The finding of an aortic phenotype in a

The finding of an aortic phospholipase a2 inhibitor in a mouse model with a complete Smad3 deficiency very similar to the human disease supports the idea that also lack of functional SMAD3 could cause the human clinical phenotype. A similar scenario has been described for the TGF-β2 mouse model (Boileau et al., 2012). Moreover, in aneurysmal diseases such as Marfan\’s syndrome (MFS), the efficacy of interventions that target the TGF-β signaling pathway is being explored. So far the effects on delay of aneurysmal growth are quite promising (Neptune et al., 2003; Ng et al., 2004; Habashi et al., 2006; Cohn et al., 2007). However, similar intervention strategies might not be beneficial in case of a Smad3 deficiency. Since downstream transcriptional activation is hampered in the absence of Smad3, inhibition of components in the TGF-β signaling pathway might in this case worsen the outcome as even less ECM would be generated, and alternative ‘escape’ pathways would be blocked.
In conclusion, Smad3 deficiency leads to aortic aneurysms and sudden death in the Smad3 knockout animal model. This phenotype is influenced by age and gender of the animals. Although Smad3 is absent, we observed increased nuclear translocation of pSmad2, and upregulated pERK signaling, inferring increased upstream TGF-β receptor activation. However, the downstream TGF-β-activated transcriptional response seemed impaired as derived from the absence of MMP activation and lack of amorphous ECM accumulation phospholipase a2 inhibitor in Smad3−/− mouse aortas. Together our data stress the importance of identifying the molecular mechanism of aneurysmal disease, as the outcome, and therefore treatment options, can differ dramatically. At the same time, the Smad3−/− mouse proves to be an ideal model to start testing these different interventional options on.
The following are the supplementary data related to this article.

Funding Sources
This study was funded by ‘Stichting lijf en leven’ (project: dilating versus stenosing arterial disease, 2011–2015), and partially funded by an Erasmus Fellowship (2009) to AM Bertoli-Avella. Funders had no role in study design, data collection, data analysis, interpretation or writing of the manuscript.

Conflicts of Interest

Author contributions

Acknowledgements
We would like to acknowledge Fumiko Itoh, Ph.D. from the Department of Experimental Pathology, University of Tsukuba, Japan, for generous donation of Smad3 mutant mice.

Introduction
Vaccine confidence is an increasingly important global public health issue, with decreases in confidence leading to well-documented cases of disease outbreaks, setbacks to global polio eradication as well as other immunization goals, and contentious political debates in high and low-income countries alike (Brown et al., 2010; Hanley et al., 2015; Khetsuriani et al., 2010; Larson et al., 2011; Yu et al., 2016). The World Health Organization\’s (WHO) Strategic Advisory Group of Experts (SAGE) on Immunization (WHO, 2014) as well as national immunization programmes (US Dept. Health and Human Services, 2015) have called for better monitoring of vaccine confidence and hesitancy to inform the development of communication and other interventions to address confidence gaps, to sustain confidence in vaccines and immunization programmes and to avert confidence crises and their public health consequences.
In March 2012, the SAGE Working Group on Vaccine Hesitancy convened to define “vaccine hesitancy” and to develop and standardize survey frameworks within which the scale and determinants of vaccine hesitancy and vaccine confidence can be measured (Larson et al., 2015a, 2015b). A number of studies have since investigated attitudes towards vaccines in diverse contexts, including investigation of attitudes towards immunization programmes (Dubé et al., 2016), vaccine hesitancy among general practitioners (GPs) (Verger et al., 2015), the detrimental effects of non-voluntary immunization campaigns (especially amongst those already expressing negative vaccine sentiment) (Betsch and Böhm, 2016), and social network analyses identifying clustering of vaccine-refusing households (Onnela et al., 2016).