6 resultados para Opioid Dependence

em Université de Lausanne, Switzerland


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This paper reviews the literature on clinical signs such as imitation behavior, grasp reaction, manipulation of tools, utilization behavior, environmental dependency, hyperlexia, hypergraphia and echolalia. Some aspects of this semiology are of special interest because they refer to essential notions such as free-will and autonomy.

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β-Arrestin2 (ARRB2) is a component of the G-protein-coupled receptor complex and is involved in μ-opioid and dopamine D(2) receptor signaling, two central processes in methadone signal transduction. We analyzed 238 patients in methadone maintenance treatment (MMT) and identified a haplotype block (rs34230287, rs3786047, rs1045280 and rs2036657) spanning almost the entire ARRB2 locus. Although none of these single nucleotide polymorphisms (SNPs) leads to a change in amino-acid sequence, we found that for all the SNPs analyzed, with exception of rs34230287, homozygosity for the variant allele confers a nonresponding phenotype (n=73; rs1045280C and rs2036657G: OR=3.1, 95% CI=1.5-6.3, P=0.004; rs3786047A: OR=2.5, 95% CI=1.2-5.1, P=0.02) also illustrated by a 12-fold shorter period of negative urine screening (P=0.01). The ARRB2 genotype may thus contribute to the interindividual variability in the response to MMT and help to predict response to treatment.

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ABSTRACT: In order to evaluate the one-year evolution of web-based information on alcohol dependence, we re-assessed alcohol-related sites in July 2007 with the same evaluating tool that had been used to assess these sites in June 2006. Websites were assessed with a standardized form designed to rate sites on the basis of accountability, presentation, interactivity, readability, and content quality. The DISCERN scale was also used, which aimed to assist persons without content expertise in assessing the quality of written health publications. Scores were highly stable for all components of the form one year later (r = .77 to .95, p < .01). Analysis of variance for repeated measures showed no time effect, no interaction between time and scale, no interaction between time and group (affiliation categories), and no interaction between time, group, and scale. The study highlights lack of change of alcohol-dependence-related web pages across one year.

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INTRODUCTION: Several studies have shown an increased risk of type 2 diabetes among smokers. Therefore, the aim of this analysis was to assess the relationship between smoking, cumulative smoking exposure and nicotine dependence with pre-diabetes. METHODS: We performed a cross-sectional analysis of healthy adults aged 25-41 in the Principality of Liechtenstein. Individuals with known diabetes, Body Mass Index (BMI) >35 kg/m² and prevalent cardiovascular disease were excluded. Smoking behaviour was assessed by self-report. Pre-diabetes was defined as glycosylated haemoglobin between 5.7% and 6.4%. Multivariable logistic regression models were done. RESULTS: Of the 2142 participants (median age 37 years), 499 (23.3%) had pre-diabetes. There were 1,168 (55%) never smokers, 503 (23%) past smokers and 471 (22%) current smokers, with a prevalence of pre-diabetes of 21.2%, 20.9% and 31.2%, respectively (p <0.0001). In multivariable regression models, current smokers had an odds ratio (OR) of pre-diabetes of 1.82 (95% confidential interval (CI) 1.39; 2.38, p <0.0001). Individuals with a smoking exposure of <5, 5-10 and >10 pack-years had an OR (95% CI) for pre-diabetes of 1.34 (0.90; 2.00), 1.80 (1.07; 3.01) and 2.51 (1.80; 3.59) (p linear trend <0.0001) compared with never smokers. A Fagerström score of 2, 3-5 and >5 among current smokers was associated with an OR (95% CI) for pre-diabetes of 1.27 (0.89; 1.82), 2.15 (1.48; 3.13) and 3.35 (1.73; 6.48) (p linear trend <0.0001). DISCUSSION: Smoking is strongly associated with pre-diabetes in young adults with a low burden of smoking exposure. Nicotine dependence could be a potential mechanism of this relationship.

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We show that a simple mixing idea allows one to establish a number of explicit formulas for ruin probabilities and related quantities in collective risk models with dependence among claim sizes and among claim inter-occurrence times. Examples include compound Poisson risk models with completely monotone marginal claim size distributions that are dependent according to Archimedean survival copulas as well as renewal risk models with dependent inter-occurrence times.