Recombinant low-seroprevalent adenoviral vectors Ad26 and Ad35 expressing the respiratory syncytial virus (RSV) fusion protein induce protective immunity against RSV infection in cotton rats

AbstractRSV is an important cause of lower respiratory tract infections in children, the elderly and in those with underlying medical conditions. Although the high disease burden indicates an urgent need for a vaccine against RSV, no licensed RSV vaccine is currently available. We developed an RSV vaccine candidate based on the low-seroprevalent human adenovirus serotypes 26 and 35 (Ad26 and Ad35) encoding the RSV fusion (F) gene. Single immunization of mice with either one of these vectors induced high titers of RSV neutralizing antibodies and high levels of F specific interferon-gamma-producing T cells. A Th1-type immune response was indicated by a high IgG2a/IgG1 ratio of RSV-specific antibodies, strong induction of RSV-specific interferon-gamma and tumor necrosis factor-alpha cytokine producing CD8 Tcells, and low RSV-specific CD4 T-cell induction. Both humoral and cellular responses were increased upon a boost with RSV-F expressing heterologous adenovirus vector (Ad35 boost after Ad26 prime or vice versa). Both single immunization and prime-boost immunization of cotton rats induced high and long-lasting RSV neutralizing antibody titers and protective immunity against lung and nasal RSV A2 virus load up to at least 30 weeks after immunization. Cotton rats were also completely protected against challenge with a RSV B strain (B15/97) after heterologous prime-boost immunization. Lungs from vaccinated animals showed minimal damage or inflammatory infiltrates post-challenge, in contrast to animals vaccinated with formalin-inactivated virus. Our results suggest that recombinant human adenoviral Ad26 and Ad35 vectors encoding the RSV F gene have the potential to provide broad and durable protection against RSV in humans, and appear safe to be investigated in infants

Differential effects of proteins and carbohydrates on postprandial blood pressure-related responses

Diet composition may affect blood pressure (BP), but the mechanisms are unclear. The aim of the present study was to compare postprandial BP-related responses to the ingestion of pea protein, milk protein and egg-white protein. In addition, postprandial BP-related responses to the ingestion of maltodextrin were compared with those to the ingestion of sucrose and a protein mix. We hypothesised that lower postprandial total peripheral resistance (TPR) and BP levels would be accompanied by higher plasma concentrations of nitric oxide, insulin, glucagon-like peptide 1 (GLP-1) and glucagon. On separate occasions, six meals were tested in a randomised order in forty-eight overweight or obese adults with untreated elevated BP. Postprandial responses of TPR, BP and plasma concentrations of insulin, glucagon, GLP-1 and nitrite, nitroso compounds (RXNO) and S-nitrosothiols (NO(x)) were measured for 4 h. No differences were observed in TPR responses. Postprandial BP levels were higher after the ingestion of the egg-white-protein meal than after that of meals containing the other two proteins (P≤ 0·01). The ingestion of the pea-protein meal induced the highest NO(x) response (P≤ 0·006). Insulin and glucagon concentrations were lowest after the ingestion of the egg-white-protein meal (P≤ 0·009). Postprandial BP levels were lower after the ingestion of the maltodextrin meal than after that of the protein mix and sucrose meals (P≤ 0·004), while postprandial insulin concentrations were higher after the ingestion of the maltodextrin meal than after that of the sucrose and protein mix meals after 1-2 h (P≤ 0·0001). Postprandial NO(x), GLP-1 and glucagon concentrations were lower after the ingestion of the maltodextrin meal than after that of the protein mix meal (P≤ 0·008). In conclusion, different protein and carbohydrate sources induce different postprandial BP-related responses, which may be important for BP management. Lower postprandial BP levels are not necessarily accompanied by higher NO(x), insulin, glucagon or GLP-1 responses

Nonlinear Models for Longitudinal Data

While marginal models, random-effects models, and conditional models are routinely considered to be the three main modeling families for continuous and discrete repeated measures with linear and generalized linear mean structures, respectively, it is less common to consider non-linear models, let alone frame them within the above taxonomy. In the latter situation, indeed, when considered at all, the focus is often exclusively on random-effects models. In this paper, we consider all three families, exemplify their great flexibility and relative ease of use, and apply them to a simple but illustrative set of data on tree circumference growth of orange trees.status: publishe

Characterizing persistent disturbing behavior using longitudinal and multivariate techniques

Persistent disturbing behavior (PDB) refers to a chronic condition in therapy-resistant psychiatric patients. Since these patients are highly unstable and difficult to maintain in their natural living environment and even in hospital wards, it is important to properly characterize this group. Previous studies in the Belgian province of Limburg indicated that the size of this group was larger than anticipated. Here, using a score calculated from longitudinal psychiatric registration data in 611 patients, we characterize the difference between PDB patients and a set of control patients. These differences are studied both at a given point in time, using discriminant analysis, as well as in terms of the evolution of the score over time, using longitudinal data analysis methods. Further, using clustering techniques, the group of PDB patients is split into two subgroups, characterized in terms of a number of ordinal scores. Such findings are useful from a scientific as well as from an organizational point of view

Latent class analysis of persistent disturbing behaviour patients by using longitudinal profiles

"Persistent disturbing behaviour" refers to a chronic condition in highly unstable, therapy resistant psychiatric patients. Because these patients are difficult to maintain in their natural living environment and even in hospital wards, purposely designed residential psychiatric facilities need to be established. Therefore, it is important to define and circumscribe the group carefully. Serroyen and co-workers, starting from the longitudinal analysis of a score based on data from the Belgian national psychiatric registry, undertook a discriminant analysis to distinguish persistent disturbing behaviour patients from a control group. They also indicated that there is scope for further subdividing the persistent disturbing behaviour patients into two subgroups, using conventional cluster analysis techniques. We employ a variety of novel longitudinal-data-based cluster analysis techniques. These are based on either conventional growth models, growth-mixture models or latent class growth models. Unlike in earlier analyses, where some evidence for two groups was found, there now is an indication of three groups, which is a finding with high practical and organizational relevance. Copyright (c) 2010 Royal Statistical Society.

The impact of personal attributes on the association between cumulative exposure to work stressors and cardiovascular disease

Objective: To examine the impact of personal attributes (coping and negative affectivity) on the association between cumulative exposure to work stressors and cardiovascular disease (CVD). Methods: 6154 employees from the Maastricht Cohort Study (MCS) were selected who had complete data on the MCS Work Stressor Score (MCS-WSS) at three consecutive time points during 1998-2000 and free of CVD during the exposure assessment period. CVD was assessed from 2000 until 2008 with questionnaires. Cumulative exposure to work stressors was calculated as the frequency of exposure in the upper quartile of the MCS-WSS and consisted of the categories: none, once, twice or thrice exposed. Cox regression analyses were used to examine the direct effect of personal attributes on CVD incidence and the intermediating, confounding and moderating effect on the association between cumulative exposure to work stressors and CVD. Logistic regression was used to examine the influence of personal attributes on the odds of high cumulative exposure (thrice exposed). Results: High NA was significantly associated with high cumulative exposure to work stressors. There was no significant evidence supporting an intermediating, confounding or moderating effect of NA or coping on the association between cumulative exposure to work stressors and CVD. Conclusion: The association between cumulative exposure to work stressors and CVD is not confounded or intermediated by NA or coping. High NA showed a direct effect on high cumulative exposure to work stressors. However, it should be examined whether high NA individuals experience more work stressors or are actually exposed to more work stressors

A simple formula for the calculation of sample size in pilot studies

One of the goals of a pilot study is to identify unforeseen problems, such as ambiguous inclusion or exclusion criteria or misinterpretations of questionnaire items. Although sample size calculation methods for pilot studies have been proposed, none of them are directed at the goal of problem detection. In this article, we present a simple formula to calculate the sample size needed to be able to identify, with a chosen level of confidence, problems that may arise with a given probability. If a problem exists with 5% probability in a potential study participant, the problem will almost certainly be identified (with 95% confidence) in a pilot study including 59 participants.publisher: Elsevier articletitle: A simple formula for the calculation of sample size in pilot studies journaltitle: Journal of Clinical Epidemiology articlelink: http://dx.doi.org/10.1016/j.jclinepi.2015.04.014 content_type: article copyright: Copyright © 2015 Elsevier Inc. All rights reserved.status: publishe