The efficacy of behavioural activation treatment for co-occurring depression and substance use disorder (the activate study): a randomized controlled trial.

BACKGROUND: Epidemiological studies suggest that compared with the general population, mood disorders are up to 4.7 times more prevalent in substance dependent samples. Comorbid substance use disorder (SUD) and depression has been associated with a more severe and protracted illness course and poorer treatment outcomes. Despite this, the development and assessment of behavioural interventions for treating depression among individuals with SUDs have received little empirical attention. Behavioural Activation Treatment for Depression (BATD-R) is an empirically supported treatment for depression that has shown some efficacy among substance users. This paper describes the study protocol of a parallel, single blind, randomised controlled trial to determine the efficacy and feasibility of a modified version of the BATD-R (Activate) in reducing symptoms of depression and substance dependence among individuals in residential rehabilitation (RR) and opioid substitution therapy (OST). METHODS/DESIGN: A sample of approximately 200 individuals with depressive symptomatology in treatment for SUD will be recruited from RR and OST services in New South Wales, Australia. Dynamic random allocation following minimisation methodology will be used to assign participants to one of two groups. The control group will receive treatment as usual (TAU), which will be the model of care provided in accordance with standard practice at participating RR and OST services. The intervention group will receive Activate, comprising 10 individual 60-min therapy sessions with a psychologist employed on the research team, in addition to TAU. Data collection will occur at baseline (pre-intervention), and 3-months and 12-months post baseline. DISCUSSION: The association between depression and substance dependence has been well documented, yet practical and effective treatments are scarce. The findings of the present study will contribute significantly to understanding the types of programs that are effective in treating this comorbidity. TRIAL REGISTRATION: This trial is registered with the Australian and New Zealand Clinical Trials registry, ACTRN12613000876796 . Registered on 7 August, 2013.

A novel segmentation-based algorithm for the quantification of magnified cells.

Cell segmentation and counting is often required in disciplines such as biological research and medical diagnosis. Manual counting, although still employed, suffers from being time consuming and sometimes unreliable. As a result, several automated cell segmentation and counting methods have been developed. A main component of automated cell counting algorithms is the image segmentation technique employed. Several such techniques were investigated and implemented in the present study. The segmentation and counting was performed on antibody stained brain tissue sections that were magnified by a factor of 40. Commonly used methods such as the circular Hough transform and watershed segmentation were analysed. These tests were found to over-segment and therefore over-count samples. Consequently, a novel cell segmentation and counting algorithm was developed and employed. The algorithm was found to be in almost perfect agreement with the average of four manual counters, with an intraclass correlation coefficient (ICC) of 0.8.