Post-natal heart adaptation in a knock-in mouse model of calsequestrin 2-linked recessive catecholaminergic polymorphic ventricular tachycardia

Cardiac calsequestrin (CASQ2) contributes to intracellular Ca²⁺ homeostasis by virtue of its low-affinity/high-capacity Ca²⁺ binding properties, maintains sarcoplasmic reticulum (SR) architecture and regulates excitation–contraction coupling, especially or exclusively upon β-adrenergic stimulation. Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an inherited arrhythmogenic disease associated with cardiac arrest in children or young adults. Recessive CPVT variants are due to mutations in the CASQ2 gene. Molecular and ultra-structural properties were studied in hearts of CASQ2^R33Q/R33Q and of CASQ2^−/− mice from post-natal day 2 to week 8. The drastic reduction of CASQ2-R33Q is an early developmental event and is accompanied by down-regulation of triadin and junctin, and morphological changes of jSR and of SR-transverse-tubule junctions. Although endoplasmic reticulum stress is activated, no signs of either apoptosis or autophagy are detected. The other model of recessive CPVT, the CASQ2^−/− mouse, does not display the same adaptive pattern. Expression of CASQ2-R33Q influences molecular and ultra-structural heart development; post-natal, adaptive changes appear capable of ensuring until adulthood a new pathophysiological equilibrium.     

A plasma global metabolic profiling approach applied to an exercise study monitoring the effects of glucose, galactose and fructose drinks during post-exercise recovery

A global metabolic profiling methodology based on gas chromatography coupled to time-of-flight mass spectrometry (GC-TOFMS) for human plasma was applied to a human exercise study focused on the effects of beverages containing glucose, galactose, or fructose taken after exercise and throughout a recovery period of 6 h and 45 min. One group of 10 well trained male cyclists performed 3 experimental sessions on separate days (randomized, single center). After performing a standardized depletion protocol on a bicycle, subjects consumed one of three different beverages: maltodextrin (MD)+glucose (2:1 ratio), MD+galactose (2:1), and MD+fructose (2:1), consumed at an average of ～1.25 g of carbohydrate (CHO) ingested per minute. Blood was taken straight after exercise and every 45 min within the recovery phase. With the resulting blood plasma, insulin, free fatty acid (FFA) profile, glucose, and GC-TOFMS global metabolic profiling measurements were performed. The resulting profiling data was able to match the results obtained from the other clinical measurements with the addition of being able to follow many different metabolites throughout the recovery period. The data quality was assessed, with all the labelled internal standards yielding values of <15% CV for all samples (n=335), apart from the labelled sucrose which gave a value of 15.19%. Differences between recovery treatments including the appearance of galactonic acid from the galactose based beverage were also highlighted.     

Regression Calibration in Semiparametric Accelerated Failure Time Models

Summary In large cohort studies, it often happens that some covariates are expensive to measure and hence only measured on a validation set. On the other hand, relatively cheap but error‐prone measurements of the covariates are available for all subjects. Regression calibration (RC) estimation method ( Prentice, 1982 , Biometrika 69 , 331–342) is a popular method for analyzing such data and has been applied to the Cox model by Wang et al. (1997, Biometrics 53 , 131–145) under normal measurement error and rare disease assumptions. In this article, we consider the RC estimation method for the semiparametric accelerated failure time model with covariates subject to measurement error. Asymptotic properties of the proposed method are investigated under a two‐phase sampling scheme for validation data that are selected via stratified random sampling, resulting in neither independent nor identically distributed observations. We show that the estimates converge to some well‐defined parameters. In particular, unbiased estimation is feasible under additive normal measurement error models for normal covariates and under Berkson error models. The proposed method performs well in finite‐sample simulation studies. We also apply the proposed method to a depression mortality study.     

Correction for Covariate Measurement Error in Nonparametric Longitudinal Regression

Summary We introduce a correction for covariate measurement error in nonparametric regression applied to longitudinal binary data arising from a study on human sleep. The data have been surveyed to investigate the association of some hormonal levels and the probability of being asleep. The hormonal effect is modeled flexibly while we account for the error‐prone measurement of its concentration in the blood and the longitudinal character of the data. We present a fully Bayesian treatment utilizing Markov chain Monte Carlo inference techniques, and also introduce block updating to improve sampling and computational performance in the binary case. Our model is partly inspired by the relevance vector machine with radial basis functions, where usually very few basis functions are automatically selected for fitting the data. In the proposed approach, we implement such data‐driven complexity regulation by adopting the idea of Bayesian model averaging. Besides the general theory and the detailed sampling scheme, we also provide a simulation study for the Gaussian and the binary cases by comparing our method to the naive analysis ignoring measurement error. The results demonstrate a clear gain when using the proposed correction method, particularly for the Gaussian case with medium and large measurement error variances, even if the covariate model is misspecified.     

Influence of castration-induced testosterone and estradiol deficiency on obesity and glucose metabolism in male Göttingen minipigs

Low testosterone and estradiol concentrations are predictive for the development of the metabolic syndrome in men and women, respectively. The aim of this study was to investigate the influence of sex hormone deficiency on food intake, body weight, body composition and glucose metabolism in male Göttingen minipigs.Five adult male Göttingen minipigs were studied before castration (pre-cast), 10-18 days (post-cast 1) and 10-11 weeks (post-cast 2) after castration. Parameters of interest were food intake, body weight, body fat percentage and sex hormone concentrations. Furthermore glucose tolerance, glucagon suppression, insulin resistance, beta cell function and disposition index were evaluated by oral and intravenous glucose tolerance tests.Castration led to almost complete disappearance of circulating testosterone and estradiol and secondarily to increased food intake, body weight and body fat percentage. Ten-eighteen days sex hormone deficiency (post-cast 1) did not significantly change any of the investigated metabolic parameters compared to pre-cast levels. Ten weeks after castration (post-cast 2) significant insulin resistance, glucose intolerance and hyperglucagonemia was found, and the beta cell function and the disposition index both were decreased.In conclusion, castration-induced sex hormone deficiency in male Göttingen minipigs results in hyperphagia, obesity and disturbed glucose metabolism, which are some of the features typical for the human metabolic syndrome.     

Uncertainty in allometric exponent estimation: a case study in scaling metabolic rate with body mass

Many factors could influence the allometric scaling exponent β estimation, but have not been explored systematically. We investigated the influences of three factors on the estimate of β based on a data set of 626 species of basal metabolic rate and mass in mammals. The influence of sampling error was tested by re-sampling with different sample sizes using a Monte Carlo method. Small random errors were introduced to measured data to examine their influence on parameter estimations. The influence of analysis method was also evaluated by applying nonlinear and linear regressions to the original data. Results showed that a relative large sample size was required to lower statistical inference errors. When sample size n was 10% of the base population size (n=63), 35% of the samples supported β=2/3, 39% supported β=3/4, and 15% rejected β=0.711, even though the base population had a β=0.711. The controversy surrounding the estimation of β in the literature could be partially attributable to such small sample sizes in many studies. Measurement errors in body mass and base metabolic rate, especially in body mass, could largely increase alpha and beta errors. Analysis methods also affected parameter estimations. Nonlinear regressions provided better estimates of the scaling exponent that were significantly higher than these commonly estimated by linear regressions. This study demonstrated the importance of the quantity and quality of data as well as analysis method in power law analysis, raising caution in interpreting power law results. Meta-data synthesis using data from independent studies seems to be a proper approach in the future, but caution should be taken to make sure that such measurements are made using similar protocols.     

Noninvasive Estimation of Black Bear Abundance Incorporating Genotyping Errors and Harvested Bear

ABSTRACT Estimating black bear (Ursus americanus) population size is a difficult but important requirement when justifying harvest quotas and managing populations. Advancements in genetic techniques provide a means to identify individual bears using DNA contained in tissue and hair samples, thereby permitting estimates of population abundance based on established mark-capture-recapture methodology. We expand on previous noninvasive population-estimation work by geographically extending sampling areas (36,848 km²) to include the entire Northern Lower Peninsula (NLP) of Michigan, USA. We selected sampling locations randomly within biologically relevant bear habitat and used barbed wire hair snares to collect hair samples. Unlike previous noninvasive studies, we used tissue samples from harvested bears as an additional sampling occasion to increase recapture probabilities. We developed subsampling protocols to account for both spatial and temporal variance in sample distribution and variation in sample quality using recently published quality control protocols using 5 microsatellite loci. We quantified genotyping errors using samples from harvested bears and estimated abundance using statistical models that accounted for genotyping error. We estimated the population of yearling and adult black bears in the NLP to be 1,882 bears (95% CI = 1,389-2,551 bears). The derived population estimate with a 15% coefficient of variation was used by wildlife managers to examine the sustainability of harvest over a large geographic area.