When is enough evidence enough? - Using systematic decision analysis and value-of-information analysis to determine the need for further evidence

Decision analysis (DA) and value-of-information (VOI) analysis provide a systematic, quantitative methodological framework that explicitly considers the uncertainty surrounding the currently available evidence to guide healthcare decisions. In medical decision making under uncertainty, there are two fundamental questions: 1) What decision should be made now given the best available evidence (and its uncertainty)?; 2) Subsequent to the current decision and given the magnitude of the remaining uncertainty, should we gather further evidence (i.e., perform additional studies), and if yes, which studies should be undertaken (e.g., efficacy, side effects, quality of life, costs), and what sample sizes are needed? Using the currently best available evidence, VoI analysis focuses on the likelihood of making a wrong decision if the new intervention is adopted. The value of performing further studies and gathering additional evidence is based on the extent to which the additional information will reduce this uncertainty. A quantitative framework allows for the valuation of the additional information that is generated by further research, and considers the decision maker's objectives and resource constraints. Claxton et al. summarise: "Value of information analysis can be used to inform a range of policy questions including whether a new technology should be approved based on existing evidence, whether it should be approved but additional research conducted or whether approval should be withheld until the additional evidence becomes available." [Claxton K. Value of information entry in Encyclopaedia of Health Economics, Elsevier, forthcoming 2014.] The purpose of this tutorial is to introduce the framework of systematic VoI analysis to guide further research. In our tutorial article, we explain the theoretical foundations and practical methods of decision analysis and value-of-information analysis. To illustrate, we use a simple case example of a foot ulcer (e.g., with diabetes) as well as key references from the literature, including examples for the use of the decision-analytic VoI framework by health technology assessment agencies to guide further research. These concepts may guide stakeholders involved or interested in how to determine whether or not and, if so, which additional evidence is needed to make decisions.

Subtle microstructural changes of the cerebellum in a knock-in mouse model of DYT1 dystonia

The dystonias are a group of disorders characterized by involuntary twisting and repetitive movements. DYT1 dystonia is an inherited form of dystonia caused by a mutation in the TOR1A gene, which encodes torsinA. TorsinA is expressed in many regions of the nervous system, and the regions responsible for causing dystonic movements remain uncertain. Most prior studies have focused on the basal ganglia, although there is emerging evidence for abnormalities in the cerebellum too. In the current studies, we examined the cerebellum for structural abnormalities in a knock-in mouse model of DYT1 dystonia. The gross appearance of the cerebellum appeared normal in the mutant mice, but stereological measures revealed the cerebellum to be 5% larger in mutant compared to control mice. There were no changes in the numbers of Purkinje cells, granule cells, or neurons of the deep cerebellar nuclei. However, Golgi histochemical studies revealed Purkinje cells to have thinner dendrites, and fewer and less complex dendritic spines. There also was a higher frequency of heterotopic Purkinje cells displaced into the molecular layer. These results reveal subtle structural changes of the cerebellum that are similar to those reported for the basal ganglia in the DYT1 knock-in mouse model.

Dystonia-associated protein torsinA is not detectable at the nerve terminals of central neurons

Presynaptic functions of the mammalian central neurons are regulated by a network of protein interactions. Synaptic vesicle recycling in and neurotransmitter release from the presynaptic nerve terminals are altered when a glutamate-deleting mutation is present in the torsinA protein (ΔE-torsinA). This mutation is linked with a hereditary form of the movement disorder dystonia known as DYT1 dystonia. Although torsinA expression is prevalent throughout the central nervous system, its subcellular localization - in particular with respect to presynaptic nerve terminals - remains unclear. This information would be useful in narrowing down possible models for how wild-type torsinA affects presynaptic function, as well as the nature of the presynaptic dysfunction that arises in the context of ΔE-torsinA mutation. Here we report on an analysis of the presynaptic localization of torsinA in cultured neurons obtained from a knock-in mouse model of DYT1 dystonia. Primary cultures of neurons were established from heterozygous and homozygous ΔE-torsinA knock-in mice, as well as from their wild-type littermates. Neurons were obtained from the striatum, cerebral cortex and hippocampus of these mice, and were subjected to immunocytochemistry. This analysis revealed the expression of both proteins in the somata and dendrites. However, neither the nerve terminals nor axonal shafts were immunoreactive. These results were confirmed by fluorogram-based quantitation. Our findings indicate that neither the wild-type nor the ΔE-torsinA mutant protein is present at substantial levels in the presynaptic structures of cultured neurons. Thus, the effects of torsinA, in wild-type and mutant forms, appear to influence presynaptic function indirectly, without residing in presynaptic structures.

A link between two tumorigenic proteins, CD44 and p21WAF1: CD44 increases phorbol ester-induced expression of p21WAF1 by stabilizing its mRNA and extending protein half-life

The cell surface glycoprotein CD44 enhances phorbol-12-myristate 13-acetate (TPA)-induced expression of p21WAF1 by stabilizing its mRNA and enhancing the protein's half-life in several cell lines. Only the plasma membrane-anchored cytoplasmic tail of CD44 and its interacting ezrin, radixin, moesin (ERM) proteins are required for this effect. A mitogen activated kinase (MEK) inhibitor abolishes the action of CD44 on p21. Down-regulation of p21 dramatically decreased anchorage-independence of a cancer cell line, whereas CD44 expression in this background could partially rescue the phenotype.