Reduced attentional blink for alcohol-related stimuli in heavy social drinkers

Researchers have used various paradigms to show that attentional biases for substance-related stimuli are an important feature of addictive behaviours. However, it is not clear whether these attentional biases occur at the level of encoding or at later post-attentive processing stages. We examined attentional bias at the level of encoding with the attentional blink paradigm in a sample of non-clinical heavy and light-drinking students. Our results show a diminished attentional blink effect for alcohol-related words compared with soft drink-related words among heavy drinkers. The attentional blink was equally strong for alcohol-related and soft drink-related words among light drinkers. This suggests that alcohol-related information is processed relatively more efficiently in the former group. Even though these results are promising, our study shows that the internal consistency of the attentional blink can be improved

Bioinformatic Challenges Detecting Genetic Variation in Precision Medicine Programs

Precision medicine programs to identify clinically relevant genetic variation have been revolutionized by access to increasingly affordable high-throughput sequencing technologies. A decade of continual drops in per-base sequencing costs means it is now feasible to sequence an individual patient genome and interrogate all classes of genetic variation for < $1,000 USD. However, while advances in these technologies have greatly simplified the ability to obtain patient sequence information, the timely analysis and interpretation of variant information remains a challenge for the rollout of large-scale precision medicine programs. This review will examine the challenges and potential solutions that exist in identifying predictive genetic biomarkers and pharmacogenetic variants in a patient and discuss the larger bioinformatic challenges likely to emerge in the future. It will examine how both software and hardware development are aiming to overcome issues in short read mapping, variant detection and variant interpretation. It will discuss the current state of the art for genetic disease and the remaining challenges to overcome for complex disease. Success across all types of disease will require novel statistical models and software in order to ensure precision medicine programs realize their full potential now and into the future

Detecting pathogenic variants in autoimmune diseases using high‐throughput sequencing

Sequencing the first human genome in 2003 took 15 years and cost $2.7 billion. Advances in sequencing technologies have since decreased costs to the point where it is now feasible to resequence a whole human genome for$1000 in a single day. These advances have allowed the generation of huge volumes of high‐quality human sequence data used to construct increasingly large catalogs of both population‐level and disease‐causing variation. The existence of such databases, coupled with a high‐quality human reference genome, means we are able to interrogate and annotate all types of genetic variation and identify pathogenic variants for many diseases. Increasingly, sequencing‐based approaches are being used to elucidate the underlying genetic cause of autoimmune diseases, a group of roughly 80 polygenic diseases characterized by abnormal immune responses where healthy tissue is attacked. Although sequence data generation has become routine and affordable, significant challenges remain with no gold‐standard methodology to identify pathogenic variants currently available. This review examines the latest methodologies used to identify pathogenic variants in autoimmune diseases and considers available sequencing options and subsequent bioinformatic methodologies and strategies. The development of reliable and robust sequencing and analytic workflows to detect pathogenic variants is critical to realize the potential of precision medicine programs where patient variant information is used to inform clinical practice

Alcohol approach tendencies in heavy drinkers: comparison of effects in a relevant stimulus-response compatibility task and an approach/avoidance Simon task

Several recent studies suggest that alcohol-related cues elicit automatic approach tendencies in heavy drinkers. A variety of tasks have been used to demonstrate these effects, including Relevant Stimulus-Response Compatibility (R-SRC) tasks and variants of Simon tasks. Previous work with normative stimuli suggests that the R-SRC task may be more sensitive than Simon tasks because the activation of approach tendencies may depend on encoding of the stimuli as alcohol-related, which occurs in the R-SRC task but not in Simon tasks. Our aim was to directly compare these tasks for the first time in the context of alcohol use. We administered alcohol versions of an R-SRC task and a Simon task to 62 social drinkers, who were designated as heavy or light drinkers based on a median split of their weekly alcohol consumption. Results indicated that, compared to light drinkers, heavy drinkers were faster to approach, rather than avoid, alcohol-related pictures in the R-SRC task but not in the Simon task. Theoretical implications and methodological issues are discussed

Electrophysiological responses to alcohol cues are not associated with Pavlovian-to-Instrumental Transfer in social drinkers

Peer reviewedPublisher PD

The impact of the Whistle-to-Whistle ban on the frequency and placement of gambling advertising on UK television

Abstract Introduction: There are currently no legally enforced restrictions on the broadcast time of gambling advertising on UK television. In August 2019, the Industry Group for Responsible Gambling (IGRG) introduced a voluntary policy which limited advertising around live sports, creating a “safe” window of 5 minutes either side of a match, known as the “Whistle-to-Whistle” period. This policy intended to protect young and vulnerable people from the widespread exposure to gambling adverts on television during pre-watershed (9pm) live sports games. Methods: This study will employ panel data statistical models to explore the change in pre-watershed gambling advertising around live sports, and other sporting programmes, following the introduction of the “whistle-to-whistle” ban. It will explore spill-overs to post-watershed, and non-sports channels to assess the industry’s response to the policy. Data: Data comprises six months of advertising data obtained from Nielsen Media covering 1st September to 1st December 2018, and 2019. Data is compiled using the BARB (Broadcasters Audience Research Board) definition of gambling. Analyses will compare the impact on weekdays and weekends, pre and post-watershed, for different genres of television programme. Results & Conclusions: Data analysis commences in November 2022 and is due to be completed in early 2023. Statement of implications: If such policies are effective in reducing the number of gambling adverts on television, then they might be implemented legally in the UK broadcasting codes. Identifying spread to other channels, or post-watershed television, will help to understand the potential unintended consequences of the policy

ITV vs BBC: How gambling advertising during the World Cup influences impulsive football betting

Abstract Introduction: The pervasiveness of gambling advertising and its relationship with football have been identified as areas of concern in the UK. Following the liberalisation of advertising laws in the 2005 Gambling Act, there has been increasing recognition of its public health impact. To date, there is a lack of evidence of the impact of advertising on behaviour in “real-world” settings. Methods: This study employs a natural experiment measuring the impact of television gambling advertising on the frequency of impulsive football bets placed during the World Cup. Longitudinal betting data collected from a sample of men (aged 18-45) will be linked with individual exposure to two TV channels: ITV (adverts) and BBC (no adverts). This is a unique opportunity to measure the causal impact of gambling advertising on behaviour during a widely-televised sporting event. Data Analysis: Panel data statistical models will be employed; advertising will be determined using the television channel. Statistical analyses will estimate the interaction effect between watching a live game which occurs on ITV. Alternative windows of impulsive betting around the live game will be tested. Results: Data collection commences in November, with data analysis complete by early 2023. Statement of implications: If a relationship exists between television gambling advertising and impulsive football betting, then policies which restrict advertising around live sports might be a crucial part of a public health approach to tackle gambling-related harm. If no relationship exists, then current industry policies might not be sufficient in reducing harm

Evaluation of a Brief Personalised Intervention for Alcohol Consumption in College Students.

In the current study we investigated the effect of a brief personalised feedback intervention (BPI), compared to an active control intervention, on outcome measures of (i) alcohol consumption (ii) frequency of binge drinking and (iii) readiness to change (RTC). A sample of 103 college students (mean age=23.85) who consumed alcohol regularly provided baseline measures of drinking behaviour and readiness to change before completing an alcohol-related quiz on the UK Department of Health's Change4Life website (active control). The study was a between subjects design and half the participants were randomly allocated to the BPI group (N=52), who received 10 minutes personalised feedback on their drinking in addition to the alcohol-related quiz. At a two-week follow-up, participants (N=103) repeated the questionnaire battery, and attempted to recall the answers to the alcohol quiz. Results indicated that both groups significantly reduced their alcohol consumption and frequency of binge drinking but there were no significant group differences in either of these measures. We conclude that the provision of generalised information can be as efficient as a BPI for the reduction of alcohol consumption in students

Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants

BACKGROUND: Pharmacogenetic variation is important to drug responses through diverse and complex mechanisms. Predictions of the functional impact of missense pharmacogenetic variants primarily rely on the degree of sequence conservation between species as a primary discriminator. However, idiosyncratic or off-target drug-variant interactions sometimes involve effects that are peripheral or accessory to the central systems in which a gene functions. Given the importance of sequence conservation to functional prediction tools—these idiosyncratic pharmacogenetic variants may violate the assumptions of predictive software commonly used to infer their effect. METHODS: Here we exhaustively assess the effectiveness of eleven missense mutation functional inference tools on all known pharmacogenetic missense variants contained in the Pharmacogenomics Knowledgebase (PharmGKB) repository. We categorize PharmGKB entries into sub-classes to catalog likely off-target interactions, such that we may compare predictions across different variant annotations. RESULTS: As previously demonstrated, functional inference tools perform variably across the complete set of PharmGKB variants, with large numbers of variants incorrectly classified as ‘benign’. However, we find substantial differences amongst PharmGKB variant sub-classes, particularly in variants known to cause off-target, type B adverse drug reactions, that are largely unrelated to the main pharmacological action of the drug. Specifically, variants associated with off-target effects (hence referred to as off-target variants) were most often incorrectly classified as ‘benign’. These results highlight the importance of understanding the underlying mechanism of pharmacogenetic variants and how variants associated with off-target effects will ultimately require new predictive algorithms. CONCLUSION: In this work we demonstrate that functional inference tools perform poorly on pharmacogenetic variants, particularly on subsets enriched for variants causing off-target, type B adverse drug reactions. We describe how to identify variants associated with off-target effects within PharmGKB in order to generate a training set of variants that is needed to develop new algorithms specifically for this class of variant. Development of such tools will lead to more accurate functional predictions and pave the way for the increased wide-spread adoption of pharmacogenetics in clinical practice