970 resultados para Cannabis sativa
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Two retrospective epidemiologic studies have shown that cannabis is the main psychoactive substance detected in the blood of drivers suspected of driving under the influence of psychotropic drugs. An oral administration double-blind crossover study was carried out with eight healthy male subjects, aged 22 to 30 years, all occasional cannabis smokers. Three treatments and one placebo were administered to all participants at a two week interval: 20 mg dronabinol, 16.5 mg D9-tétrahydrocannabinol (THC) and 45.7 mg THC as a cannabis milk decoction. Participants were asked to report the subjective drug effects and their willingness to drive under various circumstances on a visual analog scale. Clinical observations, a psychomotor test and a tracking test on a driving simulator were also carried out. Compared to cannabis smoking, THC, 11-OH-THC and THC-COOH blood concentrations remained low through the whole study (<13.1 ng THC/mL,<24.7 ng 11-OH-THC/mL and<99.9 ng THC-COOH/mL). Two subjects experienced deep anxiety symptoms suggesting that this unwanted side-effect may occur when driving under the influence of cannabis or when driving and smoking a joint. No clear association could be found between these adverse reactions and a susceptibility gene to propensity to anxiety and psychotic symptoms (genetic polymorphism of the catechol-O-methyltransferase). The questionnaires have shown that the willingness to drive was lower when the drivers were assigned an insignificant task and was higher when the mission was of crucial importance. The subjects were aware of the effects of cannabis and their performances on the road sign and tracking test were greatly impaired, especially after ingestion of the strongest dose. The Cannabis Influence Factor (CIF) which relies on the molar ratio of active and inactive cannabinoids in blood provided a good estimate of the fitness to drive.
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PURPOSE: To assess tobacco, alcohol, cannabis and benzodiazepine use in methadone maintenance treatment (MMT) as potential sources of variability in methadone pharmacokinetics. METHODS: Trough plasma (R)- and (S)-methadone concentrations were measured on 77 Australian and 74 Swiss MMT patients with no additional medications other than benzodiazepines. Simple and multiple regression analyses were performed for the primary metric, plasma methadone concentration/dose. RESULTS: Cannabis and methadone dose were significantly associated with lower 24-h plasma (R)- and (S)-methadone concentrations/dose. The models containing these variables explained 14-16% and 17-25% of the variation in (R)- and (S)-methadone concentration/dose, respectively. Analysis of 61 patients using only CYP3A4 metabolised benzodiazepines showed this class to be associated with higher (R)-concentration/dose, which is consistent with a potential competitive inhibition of CYP3A4. CONCLUSION: Cannabis use and higher methadone doses in MMT could in part be a response to-or a cause of-more rapid methadone clearance. The effects of cannabis and benzodiazepines should be controlled for in future studies on methadone pharmacokinetics in MMT.
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BACKGROUND/AIMS: Cannabis use is a growing challenge for public health, calling for adequate instruments to identify problematic consumption patterns. The Cannabis Use Disorders Identification Test (CUDIT) is a 10-item questionnaire used for screening cannabis abuse and dependency. The present study evaluated that screening instrument. METHODS: In a representative population sample of 5,025 Swiss adolescents and young adults, 593 current cannabis users replied to the CUDIT. Internal consistency was examined by means of Cronbach's alpha and confirmatory factor analysis. In addition, the CUDIT was compared to accepted concepts of problematic cannabis use (e.g. using cannabis and driving). ROC analyses were used to test the CUDIT's discriminative ability and to determine an appropriate cut-off. RESULTS: Two items ('injuries' and 'hours being stoned') had loadings below 0.5 on the unidimensional construct and correlated lower than 0.4 with the total CUDIT score. All concepts of problematic cannabis use were related to CUDIT scores. An ideal cut-off between six and eight points was found. CONCLUSIONS: Although the CUDIT seems to be a promising instrument to identify problematic cannabis use, there is a need to revise some of its items.
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Purpose: Young cannabis users are at increased risk for cigarette initiation and later progression to nicotine dependence. The present study assesses to which extent cannabis users are exposed to nicotine through mulling, a widespread process consisting of mixing tobacco to cannabis for its consumption. Methods: Data are issued from an ongoing observational study taking place in Switzerland. A total of 267 eligible participants (mean age 19 years, 46.4% males) completed an anonymous self-administered questionnaire on their tobacco and cannabis intake in the previous 5 days. They also provided a urine sample that was blindly analyzed for cotinine (a key metabolite of nicotine) using liquid-chromatography coupled mass-spectrometry. After the exclusion of cannabis users not having smoked at least one joint/blunt in which tobacco had been mixed (n _ 2), and participants reporting other sources of nicotine exposition than cigarettes or mulling (n _37), four groups were created: cannabis and cigarette abstainers (ABS, n_ 69), cannabis only smokers (CAS; n _ 33), cigarette only smokers (CIS; n _ 62); and cannabis and cigarette smokers (CCS, n _ 64). Cotinine measures of CAS were compared to those of ABS, CIS and CCS. All comparisons were performed using ANCOVA, controlling for age, gender, ethnicity, BMI and environmental exposure to cigarette smoke in the past month (at home, in school/at work, in social settings). The number of mixed joints/blunts smoked in the previous 5 days was additionally taken into account when comparing CAS to CCS. Cotinine values (ng/ml) are reported as means with 95% confidence interval (95% CI). Results: In the previous 5 days, CAS had smoked on average 10 mixed joints/blunts, CIS 30 cigarettes, and CCS 8 mixed joints/ blunts and 41 cigarettes. Cotinine levels of participants considerably differed between groups. The lowest measure was found among ABS (3.2 [0.5-5.9]), followed in growing order by CAS (294.6 [157.1-432.0]), CIS (362.8 [258.4-467.3]), and CCS (649.9 [500.7-799.2]). In the multivariate analysis, cotinine levels of CAS were significantly higher than those of ABS (p _.001), lower than those of CCS (p _ .003), but did not differ from levels of CIS (p _ .384). Conclusions: Our study reveals cannabis users to be significantly exposed to nicotine through mulling, even after controlling for several possible confounders such as environmental exposure to cigarette smoke. Utmost, mixing tobacco to Poster cannabis can result in a substantial nicotine exposition as cotinine levels from cannabis only smokers were as high as those of moderate cigarette smokers. Our findings also suggest that mulling is adding up to the already important nicotine exposition of cigarettes smokers. Because of the addictiveness of nicotine, mulling should be part of a comprehensive assessment of substance use among adolescents and young adults, especially when supporting their cannabis and cigarette quitting attempts. Sources of Support: This study was funded by the Public Health Service of the Canton de Vaud. Dr. BÊlanger's contribution was possible through grants from the Royal College of Physicians and Surgeons of Canada, the CHUQ/CMDP Foundation and the Laval University McLaughlin program, QuÊbec, Canada.
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INTRODUCTION: This study examines the relationship between nicotine exposure and tobacco addiction among young smokers consuming either only tobacco or only tobacco and cannabis. METHODS: Data on tobacco and cannabis use were collected by a questionnaire among 313 adolescents and young adults in Western Switzerland between 2009 and 2010. In addition, a urine sample was used to determine urinary cotinine level. Nicotine addiction was measured using the Fagerström Test for Nicotine Dependence (FTND). In this study, we focused on a sample of 142 participants (mean age 19.54) that reported either smoking only tobacco cigarettes (CIG group, n = 70) or smoking both tobacco cigarettes and cannabis (CCS group, n = 72). RESULTS: The FTND did not differ significantly between CIG (1.96 ± 0.26) and CCS (2.66 ± 0.26) groups (p = 0.07). However, participants in the CCS group smoked more cigarettes (8.30 ± 0.79 vs. 5.78 ± 0.8, p = 0.03) and had a higher mean cotinine value (671.18 ± 67.6 vs. 404.32 ± 68.63, p = 0.008) than the CIG group. Further, the association between cotinine and FTND was much stronger among the CIG than among the CCS group (regression coefficient of 0.0031 vs. 0.00099, p < 0.0001). CONCLUSION: Adolescents smoking tobacco and cannabis cigarettes featured higher levels of cotinine than youth smoking only tobacco; however, there was no significant difference in the addiction score. The FTND score is intended to measure nicotine dependence from smoked tobacco cigarettes. Hence, to accurately determine nicotine exposure and the associated dependence among young smokers, it seems necessary to inquire about cannabis consumption.
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The purpose of this article is to identify tobacco and cannabis co-consumptions and consumers' perceptions of each substance. A qualitative research including 22 youths (14 males) aged 15-21 years in seven individual interviews and five focus groups. Discussions were recorded, transcribed verbatim and transferred to Atlas.ti software for narrative analysis. The main consumption mode is cannabis cigarettes which always mix cannabis and tobacco. Participants perceive cannabis much more positively than tobacco, which is considered unnatural, harmful and addictive. Future consumption forecasts thus more often exclude tobacco smoking than cannabis consumption. A substitution phenomenon often takes place between both substances. Given the co-consumption of tobacco and cannabis, in helping youths quit or decrease their consumptions, both substances should be taken into account in a global approach. Cannabis consumers should be made aware of their tobacco use while consuming cannabis and the risk of inducing nicotine addiction through cannabis use, despite the perceived disconnect between the two substances. Prevention programs should correct made-up ideas about cannabis consumption and convey a clear message about its harmful consequences. Our findings support the growing evidence which suggests that nicotine dependence and cigarette smoking may be induced by cannabis consumption.
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This article presents an experimental study about the classification ability of several classifiers for multi-classclassification of cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland lawenforcement authorities regularly ask forensic laboratories to determinate the chemotype of a seized cannabisplant and then to conclude if the plantation is legal or not. This classification is mainly performed when theplant is mature as required by the EU official protocol and then the classification of cannabis seedlings is a timeconsuming and costly procedure. A previous study made by the authors has investigated this problematic [1]and showed that it is possible to differentiate between drug type (illegal) and fibre type (legal) cannabis at anearly stage of growth using gas chromatography interfaced with mass spectrometry (GC-MS) based on therelative proportions of eight major leaf compounds. The aims of the present work are on one hand to continueformer work and to optimize the methodology for the discrimination of drug- and fibre type cannabisdeveloped in the previous study and on the other hand to investigate the possibility to predict illegal cannabisvarieties. Seven classifiers for differentiating between cannabis seedlings are evaluated in this paper, namelyLinear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), Nearest NeighbourClassification (NNC), Learning Vector Quantization (LVQ), Radial Basis Function Support Vector Machines(RBF SVMs), Random Forest (RF) and Artificial Neural Networks (ANN). The performance of each method wasassessed using the same analytical dataset that consists of 861 samples split into drug- and fibre type cannabiswith drug type cannabis being made up of 12 varieties (i.e. 12 classes). The results show that linear classifiersare not able to manage the distribution of classes in which some overlap areas exist for both classificationproblems. Unlike linear classifiers, NNC and RBF SVMs best differentiate cannabis samples both for 2-class and12-class classifications with average classification results up to 99% and 98%, respectively. Furthermore, RBFSVMs correctly classified into drug type cannabis the independent validation set, which consists of cannabisplants coming from police seizures. In forensic case work this study shows that the discrimination betweencannabis samples at an early stage of growth is possible with fairly high classification performance fordiscriminating between cannabis chemotypes or between drug type cannabis varieties.
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BACKGROUND: The use of cannabis and other illegal drugs is particularly prevalent in male young adults and is associated with severe health problems. This longitudinal study explored variables associated with the onset of cannabis use and the onset of illegal drug use other than cannabis separately in male young adults, including demographics, religion and religiosity, health, social context, substance use, and personality. Furthermore, we explored how far the gateway hypothesis and the common liability to addiction model are in line with the resulting prediction models. METHODS: The data were gathered within the Cohort Study on Substance Use Risk Factors (C-SURF). Young men aged around 20 years provided demographic, social, health, substance use, and personality-related data at baseline. Onset of cannabis and other drug use were assessed at 15-months follow-up. Samples of 2,774 and 4,254 individuals who indicated at baseline that they have not used cannabis and other drugs, respectively, in their life and who provided follow-up data were used for the prediction models. Hierarchical logistic stepwise regressions were conducted, in order to identify predictors of the late onset of cannabis and other drug use separately. RESULTS: Not providing for oneself, having siblings, depressiveness, parental divorce, lower parental knowledge of peers and the whereabouts, peer pressure, very low nicotine dependence, and sensation seeking were positively associated with the onset of cannabis use. Practising religion was negatively associated with the onset of cannabis use. Onset of drug use other than cannabis showed a positive association with depressiveness, antisocial personality disorder, lower parental knowledge of peers and the whereabouts, psychiatric problems of peers, problematic cannabis use, and sensation seeking. CONCLUSIONS: Consideration of the predictor variables identified within this study may help to identify young male adults for whom preventive measures for cannabis or other drug use are most appropriate. The results provide evidence for both the gateway hypothesis and the common liability to addiction model and point to further variables like depressiveness or practising of religion that might influence the onset of drug use.
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Delta(9)-Tetrahydrocannabinol (THC) is frequently found in the blood of drivers suspected of driving under the influence of cannabis or involved in traffic crashes. The present study used a double-blind crossover design to compare the effects of medium (16.5 mg THC) and high doses (45.7 mg THC) of hemp milk decoctions or of a medium dose of dronabinol (20 mg synthetic THC, Marinol on several skills required for safe driving. Forensic interpretation of cannabinoids blood concentrations were attempted using the models proposed by Daldrup (cannabis influencing factor or CIF) and Huestis and coworkers. First, the time concentration-profiles of THC, 11-hydroxy-Delta(9)-tetrahydrocannabinol (11-OH-THC) (active metabolite of THC), and 11-nor-9-carboxy-Delta(9)-tetrahydrocannabinol (THCCOOH) in whole blood were determined by gas chromatography-mass spectrometry-negative ion chemical ionization. Compared to smoking studies, relatively low concentrations were measured in blood. The highest mean THC concentration (8.4 ng/mL) was achieved 1 h after ingestion of the strongest decoction. Mean maximum 11-OH-THC level (12.3 ng/mL) slightly exceeded that of THC. THCCOOH reached its highest mean concentration (66.2 ng/mL) 2.5-5.5 h after intake. Individual blood levels showed considerable intersubject variability. The willingness to drive was influenced by the importance of the requested task. Under significant cannabinoids influence, the participants refused to drive when they were asked whether they would agree to accomplish several unimportant tasks, (e.g., driving a friend to a party). Most of the participants reported a significant feeling of intoxication and did not appreciate the effects, notably those felt after drinking the strongest decoction. Road sign and tracking testing revealed obvious and statistically significant differences between placebo and treatments. A marked impairment was detected after ingestion of the strongest decoction. A CIF value, which relies on the molar ratio of main active to inactive cannabinoids, greater than 10 was found to correlate with a strong feeling of intoxication. It also matched with a significant decrease in the willingness to drive, and it matched also with a significant impairment in tracking performances. The mathematic model II proposed by Huestis et al. (1992) provided at best a rough estimate of the time of oral administration with 27% of actual values being out of range of the 95% confidence interval. The sum of THC and 11-OH-THC blood concentrations provided a better estimate of impairment than THC alone. This controlled clinical study points out the negative influence on fitness to drive after medium or high dose oral THC or dronabinol.
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This naturalistic cross-sectional study explores how and to what extent cannabis dependence was associated with intrapersonal aspects (anxiety, coping styles) and interpersonal aspects of adolescent functioning (school status, family relationships, peer relationships, social life). A convenience sample of 110 adolescents (aged 12 to 19) was recruited and subdivided into two groups (38 with a cannabis dependence and 72 nondependent) according to DSM-IV-TR criteria for cannabis dependence. Participants completed the State-Trait Anxiety Inventory (STAI-Y), the Coping Across Situations Questionnaire (CASQ), and the Adolescent Drug Abuse Diagnosis (ADAD) interview investigating psychosocial and interpersonal problems in an adolescent's life. Factors associated with cannabis dependence were explored with logistic regression analyses. The results indicated that severity of problems in social life and peer relationships (OR = 1.68, 95% CI = 1.21 - 2.33) and avoidant coping (OR = 4.22, 95% CI = 1.01 - 17.73) were the only discriminatory factors for cannabis dependence. This model correctly classified 84.5% of the adolescents. These findings are partially consistent with the "self-medication hypothesis" and underlined the importance of peer relationships and dysfunctional coping strategies in cannabis dependence in adolescence. Limitations of the study and implications for clinical work with adolescents are discussed.
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Cannabis use by people suffering from schizophrenia increase relapse rate and reduce adhesion to treatment. Motivational interventions could reduce cannabis misuse. The motivational interviewing principles and techniques are presented in a concrete way as well as the required adaptations to bypass cognitive deficits associated with schizophrenia.
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INTRODUCTION: To determine if mulling, the process of adding tobacco to cannabis for its consumption, exposes young cannabis users to significant levels of nicotine. METHODS: This observational study performed in 2009-2010 among Swiss youths aged 16-25 years involved the completion of a self-administrated questionnaire and the collection of a urine sample on the same day. Measures of urinary cotinine were blindly performed using liquid chromatography coupled-mass spectrometry. A total of 197 eligible participants were divided in 3 groups based on their consumption profile in the past 5 days: 70 abstainers (ABS) not having used cigarettes or cannabis, 57 cannabis users adding tobacco to the cannabis they smoke (MUL) but not having smoked cigarettes, and 70 cigarette smokers (CIG) not having smoked cannabis. RESULTS: Exposure to nicotine was at its lowest among ABS with a mean (SE) cotinine level of 3.2 (1.4) ng/ml compared, respectively, with 214.6 (43.8) and 397.9 (57.4) for MUL and CIG (p < .001). While consumption profile appeared as the only significant factor of influence when examining nicotine exposure from the ABS and MUL participants on multivariate analysis, it did not result in substantial differences among MUL and CIG groups. CONCLUSIONS: Urinary cotinine levels found among MUL are high enough to indicate a significant exposure to nicotine originating from the mulling process. In line with our results, health professionals should pay attention to mulling as it is likely to influence cannabis and cigarette use as well as the efficacy of cessation interventions.
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[Sommaire] 1. Le cannabis à travers l'histoire. - 2. Le cannabis dans tous ses états. - 3. Le phénomène des dépendances. - 4. Le cannabis : médicament ou stupéfiant? - 5. Un enjeu pour l'individu, la famille et la société Le cannabis est la drogue illégale la plus consommée en Europe. Mais sait-on ce qu'il est vraiment? Ce livre propose de répondre aux questions concernant le cannabis: Comment agit-il sur le cerveau? Est-il un médicament utile à l'allégement de la douleur? Peut-il induire une dépendance? Si une personne est libre de commencer à consommer du cannabis, l'est-elle toujours au moment de choisir d'arrêter? Quelle est son influence sur l'adolescent?