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The Design of Experiment (DoE) is a statistical methodology, very useful in the case of multivariable assays, that permits to evaluate simultaneously the influence of different factors on a specific output and to analyse the interactions among them in order to identify their optimal combinations. Moreover, DoE allows to select from the huge numbers of combinations only a limited number, in order to cover the whole frame, allowing to save both time and money. DoE was mostly used in industrial field in order to maximize robust processes, but recently it has been also applied in biomedical research field. Different studies have demonstrated the advantages of using a DoE approach compared to the classical one (a single parameter is tested in each assay) in the context of automated experiments, determination of cell media [1] compositions or HPLC tuning [2]. In the present study, we utilize for the first time the DoE methodology to optimize the transfection protocol of neural cells, as an example of DoE application to a laboratory procedure. Neural precursor cells are hard to transfect and refractory to lipidic reagents [3], for this reason we choose as transfectant reagent the cationic not-lipidic Poliethylenimmine (PEI). The DoE approach allowed us to identify the main variables (factors) affecting the transfection efficiency and to discover their optimal combinations, developing a protocol that let us to triplicate the transfection efficiency respect to the initial conditions. Moreover, the covariance analysis unmasked significant variable interactions impossible to calculate in one factor-variation tests used in normal laboratory practices. Our results indicate that DoE might be very helpful also in research for the identification of the better experimental conditions and the analysis of interactions between different variables.

The G protein-coupled receptor 30 (GPR30) has been claimed as an estrogen receptor. However, the literature reports controversial findings and the physiological function of GPR30 is not fully understood yet. Consistent with studies assigning a role of GPR30 in the cardiovascular and metabolic systems, GPR30 expression has been reported in small arterial vessels, pancreas and chief gastric cells of the stomach. Therefore, we hypothesized a role of GPR30 in the onset and progression of cardiovascular and metabolic diseases. In order to test our hypothesis, we investigated the effects of a high-fat diet on the metabolic and cardiovascular profiles of Gpr30-deficient mice (GPR30-lacZ mice). We found that GPR30-lacZ female, rather than male, mice had significant lower levels of HDL along with an increase in fat liver accumulation as compared to control mice. However, two indicators of cardiac performance assessed by echocardiography, ejection fraction and fractional shortening were both decreased in an age-dependent manner only in Gpr30-lacZ male mice. Collectively our results point to a potential role of Gpr30 in preserving lipid metabolism and cardiac function in a sex- and age-dependent fashion. (C) 2014 Elsevier B.V. All rights reserved.

The IT Future of Medicine (ITFoM, http://www.itfom.eu/ ) initiative will produce computational models of individuals to enable the prediction of their future health risks, progression of diseases and selection and efficacy of treatments while minimising side effects. To be able to move our health care system to treat patients as individuals rather than as members of larger, divergent groups, the ITFoM initiative, proposes to integrate molecular, physiological and anatomical data of every person in ‘virtual patient’ models. The establishment of such ‘virtual patient’ models is now possible due to the enormous progress in analytical techniques, particularly in the ‘-omics’ technology areas and in imaging, as well as in sensor technologies, but also due to the immense developments in the ICT field. As one of six Future and Emerging Technologies (FET) Flagship Pilot Projects funded by the European Commission, ITFoM with more than 150 academic and industrial partners from 34 countries, will foster the development in functional genomics and computer technologies to generate ‘virtual patient’ models to make them available for clinical application. The increase in the capacity of next generation sequencing systems will enable the high-throughput analysis of a large number of individuals generating huge amounts of genome, epigenome and transcriptome data, but making it feasible to apply deep sequencing in the clinic to characterise not only the patient's genome, but also individual samples, for example, from tumours. The genome profile will be integrated with proteome and metabolome information generated via new powerful chromatography, mass spectrometry and nuclear magnetic resonance techniques. The individualised model will not only enable the analysis of the current situation, but will allow the prediction of the response of the patient to different therapy options or intolerance for certain drugs.