Prevalence and Impact of Infection during Extracorporeal Membrane Oxygenation in Oncologic Patients: A Retrospective Analysis of the Extracorporeal Life Support Organization (ELSO) Registry.

Background: Extracorporeal membrane oxygenation (ECMO) is widely utilized for severe cardiopulmonary insufficiency, but its application to the oncologic population has been debated given concern for increased risk of infection. This study aims to analyze the implications of infections acquired during ECMO runs in patients with malignancy. Methods: The Extracorporeal Life Support Organization (ELSO) database was queried for patients with an International Classification of Diseases code of neoplasms over the last two decades (2000-2019). Culture-proven infections during ECMO runs were analyzed and compared to previously reported data for all ECMO runs. Results: Two thousand, seven hundred and fifty-seven patients met inclusion criteria. Infection acquired during ECMO run was found in 687 patients, a significantly greater proportion compared to all ECMO runs (24.9% vs 11.7%; P = .001). Adult patients had a significantly higher rate of infection (27.0%; P < .001) compared to neonatal (11.0%) and pediatric (21.4%) patients. Prevalence of infection was highest in pulmonary ECMO (29.0%), while the infection rate standardized with ECMO duration was highest in extracorporeal cardiopulmonary resuscitation (55.03/1000-day ECMO run). Compared with ECMO for all diagnoses, the prevalence of Candida and Klebsiella infection was significantly higher in adult and pediatric oncologic patients. Regardless of the pathogen, the presence of infection was not associated with lower survival (38.6% vs 40.0%; P = .522). Conclusions: Oncologic patients had a significantly higher infection rate while on ECMO compared with the general ECMO population. However, the prognostic impact of these infections was minimal, thus ECMO should not be withheld in oncologic patients solely with concern for infection.

Genetic analysis of egg production traits in turkeys (Meleagris gallopavo) using a single-step genomic random regression model.

BACKGROUND: Egg production traits are economically important in poultry breeding programs. Previous studies have shown that incorporating genomic data can increase the accuracy of genetic prediction of egg production. Our objective was to estimate the genetic and phenotypic parameters of such traits and compare the prediction accuracy of pedigree-based random regression best linear unbiased prediction (RR-PBLUP) and genomic single-step random regression BLUP (RR-ssGBLUP). Egg production was recorded on 7422 birds during 24 consecutive weeks from first egg laid. Hatch-week of birth by week of lay and week of lay by age at first egg were fitted as fixed effects and body weight as a covariate, while additive genetic and permanent environment effects were fitted as random effects, along with heterogeneous residual variances over 24 weeks of egg production. Predictions accuracies were compared based on two statistics: (1) the correlation between estimated breeding values and phenotypes divided by the square root of the trait heritability, and (2) the ratio of the variance of BLUP predictions of individual Mendelian sampling effects divided by one half of the estimate of the additive genetic variance. RESULTS: Heritability estimates along the production trajectory obtained with RR-PBLUP ranged from 0.09 to 0.22, with higher estimates for intermediate weeks. Estimates of phenotypic correlations between weekly egg production were lower than the corresponding genetic correlation estimates. Our results indicate that genetic correlations decreased over the laying period, with the highest estimate being between traits in later weeks and the lowest between early weeks and later ages. Prediction accuracies based on the correlation-based statistic ranged from 0.11 to 0.44 for RR-PBLUP and from 0.22 to 0.57 for RR-ssGBLUP using the correlation-based statistic. The ratios of the variances of BLUP predictions of Mendelian sampling effects and one half of the additive genetic variance ranged from 0.17 to 0.26 for RR-PBLUP and from 0.17 to 0.34 for RR-ssGBLUP. Although the improvement in accuracies from RR-ssGBLUP over those from RR-PBLUP was not uniform over time for either statistic, accuracies obtained with RR-ssGBLUP were generally equal to or higher than those with RR-PBLUP. CONCLUSIONS: Our findings show the potential advantage of incorporating genomic data in genetic evaluation of egg production traits using random regression models, which can contribute to the genetic improvement of egg production in turkey populations.

Genetic evaluation of Atlantic salmon for growth traits incorporating SNP markers.

Genetic evaluations of individual fish were calculated for growth traits in North American Atlantic salmon with and without inclusion of genetic markers. The number of SNP markers was reduced to 6,000 and further to 270 in order to reduce the problem of overparameterization. SNP genotypes were predicted for all ungenotyped animals in the pedigree. Analysis of traits used a model with polygenic effects and SNP markers together. Polygenic effects refer to the additive genetic effects that remain after accounting for SNP genotypes. SNP marker genotypes were included as covariates to evaluate fish for growth traits (weight and length) in different environments (freshwater and seawater) with genders separated. Including regressions on SNP marker genotypes reduced the sum of squares of residuals by 2.7%-12.5% and increased the variability of Mendelian sampling effects (i.e., within-family variation) compared to traditional animal model evaluations. Genetic evaluations may be carried out with a few hundred markers which may be more affordable for genotyping large numbers of fish.

Factors that influence adherence with disease-modifying therapy in MS.

BACKGROUND: The complexity and cost of injection treatment can represent a formidable challenge for patients affected by a chronic illness, particularly those whose treatment is primarily preventative and only modestly effective on the more conspicuous symptomatic aspects of the disease process. The aim of this investigation was to identify which factors most influenced nonadherent behavior with the available disease-modifying injection therapies for multiple sclerosis (MS). METHODS: A multicenter, observational (three-wave) study using surveys was developed and administered to patients with MS through the World Wide Web. Healthcare providers at 17 neurology clinics recruited patients for the study. RESULTS: A total of 798 patients responded to the baseline wave of the study (708 responded to all three waves). The nonadherence rates for all patients (missing one or more injections) across these waves remained relatively stable at 39%, 37%, and 36%, respectively. The most common reason participants listed for missing injections was that they simply forgot to administer the medication (58%). Other factors including injection-site reactions, quality of life, patients' perceptions on the injectable medications, hope, depression, and support were also assessed in relation to adherence. CONCLUSIONS: This study characterizes factors that are associated with failure to fully adhere with disease modifying injection therapy for MS and underscores the principles associated with optimizing adherence and its implications for effective treatment of the disease process in MS.

Non-additive genetic effects for fertility traits in Canadian Holstein cattle.

The effects of additive, dominance, additive by dominance, additive by additive and dominance by dominance genetic effects on age at first service, non-return rates and interval from calving to first service were estimated. Practical considerations of computing additive and dominance relationships using the genomic relationship matrix are discussed. The final strategy utilized several groups of 1000 animals (heifers or cows) in which all animals had a non-zero dominance relationship with at least one other animal in the group. Direct inversion of relationship matrices was possible within the 1000 animal subsets. Estimates of variances were obtained using Bayesian methodology via Gibbs sampling. Estimated non-additive genetic variances were generally as large as or larger than the additive genetic variance in most cases, except for non-return rates and interval from calving to first service for cows. Non-additive genetic effects appear to be of sizeable magnitude for fertility traits and should be included in models intended for estimating additive genetic merit. However, computing additive and dominance relationships for all possible pairs of individuals is very time consuming in populations of more than 200 000 animals.

Power of QTL detection by either fixed or random models in half-sib designs.

The aim of this study was to compare the variance component approach for QTL linkage mapping in half-sib designs to the simple regression method. Empirical power was determined by Monte Carlo simulation in granddaughter designs. The factors studied (base values in parentheses) included the number of sires (5) and sons per sire (80), ratio of QTL variance to total genetic variance (lambda= 0.1), marker spacing (10 cM), and QTL allele frequency (0.5). A single bi-allelic QTL and six equally spaced markers with six alleles each were simulated. Empirical power using the regression method was 0.80, 0.92 and 0.98 for 5, 10, and 20 sires, respectively, versus 0.88, 0.98 and 0.99 using the variance component method. Power was 0.74, 0.80, 0.93, and 0.95 using regression versus 0.77, 0.88, 0.94, and 0.97 using the variance component method for QTL variance ratios (lambda) of 0.05, 0.1, 0.2, and 0.3, respectively. Power was 0.79, 0.85, 0.80 and 0.87 using regression versus 0.80, 0.86, 0.88, and 0.85 using the variance component method for QTL allele frequencies of 0.1, 0.3, 0.5, and 0.8, respectively. The log10 of type I error profiles were quite flat at close marker spacing (1 cM), confirming the inability to fine-map QTL by linkage analysis in half-sib designs. The variance component method showed slightly more potential than the regression method in QTL mapping.

Comparison between estimation of breeding values and fixed effects using Bayesian and empirical BLUP estimation under selection on parents and missing pedigree information.

Bayesian (via Gibbs sampling) and empirical BLUP (EBLUP) estimation of fixed effects and breeding values were compared by simulation. Combinations of two simulation models (with or without effect of contemporary group (CG)), three selection schemes (random, phenotypic and BLUP selection), two levels of heritability (0.20 and 0.50) and two levels of pedigree information (0% and 15% randomly missing) were considered. Populations consisted of 450 animals spread over six discrete generations. An infinitesimal additive genetic animal model was assumed while simulating data. EBLUP and Bayesian estimates of CG effects and breeding values were, in all situations, essentially the same with respect to Spearman's rank correlation between true and estimated values. Bias and mean square error (MSE) of EBLUP and Bayesian estimates of CG effects and breeding values showed the same pattern over the range of simulated scenarios. Methods were not biased by phenotypic and BLUP selection when pedigree information was complete, albeit MSE of estimated breeding values increased for situations where CG effects were present. Estimation of breeding values by Bayesian and EBLUP was similarly affected by joint effect of phenotypic or BLUP selection and randomly missing pedigree information. For both methods, bias and MSE of estimated breeding values and CG effects substantially increased across generations.