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.     

Evaluation of neurobehavioral abnormalities and immunotoxicity in response to oral imidacloprid exposure in domestic chickens (Gallus gallus domesticus)

ABSTRACT

Domestic chickens (Gallus gallus domesticus) were exposed to imidacloprid by gavage once daily for 7 consecutive days at 0, 0.03, 0.34, 3.42, 10.25, and 15.5 mg/kg/day (n = 20 per group; 5 6-week-old males, 5 6-week-old females, 5 9-week-old males, and 5 9-week-old females). The severity and duration of neurobehavioral abnormalities were recorded. Components of the innate and adaptive immune system were assessed with 7 standard functional assays. Temporary neurobehavioral abnormalities were observed in a dose-dependent manner, including muscle tremors, ataxia, and depressed mentation. Based upon mean clinical severity scores, the no observed adverse effect level (NOAEL) was 3.42 mg/kg/day, and the lowest observed adverse effect level (LOAEL) was 10.25 mg/kg/day. The effective dose value for the presence of any neurobehavioral abnormalities in 50% of the test group (ED₅₀) was 4.62 ± 0.98 mg/kg/day. The ED₅₀ for an adjusted score that included both severity and duration of neurobehavioral abnormalities was 11.24 ± 9.33 mg/kg/day. These ED₅₀ values are equivalent to a 1 kg bird ingesting 29 or 70 imidacloprid treated soybean seeds respectively. Immunotoxicity was not documented, possible causes include the assays were insensitive, relevant immune functions were not examined, or imidacloprid is not immunotoxic at this dosing schedule in this species. Neurobehavioral abnormalities were a more sensitive indicator of the sublethal effects of imidacloprid than immunotoxicity.     

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.