Long-term safety of unopposed estrogen used by women surviving myocardial infarction : 14 year follow-up of the ESPRIT randomised controlled trial

Peer reviewedPostprin

Rapid Prenatal Diagnosis and Exclusion of Epidermolysis Bullosa Using Novel Antibody Probes

Prenatal diagnosis of recessive dystrophic epidermolysis bullosa was successfully achieved at 19 weeks' gestation by indirect immunofluorescence examination of a fetal skin biopsy sample using the monoclonal antibody LH 7:2. The abortus displayed marked blistering and the diagnosis was confirmed by transmission electron microscopy (TEM). In 3 further pregnancies at risk for lethal junctional epidermolysis bullosa the diagnosis was excluded using the polyclonal antibody AA3. In all these studies the results were available within 4h of receiving the samples. These new techniques offer a quick and simple alternative to TEM for midtrimester prenatal diagnosis of 2 severe recessive forms of epidermolysis bullosa

Estimation of a semiparametric recursive bivariate probit model with nonparametric mixing

We consider an extension of the recursive bivariate probit model for estimating the effect of a binary variable on a binary outcome in the presence of unobserved confounders, nonlinear covariate effects and overdispersion. Specifically, the model consists of a system of two binary outcomes with a binary endogenous regressor which includes smooth functions of covariates, hence allowing for flexible functional dependence of the responses on the continuous regressors, and arbitrary random intercepts to deal with overdispersion arising from correlated observations on clusters or from the omission of non-confounding covariates. We fit the model by maximizing a penalized likelihood using an Expectation-Maximisation algorithm. The issues of automatic multiple smoothing parameter selection and inference are also addressed. The empirical properties of the proposed algorithm are examined in a simulation study. The method is then illustrated using data from a survey on health, aging and wealth

Susceptibility to multiple cutaneous basal cell carcinomas: significant interactions between glutathione S-transferase GSTM1 genotypes, skin type and male gender.

The factors that determine development of single and multiple primary cutaneous basal cell carcinomas (BCCs) are unclear. We describe a case-control study firstly, to examine the influence of allelism at the glutathione S-transferase GSTM1 and GSTT1 and cytochrome P450 CYP2D6 loci on susceptibility to these tumours and, secondly, to identify interactions between genotypes and relevant individual characteristics, such as skin type and gender. Frequency distributions for GSTM1 genotypes in cases and controls were not different, although the frequency of GSTM1 A/B was significantly lower (P = 0.048) in the multiple BCCs than in controls. We found no significant differences in the frequencies of GSTT1 and CYP2D6 genotypes in cases and controls. Interactions between genotypes were studied by comparing multinomial frequency distributions in mutually exclusive groups. These identified no differences between cases and controls for combinations of the putatively high risk GSTM1 null, GSTT1 null, CYP2D6 EM genotypes. Interactions between GSTM1 A/B and the CYP2D6 PM and GSTT1-positive genotypes were also not different. Frequency distributions of GSTM1 A/B with CYP2D6 EM in controls and multiple BCCs were significantly different (P = 0.033). The proportion of males in the multiple BCC group (61.3%) was greater than in controls (47.0%) and single BCC (52.2%), and the frequency of the combination GSTM1 null/male gender was significantly greater in patients with multiple tumours (P = 0.002). Frequency distributions of GSTM1 null/skin type 1 were also significantly different (P = 0.029) and the proportion of subjects who were GSTM1 null with skin type 1 was greater (P = 0.009) in the multiple BCC group. We examined the data for interactions between GSTM1 null/skin type 1/male gender by comparing frequency distributions of these factors in the single and multiple BCC groups. The distributions were almost significantly different (exact P = 0.051). No significant interactions between GSTT1 null or CYP2D6 EM and skin type 1 were identified. Comparisons of frequency distributions of smoking with the GSTM1 null, GSTT1 null and CYP2D6 EM genotypes identified no differences between patients with single and multiple tumours

Interleukin-33 rescues perivascular adipose tissue anticontractile function in obesity

Perivascular adipose tissue (PVAT) depots are metabolically active and play a major vasodilator role in healthy lean individuals. In obesity, they become inflamed and eosinophil-depleted and the anticontractile function is lost with the development of diabetes and hypertension. Moreover, eosinophil-deficient ΔdblGATA-1 mice lack PVAT anticontractile function and exhibit hypertension. Here, we have investigated the effects of inducing eosinophilia on PVAT function in health and obesity. Control, obese, and ΔdblGATA-1 mice were administered intraperitoneal injections of interleukin-33 (IL-33) for 5 days. Conscious restrained blood pressure was measured, and blood was collected for glucose and plasma measurements. Wire myography was used to assess the contractility of mesenteric resistance arteries. IL-33 injections induced a hypereosinophilic phenotype. Obese animals had significant elevations in blood pressure, blood glucose, and plasma insulin, which were normalized with IL-33. Blood glucose and insulin levels were also lowered in lean treated mice. In arteries from control mice, PVAT exerted an anticontractile effect on the vessels, which was enhanced with IL-33 treatment. In obese mice, loss of PVAT anticontractile function was rescued by IL-33. Exogenous application of IL-33 to isolated arteries induced a rapidly decaying endothelium-dependent vasodilation. The therapeutic effects were not seen in IL-33-treated ΔdblGATA-1 mice, thereby confirming that the eosinophil is crucial. In conclusion, IL-33 treatment restored PVAT anticontractile function in obesity and reversed development of hypertension, hyperglycemia, and hyperinsulinemia. These data suggest that targeting eosinophil numbers in PVAT offers a novel approach to the treatment of hypertension and type 2 diabetes in obesity

mlr3proba: An R Package for Machine Learning in Survival Analysis

As machine learning has become increasingly popular over the last few decades, so too has the number of machine learning interfaces for implementing these models. Whilst many R libraries exist for machine learning, very few offer extended support for survival analysis. This is problematic considering its importance in fields like medicine, bioinformatics, economics, engineering, and more. mlr3proba provides a comprehensive machine learning interface for survival analysis and connects with mlr3's general model tuning and benchmarking facilities to provide a systematic infrastructure for survival modeling and evaluation.Comment: Submitted to Bioinformatic

Comparison of generalized estimating equations and quadratic inference functions using data from the National Longitudinal Survey of Children and Youth (NLSCY) database

<p>Abstract</p> <p>Background</p> <p>The generalized estimating equations (GEE) technique is often used in longitudinal data modeling, where investigators are interested in population-averaged effects of covariates on responses of interest. GEE involves specifying a model relating covariates to outcomes and a plausible correlation structure between responses at different time periods. While GEE parameter estimates are consistent irrespective of the true underlying correlation structure, the method has some limitations that include challenges with model selection due to lack of absolute goodness-of-fit tests to aid comparisons among several plausible models. The quadratic inference functions (QIF) method extends the capabilities of GEE, while also addressing some GEE limitations.</p> <p>Methods</p> <p>We conducted a comparative study between GEE and QIF via an illustrative example, using data from the "National Longitudinal Survey of Children and Youth (NLSCY)" database. The NLSCY dataset consists of long-term, population based survey data collected since 1994, and is designed to evaluate the determinants of developmental outcomes in Canadian children. We modeled the relationship between hyperactivity-inattention and gender, age, family functioning, maternal depression symptoms, household income adequacy, maternal immigration status and maternal educational level using GEE and QIF. Basis for comparison include: (1) ease of model selection; (2) sensitivity of results to different working correlation matrices; and (3) efficiency of parameter estimates.</p> <p>Results</p> <p>The sample included 795, 858 respondents (50.3% male; 12% immigrant; 6% from dysfunctional families). QIF analysis reveals that gender (male) (odds ratio [OR] = 1.73; 95% confidence interval [CI] = 1.10 to 2.71), family dysfunctional (OR = 2.84, 95% CI of 1.58 to 5.11), and maternal depression (OR = 2.49, 95% CI of 1.60 to 2.60) are significantly associated with higher odds of hyperactivity-inattention. The results remained robust under GEE modeling. Model selection was facilitated in QIF using a goodness-of-fit statistic. Overall, estimates from QIF were more efficient than those from GEE using AR (1) and Exchangeable working correlation matrices (Relative efficiency = 1.1117; 1.3082 respectively).</p> <p>Conclusion</p> <p>QIF is useful for model selection and provides more efficient parameter estimates than GEE. QIF can help investigators obtain more reliable results when used in conjunction with GEE.</p