Building consensus about eHealth in Slovene primary health care: Delphi study

<p>Abstract</p> <p>Background</p> <p>Slovenia's national eHealth strategy aims to develop an efficient, flexible and modern health care informatics framework that would be comparable to the most successful EU countries. To achieve this goal, the gap between availability and usage of information and communication technology by primary care physicians needs to be reduced.</p> <p>As recent efforts show, consensus on information and communication technology purpose and usage in primary care needs to be established before any national information and communication technology solutions are developed.</p> <p>The aim of this study was to identify the most appropriate measures in implementation of Slovene national eHealth strategy and to suggest an appropriate model for success by using the three round Delphi study.</p> <p>Methods</p> <p>An e-mail based, three-round Delphi study was undertaken to achieve consensus from a selected sample of nationally recognized experts from the fields of primary health care and medical informatics. The aim of this study was to identify the most appropriate measures and key obstacles in implementation of eHealth in Slovene primary health care by using the Delphi study.</p> <p>Results</p> <p>High levels of consensus on the majority of suggested measures were achieved among all study participants, as well as between the subgroups of experts from primary health care and medical informatics. All aims of the three-round Delphi study on eHealth implementation in Slovenian primary health care were achieved.</p> <p>Conclusions</p> <p>The three round decision Delphi process has proven to be effective for developing outcomes, ranking key priorities in primary care eHealth development, and achieving consensus among the most influential experts in that field. This consensus is an important contribution to future national eHealth strategies in the field of primary health care.</p

Combined Experimental and Computational Approaches Reveal Distinct pH Dependence of Pectin Methylesterase Inhibitors.

The fine-tuning of the degree of methylesterification of cell wall pectin is a key to regulating cell elongation and ultimately the shape of the plant body. Pectin methylesterification is spatiotemporally controlled by pectin methylesterases (PMEs; 66 members in Arabidopsis [Arabidopsis thaliana]). The comparably large number of proteinaceous pectin methylesterase inhibitors (PMEIs; 76 members in Arabidopsis) questions the specificity of the PME-PMEI interaction and the functional role of such abundance. To understand the difference, or redundancy, between PMEIs, we used molecular dynamics (MD) simulations to predict the behavior of two PMEIs that are coexpressed and have distinct effects on plant development: AtPMEI4 and AtPMEI9. Simulations revealed the structural determinants of the pH dependence for the interaction of these inhibitors with AtPME3, a major PME expressed in roots. Key residues that are likely to play a role in the pH dependence were identified. The predictions obtained from MD simulations were confirmed in vitro, showing that AtPMEI9 is a stronger, less pH-independent inhibitor compared with AtPMEI4. Using pollen tubes as a developmental model, we showed that these biochemical differences have a biological significance. Application of purified proteins at pH ranges in which PMEI inhibition differed between AtPMEI4 and AtPMEI9 had distinct consequences on pollen tube elongation. Therefore, MD simulations have proven to be a powerful tool to predict functional diversity between PMEIs, allowing the discovery of a strategy that may be used by PMEIs to inhibit PMEs in different microenvironmental conditions and paving the way to identify the specific role of PMEI diversity in muro

Von Willebrand factor is dimerized by protein disulfide isomerase

Multimeric von Willebrand factor (VWF) is essential for primary hemostasis. The biosynthesis of VWF high-molecular-weight multimers requires spatial separation of each step because of varying pH value requirements. VWF is dimerized in the endoplasmic reticulum by formation of disulfide bonds between the C-terminal cysteine knot (CK) domains of 2 monomers. Here, we investigated the basic question of which protein catalyzes the dimerization. We examined the putative interaction of VWF and the protein disulfide isomerase PDIA1, which has previously been used to visualize endoplasmic reticulum localization of VWF. Excitingly, we were able to visualize the PDI–VWF dimer complex by high-resolution stochastic optical reconstruction microscopy and atomic force microscopy. We proved and quantified direct binding of PDIA1 to VWF, using microscale thermophoresis and fluorescence correlation spectroscopy (dissociation constants KD = 236 ± 66 nM and KD = 282 ± 123 nM by microscale thermophoresis and fluorescence correlation spectroscopy, respectively). The similar KD (258 ± 104 nM) measured for PDI interaction with the isolated CK domain and the atomic force microscopy images strongly indicate that PDIA1 binds exclusively to the CK domain, suggesting a key role of PDIA1 in VWF dimerization. On the basis of protein–protein docking and molecular dynamics simulations, combined with fluorescence microscopy studies of VWF CK-domain mutants, we suggest the following mechanism of VWF dimerization: PDI initiates VWF dimerization by forming the first 2 disulfide bonds Cys2771-2773′ and Cys2771′-2773. Subsequently, the third bond, Cys2811-2811′, is formed, presumably to protect the first 2 bonds from reduction, thereby rendering dimerization irreversible. This study deepens our understanding of the mechanism of VWF dimerization and the pathophysiological consequences of its inhibition

Molecular docking for predictive toxicology

Molecular docking is an in silico method widely applied in drug discovery programs to predict the binding mode of a given molecule interacting with a specific biological target. This computational technique is today emerging also in the field of predictive toxicology for regulatory purposes, being for instance successfully applied to develop classification models for the prediction of the endocrine disruptor potential of chemicals. Herein, we describe the protocol for adapting molecular docking to the purposes of predictive toxicology