Insights from examination of hearts from adults dying suddenly to the understanding of congenital cardiac malformations

Congenital heart disease is a rare but important finding in adults who experience sudden death. Examination of the congenitally malformed heart has historically been considered esoteric and best left to those with expertise. The Cardiac Risk in the Young cardiovascular pathology laboratory based at St George's University of London has now received over 6,000 cases. Of these, 21 congenitally malformed hearts were retained for research and educational purposes. Hearts were assessed using sequential segmental analysis, and causes of death were adjudicated based on thorough macroscopic examination and histology. Congenital malformations that were encountered included atrial septal defects, ventricular septal defects, tetralogy of Fallot, and transposition of the great arteries in both its regular and congenitally corrected variants. Findings also included hearts with mirror-imaged and isomeric atrial appendages. Direct causes of death included myocardial fibrosis, pulmonary hypertension, and hemorrhage. A small but notable proportion did not reveal a substrate for arrhythmia, raising the question of whether the terminal event was due to the congenital heart disease itself, or an underlying channelopathy. Here, we demonstrate the value of simple sequential segmental analysis in describing and categorizing the cases, with the concept of the “morphological method” serving to identify the distinguishing features of the cardiac components. Clin. Anat. 33:394–404, 2020. © 2019 Wiley Periodicals, Inc.     

医用粘胶相关性皮肤损伤的文献计量学分析

目的: 应用文献计量学方法,分析国内外医用粘胶相关性皮肤损伤（Medical adhesive-related skin injuries,MARSI）文献的研究主题和热点。方法: 检索Cochrane Library、PubMed、EMbase、Web of science、sinoMed、CNKI、Wanfang、VIP等数据库MARSI相关文献,检索时限为建库至2019年5月。采用SPSS 22.0 软件进行文献计量分析,利用gCLUTO统计对国内外高频主题词进行聚类分析。结果: 共纳入论文103篇,其中外文文献占总文献量的27.18%,中文文献占总文献量的72.82%。聚类分析将国内外高频主题词聚为4大类,依次为皮肤损伤及损伤类型、相关危险因素、风险评估及风险管理、护理干预措施。结论: MARSI仍是国内外研究的热点,其发病机制研究仍处于探索阶段,目前尚缺乏有效的预防策略,研究领域较局限。在今后的研究中,应加强MARSI预防的研究、注重多学科合作。     

Spontaneous recovery of fear differs among early – late adolescent and adult male mice

Adolescence is a vulnerable period for developing anxiety-related mental disorders such as post-traumatic stress disorder (PTSD), which requires a long-term course of therapy when a traumatic event has been experienced during childhood. However, the biological mechanism underlying these age-dependent characteristics remains unclear. In the present study, we used early adolescent, late adolescent and adult (4-, 8-, and 15-week old) male mice to examine age differences in fear memory, fear extinction, and spontaneous recovery of fear. We also measured the activation of extracellular signal-regulated kinase (ERK) 2 in the dorsal hippocampus (dHip) and the basolateral amygdala (BLA) following a spontaneous recovery test. Our major findings were as follows: (1) early adolescent and adult mice did not recover the fear response; only late adolescent mice recovered the fear response. (2) The ERK2 in the dHip was more activated after the spontaneous recovery test in late adolescent mice than in adult mice, and the ERK2 in the BLA was more activated after the spontaneous recovery test in adult mice than in late adolescent mice. These results suggest that there exists a unique period in which spontaneous recovery occurs and that these late adolescent behavioral signatures may be related to alteration in the ERK2 phosphorylation in the dHip and BLA.     

Perceived mechanisms of change in therapeutic yoga targeting psychological distress

IntroductionThis study was designed to investigate yoga teachers' and yoga therapists’ perceptions of the ways yoga is applied to treat symptoms of psychological distress, and identify the defining features, main components, and mechanisms of change in therapeutic practice.MethodsA qualitative design was employed. Six yoga teachers who had specific training and experience in teaching therapeutic yoga or practicing ‘yoga therapy’ took part in one-to-one interviews during which they gave accounts of their experiences of helping people cope with psychological distress through yoga. Participants' interviews were transcribed verbatim and were analysed using an integrative inductive-deductive thematic analysis.ResultsFour key themes were identified in relation to the benefits of therapeutic yoga: Awareness, Choice, Relationships, and Tailoring.ConclusionsThe findings support previous research which suggests that individualised yoga therapy is a promising intervention, and may help to form a theoretical rationale for the future treatment of psychological distress with yoga.     

基于岗位管理的基层医院专科护士使用及考核的现状调查研究

目的调查四川省基层医院专科护士使用和考核现状,对其使用和考核体系进行探索。方法于2017年9月7-15日,采用问卷调查法对四川省38所二级及三级乙等医疗单位专科护士的使用和发展现状进行调查。结果共有38所医院参与网络问卷调查,其中34.21%的医院未针对专科护士设置岗位,岗位职责不清晰。有73.68%的专科护士未进行资质再认证,60.53%的专科护士学成后未进行专科资质再考核。结论我国对专科护士能力的评价认证体系尚未形成统一标准,对专科护士的培养条件也未形成统一标准。护士岗位管理中设置专科护士岗位能促进专科护士的职业发展规划,有利于专科护士使用和考核。     

Group testing in mediation analysis

We consider the scenario where there is an exposure, multiple biologically defined sets of biomarkers, and an outcome. We propose a new two-step procedure that tests if any of the sets of biomarkers mediate the exposure/outcome relationship, while maintaining a prespecified familywise error rate. The first step of the proposed procedure is a screening step that removes all groups that are unlikely to be strongly associated with both the exposure and the outcome. The second step adapts recent advances in postselection inference to test if there are true mediators in each of the remaining candidate sets. We use simulation to show that this simple two-step procedure has higher statistical power to detect true mediating sets when compared with existing procedures. We then use our two-step procedure to identify a set of Lysine-related metabolites that potentially mediate the known relationship between increased body mass index and the increased risk of estrogen-receptor positive breast cancer in postmenopausal women.     

Immunohistochemical and molecular analysis of PI3K/AKT/mTOR pathway in esophageal carcinoma

Among the numerous signaling pathways involved in tumorigenesis, PI3K‐AKT‐mTOR is a key one that regulates diverse cellular functions. However, its prognostic value in esophageal carcinoma remains unclear. In our study, we examined the immunohistochemical expression of phosphorylated (p‐) AKT, mTOR, p70S6K and 4E‐BP1 along with the mutational status of PIK3CA and AKT1 genes by High Resolution Melting Analysis and Pyrosequencing in 44 esophageal carcinomas. The results were correlated with the clinicopathological characteristics of the patients in an effort to define their possible prognostic significance. Total p‐mTOR cytoplasmic expression, assessed in 10 random areas, was positively correlated with tumor stage (Kruskal–Wallis ANOVA, I/II vs III/IV, p = 0.0500). Μoreover, maximum p‐mTOR cytoplasmic immunoexpression, estimated in hot spot areas, was positively associated with tumor grade (Mann–Whitney U test, I/II vs III, p = 0.0565). Interestingly, p‐4E‐BP1 immunoreactivity was negatively correlated with tumor histological grade (Mann–Whitney U test, I/II vs III, p = 0.0427). No mutation was observed in exons 9 and 20 of PIK3CA gene and in exon 4 of AKT1 gene. In conclusion, our findings depict the presence of activated PI3K/AKT/mTOR pathway in esophageal cancer bringing forward p‐mTOR and p‐4E‐BP1 for their potential role in esophageal carcinogenesis. Additional studies are warranted to validate our findings.     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.