In our studies, immuno regulatory effects on human monocyte-derived DC were observed at lower THC concentrations , more akin to peak levels that occur in the blood of marijuana smokers , and had no effect on cell recovery or surface staining by Annexin-V. Instead of apoptosis, we observed broad-ranging effects of THC on the expression of MHC class II and costimulatory molecules, and the capacity for antigen uptake and IL-12 production. Furthermore, DC that had been exposed to THC during their in vitro differentiation were impaired in their capacity to activate T cells – including both CD4+ and CD8+ responders. T cell proliferation and the acquisition of a memory/effector phenotype were both impaired as was the release of Th1 cytokines. These effects of THC on the capacity for monocyte-derived DC to stimulate T cells are almost identical to the direct effects of THC on T cell activation , suggesting a coordinated immuno regulatory effect. It is interesting that other immuno suppressive factors, including IL-10 and TGF-β3, share this capacity to act in a coordinated manner on both DC and T cells . As is the case with IL-10–/– knockout mice , CB1–/–/CB2–/– double-knockout mice exhibit elevated levels of activated T cells and respond to antigen challenges by producing a higher number of activated effector cells and stronger IFN-γ responses . Collectively,vertical marijuana growing systems these findings suggest an intrinsic role for endocannabinoid signaling as a homeostatic regulator of T cell activation. There are a number of critical features that develop during the transition from monocytes into DC that enable them to activate antigen specific T cells .

Among these are high levels of antigen expression in the context of cell-surface MHC, the upregulation of adhesion and costimulatory molecules, and the elaboration of immuno stimulatory cytokines. Our studies suggest that cannabinoid receptor activation impacts on all of these. Exposure to THC during the differentiation of monocyte-derived DC impaired antigen uptake and prevented the normal upregulation of MHC class II. These findings are consistent with earlier reports by McCoy et al. , where THC was found to impair the presentation of whole hen egg lysozyme, which required uptake and processing, but not the presentation of its immuno dominant peptides, which bound directly to existing cell surface MHC. Dendritic cells that present antigen in the absence of adequate costimulatory molecules cannot fully activate T cells and may contribute to the development of T cell anergy . The inhibitory effects of THC on the expression of CD40, CD86 and other costimulatory molecules likely contributed to the failure of THC-DC to stimulate T cell proliferation. Finally, the relative production of IL-10 and IL-12 by DC plays a central role in their capacity to activate either Th1 or Th2 responses. In our studies, THC-DC produced only limited amounts of IL-12 but normal levels of IL-10. Lu et al. reported a similar suppressive effect of THC on the expression of MHC and costimulatory molecules and on production of IL-12 by mouse bone marrow-derived DC that had been infected with Legionella pneumophila. While these findings add to other compelling evidence that cannabinoids can exert important immuno suppressive effects, clinical evidence that marijuana smoking significantly impairs immune function in humans is limited. One explanation may be that inhaled THC never produces sufficient systemic levels, or that exposures may not be sustained for a sufficient period of time. to mediate immuno suppressive effects . Another explanation may be that the effects are short-lived or counterbalanced by the presence of other immune regulatory factors.

The study of purified cells in vitro culture does not adequately replicate the complex environment that occurs during an immune challenge in vivo. In this study we hypothesized that the processes of DC activation and cytokine exposure that occur in response to an infectious challenge might modulate the impact of THC. Exposing DC and THC-DC to heat-killed and fixed SAC for 18– 24 h enhanced their capacity for Tcell activation; an effect that was more pronounced with THC-DC than with control DC. Adding IL-12 and IL-15 to the DC:T cell co-culture also enhanced T cell activation and proliferation, but these effects occurred equally with control and THC-DC. Furthermore, these cytokines promoted T cell proliferation and cytokine production even in the absence of stimulation by DC . However, the addition of IL-7 to DC:T cell co-cultures had a dramatic effect on T cell proliferation, maturation and cytokine production that was restricted in part to co-cultures containing THC-DC. These studies suggest that the immuno regulatory effects of THC might be counterbalanced by thepresence of a combination of DC activating signals and the production of cytokines by other cell types present in the local immune environment. In summary, our experiments demonstrate that human monocytes express functional cannabinoid receptors, even if they are not detectable on the cell surface, and that exposure to THC alters their capacity to differentiate into immuno stimulatory DC with prominent effects on antigen uptake and presentation, expression of costimulatory molecules, and production of IL-12. The end result is the generation of DC that fail to stimulate T cell proliferation or promote maturation into functional effector/memory T cells. While the effects are relatively potent when studied in isolation in vitro, there may be a number of immunoregulatory factors that could counteract or moderate the impact of cannabinoid exposure in vivo. The functional role that marijuana smoking has on host immunity and the response to immune challenges in vivo remains to be clarified.Mobile technologies have the potential to revolutionize treatment programs for adolescent substance users. Current practices center on cognitive-behavior altherapies in which youth engage in group therapy, and which rely on retrospective assessments to self-monitor and identify relapse triggers.

Cell phones expand the feasibility and reach of ecological momentary assessment ; events are recorded in near real-time as they occur to elicit ecologically-valid data, reduce reliance on autobiographical memory and reduce recall biases . Mobile technolo-gies also enable ecological momentary interventions , for example, as tested in cell phone-based smoking cessation interventions for youth . Before EMI can be fully realized in supporting drug treatment, a greater degree of granularity is needed in understanding daily behaviors, social contexts, and internal states in order to optimize the personalization inherent in EMI. To date, most information on substance use and contextual factors has been captured through retrospective assessments. Cell phone-based EMA studies in treatment settings are crucial for EMI development, particularly for adolescents given the prevalence of substance use problems, especially in Latino youth, and the high use of cell phones in adolescents’ daily routines . Higher levels of alcohol and drug use across multiple categories have been shown for Latino youth in the 8th and 10th grades compared to African American and Caucasian youth . Moreover, Latino youth with substance use disorders are less likely to receive treatment than White adolescents . In this vein, we pilot tested CEMA of alcohol, marijuana, and other drug use in a sample of mostly Latino youth in outpatient substance abuse treatment. We previously reported high compliance rate for completing CEMA reports . Here we explore contextual factors that were assessed along with AOD use in order to fill gaps in the literature related to the context in which adolescent AOD use occurs. We highlight practical applications of our findings for the development of EMI. Our pilot study tested different CEMA strategies that would likely be used in a treatment setting, including prompted daily recall and event-based reporting. As a secondary aim, we examine if context related to AOD use that is reported during daily recall differs from context reported through event-based reporting. To the best of our knowledge, this has not been explored in prior studies. First, we summarize AOD-related contextual factors that have been evaluated in prior adolescent studies and that are evaluated in our study. We hypothesize similar findings in our sample,vertical rack system grow although we do so with caution. Prior research has mostly focused on social contextual factors ; this study makes a valuable contribution to the literature by giving equal attention to other contextual factors and affect. Moreover, prior findings are not generally based on Latino youth and are mostly based on retrospective assessments. Findings from EMA studies are specified as such.Numerous studies have shown associations between AOD use in adolescents and AOD use in their peers , as well as peer socioeconomic characteristics . What warrants further study are nuances in types of peer relations that relate to AOD use.

For example, minority youth reported alcohol and marijuana use “among young people they knew”, relative to other substances in a qualitative study . Similarly, a study of young Australians found the majority of drinking episodes to occur with “close friends” .Alcohol and marijuana use have been more frequently reported by youth on weekends versus weeknights and after school relative to time periods before or during school . This is in line with the notion that alcohol is easier to detect and more limited to nighttime and weekend parties as reported by youth in qualitative interviews . It has also been noted that youth use alcohol and marijuana to attenuate sleep problems and sleep disturbances from other substances, such as stimulants used to increase daytime alertness .Youth were recruited from an adolescent outpatient substance abuse treatment setting in a large U.S. city from 2010 to 2011. All youth were in the treatment program because they exhibited some degree of impairment in school, social, or family environments. Eligible youth were: 1) between the ages of 12–18, 2) enrolled in treatment with an expected duration of at least a month, 3) able to use a cell phone, and 4) English speaking in order to fill out the CEMA . Youth who were 18 years old signed a consent form while younger youth signed assent forms and parental consent was also obtained. Participating youth received a $15 gift certificate for completing a baseline assessment. Over the course of the study, participants received $25 per week and 500 free cell phone minutes per month. Study procedures were approved by the Institutional Review Board of the University .Assignment to a CEMA strategy was based on anticipated AOD use; youth who were newly enrolled in treatment were more likely to be assigned to EBA than remaining strategies because they were anticipated to have more AOD use events to report. Youth participated in multiple one-month CEMA periods and were rotated through different assessment strategies so that the likelihood of repeating the same assessment strategy was low. During the last two assessment periods, youth could also be assigned to a combination assessment strategy in which youth received EoDA and were also asked to initiate EBA whenever they engaged in AOD use. A total of 28 youth were enrolled. Eleven youth were initially enrolled and followed for one month with four youth assigned to EoDA, three youth assigned to EBA, and four youth assigned to RA. After the initial assessment period, youth could participate in three more month-long assessment periods with month-long breaks in between assessment periods. Six new youth were enrolled during the second assessment period, three youth during the third assessment period, and eight youth were enrolled during the last assessment period. Half of the participants participated in two or more month-long CEMA periods . Four youth participated in all four possible assessment periods.We present descriptive statistics for contextual factors, affect, and cravings by types of AOD. There was a high degree of overlap between reported use of alcohol and both marijuana and other drugs in the same reports. We categorize AOD use in a hierarchical fashion as use of alcohol only, use of marijuana and no other drugs, and use of other drugs. Use of marijuana and other drugs includes reports where alcohol use was also reported. Assessment questions for daily reports shared similar wordings, time frames, and results. In a parallel fashion, assessment questions for in-the-moment reports also shared similar properties and led to similar results. Results on daily reports and results on in-the-moment reports are grouped together for presentation. Percentages for context, affect, and cravings are compared between CEMA when AOD use was and was not reported, where possible. Specifically, comparisons are made for affect and cravings reported during EoDA and for context, affect, and cravings reported during in-the-moment RA. Comparisons are conducted through random-effects logistic regression with random effects for each participant.Models are fit in SAS software version 9.4 through the GLIMMIX procedure.