Could a lipid oxidative biomarker be applied to improve risk stratification in prevention for cardiovascular disease?

BACKGROUND AND AIMS: There is evidence demonstrating the influence of oxidative stress on atherosclerosis progression and cardiovascular diseases (CVD). However, different from dyslipidemia and inflammation, nonoxidative biomarkers have been applied to analyze the primary or secondary prevention treatment of these patients. Many factors can explain this paradox: the higher complexity of the methods applied to quantify oxidative markers, the high variability observed among the studies, lack of reference values and weak correlation with clinical endpoints. METHODS: In this review, data from 116 treatments in 55 studies that evaluated oxidative stress markers under the atherosclerotic context were included RESULTS: showed that antioxidant capacity measured as Ferric Reducing Antioxidant Power (FRAP), Superoxide Dismutase (SOD), Glutathione (GSH), Malondialdehyde (MDA), oxidized LDL (oxLDL) and Isoprostanes (F2-IsoP) were the oxidative markers more present. From them, MDA, IsoPs and oxLDL are directly formed from lipid oxidation, while FRAP, SOD and GSH have their values associated to general oxidative conditions. Among the lipid oxidative markers, MDA had the highest proportion among the treatments. A higher concentration of MDA (p»0.041) in patients with CVD (17.05 ± 37.24 mmol/L, n»51) was found than in healthy individuals (5.07 ± 7.54 mmol/L, n»21), despite the high general variability (235.85%). CONCLUSIONS: Multivariate analysis suggested that MDA was an independent factor compared with traditional markers used in the algorithms to stratify the patient's risk. Thus, it is necessary to achieve a reference value for patients under prevention, and correlate MDA increase according to the disease's progression before including it in the algorithms applied to estimate CVD risk.

Immunolocalization of melatonin receptors in bovine ovarian follicles and in vitro effects of melatonin on growth, viability and gene expression in secondary follicles.

This study aims to investigate the (1) expression of melatonin receptors types 1A/B (MTNR1A/B) in bovine ovaries and (2) the in vitro effects of melatonin on secondary follicle development, antrum formation, viability, and expression of messenger ribonucleic acid (mRNA) for superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase-1 (GPX1) and peroxiredoxin 6 (PRDX6). The expression of MTNR1A/B in bovine ovarian follicles was demonstrated by immunohistochemistry. To choose the most effective concentration of melatonin on follicular growth and viability, isolated secondary follicles were cultured individually at 38.5°C, with 5% CO2 in air, for 18 d in TCM-199+ alone or supplemented with 10-11, 10-9, 10-7 or 10-5 M melatonin. Then, melatonin receptor antagonist, luzindole, was tested to further evaluate the mechanisms of actions of melatonin, that is, the follicles were cultured in control medium alone or supplemented with 10-7 M melatonin, 10 µM luzindole and both 10-7 M melatonin and 10 µM luzindole. Follicular growth, morphology and antrum formation were evaluated at days 6, 12 and 18. At the end of culture, viability of secondary follicles was analyzed by calcein-AM and ethidium homodimer-1, and the relative levels of mRNA for SOD, CAT, GPX1 and PRDX6 were evaluated by real time polymerase chain reaction. Immunohistochemistry results showed expression of MTNR1A/B in oocyte and granulosa cells of primordial, primary, secondary and antral follicles. Secondary follicles cultured in medium supplemented with melatonin at different concentrations had well preserved follicles after 18 d of culture. Furthermore, follicles cultured in presence of 10-7 M melatonin presented significantly higher diameters than those cultured in other treatments. The presence of melatonin receptor antagonist, luzindole, blocked the effects of melatonin on follicular growth and viability. In addition, follicles cultured in medium containing only melatonin had significantly higher rates of antrum formation. Follicles cultured in medium containing only melatonin had higher relative levels of mRNA for CAT, SOD and PRDX-6 than those cultured with both melatonin and luzindole. Follicles cultured with luzindole only or both melatonin and luzindole had lower relative levels of mRNA for PRDX6 and GPX1 than those cultured control medium. In conclusion, melatonin promotes growth of bovine secondary follicles through its membrane-coupled receptors, while luzindole blocks the effects of melatonin on follicle growth and reduces the expression of antioxidant enzymes in cultured follicles.

Design and implementation of a low-cost multiparameter probe to evaluate the temporal variations of water quality conditions on an estuarine lagoon system.

The measurement of physicochemical variables to infer water quality is important since they help determine the distribution and abundance of aquatic organisms or pollution-related problems. Recently, the development of low-cost probes is a suitable alternative for continuous monitoring of these variables rather than the use of expensive instruments. In this work, a low-cost multiparameter probe (LCMP) has been developed to monitor water quality in an estuary located in Northwestern Mexico during a 3-month period. The LCMP integrates different sensors to an Arduino Nano microcontroller allowing to measure electrical conductivity, dissolved oxygen, pH, salinity, water temperature, and tide level. Data files were stored in a data logger system consisting of a secure digital (SD) card module and a real-time clock module coupled to the Arduino microcontroller. To ensure continuous operation, the system was powered by four 3.7 V, 10,000 mAh rechargeable LiPo batteries. All LCMP components were encapsulated in a polyvinyl chloride pipe. The results show that the LCMP had a good agreement with a commercial-grade multiparameter probe and was able to monitor continuously in hourly time steps. Finally, the LCMP proved to be an alternative for the establishment of coastal observatories, which has been deficient due to limited funding.

COVID-19: Rational discovery of the therapeutic potential of Melatonin as a SARS-CoV-2 main Protease Inhibitor.

The SARS-CoV-2 spread quickly across the globe. The World Health Organization (WHO) on March 11 declared COVID-19 a pandemic. The mortality rate, hospital disorders and incalculable economic and social damages, besides the unproven efficacy of the treatments evaluated against COVID-19, raised the need for immediate control of this disease. Therefore, the current study employed in silico tools to rationally identify new possible SARS-CoV-2 main protease (Mpro) inhibitors. That is an enzyme conserved among the coronavirus species; hence, the identification of an Mpro inhibitor is to make it a broad-spectrum drug. Molecular docking studies described the binding sites and the interaction energies of 74 Mpro-ligand complexes deposited in the Protein Data Bank (PDB). A structural similarity screening was carried out in order to identify possible Mpro ligands that show additional pharmacological properties against COVID-19. We identified 59 hit compounds and among them, melatonin stood out due to its prominent immunomodulatory and anti-inflammatory activities; it can reduce oxidative stress, defence cell mobility and efficiently combat the cytokine storm and sepsis. In addition, melatonin is an inhibitor of calmodulin, an essential intracellular component to maintain angiotensin-converting enzyme 2 (ACE-2) on the cell surface. Interestingly, one of the most promising hits in our docking study was melatonin. It revealed better interaction energy with Mpro compared to ligands in complexes from PDB. Consequently, melatonin can have response potential in early stages for its possible effects on ACE-2 and Mpro, although it is also promising in more severe stages of the disease for its action against hyper-inflammation. These results definitely do not confirm antiviral activity, but can rather be used as a basis for further preclinical and clinical trials.

Regularized quantile regression applied to genome-enabled prediction of quantitative traits.

Genomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qτ(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that "best" represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively.

Using artificial neural networks to select upright cowpea (Vigna unguiculata) genotypes with high productivity and phenotypic stability.

Cowpea (Vigna unguiculata) is grown in three Brazilian regions: the Midwest, North, and Northeast, and is consumed by people on low incomes. It is important to investigate the genotype x environment (GE) interaction to provide accurate recommendations for farmers. The aim of this study was to identify cowpea genotypes with high adaptability and phenotypic stability for growing in the Brazilian Cerrado, and to compare the use of artificial neural networks with the Eberhart and Russell (1966) method. Six trials with upright cowpea genotypes were conducted in 2005 and 2006 in the States of Mato Grosso do Sul and Mato Grosso. The data were subjected to adaptability and stability analysis by the Eberhart and Russell (1966) method and artificial neural networks. The genotypes MNC99-537F-4 and EVX91-2E-2 provided grain yields above the overall environment means, and exhibited high stability according to both methods. Genotype IT93K-93-10 was the most suitable for unfavorable environments. There was a high correlation between the results of both methods in terms of classifying the genotypes by their adaptability and stability. Therefore, this new approach would be effective in quantifying the GE interaction in upright cowpea breeding programs.

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs.

The aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genome-wide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: "weight", "fat", "loin", and "performance". These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS studies; accuracy measurements of the factors were similar to those obtained when the original traits were considered individually. The similarities between the top 10% of individuals selected by the factor, and those selected by the individual traits, were also satisfactory. Moreover, the estimated markers effects for the traits were similar to those found for the relevant factor.

Bayesian forecasting of temporal gene expression by using an autoregressive panel data approach.

We propose and evaluate a novel approach for forecasting gene expression over non-observed times in longitudinal trials under a Bayesian viewpoint. One of the aims is to cluster genes that share similar expression patterns over time and then use this similarity to predict relative expression at time points of interest. Expression values of 106 genes expressed during the cell cycle of Saccharomyces cerevisiae were used and genes were partitioned into five distinct clusters of sizes 33, 32, 21, 16, and 4. After removing the last observed time point, the agreements of signals (upregulated or downregulated) considering the predicted expression level were 72.7, 81.3, 76.2, 68.8, and 50.0%, respectively, for each cluster. The percentage of credibility intervals that contained the true values of gene expression for a future time was ~90%. The methodology performed well, providing a valid forecast of gene expression values by fitting an autoregressive panel data model. This approach is easily implemented with other time-series models and when Poisson and negative binomial probability distributions are assumed for the gene expression data.

Adaptability and phenotypic stability of common bean genotypes through Bayesian inference.

This study used Bayesian inference to investigate the genotype x environment interaction in common bean grown in Mato Grosso do Sul State, and it also evaluated the efficiency of using informative and minimally informative a priori distributions. Six trials were conducted in randomized blocks, and the grain yield of 13 common bean genotypes was assessed. To represent the minimally informative a priori distributions, a probability distribution with high variance was used, and a meta-analysis concept was adopted to represent the informative a priori distributions. Bayes factors were used to conduct comparisons between the a priori distributions. The Bayesian inference was effective for the selection of upright common bean genotypes with high adaptability and phenotypic stability using the Eberhart and Russell method. Bayes factors indicated that the use of informative a priori distributions provided more accurate results than minimally informative a priori distributions. According to Bayesian inference, the EMGOPA-201, BAMBUÍ, CNF 4999, CNF 4129 A 54, and CNFv 8025 genotypes had specific adaptability to favorable environments, while the IAPAR 14 and IAC CARIOCA ETE genotypes had specific adaptability to unfavorable environments.

Artificial intelligence in the selection of common bean genotypes with high phenotypic stability.

Artificial neural networks have been used for various purposes in plant breeding, including use in the investigation of genotype x environment interactions. The aim of this study was to use artificial neural networks in the selection of common bean genotypes with high phenotypic adaptability and stability, and to verify their consistency with the Eberhart and Russell method. Six trials were conducted using 13 genotypes of common bean between 2002 and 2006 in the municipalities of Aquidauana and Dourados. The experimental design was a randomized block with three replicates. Grain yield data were submitted to individual and joint variance analyses. The data were then submitted to analysis of adaptability and stability through the Eberhart and Russell and artificial neural network methods. There was high concordance between the methodologies evaluated for discrimination of phenotypic adaptability of common bean genotypes, indicating that artificial neural networks can be used in breeding programs. Based on both approaches, the genotypes Aporé, Rudá, and CNFv 8025 are recommended for use in unfavorable, general and favorable environments, respectively by the grain yield above the overall average of environments and high phenotypic stability.

Measurements of experimental precision for trials with cowpea (Vigna unguiculata L. Walp.) genotypes.

The aim of this study was to evaluate the suitability of statistics as experimental precision degree measures for trials with cowpea (Vigna unguiculata L. Walp.) genotypes. Cowpea genotype yields were evaluated in 29 trials conducted in Brazil between 2005 and 2012. The genotypes were evaluated with a randomized block design with four replications. Ten statistics that were estimated for each trial were compared using descriptive statistics, Pearson correlations, and path analysis. According to the class limits established, selective accuracy and F-test values for genotype, heritability, and the coefficient of determination adequately estimated the degree of experimental precision. Using these statistics, 86.21% of the trials had adequate experimental precision. Selective accuracy and the F-test values for genotype, heritability, and the coefficient of determination were directly related to each other, and were more suitable than the coefficient of variation and the least significant difference (by the Tukey test) to evaluate experimental precision in trials with cowpea genotypes.

Bayesian approach increases accuracy when selecting cowpea genotypes with high adaptability and phenotypic stability.

This study aimed to verify that a Bayesian approach could be used for the selection of upright cowpea genotypes with high adaptability and phenotypic stability, and the study also evaluated the efficiency of using informative and minimally informative a priori distributions. Six trials were conducted in randomized blocks, and the grain yield of 17 upright cowpea genotypes was assessed. To represent the minimally informative a priori distributions, a probability distribution with high variance was used, and a meta-analysis concept was adopted to represent the informative a priori distributions. Bayes factors were used to conduct comparisons between the a priori distributions. The Bayesian approach was effective for selection of upright cowpea genotypes with high adaptability and phenotypic stability using the Eberhart and Russell method. Bayes factors indicated that the use of informative a priori distributions provided more accurate results than minimally informative a priori distributions.

Analysis of sexual function in kidney transplanted men.

INTRODUCTION: Sexual dysfunction among renal failure and kidney transplant patients remains controversial. The aim of this study was to evaluate sexual functions of men on hemodialysis compared with patients undergoing kidney transplantation. MATERIALS AND METHODS: Our study was based on 36 end-stage renal disease (ESRD) patients undergoing hemodialysis versus 32 kidney transplanted patients. A control group was composed of 23 healthy patients. The patients underwent an anamnesis, a physical examination, and the International Index of Erectile Function about sexual performance. Statistical analysis was performed by Student's t-test or the chi-square test with the level of significance set at P < .05. Data are reported as mean values +/- standard error of the means. RESULTS: The mean scores of the control, ESRD, and transplanted group were, respectively: for erectile function, 27.4 +/- 0.5, 22.4 +/- 1.3, 23.4 +/- 1.3; for orgasmic function, 9.5 +/- 0.1, 7.6 +/- 0.5, 8.9 +/- 0.5; for sexual desire function 9.4 +/- 0.1, 7.1 +/- 0.3, 9.0 +/- 0.5; for intercourse satisfaction 12.8 +/- 0.3, 9.4 +/- 0.7, 11.0 +/- 0.7; and for satisfaction related to sexual life 9.2 +/- 0.2, 7.7 +/- 0.3, 8.6 +/- 0.6, proving that there were significant differences regarding orgasmic function, sexual desire, and intercourse satisfaction. CONCLUSION: It was possible to conclude from our study that kidney transplants do improve sexual function of patients with ESRD on hemodialysis.