Vitamin D Receptor and Jak-STAT Signaling Crosstalk Results in Calcitriol-Mediated Increase of Hepatocellular Response to IFN-α.

Recent clinical research suggests a role for vitamin D in the response to IFN-α-based therapy of chronic hepatitis C. Therefore, we aimed to explore the underlying mechanisms in vitro. Huh-7.5 cells harboring subgenomic hepatitis C virus (HCV) replicons or infected with cell culture-derived HCV were exposed to bioactive 1,25-dihydroxyvitamin D3 (calcitriol) with or without IFN-α. In these experiments, calcitriol alone had no effect on the HCV life cycle. However, calcitriol enhanced the inhibitory effect of IFN-α on HCV replication. This effect was based on a calcitriol-mediated increase of IFN-α-induced gene expression. Further mechanistic studies revealed a constitutive inhibitory interaction between the inactive vitamin D receptor (VDR) and Stat1, which was released upon stimulation with calcitriol and IFN-α. As a consequence, IFN-α-induced binding of phosphorylated Stat1 to its DNA target sequences was enhanced by calcitriol. Importantly, and in line with these observations, silencing of the VDR resulted in an enhanced hepatocellular response to IFN-α. Our findings identify the VDR as a novel suppressor of IFN-α-induced signaling through the Jak-STAT pathway.

IFN Stimulated Gene Expression in the Liver is a Better Predictor of Treatment Response in Chronic Hepatitis C than the IL28B (IFN lambda 3) Genotype

BACKGROUND: Therapy of chronic hepatitis C (CHC) with pegIFNα/ribavirin achieves a sustained virologic response (SVR) in ∼55%. Pre-activation of the endogenous interferon system in the liver is associated with non-response (NR). Recently, genome-wide association studies described associations of allelic variants near the IL28B (IFNλ3) gene with treatment response and with spontaneous clearance of the virus. We investigated if the IL28B genotype determines the constitutive expression of IFN stimulated genes (ISGs) in the liver of patients with CHC. METHODS: We genotyped 93 patients with CHC for 3 IL28B single nucleotide polymorphisms (SNPs, rs12979860, rs8099917, rs12980275), extracted RNA from their liver biopsies and quantified the expression of IL28B and of 8 previously identified classifier genes which discriminate between SVR and NR (IFI44L, RSAD2, ISG15, IFI22, LAMP3, OAS3, LGALS3BP and HTATIP2). Decision tree ensembles in the form of a random forest classifier were used to calculate the relative predictive power of these different variables in a multivariate analysis. RESULTS: The minor IL28B allele (bad risk for treatment response) was significantly associated with increased expression of ISGs, and, unexpectedly, with decreased expression of IL28B. Stratification of the patients into SVR and NR revealed that ISG expression was conditionally independent from the IL28B genotype, i.e. there was an increased expression of ISGs in NR compared to SVR irrespective of the IL28B genotype. The random forest feature score (RFFS) identified IFI27 (RFFS = 2.93), RSAD2 (1.88) and HTATIP2 (1.50) expression and the HCV genotype (1.62) as the strongest predictors of treatment response. ROC curves of the IL28B SNPs showed an AUC of 0.66 with an error rate (ERR) of 0.38. A classifier with the 3 best classifying genes showed an excellent test performance with an AUC of 0.94 and ERR of 0.15. The addition of IL28B genotype information did not improve the predictive power of the 3-gene classifier. CONCLUSIONS: IL28B genotype and hepatic ISG expression are conditionally independent predictors of treatment response in CHC. There is no direct link between altered IFNλ3 expression and pre-activation of the endogenous system in the liver. Hepatic ISG expression is by far the better predictor for treatment response than IL28B genotype.

Molecular predictors of gemcitabine response in pancreatic cancer.

Gemcitabine is one of the most used anti-neoplastic drugs with documented activity in almost all major localizations of cancer. In pancreatic cancer treatment, gemcitabine occupies a prominent place as a first line chemotherapy, partly because of the paucity of other efficacious chemotherapy options. In fact, only a minority of pancreatic cancer patients display a response or even stability of disease with the drug. There are currently no clinically applicable means of predicting which patient will derive a clinical benefit from gemcitabine although several proposed markers have been studied. These markers are proteins involved in drug up-take, activation and catabolism or proteins that define the ability of the cell to undergo apoptosis in response to the drug. Several of these markers are reviewed in this paper. We also briefly discuss the possible role of stem cells in drug resistance to gemcitabine.

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.

Reliable computation of robust response tori on the verge of breakdown

We prove the existence and local uniqueness of invariant tori on the verge of breakdown for two systems: the quasi-periodically driven logistic map and the quasi-periodically forced standard map. These systems exemplify two scenarios: the Heagy-Hammel route for the creation of strange non- chaotic attractors and the nonsmooth bifurcation of saddle invariant tori. Our proofs are computer- assisted and are based on a tailored version of the Newton-Kantorovich theorem. The proofs cannot be performed using classical perturbation theory because the two scenarios are very far from the perturbative regime, and fundamental hypotheses such as reducibility or hyperbolicity either do not hold or are very close to failing. Our proofs are based on a reliable computation of the invariant tori and a careful study of their dynamical properties, leading to the rigorous validation of the numerical results with our novel computational techniques.

Biomarkers of Host Response Predict Primary End-Point Radiological Pneumonia in Tanzanian Children with Clinical Pneumonia: A Prospective Cohort Study.

BACKGROUND: Diagnosing pediatric pneumonia is challenging in low-resource settings. The World Health Organization (WHO) has defined primary end-point radiological pneumonia for use in epidemiological and vaccine studies. However, radiography requires expertise and is often inaccessible. We hypothesized that plasma biomarkers of inflammation and endothelial activation may be useful surrogates for end-point pneumonia, and may provide insight into its biological significance. METHODS: We studied children with WHO-defined clinical pneumonia (n = 155) within a prospective cohort of 1,005 consecutive febrile children presenting to Tanzanian outpatient clinics. Based on x-ray findings, participants were categorized as primary end-point pneumonia (n = 30), other infiltrates (n = 31), or normal chest x-ray (n = 94). Plasma levels of 7 host response biomarkers at presentation were measured by ELISA. Associations between biomarker levels and radiological findings were assessed by Kruskal-Wallis test and multivariable logistic regression. Biomarker ability to predict radiological findings was evaluated using receiver operating characteristic curve analysis and Classification and Regression Tree analysis. RESULTS: Compared to children with normal x-ray, children with end-point pneumonia had significantly higher C-reactive protein, procalcitonin and Chitinase 3-like-1, while those with other infiltrates had elevated procalcitonin and von Willebrand Factor and decreased soluble Tie-2 and endoglin. Clinical variables were not predictive of radiological findings. Classification and Regression Tree analysis generated multi-marker models with improved performance over single markers for discriminating between groups. A model based on C-reactive protein and Chitinase 3-like-1 discriminated between end-point pneumonia and non-end-point pneumonia with 93.3% sensitivity (95% confidence interval 76.5-98.8), 80.8% specificity (72.6-87.1), positive likelihood ratio 4.9 (3.4-7.1), negative likelihood ratio 0.083 (0.022-0.32), and misclassification rate 0.20 (standard error 0.038). CONCLUSIONS: In Tanzanian children with WHO-defined clinical pneumonia, combinations of host biomarkers distinguished between end-point pneumonia, other infiltrates, and normal chest x-ray, whereas clinical variables did not. These findings generate pathophysiological hypotheses and may have potential research and clinical utility.

Biological Characterization of Gene Response to Insulin-Induced Hypoglycemia in Mouse Retina.

Glucose is the most important metabolic substrate of the retina and maintenance of normoglycemia is an essential challenge for diabetic patients. Chronic, exaggerated, glycemic excursions could lead to cardiovascular diseases, nephropathy, neuropathy and retinopathy. We recently showed that hypoglycemia induced retinal cell death in mouse via caspase 3 activation and glutathione (GSH) decrease. Ex vivo experiments in 661W photoreceptor cells confirmed the low-glucose induction of death via superoxide production and activation of caspase 3, which was concomitant with a decrease of GSH content. We evaluate herein retinal gene expression 4 h and 48 h after insulin-induced hypoglycemia. Microarray analysis demonstrated clusters of genes whose expression was modified by hypoglycemia and we discuss the potential implication of those genes in retinal cell death. In addition, we identify by gene set enrichment analysis, three important pathways, including lysosomal function, GSH metabolism and apoptotic pathways. Then we tested the effect of recurrent hypoglycemia (three successive 4h periods of hypoglycemia spaced by 48 h recovery) on retinal cell death. Interestingly, exposure to multiple hypoglycemic events prevented GSH decrease and retinal cell death, or adapted the retina to external stress by restoring GSH level comparable to control situation. We hypothesize that scavenger GSH is a key compound in this apoptotic process, and maintaining "normal" GSH level, as well as a strict glycemic control, represents a therapeutic challenge in order to avoid side effects of diabetes, especially diabetic retinopathy.

Aetiologies of pulseless electrical activity in out-of-hospital cardiac arrests:A retrospective study and analysis of specific causes

Background: Pulseless electrical activity (PEA) cardiac arrest is defined as a cardiac arrest (CA) presenting with a residual organized electrical activity on the electrocardiogram. In the last decades, the incidence of PEA has regularly increased, compared to other types of CA like ventricular fibrillation or pulseless ventricular tachycardia. PEA is frequently induced by reversible conditions. The "4 (or 5) H" & "4 (or 5) T" are proposed as a mnemonic to asses for Hypoxia, Hypovolemia, Hypo- /Hyperkalaemia, Hypothermia, Thrombosis (cardiac or pulmonary), cardiac Tamponade, Toxins, and Tension pneumothorax. Other pathologies (intracranial haemorrhage, severe sepsis, myocardial contraction dysfunction) have been identified as potential causes for PEA, but their respective probability and frequencies are unclear and they are not yet included into the resuscitation guidelines. The aim of this study was to analyse the aetiologies of PEA out-of-hospital CA, in order to evaluate the relative frequencies of each cause and therefore to improve the management of patients suffering a PEA cardiac arrest. Method: This retrospective study was based on data routinely and prospectively collected for each PEMS intervention. All adult patients treated from January 1st 2002 to December 2012 31st by the PEMS for out-of-hospital cardiac arrest, with PEA as the first recorded rhythm, and admitted to the emergency department (ED) of the Lausanne University Hospital were included. The aetiologies of PEA cardiac arrest were classified into subgroups, based on the classical H&T's classification, supplemented by four other subgroups analysis: trauma, intra-cranial haemorrhage (ICH), non-ischemic cardiomyopathy (NIC) and undetermined cause. Results: 1866 OHCA were treated by the PEMS. PEA was the first recorded rhythm in 240 adult patients (13.8 %). After exclusion of 96 patients, 144 patients with a PEA cardiac arrest admitted to the ED were included in the analysis. The mean age was 63.8 ± 20.0 years, 58.3% were men and the survival rate at 48 hours was 29%. 32 different causes of OHCA PEA were established for 119 patients. For 25 patients (17.4 %), we were unable to attribute a specific cause for the PEA cardiac arrest. Hypoxia (23.6 %), acute coronary syndrome (12.5%) and trauma (12.5 %) were the three most frequent causes. Pulmonary embolism, Hypovolemia, Intoxication and Hyperkaliemia occurs in less than 10% of the cases (7.6 %, 5.6 %, 3.5%, respectively 2.1 %). Non ischemic cardiomyopathy and intra-cranial haemorrhage occur in 8.3 % and 6.9 %, respectively. Conclusions: According to our results, intra-cranial haemorrhage and non-ischemic cardiomyopathy represent noticeable causes of PEA in OHCA, with a prevalence equalling or exceeding the frequency of classical 4 H's and 4 T's aetiologies. These two pathologies are potentially accessible to simple diagnostic procedures (native CT-scan or echocardiography) and should be included into the 4 H's and 4 T's mnemonic.

Validation of plant dynamic model by online and laboratory measurements – a tool to predict online COD loads out of production of mechanical printing papers

COD discharges out of processes have increased in line with elevating brightness demands for mechanical pulp and papers. The share of lignin-like substances in COD discharges is on average 75%. In this thesis, a plant dynamic model was created and validated as a means to predict COD loading and discharges out of a mill. The assays were carried out in one paper mill integrate producing mechanical printing papers. The objective in the modeling of plant dynamics was to predict day averages of COD load and discharges out of mills. This means that online data, like 1) the level of large storage towers of pulp and white water 2) pulp dosages, 3) production rates and 4) internal white water flows and discharges were used to create transients into the balances of solids and white water, referred to as “plant dynamics”. A conversion coefficient was verified between TOC and COD. The conversion coefficient was used for predicting the flows from TOC to COD to the waste water treatment plant. The COD load was modeled with similar uncertainty as in reference TOC sampling. The water balance of waste water treatment was validated by the reference concentration of COD. The difference of COD predictions against references was within the same deviation of TOC-predictions. The modeled yield losses and retention values of TOC in pulping and bleaching processes and the modeled fixing of colloidal TOC to solids between the pulping plant and the aeration basin in the waste water treatment plant were similar to references presented in literature. The valid water balances of the waste water treatment plant and the reduction model of lignin-like substances produced a valid prediction of COD discharges out of the mill. A 30% increase in the release of lignin-like substances in the form of production problems was observed in pulping and bleaching processes. The same increase was observed in COD discharges out of waste water treatment. In the prediction of annual COD discharge, it was noticed that the reduction of lignin has a wide deviation from year to year and from one mill to another. This made it difficult to compare the parameters of COD discharges validated in plant dynamic simulation with another mill producing mechanical printing papers. However, a trend of moving from unbleached towards high-brightness TMP in COD discharges was valid.

Feature selection for automatic analysis of emotional response based on nonlinear speech modeling suitable for diagnosis of Alzheimer׳s disease

Alzheimer׳s disease (AD) is the most common type of dementia among the elderly. This work is part of a larger study that aims to identify novel technologies and biomarkers or features for the early detection of AD and its degree of severity. The diagnosis is made by analyzing several biomarkers and conducting a variety of tests (although only a post-mortem examination of the patients’ brain tissue is considered to provide definitive confirmation). Non-invasive intelligent diagnosis techniques would be a very valuable diagnostic aid. This paper concerns the Automatic Analysis of Emotional Response (AAER) in spontaneous speech based on classical and new emotional speech features: Emotional Temperature (ET) and fractal dimension (FD). This is a pre-clinical study aiming to validate tests and biomarkers for future diagnostic use. The method has the great advantage of being non-invasive, low cost, and without any side effects. The AAER shows very promising results for the definition of features useful in the early diagnosis of AD.