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Background: Gambling and tobacco smoking are highly comorbid among North American adults. However, there is a paucity of treatment options that are integrated (i.e. targeting both gambling and tobacco smoking simultaneously), accessible, and evidence based., Methods: The aim of this two-arm open-label randomized controlled trial is to examine the effectiveness of an online, self-guided integrated treatment for problem gambling and tobacco smoking. A target sample of 214 participants will be recruited and be randomized into either an 8-week integrated or gambling only control condition. Both conditions will consist of seven online modules following cognitive behavioural therapy and motivational interviewing principles. Our three primary outcomes are (1) the number of days gambled, (2) money spent on gambling activities, and (3) time spent in gambling activities. Secondary outcomes include gambling disorder symptoms, cigarette use, and nicotine dependence symptoms. Assessments will be completed at baseline, at completion (i.e. 8 weeks from baseline), and at follow-up (i.e. 24 weeks from baseline). Generalized linear mixed modelling will be used to evaluate our primary and secondary outcomes. We expect that participants receiving online integrated treatment will show larger reductions in gambling relative to those receiving a control gambling only intervention. We further hypothesize that reductions in smoking will mediate these group differences., Discussion: The rates of problem gambling and tobacco smoking are high in North America; yet, the treatment options for both are limited, with no integrated treatments available. If supported, our pilot study will be a cost-effective and accessible way to improve treatments for co-occurring problem gambling and tobacco use., Trial Registration: ClinicalTrials.gov NCT03614884 . Registered on August 3, 2019.

We present a novel approach that combines MALDI-TOF profile analysis and bioinformatics-based inclusion criteria to comprehensively predict phosphorylation sites on a single protein of interest from limiting sample. It is technologically difficult to unambiguously identify phosphorylated residues, as many physiologically important phosphorylation sites are of too low abundance in vivo to be unambiguously assigned by mass spectrometry. Conversely, phosphorylation site prediction algorithms, while increasingly accurate, nevertheless overestimate the number of phosphorylation sites. In this study, we show that MODICAS, an MS data management and analysis tool, can be effectively merged with the bioinformatics attributes of residue conservation and phosphosite prediction to generate a short list of putative phosphorylation sites that can be subsequently verified by additional methodologies such as phosphospecific antibodies or mutational analysis. Therefore, the combination of MODICAS driven MS data analysis with bioinformatics-based filtering represents a substantial increase in the ability to putatively identify physiologically relevant phosphosites from limited starting material.