Accuracy of serological tests for bovine brucellosis: A systematic review and meta-analysis.

The direct methods for diagnosis of bovine brucellosis have several limitations, therefore serological tests are the basis for the diagnosis of the disease. However, a meta-analysis estimating the diagnostic sensitivity (DSe) and diagnostic specificity (DSp) on the main tests used in bovine brucellosis control programs worldwide has not been performed. This systematic review and meta-analysis aimed to estimate the DSe, DSp and thereby accuracy of serological tests individually used in the diagnosis of bovine brucellosis. The databases CABI, Cochrane Library, PubMed/MEDLINE, SciELO, Scopus and Web of Science were used to select articles. The search resulted in 5308 studies, of which 71 were selected for systematic review using quality assessment tools and 65 studies were included in the meta-analysis. For the meta-analysis, 178 assays and 11 different serological tests were considered. To estimate DSe and DSp of the tests, studies were divided according to animal selection for the studies: (1) studies that carried out a random or consecutive selection of participants (noncasecontrol studies) and (2) all studies, including casecontrol studies. Considering only the non-case-control studies to estimate the DSe, the tests that exhibited the best and worst performance were the iELISA test (indirect enzyme immunoassay - bacterial suspension as antigen - BS) (96.5%, 95% CI: 94.1-97.9%) and 2ME (2- mercaptoethanol test) (85.0%, 95% CI: 79.6-89.1%), respectively; while for DSp, the FPA (fluorescence polarization assay) (99, 7%, 95% CI: 99.5-99.8%) and PCFIA tests (protein concentration fluorescence immunoassay) (78.5%, 95% CI: 70.0-85.1%) showed better and worse performance, respectively. Overall, our results showed an overestimation in the DSe and DSp of the eleven serological tests assessed when casecontrol studies were included in the meta-analysis, which is a concern considering its impacts on the time and costs associated with populational diagnosis of the diseases, since several of these tests are routinely used in the control and eradication programs of bovine brucellosis worldwide. Furthermore, the tests that exhibited the best DSe and DSp, iELISA (BS) and FPA, respectively, are relatively easy to perform and interpret and the test which showed the best overall accuracy was FPA.

HLA-B27 positivity in a large miscegenated population of 5,389,143 healthy blood marrow donors in Brazil

Abstract Background The prevalence of HLA-B27 gene positivity in healthy Caucasian communities varies between 8 and 14%. However, there is a lack of information in countries with a high rate of miscegenation, such as Brazil. Aim To estimate the frequency of HLA-B27 in the Brazilian general population using a large national registry database. Methods This is a cross-sectional ecological study using the Brazilian Registry of Volunteer Bone Marrow Donors (REDOME) database on HLA-B27 allelic frequency and proportion of positives of healthy donors (18-60 years old). Data were analyzed according to sex, age, race (by self-reported skin color recommended by the Brazilian Institute of Geography and Statistics - IBGE), and geographic region of residence. Results From 1994 to 2022, a total of 5,389,143 healthy bone marrow donors were included. The overall positivity for HLA-B27 was 4.35% (CI 95% 4.32-4.37%), regardless of sex and age (57.2% were women, mean age was 41.7yo). However, there was a difference between races: 4.85% in Whites; 2.92% in Blacks; 3.76% in Pardos (Browns i.e. mixed races); 3.95% in Amarelos (Yellows i.e. Asian Brazilians); and 3.18% in Indigenous. There was also a difference regarding geographic region of residence (North: 3.62%; Northeast: 3.63%; Southeast: 4.29%; Midwest: 4.5% and 5.25% in South). The homozygosity rate for the HLA-B27 was 1.32% of all the positives and only 0.06% in the general population. Conclusions Our findings provide the first Brazilian national prevalence for HLA-B27 in 4.35%. There is a gradient gene positivity from North to South, suggesting that the genetic background related to the miscegenation due to colonization, slavery, and some later waves of immigration together with internal migratory flows, could explain our findings.

Shrinkage in the Bayesian analysis of the GGE model: A case study with simulation.

The genotype main effects plus the genotype × environment interaction effects model has been widely used to analyze multi-environmental trials data, especially using a graphical biplot considering the first two principal components of the singular value decomposition of the interaction matrix. Many authors have noted the advantages of applying Bayesian inference in these classes of models to replace the frequentist approach. This results in parsimonious models, and eliminates parameters that would be present in a traditional analysis of bilinear components (frequentist form). This work aims to extend shrinkage methods to estimators of those parameters that composes the multiplicative part of the model, using the maximum entropy principle for prior justification. A Bayesian version (non-shrinkage prior, using conjugacy and large variance) was also used for comparison. The simulated data set had 20 genotypes evaluated across seven environments, in a complete randomized block design with three replications. Cross-validation procedures were conducted to assess the predictive ability of the model and information criteria were used for model selection. A better predictive capacity was found for the model with a shrinkage effect, especially for unorthogonal scenarios in which more genotypes were removed at random. In these cases, however, the best fitted models, as measured by information criteria, were the conjugate flat prior. In addition, the flexibility of the Bayesian method was found, in general, to attribute inference to the parameters of the models which related to the biplot representation. Maximum entropy prior was the more parsimonious, and estimates singular values with a greater contribution to the sum of squares of the genotype + genotype × environmental interaction. Hence, this method enabled the best discrimination of parameters responsible for the existing patterns and the best discarding of the noise than the model assuming non-informative priors for multiplicative parameters.

The Brazilian Society of Rheumatology guidelines for axial spondyloarthritis - 2019.

Spondyloarthritis is a group of chronic inflammatory systemic diseases characterized by axial and/or peripheral joints inflammation, as well as extra-articular manifestations. The classification axial spondyloarthritis is adopted when the spine and/or the sacroiliac joints are predominantly involved. This version of recommendations replaces the previous guidelines published in May 2013.A systematic literature review was performed, and two hundred thirty-seven studies were selected and used to formulate 29 recommendations answering 15 clinical questions, which were divided into four sections: diagnosis, non-pharmacological therapy, conventional drug therapy and biological therapy. For each recommendation the level of evidence supporting (highest available), the strength grade according to Oxford, and the degree of expert agreement (inter-rater reliability) is informed.These guidelines bring evidence-based information on clinical management of axial SpA patients, including, diagnosis, treatment, and prognosis.

The Brazilian Society of Rheumatology guidelines for axial spondyloarthritis - 2019

Abstract Spondyloarthritis is a group of chronic inflammatory systemic diseases characterized by axial and/or peripheral joints inflammation, as well as extra-articular manifestations. The classification axial spondyloarthritis is adopted when the spine and/or the sacroiliac joints are predominantly involved. This version of recommendations replaces the previous guidelines published in May 2013. A systematic literature review was performed, and two hundred thirty-seven studies were selected and used to formulate 29 recommendations answering 15 clinical questions, which were divided into four sections: diagnosis, non-pharmacological therapy, conventional drug therapy and biological therapy. For each recommendation the level of evidence supporting (highest available), the strength grade according to Oxford, and the degree of expert agreement (inter-rater reliability) is informed. These guidelines bring evidence-based information on clinical management of axial SpA patients, including, diagnosis, treatment, and prognosis.

Bayesian mapping of multiple traits in maize: the importance of pleiotropic effects in studying the inheritance of quantitative traits.

Pleiotropy has played an important role in understanding quantitative traits. However, the extensiveness of this effect in the genome and its consequences for plant improvement have not been fully elucidated. The aim of this study was to identify pleiotropic quantitative trait loci (QTLs) in maize using Bayesian multiple interval mapping. Additionally, we sought to obtain a better understanding of the inheritance, extent and distribution of pleiotropic effects of several components in maize production. The design III procedure was used from a population derived from the cross of the inbred lines L-14-04B and L-08-05F. Two hundred and fifty plants were genotyped with 177 microsatellite markers and backcrossed to both parents giving rise to 500 backcrossed progenies, which were evaluated in six environments for grain yield and its components. The results of this study suggest that mapping isolated traits limits our understanding of the genetic architecture of quantitative traits. This architecture can be better understood by using pleiotropic networks that facilitate the visualization of the complexity of quantitative inheritance, and this characterization will help to develop new selection strategies. It was also possible to confront the idea that it is feasible to identify QTLs for complex traits such as grain yield, as pleiotropy acts prominently on its subtraits and as this "trait" can be broken down and predicted almost completely by the QTLs of its components. Additionally, pleiotropic QTLs do not necessarily signify pleiotropy of allelic interactions, and this indicates that the pervasive pleiotropy does not limit the genetic adaptability of plants.

Análise bayesiana do modelo de herança monogênica no melhoramento vegetal: um exemplo com abobrinha / Bayesian analisys of monogenic inheritance model in plant breeding: a case study with zucchini

Uma estratégia comum em programas de melhoramento é conduzir estudos básicos de herança para investigar a hipótese de controle do caráter por um ou poucos genes de efeito principal, associados ou não a genes modificadores de pequeno efeito. Neste trabalho, foi utilizada a inferência bayesiana para ajustar modelos de herança genética aditiva-dominante a experimentos de genética vegetal com várias gerações. Densidades normais com médias associadas aos efeitos genéticos das gerações foram consideradas em um modelo linear em que a matriz de delineamento dos efeitos genéticos tinha coeficientes indeterminados (precisando ser estimada para cada indivíduo). A metodologia foi ilustrada com um conjunto de dados de um estudo de herança da partenocarpia em abobrinha (Cucurbita pepo L). Tal ajuste permitiu explicitar a distribuição a posteriori das probabilidades genotípicas. A análise corrobora resultados anteriores da literatura, porém foi mais eficiente que alternativas prévias que supunham a matriz de delineamento conhecida para as gerações. Conclui-se que a partenocarpia em abobrinha é governada por um gene principal com dominância parcial.

A common breeding strategy is to carry out basic studies to investigate the hypothesis of a single gene controlling the trait (major gene) with or without polygenes of minor effect. In this study we used Bayesian inference to fit genetic additive-dominance models of inheritance to plant breeding experiments with multiple generations. Normal densities with different means, according to the major gene genotype, were considered in a linear model in which the design matrix of the genetic effects had unknown coefficients (which were estimated in individual basis). An actual data set from an inheritance study of partenocarpy in zucchini (Cucurbita pepo L.) was used for illustration. Model fitting included posterior probabilities for all individual genotypes. Analysis agrees with results in the literature but this approach was far more efficient than previous alternatives assuming that design matrix was known for the generations. Partenocarpy in zucchini is controlled by a major gene with important additive effect and partial dominance.

Selection in several environments by BLP as an alternative to pooled anova in crop breeding / Seleção em diversos ambientes pelo BLP como alternativa à anava conjunta

Plant breeders often carry out genetic trials in balanced designs. That is not always the case with animal genetic trials. In plant breeding is usual to select progenies tested in several environments by pooled analysis of variance (ANOVA). This procedure is based on the global averages for each family, although genetic values of progenies are better viewed as random effects. Thus, the appropriate form of analysis is more likely to follow the mixed models approach to progeny tests, which became a common practice in animal breeding. Best Linear Unbiased Prediction (BLUP) is not a "method" but a feature of mixed model estimators (predictors) of random effects and may be derived in so many ways that it has the potential of unifying the statistical theory of linear models (Robinson, 1991). When estimates of fixed effects are present is possible to combine information from several different tests by simplifying BLUP, in these situations BLP also has unbiased properties and this lead to BLUP from straightforward heuristics. In this paper some advantages of BLP applied to plant breeding are discussed. Our focus is on how to deal with estimates of progeny means and variances from many environments to work out predictions that have "best" properties (minimum variance linear combinations of progenies' averages). A practical rule for relative weighting is worked out.

Os melhoristas de plantas em geral conduzem testes genéticos em delineamentos balanceados, ao contrário do que ocorre com o melhoramento animal. É possível selecionar progênies pela ANAVA conjunta, com base nas médias gerais de cada família. Sabe-se, no entanto, que os valores genéticos de progênies são melhor representados por efeitos aleatórios. As formas de análise dos testes de progênie que parecem mais apropriadas são as que seguem a metodologia de modelos mistos, como no melhoramento animal. Segundo Robinson (1991) o Melhor Preditor Linear Não-Viesado (do inglês, BLUP) não é um método, mas uma propriedade dos estimadores (preditores) dos efeitos aleatórios e pode ser derivada de tantas maneiras diferentes que tem o potencial de unificar as teorias estatísticas de modelos lineares. A presença de bons estimadores para os efeitos fixos e componentes da variância torna possível combinar informações de diferentes testes por algumas simplificações do BLUP. Este trabalho exemplifica as vantagens do Melhor Preditor Linear (BLP) aplicado ao melhoramento de plantas. Procurou-se ilustrar como proceder com estimativas de médias e de variâncias de progênies obtidas em diferentes ambientes para produzir preditores que tenham propriedades "melhor" (no sentido de variância mínima entre todas as combinações lineares entre as médias de progênies). Derivou-se uma regra prática para a produção dos pesos relativos de cada ambiente. O BLP, em alguns casos, é também não-viesado produzindo BLUPs a partir de lógica mais direta.