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OBJECTIVE: Our study aimed to revaluate the significant data from meta-analyses on genetic variations in immune mediators and the risk of prostate cancer (PCa) by Bayesian approaches. METHODS: We performed a search on the literature before September 5th, 2023, for meta-analytic studies on polymorphisms in immune mediator genes and the risk of PCa. Two Bayesian approaches were used to assess the level of noteworthiness in the meta-analytic data: the False-Positive Rate Probability (FPRP) and the Bayesian False Discovery Probability (BFDP) with a statistical power of 1.2 and 1.5 of Odds Ratio at a prior probability of 10-3 and 10-6. The quality evaluation of studies was performed with the Venice criteria. Gene-gene and protein-protein networks were designed for the genes and products enrolled in the results. RESULTS: As results, 18 meta-analyses on 17 polymorphisms in several immune mediator genes were included (IL1B rs16944/rs1143627, IL4 rs2243250/rs2227284/rs2070874, IL6 1800795/rs1800796/rs1800797, IL8 rs4073, IL10 rs1800896/rs1800871/rs1800872, IL18 rs1946518, COX2 rs2745557, TNFA rs361525 and PTGS2 rs20417/689470). The Bayesian calculations showed the rs1143627 and the rs1946518 polymorphisms in IL1B and IL18 genes, respectively, were noteworthy. The Venice criteria showed that only four studies received the highest level of evidence. The gene-gene and protein-protein networks reinforced the findings on IL1B and IL18 genes. CONCLUSION: In conclusion, this current Bayesian revaluation showed that the rs1143627 and the rs1946518 polymorphisms in the IL1B and IL18 genes, respectively, were noteworthy biomarker candidates for PCa risk.

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The blue shark Prionace glauca plays a critical role as a predator in marine ecosystems but is threatened by by-catch. To obtain more precise biological data, a Bayesian approach was used, and 536 vertebrae samples collected during 1 year at the landing stage called "Playita Mía" Manta, Ecuador, were analysed. The objective was to estimate the age and growth parameters of the species. The size of the specimens varied between 116 and 310 cm in total length (TL). Using a Bayesian approach based on the Markov Chain Monte Carlo (MCMC) method, growth parameters were evaluated. The von Bertalanffy model was the one that best fitted the data and provided more adequate estimates (females: L ∞ = 325.50 cm, L 0 = 53.23 cm, and k = 0.12 years; males: L ∞ = 331.47 cm, L 0 = 51.59 cm, k = 0.12 years -1; combined sexes: L ∞ = 329.65 cm, L 0 = 53.64 cm, k = 0.11 year-1). The results indicated that females and males have a similar growth, and that the species has a slow growth. Further studies using multi-model Bayesian approaches and covering a broader range of sizes in the Pacific Ocean are suggested. These studies will provide crucial information for the management and conservation of this species.

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In this paper, we propose a hierarchical Bayesian approach for modeling the evolution of the 7-day moving average for the number of deaths due to COVID-19 in a country, state or city. The proposed approach is based on a Gaussian process regression model. The main advantage of this model is that it assumes that a nonlinear function f used for modeling the observed data is an unknown random parameter in opposite to usual approaches that set up f as being a known mathematical function. This assumption allows the development of a Bayesian approach with a Gaussian process prior over f. In order to estimate the parameters of interest, we develop an MCMC algorithm based on the Metropolis-within-Gibbs sampling algorithm. We also present a procedure for making predictions. The proposed method is illustrated in a case study, in which, we model the 7-day moving average for the number of deaths recorded in the state of São Paulo, Brazil. Results obtained show that the proposed method is very effective in modeling and predicting the values of the 7-day moving average.

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This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner-Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.

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The present study introduces a new multivariate mixture cure rate model based on the Chen probability distribution to model recurrent event data in the presence of cure fraction. In this context, we provide an alternative for the use of some usual modeling approaches as the semiparametric Cox proportional hazards model commonly used in lifetime data analysis, considering a new bivariate parametric model to be used in the data analysis of bivariate lifetime data assuming a mixture structure for the bivariate data in presence of covariates, censored data and cure fraction. Under a Bayesian setting, the proposed methodology was considered to analyze two real medical datasets from a retrospective cohort study related to leukemia and diabetic retinopathy diseases. The model validation process was addressed by using the Cox-Snell residuals, which allowed us to identify the suitability of the new proposed mixture cure rate model.

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This study was undertaken to compare different non-linear models for fitting growth curves of Polled Nellore animals as well as to estimate genetic parameters for the components of the growth curve. The study involved body weight-age data of 6,717 Polled Nellore cattle from birth to 650 days of age, which belonged to the Brazilian Association of Zebu Breeders (ABCZ), corresponding to the period from 1980 to 2011. Four non-linear models (Brody, Bertalanffy, Logistic, and Gompertz) were fitted and compared by the adjusted coefficient of determination (R2adj), mean absolute deviation of residuals (MAD), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC). To estimate the genetic parameters and genetic values of asymptotic weight (A), integration constant (B), and maturation rate (K), the Bayesian inference method was adopted. The Brody model showed the lowest values of MAD, RMSE, AIC, and BIC and the highest R2adj. Heritability estimates for parameters A, B, and K were 0.11, 0.16, and 0.30, respectively, whereas genetic correlations were 0.01 (A-B), -0.91 (A-K), and 0.24 (B-K). The Brody model provided the best fit. The K parameter shows enough genetic variability for selection in the herd. Heavier animals in adulthood tend to exhibit lower growth rates. Despite the low heritability estimate of parameter A, there were genetic gains, indicating that selection is being efficient on asymptotic weight.(AU)

O objetivo deste estudo foi comparar diferentes modelos não lineares para o ajuste das curvas de crescimento de animais da raça Nelore Mocho e estimar os parâmetros genéticos para os componentes da curva de crescimento. Foram utilizados dados de peso corporal-idade do nascimento aos 650 dias de idades de 6.717 bovinos da raça Nelore Mocho, pertencentes à Associação Brasileira de Criadores de Zebu (ABCZ), referentes ao período de 1980 e 2011. Quatro modelos não lineares (Brody, Bertalanffy, Logístico e Gompertz) foram ajustados e comparados pelo coeficiente de determinação ajustado (R2adj), desvio médio absoluto dos resíduos (DMA), raiz quadrada do quadrado médio do resíduo (RMSE), critério de informação de Akaike (AIC) e o critério de informação bayesiano (BIC). Para estimativas dos parâmetros genéticos e valores genéticos do peso assintótico (A), constante de integração (B) e taxa de maturação (K), utilizou-se o método de inferência Bayesiana. O modelo Brody apresentou os menores valores de DMA, RMSE, AIC e BIC e o maior R2adj. As estimativas de herdabilidade foram 0,11; 0,16 e 0,30 para os parâmetros A, B e K, respectivamente, enquanto as correlações genéticas foram de 0,01 (A-B), -0,91 (A-K) e 0,24 (B-K). Constatou-se que o modelo Brody forneceu o melhor ajuste. O parâmetro K apresenta variabilidade genética suficiente para seleção no rebanho. Animais com maior peso na idade adulta tendem a apresentar menores taxas de crescimento. Apesar da baixa estimativa de herdabilidade do parâmetro A, observou-se ganhos genéticos, indicando que a seleção está sendo eficiente sobre o peso assintótico.(AU)

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Gold mining is a significant source of metal(loid)s released into the environment. It is an issue of concern due to the potential adverse health effects associated with exposure to toxic elements. This study aimed to assess the ecological and human health risk caused by heavy metal(loid)s exposure in river sediments in Ponce Enríquez, one of the most important mining sites in Ecuador. Concentrations of As, Cd, Cu, Pb, and Zn were evaluated in 172 sediment samples to determine the Potential ecological risk (RI) and the carcinogenic (CR) and non-carcinogenic risk (HQ). The human exposure to polluted sediments during recreational activities was computed using Bayesian probabilistic models. Residents were randomly surveyed to adjust the risk models to the specific population data. More than 68% of the sampling stations pose a severe As and Cd ecological risk index ([Formula: see text] > 320). Likewise, residents exposed to river sediments showed a non-acceptable carcinogenic risk by incidental ingestion, being As the primary contributor to overall cancer in both children and adults receptors. Moreover, non-carcinogenic risk through the incidental ingestion of sediments was above the safe limit for children. This is the first study conducted in a mining region in Ecuador that reveals the severe levels of ecological and human health risk to which the population is exposed. These results can be applied as a baseline to develop public health strategies to monitor and reduce the health hazards of the residents of mining communities.

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BACKGROUND: The utility of insulin-like growth factor type 1 (IGF-1) is well established in the diagnosis of growth hormone deficiency (GHD), whereas IGF-binding protein type 3 (IGFBP-3) has a more controversial role. Most studies evaluated the value of these peptides by assessing their sensitivity and specificity but not considering the low prevalence of GHD among short children (<2%). OBJECTIVE: To evaluate the utility of basal IGF-1 and IGFBP-3 values in the GHD diagnosis process with a Bayesian approach, based on pre- and post-test probability. METHODS: We determined ROC curves, sensitivity, specificity, and positive and negative predictive values for IGF-1 and IGFBP-3 obtained from patients with GHD (n = 48) and GH-sufficient children (n = 175). The data were also analyzed by classifying the children into early childhood and late childhood (girls and boys younger and older than 8 and 9 years, respectively). RESULTS: The area under the curve (AUC) of the receiver operating characteristic curve of IGF-1-SDS (standard deviation score) was greater than that of IGFBP-3-SDS (AUC 0.886 and 0.786, respectively, p = 0.001). In early childhood, the AUC of IGFBP-3-SDS was significantly improved (0.866) and similar to IGF-1-SDS (0.898). IGF-1-SDS, in comparison to IGFBP-3-SDS, had a greater sensitivity (92 vs. 45.8%, respectively), lower specificity (69 vs. 93.8%, respectively), and lower positive predictive value (5.7 vs. 13.1%, respectively), with similar negative predictive values. CONCLUSION: IGF-1-SDS is a useful screening tool in the diagnosis of GHD. Although IGFBP-3-SDS lacks sensitivity, its high specificity supports the role to confirm GHD in short children, especially in early childhood. This strategy could simplify and reduce the necessity of a second laborious and expensive GH stimulation test to confirm the diagnosis of GHD.

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This study aimed to model and compare the shade-seeking behavior and grazing activity of Pantaneira and Girolando heifers by time of day. Eight heifers were used, four Pantaneira and four Girolando (3/4 Holstein × 1/4 Gir). The animals were about 18 months old and weighed 328 ± 14 kg (Pantaneira) and 430 ± 28 kg (Girolando). Shade-seeking behavior and grazing activity were recorded during the summer from 06:00 to 18:00 h in 10-min intervals, totaling 288 observations for each breed. A binary regression model was developed by means of a Bayesian approach considering logit, probit, cloglog, and Cauchy link functions. The most parsimonious model was constructed using the Cauchy function. The variables varied according to the time of day. Heifers of both breeds were more likely to graze toward the end of the day, when ambient temperatures were lower. Girolando heifers were more likely to seek shade between 06:00 and 14:00 h. From 12:00 to 13:00, the probability of seeking shade between breeds differed 22 percentage points. Pantaneira heifers were more likely to graze from 06:00 to 13:00 h, and their probability of grazing at 11:00 h was 21 percentage points higher than that of Girolando heifers. The results suggest that Pantaneira heifers are more heat-tolerant.

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Mosquito-borne diseases affect millions of individuals worldwide; the area of endemic transmission has been increasing due to several factors linked to globalization, urban sprawl, and climate change. The Aedes aegypti mosquito plays a central role in the dissemination of dengue, Zika, chikungunya, and urban yellow fever. Current preventive measures include mosquito control programs; however, identifying high-risk areas for mosquito infestation over a large geographic region based only on field surveys is labor-intensive and time-consuming. Thus, the objective of this study was to assess the potential of remote satellite images (WorldView) for determining land features associated with Ae. aegypti adult infestations in São José do Rio Preto/SP, Brazil. We used data from 60 adult mosquito traps distributed along four summers; the remote sensing images were classified by land cover types using a supervised classification method. We modeled the number of Ae. aegypti using a Poisson probability distribution with a geostatistical approach. The models were constructed in a Bayesian context using the Integrated nested Laplace Approximations and Stochastic Partial Differential Equation method. We showed that an infestation of Ae. aegypti adult mosquitoes was positively associated with the presence of asbestos roofing and roof slabs. This may be related to several other factors, such as socioeconomic or environmental factors. The usage of asbestos roofing may be more prevalent in socioeconomically poor areas, while roof slabs may retain rainwater and contribute to the generation of temporary mosquito breeding sites. Although preliminary, our results demonstrate the utility of satellite remote sensing in identifying landscape differences in urban environments using a geostatistical approach, and indicated directions for future research. Further analyses including other variables, such as land surface temperature, may reveal more complex relationships between urban mosquito micro-habitats and land cover features.

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