Management of preterm labor: atosiban or nifedipine?

Preterm birth is strongly associated with neonatal death and long-term neurological morbidity. The purpose of tocolytic drug administration is to postpone threatening preterm delivery for 48 hours to allow maximal effect of antenatal corticosteroids and maternal transportation to a center with specialized neonatal care facilities. There is uncertainty about the value of atosiban (oxytocin receptor antagonist) and nifedipine (calcium channel blocker) as first-line tocolytic drugs in the management of preterm labor. For nifedipine, concerns have been raised about unproven safety, lack of placebo-controlled trials, and its off-label use. The tocolytic efficacy of atosiban has also been questioned because of a lack of reduction in neonatal morbidity. This review discusses the available evidence, the pros and cons of either drug and aims to provide information to support a balanced choice of first-line tocolytic drug: atosiban or nifedipine

Simultaneous QTL detection and genomic breeding value estimation using high density SNP chips

Background: The simulated dataset of the 13th QTL-MAS workshop was analysed to i) detect QTL and ii) predict breeding values for animals without phenotypic information. Several parameterisations considering all SNP simultaneously were applied using Gibbs sampling. Results: Fourteen QTL were detected at the different time points. Correlations between estimated breeding values were high between models, except when the model was used that assumed that all SNP effects came from one distribution. The model that used the selected 14 SNP found associated with QTL, gave close to unity correlations with the full parameterisations. Conclusions: Nine out of 18 QTL were detected, however the six QTL for inflection point were missed. Models for genomic selection were indicated to be fairly robust, e.g. with respect to accuracy of estimated breeding values. Still, it is worthwhile to investigate the number QTL underlying the quantitative traits, before choosing the model used for genomic selection

Comparison of laser Doppler flowmetry and thermometry in the postoperative monitoring of replantations

Reliable postoperative monitoring in microvascular surgery is necessary to improve the success rate of reexplorations following vascular compromise. Surface thermometry is known as an easy and inexpensive objective postoperative monitor and therefore is used by many microsurgeons. Reliability, however, is not satisfactory, and therefore several other instrumental methods have been tested of which laser Doppler flowmetry shows the most promising results. This study compared laser Doppler flowmetry to thermometry in the postoperative monitoring after replantation surgery. In 34 patients, 45 replantations and revascularizations were monitored by laser Doppler flowmetry and thermometry. A reliable alarm value of 10 PU was defined for replantations and revascularizations, with a sensitivity of 93% and a specificity of 94%. Thermometry showed a sensitivity of 84% and a specificity of 86% at 29°C

Estimation of genetic variance for macro- and micro-environmental sensitivity using double hierarchical generalized linear models

Background Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike?s information criterion using h-likelihood to select the best fitting model. Methods We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike?s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike?s information criterion the true genetic model was selected as the best statistical model in at least 90% of 100 replicates when the number of offspring per sire was 100. Application of the model to lactation milk yield in dairy cattle showed that genetic variance for micro- and macro-environmental sensitivities existed. Conclusion The algorithm and model selection criterion presented here can contribute to better understand genetic control of macro- and micro-environmental sensitivities. Designs or datasets should have at least 100 sires each with 100 offspring

Estimating genomic breeding values and detecting QTL using univariate and bivariate models

Background Genomic selection is particularly beneficial for difficult or expensive to measure traits. Since multi-trait selection is an important tool to deal with such cases, an important question is what the added value is of multi-trait genomic selection. Methods The simulated dataset, including a quantitative and binary trait, was analyzed with four univariate and bivariate linear models to predict breeding values for juvenile animals. Two models estimated variance components with REML using a numerator (A), or SNP based relationship matrix (G). Two SNP based Bayesian models included one (BayesA) or two distributions (BayesC) for estimated SNP effects. The bivariate BayesC model sampled QTL probabilities for each SNP conditional on both traits. Genotypes were permuted 2,000 times against phenotypes and pedigree, to obtain significance thresholds for posterior QTL probabilities. Genotypes were permuted rather than phenotypes, to retain relationships between pedigree and phenotypes, such that polygenic effects could still be estimated. Results Correlations between estimated breeding values (EBV) of different SNP based models, for juvenile animals, were greater than 0.93 (0.87) for the quantitative (binary) trait. Estimated genetic correlation was 0.71 (0.66) for model G (A). Accuracies of breeding values of SNP based models were for both traits highest for BayesC and lowest for G. Accuracies of breeding values of bivariate models were up to 0.08 higher than for univariate models. The bivariate BayesC model detected 14 out of 32 QTL for the quantitative trait, and 8 out of 22 for the binary trait. Conclusions Accuracy of EBV clearly improved for both traits using bivariate compared to univariate models. BayesC achieved highest accuracies of EBV and was also one of the methods that found most QTL. Permuting genotypes against phenotypes and pedigree in BayesC provided an effective way to derive significance thresholds for posterior QTL probabilitie

A Gene Co-Expression Network in Whole Blood of Schizophrenia Patients Is Independent of Antipsychotic-Use and Enriched for Brain-Expressed Genes

Despite large-scale genome-wide association studies (GWAS), the underlying genes for schizophrenia are largely unknown. Additional approaches are therefore required to identify the genetic background of this disorder. Here we report findings from a large gene expression study in peripheral blood of schizophrenia patients and controls. We applied a systems biology approach to genome-wide expression data from whole blood of 92 medicated and 29 antipsychotic-free schizophrenia patients and 118 healthy controls. We show that gene expression profiling in whole blood can identify twelve large gene co-expression modules associated with schizophrenia. Several of these disease related modules are likely to reflect expression changes due to antipsychotic medication. However, two of the disease modules could be replicated in an independent second data set involving antipsychotic-free patients and controls. One of these robustly defined disease modules is significantly enriched with brain-expressed genes and with genetic variants that were implicated in a GWAS study, which could imply a causal role in schizophrenia etiology. The most highly connected intramodular hub gene in this module (ABCF1), is located in, and regulated by the major histocompatibility (MHC) complex, which is intriguing in light of the fact that common allelic variants from the MHC region have been implicated in schizophrenia. This suggests that the MHC increases schizophrenia susceptibility via altered gene expression of regulatory genes in this network

Clinical Course of TGA After Arterial Switch Operation in the Current Era

Background: The number of patients with an arterial switch operation (ASO) for transposition of the great arteries (TGA) is steadily growing; limited information is available regarding the clinical course in the current era. Objectives: The purpose was to describe clinical outcome late after ASO in a national cohort, including survival, rates of (re-)interventions, and clinical events. Methods: A total of 1,061 TGA-ASO patients (median age 10.7 years [IQR: 2.0-18.2 years]) from a nationwide prospective registry with a median follow-up of 8.0 years (IQR: 5.4-8.8 years) were included. Using an analysis with age as the primary time scale, cumulative incidence of survival, (re)interventions, and clinical events were determined. Results: At the age of 35 years, late survival was 93% (95% CI: 88%-98%). The cumulative re-intervention rate at the right ventricular outflow tract and pulmonary branches was 36% (95% CI: 31%-41%). Other cumulative re-intervention rates at 35 years were on the left ventricular outflow tract (neo-aortic root and valve) 16% (95% CI: 10%-22%), aortic arch 9% (95% CI: 5%-13%), and coronary arteries 3% (95% CI: 1%-6%). Furthermore, 11% (95% CI: 6-16%) of the patients required electrophysiological interventions. Clinical events, including heart failure, endocarditis, and myocardial infarction occurred in 8% (95% CI: 5%-11%). Independent risk factors for any (re-)intervention were TGA morphological subtype (Taussig-Bing double outlet right ventricle [HR: 4.9, 95% CI: 2.9-8.1]) and previous pulmonary artery banding (HR: 1.6, 95% CI: 1.0-2.2). Conclusions: TGA-ASO patients have an excellent survival. However, their clinical course is characterized by an ongoing need for (re-)interventions, especially on the right ventricular outflow tract and the left ventricular outflow tract indicating a strict lifelong surveillance, also in adulthood.</p

Gene expression patterns in four brain areas associate with quantitative measure of estrous behavior in dairy cows

<p>Abstract</p> <p>Background</p> <p>The decline noticed in several fertility traits of dairy cattle over the past few decades is of major concern. Understanding of the genomic factors underlying fertility, which could have potential applications to improve fertility, is very limited. Here, we aimed to identify and study those genes that associated with a key fertility trait namely estrous behavior, among genes expressed in four bovine brain areas (hippocampus, amygdala, dorsal hypothalamus and ventral hypothalamus), either at the start of estrous cycle, or at mid cycle, or regardless of the phase of cycle.</p> <p>Results</p> <p>An average heat score was calculated for each of 28 primiparous cows in which estrous behavior was recorded for at least two consecutive estrous cycles starting from 30 days post-partum. Gene expression was then measured in brain tissue samples collected from these cows, 14 of which were sacrificed at the start of estrus and 14 around mid cycle. For each brain area, gene expression was modeled as a function of the orthogonally transformed average heat score values using a Bayesian hierarchical mixed model. Genes whose expression patterns showed significant linear or quadratic relationships with heat scores were identified. These included genes expected to be related to estrous behavior as they influence states like socio-sexual behavior, anxiety, stress and feeding motivation (<it>OXT, AVP, POMC, MCHR1</it>), but also genes whose association with estrous behavior is novel and warrants further investigation.</p> <p>Conclusions</p> <p>Several genes were identified whose expression levels in the bovine brain associated with the level of expression of estrous behavior. The genes <it>OXT </it>and <it>AVP </it>play major roles in regulating estrous behavior in dairy cows. Genes related to neurotransmission and neuronal plasticity are also involved in estrous regulation, with several genes and processes expressed in mid-cycle probably contributing to proper expression of estrous behavior in the next estrus. Studying these genes and the processes they control improves our understanding of the genomic regulation of estrous behavior expression.</p