Effect of Brazil nut supplementation on the blood levels of selenium and glutathione peroxidase in hemodialysis patients

Objective: In patients who have undergone hemodialysis, large amounts of reactive oxygen species (ROS) are produced and, at higher concentrations, ROS are thought to be involved in the pathogenesis of cardiovascular disease. It has been proposed that selenium (Se) may exert an anti-atherogenic influence by reducing oxidative stress. The richest known food source of selenium is the Brazil nut (Bertholletia excelsa, family Lecythidaceae), found in the Amazon region. We evaluated the effect of Brazil nut supplementation on blood levels of Se and glutathione peroxidase (GSH-Px) activity in patients on hemodialysis. Methods: A total of 81 patients on hemodialysis (52.0 +/- 15.2 y old, average time on dialysis 82.3 +/- 91.4 mo, body mass index 24.9 +/- 4.4 kg/m(2)) from the RenalCor and RenalVida Clinics in Rio de Janeiro, Brazil, were studied. All patients received one nut (around 5 g, averaging 58.1 mu g Se/g) a day for 3 mo. The Se concentrations in the nuts and in plasma and erythrocytes were determined by atomic absorption spectrophotometry with hydride generation (Hitachi, Z-500). GSH-Px levels were measured using Randox commercial kits. Results: Plasma Se (18.8 +/- 17.4 mu g/L) and erythrocyte (72.4 +/- 37.9 mg/L) levels were below the normal, range before nut supplementation. After supplementation, the plasma level increased to 104.0 +/- 65.0 mu g/L and erythrocytes to 244.1 +/- 119.5 mg/L (P<0.0001). The activity of GSH-Px also increased after supplementation, from 46.6 +/- 14.9 to 55.9 +/- 23.6 U/g of hemoglobin (P<0.0001). Before supplementation, 11% of patients had GSH-Px activity below the normal range (27.5-73.6 U/g of hemoglobin). After supplementation, all patients showed GSH-Px activity within the normal range. Conclusion: The data revealed that the investigated patients presented Se deficiency and that the consumption of only one Brazil nut a day (5 g) during 3 mo was effective to increase the Se concentration and GSH-Px activity in these patients, thus improving their antioxidant status. (C) 2010 Elsevier Inc. All rights reserved.

Methods for prediction of peptide binding to MHC molecules: A comparative study

Background: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. Materials and Methods: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. Results: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. Conclusion: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.

Computational methods for prediction of T-cell epitopes - a framework for modelling, testing, and applications

Computational models complement laboratory experimentation for efficient identification of MHC-binding peptides and T-cell epitopes. Methods for prediction of MHC-binding peptides include binding motifs, quantitative matrices, artificial neural networks, hidden Markov models, and molecular modelling. Models derived by these methods have been successfully used for prediction of T-cell epitopes in cancer, autoimmunity, infectious disease, and allergy. For maximum benefit, the use of computer models must be treated as experiments analogous to standard laboratory procedures and performed according to strict standards. This requires careful selection of data for model building, and adequate testing and validation. A range of web-based databases and MHC-binding prediction programs are available. Although some available prediction programs for particular MHC alleles have reasonable accuracy, there is no guarantee that all models produce good quality predictions. In this article, we present and discuss a framework for modelling, testing, and applications of computational methods used in predictions of T-cell epitopes. (C) 2004 Elsevier Inc. All rights reserved.

Early identification of leptospirosis-associated pulmonary hemorrhage syndrome by use of a validated prediction model

Objective: To identify prediction factors for the development of leptospirosis-associated pulmonary hemorrhage syndrome (LPHS). Methods: We conducted a prospective cohort study. The study comprised of 203 patients, aged >= 14 years, admitted with complications of the severe form of leptospirosis at the Emilio Ribas Institute of Infectology (Sao Paulo, Brazil) between 1998 and 2004. Laboratory and demographic data were obtained and the severity of illness score and involvement of the lungs and others organs were determined. Logistic regression was performed to identify independent predictors of LPHS. A prospective validation cohort of 97 subjects with severe form of leptospirosis admitted at the same hospital between 2004 and 2006 was used to independently evaluate the predictive value of the model. Results: The overall mortality rate was 7.9%. Multivariate logistic regression revealed that five factors were independently associated with the development of LPHS: serum potassium (mmol/L) (OR = 2.6; 95% CI = 1.1-5.9); serum creatinine (mmol/L) (OR = 1.2; 95% CI = 1.1-1.4); respiratory rate (breaths/min) (OR = 1.1; 95% CI = 1.1-1.2); presenting shock (OR = 69.9; 95% CI = 20.1-236.4), and Glasgow Coma Scale Score (GCS) < 15 (OR = 7.7; 95% CI = 1.3-23.0). We used these findings to calculate the risk of LPHS by the use of a spreadsheet. In the validation cohort, the equation classified correctly 92% of patients (Kappa statistic = 0.80). Conclusions: We developed and validated a multivariate model for predicting LPHS. This tool should prove useful in identifying LPHS patients, allowing earlier management and thereby reducing mortality. (C) 2009 The British Infection Society. Published by Elsevier Ltd. All rights reserved.

Cytokine Profile in Pleural Fluid and Serum After Lung Transplantation

Background. Lung transplantation is the procedure of choice in several end-stage lung diseases. Despite improvements in surgical techniques and immunosuppression, early postoperative complications occur frequently. Objective. To evaluate the pleural inflammatory response after surgery. Patients and Methods. Twenty patients aged 18 to 63 years underwent unilateral or bilateral lung transplantation between August 2006 and March 2008. Proinflammatory cytokines interleukin (IL)-1 beta, IL-6, and IL-8 and vascular endothelial growth factor in pleural fluid and serum were analyzed. For cytokine evaluation, 20-mL samples of pleural fluid and blood (right, left, or both chest cavities) were obtained at 6 hours after surgery and daily until removal of the chest tube or for a maximum of 10 days. Data were analyzed using analysis of variance followed by the Holm-Sidak test. Results. All effusions were exudates according to Light`s criteria. Pleural fluid cytokine concentrations were highest at 6 hours after surgery. Serum concentrations were lower than those in pleural fluid, and IL-1 beta, IL-6, and IL-8 were undetectable at all time points. Conclusions. There is a peak concentration of inflammatory cytokines in the first 6 hours after transplantation, probably reflecting the effects of surgical manipulation. The decrease observed from postoperative day 1 and thereafter suggests the action of the immunosuppression agents and a temporal reduction in pleural inflammation.

Blood Cardioplegia with N-Acetylcysteine May Reduce Coronary Endothelial Activation and Myocardial Oxidative Stress

Objectives: The aim of this prospective study was to compare the efficacy of intermittent antegrade blood cardioplegia with or without n-acetylcysteine (NAC) in reducing myocardial oxidative stress and coronary endothelial activation. Methods: Twenty patients undergoing elective isolated coronary artery bypass graft surgery were randomly assigned to receive intermittent antegrade blood cardioplegia (32 degrees C-34 degrees C) with (NAC group) or without (control group) 300 mg of NAC. For these 2 groups we compared clinical outcome, hemodynamic evolution, systemic plasmatic levels of troponin I, and plasma concentrations of malondialdehyde (MDA) and soluble vascular adhesion molecule 1 (sVCAM-1) from coronary sinus blood samples. Results: Patient demographic characteristics and operative and postoperative data findings in both groups were similar. There was no hospital mortality. Comparing the plasma levels of MDA 10 min after the aortic cross-clamping and of sVCAM-1 30 min after the aortic cross-clamping period with the levels obtained before the aortic clamping period, we observed increases of both markers, but the increase was significant only in the control group (P=.039 and P=.064 for MDA; P=.004 and P=.064 for sVCAM- 1). In both groups there was a significant increase of the systemic serum levels of troponin I compared with the levels observed before cardiopulmonary bypass (P<.001), but the differences between the groups were not significant (P=.570). Conclusions: Our investigation showed that NAC as an additive to blood cardioplegia in patients undergoing on-pump coronary artery bypass graft surgery may reduce oxidative stress and the resultant coronary endothelial activation.

Long-Term Maternal Separation Differentially Alters Serum Corticosterone Levels and Blood Neutrophil Activity in A/J and C57BL/6 Mouse Offspring

Objectives: In this work, we searched for maternal separation effects on serum corticosterone levels and blood neutrophil activity in adult male A/J and C57BL/6 mouse offspring. Methods: 40 male A/J mice and 40 male C57BL/6 mice were divided within each strain into two groups. Mice in the maternal separation group were separated from their mothers (1 h/day) on postnatal days 0-13. Mice in the control group were left undisturbed. On postnatal day 45, blood was drawn from all mice and used to assess neutrophil activity by flow cytometry and serum corticosterone levels by radioimmunoassay. Results: The results showed that each mouse strain responded differently to maternal separation, but in both cases, serum corticosterone levels were affected. In both strains, adult mice that experienced maternal separation showed lower serum corticosterone levels than control mice. In relation to control mice kept together with their mothers, the levels of serum corticosterone were 72.7 and 36.36% lower in A/J and C57BL/6 mice submitted to maternal separation, respectively. The current findings showed that maternal separation increased neutrophil activity in mice after reaching adulthood. The observed effects, although in the same direction, differed between A/J and C57BL/6 mice. Maternal separation increased both the percentage and intensity of phagocytosis in C57BL/6 mice, but had no effects on A/J mice. Furthermore, maternal separation increased basal and propidium iodide-labeled Staphylococcus aureus-induced oxidative burst in A/J mice but did not affect oxidative burst in C57BL/6 mice. Finally, phorbol myristate acetate-induced oxidative burst increased in both strains. Conclusion: These results indicate that early maternal separation increases innate immunity, most likely by modifying hypothalamus-pituitary-adrenal axis activity. This suggests that maternal separation is a good model for stress which produces long-term neuroimmune changes whatever the animal species and strain used. Copyright (C) 2011 S. Karger AG, Basel

Genetic Covariation between Serum {gamma}-Glutamyltransferase Activity and Cardiovascular Risk Factors

Background: Several studies have shown that variation in serum gamma-glutamyltransferase (GGT) in the population is associated with risk of death or development of cardiovascular disease, type 2 diabetes, stroke, or hypertension. This association is only partly explained by associations between GGT and recognized risk factors. Our aim was to estimate the relative importance of genetic and environmental sources of variation in GGT as well as genetic and environmental sources of covariation between GGT and other liver enzymes and markers of cardiovascular risk in adult twin pairs. Methods: We recruited 1134 men and 2241 women through the Australian Twin Registry. Data were collected through mailed questionnaires, telephone interviews, and by analysis of blood samples. Sources of variation in GGT, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) and of covariation between GGT and cardiovascular risk factors were assessed by maximum-likelihood model-fitting. Results: Serum GGT, ALT, and AST were affected by additive genetic and nonshared environmental factors, with heritabilities estimated at 0.52, 0.48, and 0.32, respectively. One-half of the genetic variance in GGT was shared with ALT, AST, or both. There were highly significant correlations between GGT and body mass index; serum lipids, lipoproteins, glucose, and insulin; and blood pressure. These correlations were more attributable to genes that affect both GGT and known cardiovascular risk factors than to environmental factors. Conclusions: Variation in serum enzymes that reflect liver function showed significant genetic effects, and there was evidence that both genetic and environmental factors that affect these enzymes can also affect cardiovascular risk. (C) 2002 American Association for Clinical Chemistry.

Using data mining with time series data in short-term stocks prediction

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on short- time stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.

Detection of EBV-DNA in serum samples of an immunosuppressed child during a three years follow-up: association of clinical and PCR data with active infection

Twenty-four whole blood and serum samples were drawn from an eight year-old heart transplant child during a 36 months follow-up. EBV serology was positive for VCA-IgM and IgG, and negative for EBNA-IgG at the age of five years old when the child presented with signs and symptoms suggestive of acute infectious mononucleosis. After 14 months, serological parameters were: positive VCA-IgG, EBNA-IgG and negative VCA-IgM. This serological pattern has been maintained since then even during episodes suggestive of EBV reactivation. PCR amplified a specific DNA fragment from the EBV gp220 (detection limit of 100 viral copies). All twenty-four whole blood samples yielded positive results by PCR, while 12 out of 24 serum samples were positive. We aimed at analyzing whether detection of EBV-DNA in serum samples by PCR was associated with overt disease as stated by the need of antiviral treatment and hospitalization. Statistical analysis showed agreement between the two parameters evidenced by the Kappa test (value 0.750; p < 0.001). We concluded that detection of EBV-DNA in serum samples of immunosuppressed patients might be used as a laboratory marker of active EBV disease when a Real-Time PCR or another quantitative method is not available.

Canine visceral leishmaniasis: study of methods for the detection of IgG in serum and eluate samples

The Brazilian Ministry of Health recommends the culling and euthanasia of dogs with a positive serological test for canine visceral leishmaniasis (CVL). In the Municipality of Rio de Janeiro, the technique used for the diagnosis of CVL is the indirect fluorescent antibody test (IFAT), using blood samples eluted on filter paper (eluate). A dog survey was conducted over a period of one year in the region of Carapiá, in order to evaluate the diagnosis of CVL in this region. All animals underwent clinical examination, and blood samples (serum and eluate) were collected for analysis by enzyme immunoassay (ELISA) and IFAT. A skin biopsy was obtained for parasitological examination (culture). A total of 305 animals were studied and Leishmania chagasi was isolated from nine animals. Sensitivity and specificity were 100% and 96.6% for ELISA, respectively, 100% and 65.5% for IFAT (cut-off at a 1:40 dilution), 100% and 83.4% for IFAT (cut-off at a 1:80 dilution), and 22.2% and 97.0% for eluate IFAT. In conclusion, ELISA was the best tool for the diagnosis of CVL among the serological techniques tested. The present results suggest the need for a better evaluation of filter paper IFAT as the only diagnostic method for CVL in the Municipality of Rio de Janeiro.

Establishing a cell biology platform: isolation and preservation of human blood products

Dissertação para obtenção do Grau de Mestre em Genética Molecular e Biomedicina

Prevalence of antinuclear autoantibodies in the serum of normal blood dornors

OBJECTIVE:To examine the presence of serum antinuclear autoantibodies in a healthy population. METHODS: Serum of 500 normal blood donors between 18 and 60 years of age were tested for the presence of autoantibodies. Antinuclear antibodies were detected by indirect immunofluorescence technique using HEp-2 epithelial cells as the substrate. The presence of dnaN was detected by indirect immunofluorescence technique using Critidia lucillae as the substrate. Anti-SSA (RO), anti-SSB (LA), anti-Sm, and anti-RNP were determined by double radial immunodiffusion. RESULTS: In the evaluation of the presence of serum antibodies, antinuclear antibodies were detected in 22.6% of the sera. The presence of other antibodies was not significant. The majority of the titers were 1:40. CONCLUSION: The presence of autoantibodies is not necessarily pathologic and has to be related to the age group, gender, and clinical condition of the patient.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.