Keeping Secrets or Educating Others: A Dyadic Analysis of Group Entitativity’s Influence on Spouses’ Label Management Connected to AATD

In 1963, Goffman argued that forming a group based on shared stigma may provide benefits. However, there is no empirical research on whether perception that a separate, unique, coherent group exists (i.e., group entitativity) influences coping, such as educating others or secrecy, for the stigmatized individual or his or her spouse. Further, little is known about how spouses influence each other in terms of promoting the education of others about a stigmatizing condition, especially when it comes to the role of believing that stigma-based groups, to which they may both belong, exist. This study provides a step toward bridging this gap in the research by applying the label management model in efforts to understand coping for couples in which one spouse is diagnosed with genetic mutations leading to alpha-1 antitrypsin deficiency (AATD). This study included 50 married couples in which one spouse is diagnosed with genetic mutations leading to alpha-1 antitrypsin deficiency (AATD). We found that group entitativity related to those with AATD counterbalanced the influence of genetic stigma on spouses’ intentions to keep the diagnosis secret or to educate others about it. Intrapersonal and interpersonal influences appeared among spouses. Attention is needed on the power of creating groups for stigmatized persons and their relatives. Indeed, people live within a dynamic world of group entities, and multiple social identities including spousal and familial. While attention has been paid to the diffusion of stigmas to loved ones, less has been paid to the uplift of group entities for them.     

Symptoms During or Shortly After Isolated Carpal Tunnel Release and Problems Within 24?hours After Surgery

This study used the National Survey of Ambulatory Surgery (NSAS) database to measure the incidence of and risk factors for symptoms in the ambulatory surgery center and problems within 24 h after isolated carpal tunnel release (CTR). The NSAS contained records on 400,000 adult patients with carpal tunnel syndrome who were treated with CTR in 2006, based on ICD-9 codes. The type of anesthesia used and factors associated with symptoms and problems were sought in bivariate and multivariable statistical analyses. The mean duration of the procedure was 16 ± 8.8 min. Only 5 % were performed under local anesthesia without sedation, 45 % with IV sedation, 28 % regional anesthesia, and 19 % general anesthesia. Symptoms in the ambulatory surgery center or a problem within 24 h after discharge were recorded in 10 % of patients, all of them minor and transient, including difficulties with pain and its treatment. The strongest risk factors were male sex, age of 45 years and older, and participation of an anesthesiologist. Local anesthesia and regional anesthesia were associated with more perioperative symptoms and postoperative problems. Most CTR are performed with some sedation in the United States. CTR is a safe procedure: one in 10 patients will experience a minor issue in the perioperative or immediate postoperative period.     

Characterization of Benign Myocarditis Using Quantitative Delayed-Enhancement Imaging Based on Molli T1 Mapping

Delayed contrast enhancement after injection of a gadolinium-chelate (Gd-chelate) is a reference imaging method to detect myocardial tissue changes. Its localization within the thickness of the myocardial wall allows differentiating various pathological processes such as myocardial infarction (MI), inflammatory myocarditis, and cardiomyopathies. The aim of the study was first to characterize benign myocarditis using quantitative delayed-enhancement imaging and then to investigate whether the measure of the extracellular volume fraction (ECV) can be used to discriminate between MI and myocarditis.In 6 patients with acute benign myocarditis (32.2 ± 13.8 year-old, subepicardial late gadolinium enhancement [LGE]) and 18 patients with MI (52.3 ± 10.9 year-old, subendocardial/transmural LGE), myocardial T1 was determined using the Modified Look-Locker Imaging (MOLLI) sequence at 3 Tesla before and after Gd-chelate injection. T1 values were compared in LGE and normal regions of the myocardium. The myocardial T1 values were normalized to the T1 of blood, and the ECV was calculated from T1 values of myocardium and blood pre- and post-Gd injection.In both myocarditis and MI, the T1 was lower in LGE regions than in normal regions of the left ventricle. T1 of LGE areas was significantly higher in myocarditis than in MI (446.8 ± 45.8 vs 360.5 ± 66.9 ms, P = 0.003) and ECV was lower in myocarditis than in MI (34.5 ± 3.3 vs 53.8 ± 13.0 %, P = 0.004).Both inflammatory process and chronic fibrosis induce LGE (subepicardial in myocarditis and subendocardial in MI). The present study demonstrates that the determination of T1 and ECV is able to differentiate the 2 histological patterns.Further investigation will indicate whether the severity of ECV changes might help refine the predictive risk of LGE in myocarditis.     

Population Genetics Identifies Challenges in Analyzing Rare Variants

Geneticists have, for years, understood the nature of genome‐wide association studies using common genomic variants. Recently, however, focus has shifted to the analysis of rare variants. This presents potential problems for researchers, as rare variants do not always behave in the same way common variants do, sometimes rendering decades of solid intuition moot. In this paper, we present examples of the differences between common and rare variants. We show why one must be significantly more careful about the origin of rare variants, and how failing to do so can lead to highly inflated type I error. We then explain how to best avoid such concerns with careful understanding and study design. Additionally, we demonstrate that a seemingly low error rate in next‐generation sequencing can dramatically impact the false‐positive rate for rare variants. This is due to the fact that rare variants are, by definition, seen infrequently, making it hard to distinguish between errors and real variants. Compounding this problem is the fact that the proportion of errors is likely to get worse, not better, with increasing sample size. One cannot simply scale their way up in order to solve this problem. Understanding these potential pitfalls is a key step in successfully identifying true associations between rare variants and diseases.