phosphates on soil P forms and availability to soybean was studied with or without fertilization with soluble P in the planting furrow in a long-term experiment in which soybean was grown in rotation with Ruzigrass (Brachiaria ruziziensis). No P or 80 kg ha-1 of P2O5 in the form of triple superphosphate or Arad reactive rock phosphate was applied on the surface of a soil with variable P fertilization history. Soil samples were taken to a depth of 60 cm and soil P was fractionated. Soybean was grown with 0, 30, and 60 kg ha-1 of P2O5 in the form of triple phosphate applied in the seed furrow. Both fertilizers applied increased available P in the uppermost soil layers and the moderately labile organic and inorganic forms of P in the soil profile, probably as result of root decay. Soybean responded to phosphates applied on the soil surface or in the seed furrow; however, application of soluble P in the seed furrow should not be discarded. In tropical soils with a history of P fertilization, soluble P sources may be substituted for natural reactive phosphates broadcast on the surface. The planting operation may be facilitated through reduction in the rate of P applied in the planting furrow in relation to the rates currently applied.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

LOSS OF EXTRACTION CAPACITY OF MEHLICH-1 AND MONOCALCIUM PHOSPHATE AS A VARIABLE OF REMAINING P AND ITS RELATIONSHIP TO CRITICAL LEVELS OF SOIL PHOSPHORUS AND SULFUR

The Mehlich-1 (M-1) extractant and Monocalcium Phosphate in acetic acid (MCPa) have mechanisms for extraction of available P and S in acidity and in ligand exchange, whether of the sulfate of the extractant by the phosphate of the soil, or of the phosphate of the extractant by the sulfate of the soil. In clayey soils, with greater P adsorption capacity, or lower remaining P (Rem-P) value, which corresponds to soils with greater Phosphate Buffer Capacity (PBC), more buffered for acidity, the initially low pH of the extractants increases over their time of contact with the soil in the direction of the pH of the soil; and the sulfate of the M-1 or the phosphate of the MCPa is adsorbed by adsorption sites occupied by these anions or not. This situation makes the extractant lose its extraction capacity, a phenomenon known as loss of extraction capacity or consumption of the extractant, the object of this study. Twenty soil samples were chosen so as to cover the range of Rem-P (0 to 60 mg L-1). Rem-P was used as a measure of the PBC. The P and S contents available from the soil samples through M-1 and MCPa, and the contents of other nutrients and of organic matter were determined. For determination of loss of extraction capacity, after the rest period, the pH and the P and S contents were measured in both the extracts-soils. Although significant, the loss of extraction capacity of the acidity of the M-1 and MCPa extractants with reduction in the Rem-P value did not have a very expressive effect. A “linear plateau” model was observed for the M-1 for discontinuous loss of extraction capacity of the P content in accordance with reduction in the concentration of the Rem-P or increase in the PBC, suggesting that a discontinuous model should also be adopted for interpretation of available P of soils with different Rem-P values. In contrast, a continuous linear response was observed between the P variables in the extract-soil and Rem-P for the MCPa extractor, which shows increasing loss of extraction capacity of this extractor with an increase in the PBC of the soil, indicating the validity of the linear relationship between the available S of the soil and the PBC, estimated by Rem-P, as currently adopted.

Estimating biophysical variable dependences with kernels

This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is very easy to compute and has good theoretical and practical properties. We exploit the capabilities of HSIC to explain nonlinear dependences in two remote sensing problems: temperature estimation and chlorophyll concentration prediction from spectra. Results show that, when the relationship between random variables is nonlinear or when few data are available, the HSIC criterion outperforms other standard methods, such as the linear correlation or mutual information.

Identification of human IKK-2 inhibitors of natural origin (Part II): in Silico prediction of IKK-2 inhibitors in natural extracts with known anti-inflammatory activity.

Human inhibitor NF-κB kinase 2 (hIKK-2) is the primary component responsible for activating NF-κB in response to various inflammatory stimuli. Thus, synthetic ATP-competitive inhibitors for hIKK-2 have been developed as anti-inflammatory compounds. We recently reported a virtual screening protocol (doi:10.1371/journal.pone.0016903) that is able to identify hIKK-2 inhibitors that are not structurally related to any known molecule that inhibits hIKK-2 and that have never been reported to have anti-inflammatory activity. In this study, a stricter version of this protocol was applied to an in-house database of 29,779 natural products annotated with their natural source. The search identified 274 molecules (isolated from 453 different natural extracts) predicted to inhibit hIKK-2. An exhaustive bibliographic search revealed that anti-inflammatory activity has been previously described for: (a) 36 out of these 453 extracts; and (b) 17 out of 30 virtual screening hits present in these 36 extracts. Only one of the remaining 13 hit molecules in these extracts shows chemical similarity with known synthetic hIKK-2 inhibitors. Therefore, it is plausible that a significant portion of the remaining 12 hit molecules are lead-hopping candidates for the development of new hIKK-2 inhibitors.

Multivariate prediction

The problem of prediction is considered in a multidimensional setting. Extending an idea presented by Barndorff-Nielsen and Cox, a predictive density for a multivariate random variable of interest is proposed. This density has the form of an estimative density plus a correction term. It gives simultaneous prediction regions with coverage error of smaller asymptotic order than the estimative density. A simulation study is also presented showing the magnitude of the improvement with respect to the estimative method.

Experimental analysis and computer prediction of CTF/NFI transcription factor DNA binding sites.

Accurate prediction of transcription factor binding sites is needed to unravel the function and regulation of genes discovered in genome sequencing projects. To evaluate current computer prediction tools, we have begun a systematic study of the sequence-specific DNA-binding of a transcription factor belonging to the CTF/NFI family. Using a systematic collection of rationally designed oligonucleotides combined with an in vitro DNA binding assay, we found that the sequence specificity of this protein cannot be represented by a simple consensus sequence or weight matrix. For instance, CTF/NFI uses a flexible DNA binding mode that allows for variations of the binding site length. From the experimental data, we derived a novel prediction method using a generalised profile as a binding site predictor. Experimental evaluation of the generalised profile indicated that it accurately predicts the binding affinity of the transcription factor to natural or synthetic DNA sequences. Furthermore, the in vitro measured binding affinities of a subset of oligonucleotides were found to correlate with their transcriptional activities in transfected cells. The combined computational-experimental approach exemplified in this work thus resulted in an accurate prediction method for CTF/NFI binding sites potentially functioning as regulatory regions in vivo.

S100A1 deficiency results in prolonged ventricular repolarization in response to sympathetic activation.

S100A1 is a Ca(2+)-binding protein and predominantly expressed in the heart. We have generated a mouse line of S100A1 deficiency by gene trap mutagenesis to investigate the impact of S100A1 ablation on heart function. Electrocardiogram recordings revealed that after beta-adrenergic stimulation S100A1-deficient mice had prolonged QT, QTc and ST intervals and intraventricular conduction disturbances reminiscent of 2 : 1 bundle branch block. In order to identify genes affected by the loss of S100A1, we profiled the mutant and wild type cardiac transcriptomes by gene array analysis. The expression of several genes functioning to the electrical activity of the heart were found to be significantly altered. Although the default prediction would be that mRNA and protein levels are highly correlated, comprehensive immunoblot analyses of salient up- or down-regulated candidate genes of any cellular network revealed no significant changes on protein level. Taken together, we found that S100A1 deficiency results in cardiac repolarization delay and alternating ventricular conduction defects in response to sympathetic activation accompanied by a significantly different transcriptional regulation.