Factors Relating To Failure To Quit Smoking: A Prospective Cohort Study [fatores Relacionados Ao Insucesso Na Cessação Tabágica: Um Estudo De Coorte Prospectivo]

CONTEXT AND OBJECTIVE: Considering the difficulties in stopping smoking, this article aimed to identify factors relating to failure of attempts to quit smoking among smokers who sought care at an outpatient clinic in a general university hospital. DESIGN AND SETTING: Prospective cohort study evaluating 100 smokers who sought treatment at the Psychoactive Substances Outpatient Clinic. METHODS: The variables gathered were sociodemographic factors; degree of dependence (Fagerström questionnaire); stage of motivation for change (University of Rhode Island Change Assessment Scale); and presence of depression and anxiety (Hospital Anxiety and Depression Scale). The patients were followed up after 4, 8, 12 and 24 weeks to identify factors relating to failure to quit smoking. RESULTS: The patients were mostly women (75%), between 40 and 59 years of age (67%); with incomplete elementary education (60%); with leisure activities (57%); suffering from tobacco-related disease (53%); with previous attempts to quit smoking (70%); with a medical recommendation to stop (51%); with encouragement to stop (66%); and with a high degree of dependence (78%). The main motivational stage was contemplation/action (43%); the anxiety rate was 64% and the depression rate was 39%. The quitting rate was 66% among adherents and 17% among non-adherents (P < 0.001). Lack of success was correlated with absence of leisure, higher education and absence of tobacco-related disease. CONCLUSION: The variables of lack of leisure activities, higher education and/or lack of tobacco-related disease correlated with failure to quit smoking among smokers who sought treatment at an outpatient clinic in a tertiary general hospital.1296380386Tabaco e pobreza: Um ciclo vicioso, , http://www.opas.org.br/MOSTRANT.CFM?CODIGODEST=213, Organização Panamericana de Saúde. Available from: Accessed in 2011 (Jun 2)(2009) WHO Report on the Global Tobacco Epidemic, 2009: Implementing smoke-free environments, , http://whqlibdoc.who.int/publications/2009/9789241563918_eng_full.pdf, World Health Organization. Geneva: World Health OrganizationAvailable from: Accessed in 2011 (Jun 2)Tabagismo 2008, , http://www.ibge.gov.br/home/estatistica/populacao/trabalhoerendimento/pnad2008/suplementos/tabagismo/tab3_2.pdf, Brasil. Ministério do Planejamento, Orçamento e Gestão. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa Nacional por Amostra de Domicílios. 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How much should one sample to accurately predict the distribution of species assemblages? A virtual community approach

Correlative species distribution models (SDMs) are widely used to predict species distributions and assemblages, with many fundamental and applied uses. Different factors were shown to affect SDM prediction accuracy. However, real data cannot give unambiguous answers on these issues, and for this reason, artificial data have been increasingly used in recent years. Here, we move one step further by assessing how different factors can affect the prediction accuracy of virtual assemblages obtained by stacking individual SDM predictions (stacked SDMs, S-SDM). We modeled 100 virtual species in a real study area, testing five different factors: sample size (200-800-3200), sampling method (nested, non-nested), sampling prevalence (25%, 50%, 75% and species true prevalence), modelling technique (GAM, GLM, BRT and RF) and thresholding method (ROC, MaxTSS, and MaxKappa). We showed that the accuracy of S-SDM predictions is mostly affected by modelling technique followed by sample size. Models fitted by GAM/GLM had a higher accuracy and lower variance than BRT/RF. Model accuracy increased with sample size and a sampling strategy reflecting the true prevalence of the species was most successful. However, even with sample sizes as high as &gt;3000 sites, residual uncertainty remained in the predictions, potentially reflecting a bias introduced by creating and/or resampling the virtual species. Therefore, when evaluating the accuracy of predictions from S-SDMs fitted with real field data, one can hardly expect reaching perfect accuracy, and reasonably high values of similarity or predictive success can already be seen as valuable predictions. We recommend the use of a ‘plot-like’ sampling method (best approximation of the species’ true prevalence) and not simply increasing the number of presences-absences of species. As presented here, virtual simulations might be used more systematically in future studies to inform about the best accuracy level that one could expect given the characteristics of the data and the methods used to fit and stack SDMs

Using species richness and functional traits predictions to constrain assemblage predictions from stacked species distribution models

Aim: Modelling species at the assemblage level is required to make effective forecast of global change impacts on diversity and ecosystem functioning. Community predictions may be achieved using macroecological properties of communities (MEM), or by stacking of individual species distribution models (S-SDMs). To obtain more realistic predictions of species assemblages, the SESAM framework suggests applying successive filters to the initial species source pool, by combining different modelling approaches and rules. Here we provide a first test of this framework in mountain grassland communities. Location: The western Swiss Alps. Methods: Two implementations of the SESAM framework were tested: a "Probability ranking" rule based on species richness predictions and rough probabilities from SDMs, and a "Trait range" rule that uses the predicted upper and lower bound of community-level distribution of three different functional traits (vegetative height, specific leaf area and seed mass) to constraint a pool of environmentally filtered species from binary SDMs predictions. Results: We showed that all independent constraints expectedly contributed to reduce species richness overprediction. Only the "Probability ranking" rule allowed slightly but significantly improving predictions of community composition. Main conclusion: We tested various ways to implement the SESAM framework by integrating macroecological constraints into S-SDM predictions, and report one that is able to improve compositional predictions. We discuss possible improvements, such as further improving the causality and precision of environmental predictors, using other assembly rules and testing other types of ecological or functional constraints

Avaliação da atividade fenilalanina amônia liásica em folhas de bananeira inoculadas com o fungo Mycosphaerella fijiensis.

O objetivo deste trabalho foi avaliar os atributos bioquímicos envolvidos na defesa da bananeira ao ataque do fungo M. fijiensis, notadamente avaliar o papel da enzima fenilalanina amônia liase nesta resposta, nas variedades Caprichosa, Garantida, FHIA-18, Prata Ken, Prata Anã, Maçã e Grande Naine, nos tempos: 0, 6h, 24h, 48h, 72h dias após a inoculação. Após a coleta, as folhas foram pesadas e obtidos os extratos totais por meio de maceração das folhas com tampão acetato de sódio 50 mM, pH 5,2, os quais foram utilizados nas determinações de proteínas e atividade enzimática

New cathepsin V inhibitor from stems of Bowdichia virgilioides

Bioassay-guided fractionation of Bowdichia virgilioides Kunth, Fabaceae, extracts has led to the isolation of cathepsin V inhibitors. The investigation of the hexane and ethyl acetate extracts allowed the characterization of eleven compounds: lupeol, lupenone, β-sitosterol and stigmasterol in mixture, trans p-coumaric acid ester derivative, syringaresinol, bowdenol, 8-methoxycoumestrol, 3,4-hydroxy-7-methoxyisoflavone, 7,3′-dihydroxy-4′-methoxyisoflavone, and 5,4′-dihydroxy-7′-methoxyisoflavone. Structures of compounds were stablished by 1D and 2D NMR, and MS experiments. Among the isolated compounds, trans p-coumaric acid ester derivative and 8-methoxycoumestrol showed significant inhibition on cathepsin V, which is up to now unexplored. Keywords: Cathepsin, Cysteine peptidases, Inhibitors, Cerrado biom

Aerobic exercise prevents cardiomyocyte damage caused by oxidative stress in early cardiovascular disease by increasing vascularity while L-arginine supplementation prevents it by increasing activation of the enzyme nitric oxide synthase

L-Arginine and chronic exercise reduce oxidative stress. However, it is unclear how they affect cardiomyocytes during cardiovascular disease (CVD) development. The aim of this research was to investigate the possible effects of L-arginine supplementation and aerobic training on systemic oxidative stress and their consequences on cardiomyocytes during cardiometabolic disease onset caused by excess fructose. Wistar rats were allocated into four groups: control (C), fructose (F, 10% fructose in water), fructose training (FT; moderate running, 50-70% of the maximal velocity), and fructose arginine (FA; 880 mg/kg/day). Fructose was given for two weeks and fructose plus treatments for the subsequent eight weeks. Body composition, blood glucose, insulin, lipid profile, lipid peroxidation, nitrite, metalloproteinase-2 (MMP-2) activity, left ventricle histological changes, microRNA-126, -195, and -146, eNOS, p-eNOS, and TNF-α expressions were analyzed. Higher abdominal fat mass, triacylglycerol level, and insulin level were observed in the F group, and both treatments reversed these alterations. Myocardial vascularization was impaired in fructose-fed groups, except in FT. Cardiomyocyte hypertrophy was observed in all fructose-fed groups. TNF-α levels were higher in fructose-fed groups than in the C group, and p-eNOS levels were higher in the FA than in the C and F groups. Lipid peroxidation was higher in the F group than in the FT and C groups. During CVD onset, moderate aerobic exercise reduced lipid peroxidation, and both training and L-arginine prevented metabolic changes caused by excessive fructose. Myocardial vascularization was impaired by fructose, and cardiomyocyte hypertrophy appeared to be influenced by pro-inflammatory and oxidative environments