Search

BACKGROUND: Apathy is the most frequent neuropsychiatric symptom in patients with dementia of the Alzheimer's type (DAT). We analyzed the influence of apathy on the resource use of DAT patients and their caregivers.METHODS: Included were baseline data of 107 DAT patients from a randomized clinical trial on apathy treatment. The Resource Utilization in Dementia (RUD) instrument assessed costs over a 1-month period prior to baseline. Cost predictors were determined via a least absolute shrinkage and selection operator (LASSO).RESULTS: On average, total monthly costs were €3070, of which €2711 accounted for caregivers' and €359 for patients' costs. An increase of one point in the Apathy Evaluation Scale resulted in a 4.1% increase in total costs.DISCUSSION: Apathy is a significant cost driving factor for total costs in mild to moderate DAT. Effective treatment of apathy might be associated with reduced overall costs in DAT.

In this work, we propose an extension of the versatile joint regression framework for bivariate count responses of the R package GJRM by Marra and Radice (R package version 0.2-3, 2020) by incorporating an (adaptive) LASSO-type penalty. The underlying estimation algorithm is based on a quadratic approximation of the penalty. The method enables variable selection and the corresponding estimates guarantee shrinkage and sparsity. Hence, this approach is particularly useful in high-dimensional count response settings. The proposal’s empirical performance is investigated in a simulation study and an application on FIFA World Cup football data., AStA advances in statistical analysis;Vol. 107. 2023, pp 127-151

Alzheimer's disease (AD) is the most common neurodegenerative disease; thus, the search for a cure or causal therapy has become necessary. Despite intense research on this topic in recent decades, there is no curative therapy up today, and also no disease-modifying treatment has been approved. As promising approach passive immunization strategies have thereby come forth. In this study, we focused on naturally occurring autoantibodies against the AD-associated peptide amyloid-β. These antibodies have already reported to show beneficial functions in vitro and in mouse models of AD. However, their availability is limited due to their low abundance in peripheral blood. In a recent study, we were able to generate four recombinant antibodies against amyloid-β. In the present study, we tested these antibodies in ELISA and SPR assays for their binding behavior and by aggregation- and phagocytosis assays as functional evidences to characterize their amyloid-β-related neutralizing and clearance abilities. Further ex vivo assay on organotypic hippocampal slice cultures gave first evidence of microglial activation and inflammatory features. The tested recombinant antibodies in IgG format showed, in comparison to naturally occurring autoantibodies against amyloid-β, insufficient binding capacities and -affinities. However, after conversion of one antibody into a single chain format multimerization of the scFv-Fc construct, the investigated binding capacity and -affinity showed improvements. Further functional assays predict a protective effect of this antibody. Although, all four recombinant antibodies showed binding to amyloid-β, promising features were only detectable after conversion into a multimeric format. The multimeric scFv-Fc antibody exhibited thereby strong impact on amyloid-β clearance and inhibition of oligomerization.

We propose a versatile joint regression framework for count responses. The method is implemented in the R add-on package and allows for modelling linear and non-linear dependence through the use of several copulae. Moreover, the parameters of the marginal distributions of the count responses and of the copula can be specified as flexible functions of covariates. Motivated by competitive settings, we also discuss an extension which forces the regression coefficients of the marginal (linear) predictors to be equal via a suitable penalisation. Model fitting is based on a trust region algorithm which estimates simultaneously all the parameters of the joint models. We investigate the proposal’s empirical performance in two simulation studies, the first one designed for arbitrary count data, the other one reflecting competitive settings. Finally, the method is applied to football data, showing its benefits compared to the standard approach with regard to predictive performance.