The Cannabis Expectancy Questionnaire for Men who have Sex with Men (CEQ-MSM): A measure of substance-related beliefs

Objective Substance-related expectancies are associated with substance use and post-substance use thoughts, feelings and behaviours. The expectancies held by specific cultural or sub-cultural groups have rarely been investigated. This research maps expectancies specific to gay and other men who have sex with men (MSM) and their relationship with substance patterns and behaviours following use, including sexual practices (e.g., unprotected anal intercourse). This study describes the development of a measure of such beliefs for cannabis, the Cannabis Expectancy Questionnaire for Men who have Sex with Men (CEQ-MSM). Method Items selected through a focus group and interviews were piloted on 180 self-identified gay or other MSM via an online questionnaire. Results Factor analysis revealed six distinct substance reinforcement domains (“Enhanced sexual experience”, “Sexual negotiation”, “Cognitive impairment”, “Social and emotional facilitation”, “Enhanced sexual desire”, and “Sexual inhibition”). The scale was associated with consumption patterns of cannabis, and in a crucial test of discriminant validity not with the consumption of alcohol or stimulants. Conclusions The CEQ-MSM represents a reliable and valid measure of outcome expectancies, related to cannabis among MSM. Future applications of the CEQ-MSM in health promotion, clinical settings and research may contribute to reducing harm associated with substance use among MSM, including HIV transmission.

Morphine-induced receptor endocytosis in a novel knockin mouse reduces tolerance and dependence

Abstract Opioid drugs, such as morphine, are among the most effective analgesics available. However, their utility for the treatment of chronic pain is limited by side effects including tolerance and dependence. Morphine acts primarily through the mu-opioid receptor (MOP-R) , which is also a target of endogenous opioids. However, unlike endogenous ligands, morphine fails to promote substantial receptor endocytosis both in vitro, and in vivo. Receptor endocytosis serves at least two important functions in signal transduction. First, desensitization and endocytosis act as an "off" switch by uncoupling receptors from G protein. Second, endocytosis functions as an "on" switch, resensitizing receptors by recycling them to the plasma membrane. Thus, both the off and on function of the MOP-R are altered in response to morphine compared to endogenous ligands. To examine whether the low degree of endocytosis induced by morphine contributes to tolerance and dependence, we generated a knockin mouse that expresses a mutant MOP-R that undergoes morphine-induced endocytosis. Morphine remains an excellent antinociceptive agent in these mice. Importantly, these mice display substantially reduced antinociceptive tolerance and physical dependence. These data suggest that opioid drugs with a pharmacological profile similar to morphine but the ability to promote endocytosis could provide analgesia while having a reduced liability for promoting tolerance and dependence

Business cycle dynamics with duration dependence and leading indicators

Durland and McCurdy [Durland, J.M., McCurdy, T.H., 1994. Duration-dependent transitions in a Markov model of US GNP growth. Journal of Business and Economic Statistics 12, 279–288] investigated the issue of duration dependence in US business cycle phases using a Markov regime-switching approach, introduced by Hamilton [Hamilton, J., 1989. A new approach to the analysis of time series and the business cycle. Econometrica 57, 357–384] and extended to the case of variable transition parameters by Filardo [Filardo, A.J., 1994. Business cycle phases and their transitional dynamics. Journal of Business and Economic Statistics 12, 299–308]. In Durland and McCurdy’s model duration alone was used as an explanatory variable of the transition probabilities. They found that recessions were duration dependent whilst expansions were not. In this paper, we explicitly incorporate the widely-accepted US business cycle phase change dates as determined by the NBER, and use a state-dependent multinomial Logit modelling framework. The model incorporates both duration and movements in two leading indexes – one designed to have a short lead (SLI) and the other designed to have a longer lead (LLI) – as potential explanatory variables. We find that doing so suggests that current duration is not only a significant determinant of transition out of recessions, but that there is some evidence that it is also weakly significant in the case of expansions. Furthermore, we find that SLI has more informational content for the termination of recessions whilst LLI does so for expansions.

Psychotic symptom and cannabis relapse in recent-onset sychosis

Background Cannabis use appears to exacerbate psychotic symptoms and increase risk of psychotic relapse. However, the relative contribution of cannabis use compared with other risk factors is unclear. The influence of psychotic symptoms on cannabis use has received little attention. Aims To examine the influence of cannabis use on psychotic symptom relapse and the influence of psychotic symptom severity on relapse in cannabis use in the 6 months following hospital admission. Method At baseline, 84 participants with recent-onset psychosis were assessed and 81 were followed up weekly for 6 months, using telephone and face-to-face interviews. Results A higher frequency of cannabis use was predictive of psychotic relapse, after controlling for medication adherence, other substance use and duration of untreated psychosis. An increase in psychotic symptoms was predictive of relapse to cannabis use, and medication adherence reduced cannabis relapse risk. Conclusions The relationship between cannabis use and psychosis may be bidirectional, highlighting the need for early intervention programmes to target cannabis use and psychotic symptom severity in this population.

Substance-induced psychosis : a diagnostic conundrum

Objective: To critically examine the DSM-IV-TR criteria for Substance-Induced Psychotic Disorder (SIPD). Data sources: Leading electronic databases (such as Medline, Pubmed) were searched for the years 1992 through 2007, using combinations of the following key search terms: substance abuse/dependence, alcohol, marijuana, cannabis, methamphetamine, crack, cocaine, amphetamine, ecstasy, ketamine, phencyclidine, LSD, mental health, drug-induced psychosis, substance-induced psychosis, psychosis, schizophrenia. References identified from bibliographies of pertinent articles and books in the field were also collected and reviewed. Data extraction: Only research studies or case reports series that presented data on populations diagnosed with SIPD using clinical or structured diagnostic interviews published in English were used to assess the validity of the current SIPD criteria. Data synthesis: We identified 49 articles that presented clinical data on SIPD. The majority of these publications were case reports, with only 18 articles specifically focusing on delineating the clinical characteristics or outcomes of individuals diagnosed with SIPD. While several large studies have recently been conducted to assess the stability of SIPD, there is a dearth of research rigorously examining the validity of DSM-IV diagnostic criteria across substances. Conclusions: There remains a striking paucity of information on the outcome, treatment and best practice for substance-associated psychotic episodes. Further work is clearly required before the advent of DSM-V. We propose an alternative, broader classification that better reflects the current evidence base, inferring association rather than causation.

Multifractal analysis of geomagnetic storm and solar flare indices and their class dependence

The multifractal properties of two indices of geomagnetic activity, D st (representative of low latitudes) and a p (representative of the global geomagnetic activity), with the solar X-ray brightness, X l , during the period from 1 March 1995 to 17 June 2003 are examined using multifractal detrended fluctuation analysis (MF-DFA). The h(q) curves of D st and a p in the MF-DFA are similar to each other, but they are different from that of X l , indicating that the scaling properties of X l are different from those of D st and a p . Hence, one should not predict the magnitude of magnetic storms directly from solar X-ray observations. However, a strong relationship exists between the classes of the solar X-ray irradiance (the classes being chosen to separate solar flares of class X-M, class C, and class B or less, including no flares) in hourly measurements and the geomagnetic disturbances (large to moderate, small, or quiet) seen in D st and a p during the active period. Each time series was converted into a symbolic sequence using three classes. The frequency, yielding the measure representations, of the substrings in the symbolic sequences then characterizes the pattern of space weather events. Using the MF-DFA method and traditional multifractal analysis, we calculate the h(q), D(q), and τ (q) curves of the measure representations. The τ (q) curves indicate that the measure representations of these three indices are multifractal. On the basis of this three-class clustering, we find that the h(q), D(q), and τ (q) curves of the measure representations of these three indices are similar to each other for positive values of q. Hence, a positive flare storm class dependence is reflected in the scaling exponents h(q) in the MF-DFA and the multifractal exponents D(q) and τ (q). This finding indicates that the use of the solar flare classes could improve the prediction of the D st classes.

Variational Bayes and the reduced dependence approximation for the autologistic model on an irregular grid with applications

Discrete Markov random field models provide a natural framework for representing images or spatial datasets. They model the spatial association present while providing a convenient Markovian dependency structure and strong edge-preservation properties. However, parameter estimation for discrete Markov random field models is difficult due to the complex form of the associated normalizing constant for the likelihood function. For large lattices, the reduced dependence approximation to the normalizing constant is based on the concept of performing computationally efficient and feasible forward recursions on smaller sublattices which are then suitably combined to estimate the constant for the whole lattice. We present an efficient computational extension of the forward recursion approach for the autologistic model to lattices that have an irregularly shaped boundary and which may contain regions with no data; these lattices are typical in applications. Consequently, we also extend the reduced dependence approximation to these scenarios enabling us to implement a practical and efficient non-simulation based approach for spatial data analysis within the variational Bayesian framework. The methodology is illustrated through application to simulated data and example images. The supplemental materials include our C++ source code for computing the approximate normalizing constant and simulation studies.

Sequential fusion of decisions with favorable dependence for controlled verification errors

Statistical dependence between classifier decisions is often shown to improve performance over statistically independent decisions. Though the solution for favourable dependence between two classifier decisions has been derived, the theoretical analysis for the general case of 'n' client and impostor decision fusion has not been presented before. This paper presents the expressions developed for favourable dependence of multi-instance and multi-sample fusion schemes that employ 'AND' and 'OR' rules. The expressions are experimentally evaluated by considering the proposed architecture for text-dependent speaker verification using HMM based digit dependent speaker models. The improvement in fusion performance is found to be higher when digit combinations with favourable client and impostor decisions are used for speaker verification. The total error rate of 20% for fusion of independent decisions is reduced to 2.1% for fusion of decisions that are favourable for both client and impostors. The expressions developed here are also applicable to other biometric modalities, such as finger prints and handwriting samples, for reliable identity verification.