Quality of life of patients living with psoriasis: a qualitative study

Background: Psoriasis is a multifactorial inflammatory disease prevalent in dermatology. We aimed to understand the perceptions of patients living with psoriasis in relation to their quality of life and to identify aspects to improve it. Methods: This is qualitative research carried out in a dermatology outpatient clinic of the São Paulo State University (UNESP) medical school, Botucatu, Brazil, with 81 psoriasis patients. The interviews were transcribed and analysed using the Discourse of the Collective Subject method (DCS). Results: Quality of life was linked to well-being, happiness, leisure, good food and financial stability. However, disease symptoms, social and clothing restrictions, impairment of professional activities and the absence of a cure, negatively influenced their perceptions. Suggestions for improvements included an increase of public awareness, stress reduction, disease acceptance and multidisciplinary care. Conclusion: The meanings of quality of life revealed by the participants are subjective, multidimensional, linked to moments experienced by them and to the health-disease process. Public health policies promoting reduction in social stigma and stress as well as multidisciplinary approaches towards care can contribute to improvements of QoL in psoriasi

Training Programmes Can Change Behaviour and Encourage the Cultivation of Over-Harvested Plant Species

Cultivation of wild-harvested plant species has been proposed as a way of reducing over-exploitation of wild populations but lack of technical knowledge is thought to be a barrier preventing people from cultivating a new species. Training programmes are therefore used to increase technical knowledge to encourage people to adopt cultivation. We assessed the impact of a training programme aiming to encourage cultivation of xaté (Chamaedorea ernesti-augusti), an over-harvested palm from Central America. Five years after the training programme ended, we surveyed untrained and trained individuals focusing on four potential predictors of behaviour: technical knowledge, attitudes (what individuals think about a behaviour), subjective norms (what individuals perceive others to think of a behaviour) and perceived behavioural control (self assessment of whether individuals can enact the behaviour successfully). Whilst accounting for socioeconomic variables, we investigate the influence of training upon these behavioural predictors and examine the factors that determine whether people adopt cultivation of a novel species. Those who had been trained had higher levels of technical knowledge about xaté cultivation and higher belief in their ability to cultivate it while training was not associated with differences in attitudes or subjective norms. Technical knowledge and perceived behavioural control (along with socio-economic variables such as forest ownership and age) were predictors of whether individuals cultivate xaté. We suggest that training programmes can have a long lasting effect on individuals and can change behaviour. However, in many situations other barriers to cultivation, such as access to seeds or appropriate markets, will need to be addressed

Genome-Scale Analysis of Translation Elongation with a Ribosome Flow Model

We describe the first large scale analysis of gene translation that is based on a model that takes into account the physical and dynamical nature of this process. The Ribosomal Flow Model (RFM) predicts fundamental features of the translation process, including translation rates, protein abundance levels, ribosomal densities and the relation between all these variables, better than alternative (‘non-physical’) approaches. In addition, we show that the RFM can be used for accurate inference of various other quantities including genes' initiation rates and translation costs. These quantities could not be inferred by previous predictors. We find that increasing the number of available ribosomes (or equivalently the initiation rate) increases the genomic translation rate and the mean ribosome density only up to a certain point, beyond which both saturate. Strikingly, assuming that the translation system is tuned to work at the pre-saturation point maximizes the predictive power of the model with respect to experimental data. This result suggests that in all organisms that were analyzed (from bacteria to Human), the global initiation rate is optimized to attain the pre-saturation point. The fact that similar results were not observed for heterologous genes indicates that this feature is under selection. Remarkably, the gap between the performance of the RFM and alternative predictors is strikingly large in the case of heterologous genes, testifying to the model's promising biotechnological value in predicting the abundance of heterologous proteins before expressing them in the desired host

Albumin and mammalian cell culture: implications for biotechnology applications

Albumin has a long historical involvement in design of media for the successful culture of mammalian cells, in both the research and commercial fields. The potential application of albumins, bovine or human serum albumin, for cell culture is a by-product of the physico-chemical, biochemical and cell-specific properties of the molecule. In this review an analysis of these features of albumin leads to a consideration of the extracellular and intracellular actions of the molecule, and importantly the role of its interactions with numerous ligands or bioactive factors that influence the growth of cells in culture: these include hormones, growth factors, lipids, amino acids, metal ions, reactive oxygen and nitrogen species to name a few. The interaction of albumin with the cell in relation to these co-factors has a potential impact on metabolic and biosynthetic activity, cell proliferation and survival. Application of this knowledge to improve the performance in manufacturing biotechnology and in the emerging uses of cell culture for tissue engineering and stem cell derived therapies is an important prospect

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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