煅石膏的研究进展及含钙止血材料应用概况

矿物药是传统中医药的重要组成部分，具有悠久的应用历史。其中矿物止血药对多种出血病证的临床应用广泛、用药经验丰富且疗效显著。石膏作为临床常用的矿物药，生石膏的功效为清热泻火、除烦止渴，而煅石膏为生石膏经高温煅烧的炮制品，主要由无水硫酸钙（CaSO₄）组成，具有收湿、生肌、敛疮、止血的功效，常外用于治疗溃疡不敛、湿疹瘙痒、水火烫伤、外伤出血等。止血机制研究表明，Ca²⁺作为凝血因子Ⅳ参与凝血过程的内外凝血级联多个关键环节，并且参与调节血小板活化、聚集，促进生成不溶性纤维蛋白，最终形成血凝块防止出血。Ca²⁺是重要的止血物质基础，但是石膏、煅石膏均含有Ca²⁺，煅石膏具有显著的止血作用，而石膏没有止血作用，石膏经过炮制后性味、功效发生了明显改变，体现了炮制特色，但目前尚未完全阐明煅石膏的炮制机制。因此，该文对石膏煅制前后比较研究情况进行总结与分析，围绕煅制工艺、晶型比较、元素含量、药效比较等进行综述，并总结Ca²⁺参与止血的各个环节。此外，对其他含钙矿物药及新型含钙止血材料如海藻酸钙、介孔硅酸钙和纳米凝胶止血材料的止血性能进行探讨，以期为阐明煅石膏的炮制机制提供借鉴，为临床辨证使用煅石膏提供依据，并为后续新型含钙止血材料的开发提供借鉴。     

Generalized,severe epidermolysis bullosa simplex caused by a Keratin 5 p.E477K mutation

Epidermolysis bullosa simplex (EBS) is a skin fragility disorder resulting from mutations of structural proteins in the epidermis. We provide a brief report of long‐term survival and reproduction in a mother with EBS due to keratin 5 (KRT5) c.1429G > A (p.E477K) mutation, which causes a particularly severe form of the disease.     

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.