Addiction Mindsets and Psychological Processes of Quitting Smoking

Background: Lay belief systems about the malleability of human attributes have been shown to impact behavior change in multiple domains. Addiction mindset—i.e., beliefs about the permanence (vs. malleability) of addiction — may affect cigarette smokers’ ability to quit, but this has never been examined. Objectives: The aims of the present research were to develop a measure of addiction mindset (study 1) and examine its associations with various psychological aspects of quitting smoking (study 2). Methods: In Study 1, using factor analysis of current smokers’ and nonsmokers’ (n?=?600) responses to 22 items designed to measure addiction mindset, we developed a reliable six-item Addiction Mindset Scale (AMS). In Study 2, adult smokers (n?=?200) completed the AMS, and measures of a number of psychological processes related to smoking. Results: Higher scores on the AMS, indicative of the belief that addiction is malleable (referred to as a growth mindset), were positively and significantly associated with greater motivation to quit, greater commitment to quitting, greater self-efficacy to abstain, less attribution of failure to lack of ability to change addiction, and fewer self-reported barriers to cessation (all p’s < .05). Conclusions: The results of this study show a relationship between the beliefs about the permanence of addiction and psychological processes relevant to quitting smoking. The findings underscore the potential of future research exploring how addiction mindsets relate to successful smoking cessation as well as other types of addictive behavior and how they can be applied to change people’s behavior.     

HPLC-MS Method for Simultaneous Quantitative Determination of an Innovative Russian Gestagen and its Metabolites in Rat and Rabbit Blood Sera

Pharmaceutical Chemistry Journal - An HPLC-MS method for simultaneous quantitative determination of a novel gestagenic pharmaceutical and two of its metabolites in rat and rabbit blood sera was...     

Estimation of phase signal change in neuronal current MRI for evoke response of tactile detection with realistic somatosensory laminar network model

Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000 PNs of simulated network were positioned randomly in a voxel. Biot–savart law applied to calculate neuronal magnetic field and additional phase. The procedure repeated for eleven neuronal arrangements in the voxel. PSC signal variation with the echo time and voxel size was assessed. The simulated results show that PSC signal increases with echo time, especially 100/80 ms after stimulus for gradient echo/spin echo sequence. It can be up to 0.1 mrad for echo time = 175 ms and voxel size = 1.48 × 1.48 × 2.18 mm³. With echo time less than 25 ms after stimulus, it was just acquired effects of physiological noise on PSC signal. The absolute value of the signal increased with decrease of voxel size, but its components had complex variation. External field orthogonal to local surface of cortex maximizes the signal. Expected PSC signal for tactile detection in the somatosensory cortex increase with echo time and have no oscillation.