Modelling norovirus transmission and vaccination

BackgroundNorovirus is thought to be responsible for a fifth of all acute gastroenteritis cases globally each year. The population level transmission dynamics of this very common virus are still poorly understood, in part because illness is under-reported. With vaccines undergoing clinical trials, there is a growing need for appropriate, empirically grounded models, to predict the likely impact of vaccination.MethodsWe developed a dynamic age-specific mathematical model of norovirus transmission and vaccination, informed by available data, particularly age-stratified time series case notification data. We introduce the use of a self-reporting Markov model to account for variation by age and over time in the statutory reporting of norovirus in Germany. We estimated the model using a sequential Monte Carlo particle filter. We then extended and applied our estimated model to investigate the potential impact of a range of immunisation strategies. We performed sensitivity analyses on the mode of vaccine action and other vaccine-related parameters.ResultsWe find that routine immunisation could reduce the incidence of norovirus by up to 70.5% even when those vaccines do not provide complete protection from disease. Furthermore, we find that the relative efficiency of alternative strategies targeting different age groups are dependant on the outcome we consider and are sensitive to assumptions on the mode of vaccine action. Strategies that target infants and toddler are more efficient in preventing infection but targeting older adults is preferable for preventing severe outcomes.ConclusionsOur model provides a robust estimate of a dynamic transmission model for norovirus at the population level. Vaccination may be an effective strategy in preventing disease but further work is required to ascertain norovirus vaccine efficacy, its mode of action and to estimate the cost-effectiveness of immunisation against norovirus.     

The relationship between cognition and depressive symptoms,and factors modifying this association,in Alzheimer’s disease: A multivariate multilevel model

ObjectivesTo examine whether there was a bidirectional association between cognition and depressive symptoms in Alzheimer’s disease (AD), and to explore the role of socio-demographic factors and daily performance in this association.MethodsWe conducted a longitudinal study of 104 community-dwelling patients with confirmed AD from Taiyuan, China. We assessed cognition and depressive symptoms (dependent variables) with the Montreal Cognitive Assessment (MoCA) and the Geriatric Depression Scale-30 (GDS-30), respectively. Socio-demographic information and daily performance were treated as explanatory variables. A multivariate multilevel model was built to investigate the interrelationship between patients’ cognition and depression, as well as the effect of related factors on both outcomes.ResultsMoCA scores were negatively correlated with GDS-30 both at the subject level (correlation coefficient r₃ = −0.68, χ² = 19.26, P < 0.001) and time point level (r₂ = −0.35, χ² = 35.68, P < 0.001) in patients with AD. Multivariate analysis showed several significant factors for cognitive function, including educational level, personality, hobbies, exercise, reading, aluminum utensil use, dietary restriction and hypertension (regression coefficients: 0.60, 0.52, 0.51, 0.48, 0.45, −0.48, 0.67, and −0.74, respectively). Significant factors for depressive symptoms included family status, employment before retirement, homemaking, reading, aluminum utensil use, dietary restriction, and hypertension (regression coefficients: 3.09, −1.33, −1.62, −1.31, 0.96, −1.15, and 1.14, respectively).ConclusionThese findings indicated that cognition was negatively associated with depression in patients with AD, and both were influenced by reading, aluminum utensil use, dietary restriction, and hypertension. Considering patient factors may help to slow the progression of dementia.     

PDCA护理模式在治疗腋臭围术期中的应用

目的探讨PDCA护理模式在微创耕耙剥离搔刮法治疗腋臭围术期的应用效果。方法选择2012年1月-2012年12月本院收治的腋臭患者共80例，将其随机分为观察组和对照组各40例。观察组采用PDCA护理模式贯穿于整个围术期，对照组行常规护理。采用Zung焦虑与抑郁自评量表（SAS、SDS）、疼痛指数、创面愈合率和创面愈合时间等指标综合评价护理效果。结果观察组与对照组手术前SAS、SDS评分比较，差异无统计学意义（P〉0．05）；而两组术中疼痛指数比较，差异有统计学意义（P〈0．05）；术后两组创面愈合率和创面愈合时间比较，差异有统计学意义（P〈0．05）。结论PDCA护理模式可有效降低患者疼痛敏感度，促进患者术后康复，有利于临床护理工作的开展。     

Bleomycin in octaarginine-modified fusogenic liposomes results in improved tumor growth inhibition

Bleomycin (BLM) is an example of an anticancer drug that should be delivered into cytosol for its efficient therapeutic action. With this in mind, we developed octaarginine (R8)-modified fusogenic DOPE-liposomes (R8-DOPE-BLM). R8-modification dramatically increased (up to 50-fold) the cell-liposome interaction. R8-DOPE-liposomes were internalized via macropinocytosis and did not end up in the lysosomes. R8-DOPE-BLM led to a significantly stronger cell death and DNA damage in vitro relative to all controls. R8-DOPE-BLM demonstrated a prominent anticancer effect in the BALB/c mice bearing 4T1 tumors. Thus, R8-DOPE-BLM provided efficient intracellular delivery of BLM leading to strong tumor growth inhibition in vivo.     

“肺病及肠”动物模型建立的探索

目的:探索"肺病及肠"动物模型的最佳造模方案,为正式实验研究"肺病及肠"病理传变规律及机制做好模型复制的准备工作。方法:采用单纯烟熏法建立大鼠慢性支气管炎模型,将60只大鼠随机分成空白对照和模型组,观察两组大鼠体重和组织形态学变化。结果:模型组大鼠体重与正常对照组体重比较有差异(P0.05),模型组大鼠肺组织炎症发生率较空白对照组比较有差异(P0.05),模型组大鼠结肠组织炎症发生率较空白对照组比较有差异(P0.05)。结论:在大鼠慢性支气管炎模型的基础上出现了慢性炎症性肠炎的表现。     

Asymptotic Structure of Cosmological Burgers Flows in One and Two Space Dimensions: A Numerical Study

We study the cosmological Burgers model, as we call it, which is a nonlinear hyperbolic balance law (in one and two spatial variables) posed on an expanding or contracting background. We design a finite volume scheme that is fourth-order in time and second-order in space, and allows us to compute weak solutions containing shock waves. Our main contribution is the study of the asymptotic structure of the solutions as the time variable approaches infinity (in the expanding case) or zero (in the contracting case). We discover that a saddle competition is taking place which involves, on one hand, the geometrical effects of expanding or contracting nature and, on the other hand, the nonlinear interactions between shock waves.     

Integrative analysis of multiple cancer genomic datasets under the heterogeneity model

In the analysis of cancer studies with high‐dimensional genomic measurements, integrative analysis provides an effective way of pooling information across multiple heterogeneous datasets. The genomic basis of multiple independent datasets, which can be characterized by the sets of genomic markers, can be described using the homogeneity model or heterogeneity model. Under the homogeneity model, all datasets share the same set of markers associated with responses. In contrast, under the heterogeneity model, different studies have overlapping but possibly different sets of markers. The heterogeneity model contains the homogeneity model as a special case and can be much more flexible. Marker selection under the heterogeneity model calls for bi‐level selection to determine whether a covariate is associated with response in any study at all as well as in which studies it is associated with responses. In this study, we consider two minimax concave penalty‐based penalization approaches for marker selection under the heterogeneity model. For each approach, we describe its rationale and an effective computational algorithm. We conduct simulations to investigate their performance and compare with the existing alternatives. We also apply the proposed approaches to the analysis of gene expression data on multiple cancers. Copyright © 2013 John Wiley & Sons, Ltd.