Alterations in pulmonary structure by elastase administration in a model of emphysema in mice is associated with functional disturbances

Several experimental studies of pulmonary emphysema using animal models have been described in the literature. However, only a few of these studies have focused on the assessment of ergometric function as a non-invasive technique to validate the methodology used for induction of experimental emphysema. Additionally, functional assessments of emphysema are rarely correlated with morphological pulmonary abnormalities caused by induced emphysema. The present study aimed to evaluate the effects of elastase administered by tracheal puncture on pulmonary parenchyma and their corresponding functional impairment. This was evaluated by measuring exercise capacity in C57Bl/6 mice in order to establish a reproducible and safe methodology of inducing experimental emphysema. Thirty six mice underwent ergometric tests before and 28 days after elastase administration. Pancreatic porcine elastase solution was administered by tracheal puncture, which resulted in a significantly decreased exercise capacity, shown by a shorter distance run (-30.5%) and a lower mean velocity (-15%), as well as in failure to increase the elimination of carbon dioxide. The mean linear intercept increased significantly by 50% in tracheal elastase administration. In conclusion, application of elastase by tracheal function in C57Bl/6 induces emphysema, as validated by morphometric analyses, and resulted in a significantly lower exercise capacity, while resulting in a low mortality rate. (C) 2011 Sociedade Portuguesa de Pneumologia. Published by Elsevier Espana, S.L. All rights reserved.

Cardiac Rhythm Disturbances Associated with Amlodipine Acute Intoxication

Amlodipine is a dihydropyridine calcium channel antagonist extensively used for the treatment of arterial hypertension, with predominant effect on the peripheral vascular territory. In most cases of severe intoxication, important hypotension and reflex tachycardia are usually observed. We report a case of young man with severe amlodipine intoxication that developed important bradyarrhythmias, such as low atrial rhythm, prolonged PR interval, atrioventricular block, and left bundle branch block. These rhythm disturbances suggest that, during acute intoxication, dihydropyridine loses its selective action on the vascular territory and can depress automatism and conduction of cardiac electrical stimulus.

A Genetic and Pathologic Study of a DENV2 Clinical Isolate Capable of Inducing Encephalitis and Hematological Disturbances in Immunocompetent Mice

Dengue virus (DENV) is the causative agent of dengue fever (DF), a mosquito-borne illness endemic to tropical and subtropical regions. There is currently no effective drug or vaccine formulation for the prevention of DF and its more severe forms, i.e., dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). There are two generally available experimental models for the study of DENV pathogenicity as well as the evaluation of potential vaccine candidates. The first model consists of non-human primates, which do not develop symptoms but rather a transient viremia. Second, mouse-adapted virus strains or immunocompromised mouse lineages are utilized, which display some of the pathological features of the infection observed in humans but may not be relevant to the results with regard to the wild-type original virus strains or mouse lineages. In this study, we describe a genetic and pathological study of a DENV2 clinical isolate, named JHA1, which is naturally capable of infecting and killing Balb/c mice and reproduces some of the symptoms observed in DENV-infected subjects. Sequence analyses demonstrated that the JHA1 isolate belongs to the American genotype group and carries genetic markers previously associated with neurovirulence in mouse-adapted virus strains. The JHA1 strain was lethal to immunocompetent mice following intracranial (i.c.) inoculation with a LD50 of approximately 50 PFU. Mice infected with the JHA1 strain lost weight and exhibited general tissue damage and hematological disturbances, with similarity to those symptoms observed in infected humans. In addition, it was demonstrated that the JHA1 strain shares immunological determinants with the DENV2 NGC reference strain, as evaluated by cross-reactivity of anti-envelope glycoprotein (domain III) antibodies. The present results indicate that the JHA1 isolate may be a useful tool in the study of DENV pathogenicity and will help in the evaluation of anti-DENV vaccine formulations as well as potential therapeutic approaches.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded. Methods: We propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood. Results: For the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib. Conclusions: From the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Influence diagnostics in heteroscedastic and/or autoregressive nonlinear elliptical models for correlated data

In this paper, we propose nonlinear elliptical models for correlated data with heteroscedastic and/or autoregressive structures. Our aim is to extend the models proposed by Russo et al. [22] by considering a more sophisticated scale structure to deal with variations in data dispersion and/or a possible autocorrelation among measurements taken throughout the same experimental unit. Moreover, to avoid the possible influence of outlying observations or to take into account the non-normal symmetric tails of the data, we assume elliptical contours for the joint distribution of random effects and errors, which allows us to attribute different weights to the observations. We propose an iterative algorithm to obtain the maximum-likelihood estimates for the parameters and derive the local influence curvatures for some specific perturbation schemes. The motivation for this work comes from a pharmacokinetic indomethacin data set, which was analysed previously by Bocheng and Xuping [1] under normality.

Functional Magnetic Resonance Imaging Data Analysis by Autoregressive Estimator

The autoregressive (AR) estimator, a non-parametric method, is used to analyze functional magnetic resonance imaging (fMRI) data. The same method has been used, with success, in several other time series data analysis. It uses exclusively the available experimental data points to estimate the most plausible power spectra compatible with the experimental data and there is no need to make any assumption about non-measured points. The time series, obtained from fMRI block paradigm data, is analyzed by the AR method to determine the brain active regions involved in the processing of a given stimulus. This method is considerably more reliable than the fast Fourier transform or the parametric methods. The time series corresponding to each image pixel is analyzed using the AR estimator and the corresponding poles are obtained. The pole distribution gives the shape of power spectra, and the pixels with poles at the stimulation frequency are considered as the active regions. The method was applied in simulated and real data, its superiority is shown by the receiver operating characteristic curves which were obtained using the simulated data.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Effect of endogenous hydrogen sulfide inhibition on structural and functional renal disturbances induced by gentamicin

Animal models of gentamicin nephrotoxicity present acute tubular necrosis associated with inflammation, which can contribute to intensify the renal damage. Hydrogen sulfide (H2S) is a signaling molecule involved in inflammation. We evaluated the effect of DL-propargylglycine (PAG), an inhibitor of endogenous H2S formation, on the renal damage induced by gentamicin. Male Wistar rats (N = 8) were injected with 40 mg/kg gentamicin (im) twice a day for 9 days, some of them also received PAG (N = 8, 10 mg·kg-1·day-1, ip). Control rats (N = 6) were treated with saline or PAG only (N = 4). Twenty-four-hour urine samples were collected one day after the end of these treatments, blood samples were collected, the animals were sacrificed, and the kidneys were removed for quantification of H2S formation and histological and immunohistochemical studies. Gentamicin-treated rats presented higher sodium and potassium fractional excretion, increased plasma creatinine [4.06 (3.00; 5.87) mg%] and urea levels, a greater number of macrophages/monocytes, and a higher score for tubular interstitial lesions [3.50 (3.00; 4.00)] in the renal cortex. These changes were associated with increased H2S formation in the kidneys from gentamicin-treated rats (230.60 ± 38.62 µg·mg protein-1·h-1) compared to control (21.12 ± 1.63) and PAG (11.44 ± 3.08). Treatment with PAG reduced this increase (171.60 ± 18.34), the disturbances in plasma creatinine levels [2.20 (1.92; 4.60) mg%], macrophage infiltration, and score for tubular interstitial lesions [2.00 (2.00; 3.00)]. However, PAG did not interfere with the increase in fractional sodium excretion provoked by gentamicin. The protective effect of PAG on gentamicin nephrotoxicity was related, at least in part, to decreased H2S formation.