We chose these techniques because they work even when the sample size is small relative to the number of predictors, as is the case here.Moreover, in keeping with the common practice, the performance of these models was evaluated by examining their prediction accuracy as measured using overall accuracy , sensitivity , and specificity . Further, due to the lack of independent test data, the performance measures were computed using LOOCV. By protecting against overfitting, the LOOCV-based measures provide a more accurate assessment of model performance on future unseen data than those computed directly from the training data. By default, the models use 0.5 as the cutoff for probability, that is, a study subject is classified as having CUD if their probability of CUD exceeds 0.5. If the cutoff is increased, the sensitivity will decrease and specificity will increase. To evaluate the overall model performance, we used the receiver operating characteristic  curve, a plot of sensitivity against 1-specificity  obtained by varying the cutoffs, and computed the corresponding area under the curve  . The models were fit using the statistical software system R  with the following specific packages: glmnet  for LASSO logistic regression, knn , e1071  for SVM, randomForest , gbm  for gradient boosting, caret  for LOOCV, and pROC  for AUC. Fig. 1 presents the variables with non-zero regression coefficients from LASSO logistic regression model and the top seven variables based on variable importance measures for the other models. The seven variables selected by LASSO, namely, age, level of enjoyment from initial smoking, ImpSS-T, BIS-I, NEO-N, NEO-O, and NEO-C, were also found to be important by the other models. In particular, except ImpSS-T and NEO-O, the remaining five were selected as important by all other models.

Moreover, ImpSS-T was chosen as an important predictor by KNN, random forest, and SVM while NEO-O was indicated to be important by random forest and gradient boosting. Table 2 presents the accuracy, sensitivity, and specificity of the models based on 0.5 cutoff as computed using LOOCV as well as the AUC of the models. The associated ROC curves and the plots of accuracy versus cutoff are provided in Supplementary Materials. Although the various models performed similarly, which is reassuring, grow cannabis overall we may conclude that LASSO and gradient boosting outperformed the others. For example, the two are tied for the highest AUC. Nevertheless, an advantage of LASSO is that it provides estimates of regression coefficients and hence odds ratios. This allows easy interpretation of the effects of the risk factors. This important and desirable feature is not available in other models. Therefore, we choose the LASSO logistic regression model as our final model. It may be of interest to quantify the advantage of this model over a random guess classifier that predicts CUD with probability 0.617, the proportion of CUD cases in the data. The accuracy, sensitivity, and specificity of this classifier can be calculated to be 0.527, 0.617, and 0.383, respectively. These are much lower than the corresponding values reported in Table 2 for the LASSO model. The final LASSO model predicted the CUD status with 66% accuracy. Its sensitivity and specificity were 0.81 and 0.42, respectively. Thus, it does a much better job of correctly identifying the CUD cases than the non-CUD controls at the probability cutoff of 0.5. This cutoff may not be appropriate in all clinical settings. The appropriate cutoff can be chosen by examining its ROC curve, presented in Supplementary Materials, for the trade off between sensitivity and specificity. Its AUC is 0.65. The seven variables selected by this model together with their estimated coefficients and the associated odds ratios  are shown in Table 3. The higher probability of CUD was associated with younger age , lower level of enjoyment from initial smoking , higher score on impulsivity , greater cognitive instability , higher neuroticism, i.e., more prone to experience negative feelings , greater openness to new experiences , and lower conscientiousness . To illustrate the model, we considered two subjects from the data who had the largest and the smallest predicted probability of CUD. Their true status is CUD and non-CUD, respectively. The first subject was young ; received little enjoyment from initial smoking ; had high scores on impulsivity , cognitive instability , and neuroticism ; was quite open to new experiences ; and had low conscientiousness .

The predicted probability of CUD for this subject was 0.93. The second subject was 49 years old; received much enjoyment from initial smoking ; had low scores on impulsivity , cognitive instability , and neuroticism ; was also quite open to new experiences ; and had high conscientiousness . The predicted probability of CUD for this subject was 0.15. Substance use disorders are a growing public health problem and cannabis is the most commonly used illicit substance in the world . The legalization of cannabis for medical and recreational purposes worldwide has increased cannabis use and CUD. Therefore, there is a growing need for a CUD risk prediction tool. In this study, we built a preliminary model by identifying risk factors with the help of several statistical and machine learning algorithms. We eventually chose the LASSO logistic regression model as the final model for two reasons. First, there was no major difference among the top performing models. Second, LASSO allows the ability to interpret the effects of risk factors quantitatively, a feature unavailable in the other methods. The LASSO model gave seven risk factors with non-zero  coefficients. We had also explored the possibility of adding interaction terms to this model but did not eventually add any because the model with interactions had lower predictive accuracy than this model. The risk factors identified by our model are consistent with the literature . In particular, previous findings indicate that younger people are more likely to develop CUD . Using ImpSS and BIS scales, numerous studies have shown that high impulsivity is prevalent among users of nicotine , cocaine , and alcohol . We also found that higher ImpSS-T increases the likelihood of dependence on cannabis. The positive association between cognitive instability and CUD status that we found is also known . Similarly, the relationship of CUD with personality trait risk factors based on NEO is consistent with the previous findings . For example, cannabis users have higher openness and lower conscientiousness compared to nonusers . Generally, high neuroticism is reported in nicotine-only users  and average neuroticism is reported in cannabis only users . We found that higher neuroticism is associated with higher likelihood of CUD, which is not surprising because our sample consists of co-morbid marijuana and nicotine users.

We also found that less enjoyment from initial smoking is associated with increased likelihood of becoming cannabis dependent. This is in line with the findings from a nationally representative longitudinal study, which was conducted to identify the risk factors associated with different stages of cannabis use . This study found that greater quantity of cigarette use decreased the likelihood of reinitiation of cannabis use among participants who were cannabis users prior to reaching adolescence . Even though our overall findings are consistent with the literature, we did not find several risk factors for CUD that have been previously reported in the literature. Some of the risk factors such as childhood depression and conduct disorder symptoms were not available in these data. While some other factors such as early exposure to traumatic events had substantial missing data because of which they were excluded. Yet others may not have been identified due to limitations of the study as described in the following. Our study’s first limitation is the cross-sectional and observational nature of the study because of which it is difficult to establish a causal relationship between a risk factor and CUD, especially for the factors that can vary over time. To mitigate the latter issue, we only used risk factors that remain relatively stable over time. However, even then we need to be cautious about drawing any conclusion about causation as this is an observational study. The second is that there are not a large number of subjects and the participating subjects came from a specific metro area in the US, which may not be representative of the entire population of all cannabis users. The third is due to missing values on the variables. When the risk factors are jointly analyzed in a multivariate model, this leads to a loss of some subjects as those with missing values in any of the multiple variables are discarded. We tried to balance the loss of sample size with the inclusion of risk factors. Moreover, to mitigate the issue of small sample size, we chose the statistical and machine learning methods that work even when the sample size is small relative to the number of predictors. Nonetheless, availability of complete data on more subjects would have provided higher power for identifying association. We also acknowledge that the data used for this study were acquired in 2007–2010 and may be limited in its generalizability to current cannabis use impacts. Nonetheless, New Mexico’s cannabis policies may be more historically representative of current national policies  given that medically-indicated cannabis was legalized in New Mexico in 2007 coinciding with the study’s data collection. Thus, our findings may provide insights into future trends related to continued changes in cannabis legislation in the US. Also importantly, there has been no change in rate of current marijuana use in New Mexico in recent years, although the rate has remained significantly higher than the US rate . Thus, indoor cannabis grow system use in New Mexico has been stable and should not limit the impact of the current findings. Lastly, the mechanisms that underlie the risk for CUD likely remained relatively unchanged in the last 10 years. Despite its limitations, this study represents a novel attempt to build a CUD risk prediction tool.

To address the limitations, we are working towards building a risk prediction model using longitudinal data from a large number of subjects spread throughout the US. In addition, some people may be dependent on more than one substance  and in fact, there may be common risk factors for several substance disorders . Therefore, it would be of interest to model jointly the relationship between multiple substance disorders and potential risk factors. Finally, inclusion of genetic and/or imaging factors can also provide a more personalized model. Cannabis use is common, but most users do not progress to cannabis use disorders. About 50–70% of liability to cannabis use disorders is due to genetic factors.1 Three genome-wide association studies  of cannabis use disorders2–4 have identified variants reaching genome-wide significance, but inadequate sample sizes  and heterogeneity among samples have contributed to a paucity of replicable findings: only one locus, tagged by a cis-eQTL for CHRNA2 , has been robustly identified.A GWAS of lifetime cannabis use  identified eight genome-wide significant loci and 35 significant genes.Twin studies suggest high genetic correlations between early stages of cannabis experimentation and later cannabis use disorder.6 However, casual cannabis use is affected by a variety of socio-environmental influences and ageperiod-cohort effects, whereas progression to cannabis use disorder is related to other psychopathologies. Findings have suggested partially distinct genetic causes underlying alcohol consumption and alcohol use disorder, including different genetic associations with other psychiatric disorders and traits.7,8 Thus, in addition to examining the genomic liability for cannabis use disorder, we tested whether the genetic influences underlying cannabis use and cannabis use disorder diverge with respect to behavioural and brain measures.regulations. Investigators for each contributing study obtained informed consent from their participants and received ethics approvals from their respective review boards in accordance with applicable regulations. Personal identifiers associated with phenotypic information and samples from deCODE were encrypted using a third-party encryption system.The iPSYCH group used pseudonymised unique identifications.Psychiatric Genomics Consortium cases met criteria for a lifetime diagnosis of DSM-IV cannabis abuse or dependence11 derived from clinician ratings or semi-structured interviews.Cases from the iPSYCH sample had ICD-10 codes of F12.1 or F12.2 , or both in the Danish Psychiatric Central Research Register; the remaining individuals in the sample were used as controls.

However, the new Medical Cannabis industry needs to adopt the quality standards of the pharmaceutical industry and at the same time satisfy the specific requirements of Cannabis-legislation. This means that the Cannabis products for medicinal use need to comply with its established quality parameters and standards and to be manufactured under cGMP regulations. Tetrahydrocannabinol  and cannabidiol  are well known phytocannabinoids, considered as the most notable Cannabis components with pharmacological activity. THC acts mostly as a CB1 receptor agonist, leading to its distinguished psychoactive and pain relief effects, while CBD works through a variety of pharmacological pathways, including inhibition of endocannabinoid reuptake, activation of transient receptor potential vanilloid 1 and G protein-coupled receptor 55. Considering their immense importance in the overall pharmacological activity, as well as the current Cannabis legislation restrictions, related to the THC content of some classes of Cannabis products, one could entitle the content of CBD and THC as critical quality parameters of Cannabis products. The CBD and THC content could also be considered as critical material parameters when Cannabis flowers are used as starting material in the production. The quality control of Cannabis inflorescence and chemotype differentiation is a subject of many Pharmacopoeias focus on the assay of the content of five main cannabinoids CBDA, CBD, CBN, D9 THC and D9 -THCA. The above-referenced Pharmacopoeias comprise monographs on cannabis defining the dried drug or herbal substance ‘‘Cannabis inflorescence” that contains a minimum of 90 and a maximum of 110% the amounts of cannabinoids such as D9 -THC and CBD, as well as Cannabinoid carboxylic acids such as D9 -THCA and CBDA, calculated as D9 -THC or CBD, based on the dried drug. Depending on the content of D9 -THC and CBD, authorities have classified generally three chemotypes of Cannabis sativa L.: D9 -THC predominant type, i.e. drug-type , CBDpredominant type, i.e. fiber type and intermediate chemotype .

Regarding cannabis extracts, so far the German pharmacopeia recognizes discontinued cannabis extract – Cannabis extractum normatum defining as an extract from whole or shredded, flowering, dried shoot tips of the female plants of Cannabis sativa L. that contains D9 -THC at least 1% and at most 25%  for the extract, and CBD maximum 10%  for the extract. The chemical complexity of cannabis makes its pharmaceutical standardization challenging. Thus, vertical grow rack chemical characterization must include well-defifined methodologies that would characterize the plant chemotype and the herbal drug as well as extraction procedures. It was found that the concentrations of target cannabinoids obtained for the same plant chemotype originating from different suppliers could vary for more than 25%. scientific and technological development in regard to Cannabis sativa began, highlighting the need of sensitive, specific and robust analytical methods for identification and quantification of the active constituents of Cannabis sativa. Gas chromatography  and liquid chromatography  are regarded as analytical methods of choice for the quantification of phytocannabinoids. Both are relatively slow and costly techniques and require sample preparation that involves at least extraction of the ingredients with organic solvents. Since quality assessment in the current Medical Cannabis industry relies on end-product testing, these techniques are vastly employed. However, the variability of phytocannabinoids content in the plant material often exerts an issue in the inconsistency of the finished product quality parameters. Sampling problems and sample representativeness is a major limitation in the end-point testing, particularly when the expected variation of the product quality parameters is high. Usually, physical limitations  prevent the test sample analysis to adequately represent the whole batch variability. Besides, the critical quality parameters are not monitored frequently during the production process, thus lacking the knowledge for appropriate identification of critical process parameters in the process optimization. Therefore, there is an obvious need for the introduction of the concept of quality by design  in Cannabis products manufacturing, which means that product quality should be scientifically designed to meet specific objectives, not merely empirically derived from the performance of test batches. In this manner, the product and process characteristics important to desired performance must be derived from a combination of prior knowledge and vast experimental assessment during product development. The generated data will result in the construction of a multivariate model linking product and process measurements and desired parameters.

Implementation of Process Analytical Technology  is an important QbD tool aimed to increase the understanding essential for the quality throughout the manufacturing process, which became recognizable in the Pharmaceutical industry by the initiative launched by the FDA. Infrared spectroscopy is a promising analytical technique that is consistent with the PAT requirements of the FDA guidelines, and its implementation depends on the advances in instrumentation and chemometrics that will facilitate the qualitative and quantitative aspects of the technique. In the literature, few attempts have been made to introduce near-infrared spectroscopy for quantification of phytocannabinoids in plant material, in liquid pharma-grade Cannabis formulations and for growth-staging of Cannabis. To the best of our knowledge, so far no scientific papers are addressing the capability of mid-infrared spectroscopy  for quantification of phytocannabinoids in Cannabis plant material and extract. Therefore, our present work aims in highlighting the potential of mid-infrared  spectroscopy as PAT in the quantification of the main phytocannabinoids , considered as critical quality/material parameters in the production of Cannabis plant and extract. To achieve our goals, we have performed MIR analysis of Cannabis flowers  and Cannabis extracts from various randomized sources with various CBD and THC content and employed a multivariate statistical approach to develop and optimize the calibration models. Furthermore, a prediction set was used to estimate the prediction capability of each MIR model against the referent analytical technique .MIR spectra were collected on the attenuated total reflection module of an Alpha Platinum ATR Fourier transform infrared spectrometer . Ten milligrams of each dry sample was used for spectral acquisition whereas few drops of each extract were placed on the ATR plate compartment. After the collection of each extract IR spectrum, the solidified specimen that remained on the plate was dissolved in hexane and the compartment was cleaned to proceed with the next extract sample. Each IR spectrum was recorded in the 4000 to 400 cm 1 region and averaged from eight scans with the spectral resolution adjusted to 4 cm 1.The partial least-squares analysis was employed to build calibration models for quantification of THC and CBD in Cannabis extract and flowers, using the spectroscopy skin of Simca14 . Spectral pre-processing was performed using the spectral filters add-in. The correlation coefficients of both X and Y matrices , the predictivity coefficient , root mean square error of estimation , and the root mean square error of cross-validation  were used as the main statistical indicators. The VIP and coefficient plots were used for further analysis of the models.

The predictive capability of the models was evaluated on separate predictive sets and the root mean square error of prediction  was determined for each model.The description and assignment of the IR spectra was conducted following the available literature with an emphasis of the analytical bands responsible for differentiation of the cannabinoids of our interest: THCA, THC, CBDA, and CBD . Firstly, given the need for precise spectral examination, two air-dried, non-thermally treated flowers  were screened . The strikingly various content of CBDA and THCA in these flowers enabled to ascribe the bands originating from these cannabinoids because it was assumed that the remaining content of the chemical species of the flower is rather similar and comparable . Namely, the attempt was made to select discriminating spectral regions for THCA and CBDA native flowers that, although depict evident similarities, exhibit marked differences . The apparent spectral disparity was discussed in terms of:  shift of the position of the dominant bands,  absence/presence of discriminating bands and  differences in the intensity of the identical band . The applied approach was advantageous because the cannabis flowers contain various molecule species and was perplexing to specify an infrared band solely to a particular cannabinoid or terpene analyte. Thus, the bands in the dominant THCA flower emerge at 1120, 909, and 711 cm 1 that are not registered in the CBDA flower where, on contrary, the bands at 3402 and 620 cm 1 evolved . An intensity increase of the 888 cm 1 band in the CBDA plant was observed, largely diminishing in the spectrum of the THCA flower . Another apparent difference among both spectra is associated with a marked shift of the medium to strong pair-analogues around 1570, 1250, and 1180 cm 1 . However, due to the similar structural formula of the tracked cannabinoids, we found all these regions as reliable indicators to fortify the discrimination of the THCA and CBDA molecular entities, but we will firstly focus on the interpretation towards the absent/present bands and further encompass the assignment in the appointed shifting band regions. The band at 3402 cm 1 found in the CBDA dominant flowers is attributed to the stretching OH vibration from the aromatic OH group situated in para-position to the carboxylic group fragment . This OH group is not present in the THC and THCA molecules  and therefore lacks in the spectrum of the FL1 cannabis cultivar . On contrary, the bands at 1120 and 909 cm 1 as well as the band at 711 cm 1 in the spectrum of THCA flowers  could be related to the aryl-alkyl ether group present in the THCA molecules  and assigned to antisymmetric and symmetric C–O–C stretching, and ring symmetric C–O–C bending vibration, respectively. On the other hand, the @CH2 wagging vibrations from the  gave rise to the strong 888 cm 1 band registered in the CBDA flower spectrum because the THCA compound lacks unsaturated C@C linkage in this structural position . Further focus on the assignment in the appointed shifting band regions infers that the 1570 cm 1 maximum is attributed to the stretching m vibration within the aromatic rings in the lignin, cellulose and the remaining cannabis grow racks species. C–H bending vibrations from the CH2 and CH3 aliphatic groups in these compounds are observed around 1430 cm 1 , and the m vibration next to the carboxylic group around 1180 cm 1 .

However, the most intense absorption around 1250 cm 1 , slightly shifted among the THCA and CBDA spectra, is ascribed to the m vibration from the carboxylic group since upon decarboxylation the intensity of this band majorly decreases . Although the precise assignment of these bands poses a great challenge due to the complex matrix of the samples, the proposed tentative assignment  was made in accordance with the limited IR spectral literature data for hemp and cannabis related specimens and the recent non-destructive Raman studies aimed to differentiate between hemp and cannabis.It is worth emphasizing that the decarboxylation protocol was delivered for non-origin correlated THC and CBD matrices of 45 thermally-treated flowers and 34 extracts. The idea was driven to develop a proper protocol that can sufficiently and reliably estimate the content of the major CBD and THC in any randomized series of flower and extract samples. For ease of interpretation, we have also considered the fact that the content of the acidic cannabinoids THCA and CBDA in the non-thermally treated samples is reasonably higher compared to the thermally treated flowers. Namely, flowers dominantly contain the acidic forms of the cannabinoids which upon careful temperature action are decarboxylated and converted to THC and CBD, respectively. The results from the IR spectra filtered the most important regions that reflect the major differences appearing among the different fresh flowers, and other regions where the major differences occur among the decarboxylated flowers and extract samples in comparison to the spectra of the starting fresh flowers. These spectral outcomes were found complementary to the chemometric results . To the best of our knowledge, the literature lacks IR band assignments for pure THCA, CBDA, THC and CBD, though the spectral appearance of these compounds in the native form can be found in the German Pharmacopeia, Specac App Note, Bruker App note, and PerkinElmer App note. The IR spectra of the acidic forms of the cannabinoids of interest  were only presented  by Hazekamp et al. who isolated these compounds from the concentrated ethanolic solutions , subsequently evaporating the ethanol under vacuum.

The results demonstrated that most students do not feel knowledgeable and did not receive a passing score for adverse effects and approved indications.Notably, four studies used the same instrument , which was developed by the Regional Alcohol and Drug Abuse Research  Center at Ben Gurion University of the Negev . The search strategy resulted in data from ten countries: The United States, Canada, Serbia, Russia, Israel, Spain, South Africa, Malta, Belarus, and Poland. As shown in Table 2, which summarizes the distribution of studies by country and discipline, the largest number of studies  took place in the United States where the legalization of medical cannabis varies by state. Furthermore, medical cannabis is not legal in all of the remaining countries examined. Twelve studies out of the 23 examined, in eight of the ten countries, focused on the field of medicine, and examined the education of medical cannabis in the medical school curriculum. Pharmacy was the second most common field of study making up seven out of the 23 studies and was examined in three countries. Finally, two studies examined the topic in the field of nursing, two articles focused on the field of psychology, and one study surveyed nurse practitioner programs. In general, it was found that there was no structured curriculum or competencies on medical cannabis in most schools. Four studies revealed that students receive most of their education on medical cannabis from extracurriculars and sources outside of school. We also found that this aligns with the belief commonly expressed among students that they lack adequate education, mentorship, and guidance on this subject. In the studies assessed, students overwhelmingly reported that they do not feel knowledgeable or comfortable to counsel patients on medical cannabis, mostly due to a lack of evidence-based knowledge. However, students’ beliefs about the efficacy of medical cannabis varied depending on culture, religion, location, and prior personal use. For example, one study in Israel reported that religious students were more likely to have negative attitudes about medical cannabis.In addition, a study in Malta and Russia found that secular students were more likely to recommend medical cannabis than religious students.

Students in several studies also cited prior personal use as a factor influencing knowledge, where it was determined that, in general, these students had more knowledge on this subject and were more likely to recommend medical cannabis to patients. Faculty perspectives were also considered in five of the studies analyzed. Three of these studies focused on pharmacy curriculum, and in all three studies, the faculty described that medical cannabis was included in their curriculum. In a study based in Ohio,vertical grow rack over half of the faculty surveyed from multiple states with legalized medical cannabis stated that medical cannabis was incorporated in the curriculum either as an elective or required course.Another study from the same year concurred with these findings, reporting that over half of the respondents claimed that medical cannabis education is included in the first two years of school. The third study from Canada described that medical cannabis is taught, but there is limited teaching time to about 4 h and there is no standardized curriculum.The results of these studies also indicated that some schools are hoping to expand upon or include medical cannabis into their curriculum if not already present. The two additional studies including faculty focused on the fields of nurse practitioners and medicine, and there was agreement that trainees require more education on medical cannabis and are unprepared to counsel patients on this topic with the current education they are receiving.Cannabis is recently emerging as a potential therapeutic agent in many places, and regulatory frameworks are accordingly evolving around the world. Therefore, the objective of this study was to identify and assess the current literature about medical cannabis education among trainees and faculty of health professions, and to examine their prominent beliefs and views related to their competency to integrate cannabis into their clinical work once graduated. Overall, trainees in all health disciplines reported on low levels of perceived knowledge on medical cannabis and lack of formal education on this topic. Correspondingly, they expressed feeling unprepared to counsel patients on appropriate use of medical cannabis. Studies that have surveyed curriculum/education deans at academic institutes affirmed the lack of structured and standardized education. These results disclose the large gap between the need of guidance on medical cannabis by patients and the professional capacity of healthcare providers to deliver such guidance.The beliefs reported by trainees align with those reported by certified healthcare professionals. Specifically, cannabis was perceived to potentially benefit certain conditions, but was additionally considered to cause physical or mental harms as an addictive substance. These views reflect the innate conundrum of cannabis, as being a potential therapeutic agent but also a substance of abuse.Although cannabis has been used for medical purposes throughout history, only in recent years is it being reintroduced as an optional part of modern pharmacopeia.

Nonetheless, the legal status of medical cannabis varies among states and countries, and the relevant legal frameworks differ considerably. However, major differences were not observed between studies conducted in places where medical cannabis is legal as opposed to where it is not . Indeed, despite the fact that our scoping review revealed a limited amount of research done to assess attitudes and knowledge of medical and allied healthcare trainees about medical cannabis, there appears to be a uniform lack of education and desire to learn more about this field, regardless of its legal status. Students are not merely educated in medical schools, but are also being indoctrinated to adhere to professional norms and conceptions. While the biomedical model of medicine is a dominant paradigm in healthcare, it, in fact, clashes with the integration of cannabis into the medical practice, similar to herbal medicines as well as to other complementary practices. Ostensibly, trainees’ attitudes and perceived knowledge may be influenced from various factors, which are not necessarily related to the current state of scientific evidence. For example, one study pointed to religion as a factor which may influence perceptions about medical cannabis,and another study suggested that personal experience may be associated with more perceived knowledge on medical cannabis.Finally, a considerable amount of students reported getting their information about medical cannabis from non-scientific sources, and previous studies suggest that sources such as the online sources may have an impact on attitudes towards medical cannabis.Inevitably, there is a tangible mismatch between the reported beliefs of trainees and the current evidence-base as outlined in the 2017 NASEM report. Clearly, the lack of education on medical cannabis ought to be addressed. Currently, however, academic programs for health professions lack any structured and uniform curriculum, and previous research points to resistance among academic administrators to such additions to the core curriculum. Moreover, the Accreditation Council for Graduate Medical Education  or equivalent regulatory bodies worldwide offer no guidance for how to tackle the current educational gap. As a result, only sporadic institution-level efforts are being made to incorporate medical cannabis into the curriculum. For example, the Lerner College of Medicine in the University of Vermont has created an online course on medical cannabis, which has drawn more students than expected, but this is a non-credit course that is not integral to the medical education program.Furthermore, there is currently no standardized resource  to facilitate proper evidence-based education on medical cannabis.Given the scarcity of formal curricula within academic centers, establishing a set of ACGME-approved competencies appears to be a high priority that can aptly facilitate the development of medical cannabis education. This scoping review is inherently restricted by the limitations of the studies which were included in our analysis. Indeed, poor and inconsistent methodologies have been applied in most of the studies reviewed, which have used cross-sectional designs to survey non-representative samples; moreover, sample sizes were not justified and consisted of low response rates. In addition, the instruments which were used are not validated or uniform, and such flaws clearly undermine the ability to adequately draw comparisons between the findings and form robust conclusions. Indeed, a validated and uniform instrument is warranted in order to facilitate the generation of reliable information.

In this context, the instrument developed by the RADAR Center in Israel is noteworthy, as it has already been used in several studies worldwide.An additional limitation is that we only searched for literature in English. However, we assume that studies around the world are generally published in peer-reviewed literature in English. Notwithstanding its limitations, this novel study sheds light on the current status of medical cannabis grow racks education, and it may serve to direct the developments of future studies and academic endeavors alike. Since modern health care education is normally based on competencies, we suggest that a formal set of competencies related to medical cannabis should be established in order to guide the formation of curricular inclusion. This would dictate the scope of courses that are needed for providing adequate education, which may vary across healthcare disciplines. Although curricula are indeed horrendously overloaded, we argue that this should not serve as a reason for excluding medical cannabis from healthcare curricula. Academic education on medical cannabis may only be feasible with the support of governmental granting agencies, such as the NIH, in order to avoid potential biases andconflicts of interest if such programs were to be sponsored by the industry. In conclusion, while the medical cannabis landscape is developing, medical and allied health students are not properly educated and knowledgeable on this emerging field of clinical care. This appears to be common across disciplines and countries. Given the massive gap between public interest in medical cannabis and lack of qualified practitioners offering balanced guidance, it is essential that future providers are trained appropriately and enter the workforce well equipped with knowledge based on a set of approved medical cannabis competencies. It is well documented by the Greek historian Herodus  and the Greek physician, pharmacologist and botanist, Pedanius Dioscorides that ancient Greeks cultivated and used cannabis to treat medical conditions such as inflammation, earache, edema, nosebleeds and tapeworms.Cannabis cultivation, commerce and use were illegal in Greece during the 20th century.In 2013, the use of Cannabis Sativa L  with tetrahydrocannabinol  0,2%, was legalized for medical purposes by Law 4139.In 2017, Greece became the sixth country of the European Union partnership to produce and market cannabis-related products.Following this action, in 2018, the Greek state passed legislation  which permitted cannabis cultivation for the production, distribution and commerce of Cannabis Sativa L  0,2 %, and related/derived products.This action involved the Greek National Organization for Medicines; however, the legislation does not identify the medical conditions for which cannabis can be administered, or the healthcare professionals who are entitled to prescribe/administer cannabis for medical purposes.

Additionally, there are no official sources of information or training on medical cannabis  in Greece; and, only a few Greek associations on patient rights provide information on this topic via their webpage.Distribution, commerce and use of cannabis for recreational purposes continue to remain illegal in the country. Within this context, MC appears to be an important issue for the Greek nursing profession and, especially, students who after graduation are expected to manage MC-related therapies or be employed in substance use therapeutic settings. However, there is a lack of usable information about Greek student attitudes, beliefs and knowledge towards MC. Furthermore, internationally, few studies have investigated the MC issue among nursing students; what information is available tends to focus on such professions as medicine and pharmacy and issues about MC legalization. Regarding Greece, specifically, only one study was found that qualitatively explores the attitudes of Greek physicians on the use of MC. This study of only 10 physicians highlighted a lack of robust information and training on MC by government organizations.The present study aimed to assess the attitudes, beliefs and knowledge of Greek university nursing students towards MC. It was hypothesized that these conditions are associated with year of study and student undergraduate or postgraduate academic status. Cluster sampling was performed on 49–66 participants, depending on year of study . This method ensured better representation of the NKUA nursing students.

While there were no significant demographic differences between participants with and without legal access to cannabis, those using cannabis medically were significantly older, had more education, and reported worse physical health than those using medically and recreationally. Congruent with other studies, cannabis was most commonly used for mood/stress symptoms  and chronic pain . Forty-nine percent used cannabis daily or more frequently. Two thirds had access to legal cannabis, and 48% had medical cannabis licenses. Most  participants with legal access obtained cannabis through state-licensed dispensaries. Our results highlight a troubling trend: in response to the COVID-19 pandemic, >50% of people using cannabis medically report initiation or increased use of medications/substances – most commonly alcohol. Consistent with our hypotheses, some individuals  who started using or increased medication/substance use attributed those behaviors to lack of access to cannabis products. However, despite access concerns, 35% of participants increased their cannabis use  to cope with anxiety, symptom flares, or boredom. Although our cross-sectional design precludes us from determining causality, several factors may be influencing these results. First, many people report substituting cannabis for medications and substances  due to better symptom management and fewer side effects.

As some participants were more likely to report decreased cannabis use and increased substance/medication use due to access concerns, these results may indicate that people who previously substituted cannabis could no longer do so effectively and subsequently increased/re-initiated use of other substances. If true, such changes are concerning, as cannabis substitution is often done for harm-reduction reasons and cannabis has substantially lower lethal overdose risk than other substances. Second, medical or combined medical/recreational cannabis use has been associated with worse overall health and risky health behaviors. One recent nationally representative study showed that combined medical/recreational cannabis use  was associated with higher rates of anxiety disorders compared to the general public. This finding is congruent both with our participants using cannabis for mood/stress and also increasing cannabis use typically due to anxiety. Others have shown that people using cannabis medically report high rates of prescription drug use/misuse. Similarly,vertical grow system a longitudinal cohort study of individuals with chronic pain associated cannabis use with more severe clinical pain and pain interference. As such, our data may reflect poly-substance use and/or increasing substance use to cope with medical burdens. Third, it is possible that as with other populations, people using medical cannabis are subject to stressors of COVID-19 ), resulting in coping through medications/substances. Indeed, health research firms reported increased benzodiazepine  and antidepressant  prescriptions in February and March 2020. This increase also may be because people confined to their residence are typically in closer proximity to substances than they would be otherwise, leading to greater use. Whether these effects are magnified among people using medical cannabis remains uncertain.On October 17, 2018, Bill C-45  came into force, legalizing cannabis for non-medical use across Canada. While the availability of controlled, unadulterated alternatives to black market cannabis may increase safety, cannabis use remains associated with multiple health risks, including dependence, mental health problems , and respiratory problems . Protecting and promoting public health post-legalization will require theimplementation of prevention and harm-reduction initiatives, particularly targeting at-risk groups. Indigenous Canadians, especially youth, have been identified as a population that may be at greater risk for harms associated with non-medical cannabis use . This risk may be amplified by government inaction in preparing Indigenous communities for legalization.

Concerns have been raised regarding the federal government’s lack of consultation with Indigenous communities prior to legalization, a lack of culturally appropriate public education materials, and a lack of access and funding for addiction services . Almost 5%  of Canada’s population identifies as Indigenous , a term which comprises three distinct peoples: First Nations, Métis, and Inuit. A synthesis of cannabis use patterns across this at-risk population is needed to inform the development of targeted harm reduction and prevention initiatives. However, to our knowledge, there has been no systematic review on this topic to date. Therefore, we conducted a systematic review of the literature to describe the prevalence of non-medical cannabis use among Indigenous Canadians and factors associated with non-medical cannabis use in this population.Eligible publications were published in English after January 1st, 2000 and reported primary data on the prevalence of non-medical cannabis use among one or more Canadian Indigenous peoples . There were no restrictions regarding methodology, population age, or recruitment setting. Publications were excluded if they reported only on prevalence of medical cannabis use . Reviews, commentaries, letters to the editor, and abstracts were also excluded.We systematically searched the MEDLINE, EMBASE, Web of Science, and Scopus databases from inception through January 29, 2020. The searches included Medical Subject Headings  terms and keywords  employing a combination of the following terms: Indigenous, Aboriginal, First Nation, Inuit, Métis, cannabis, marijuana, and Canada . The search was developed and implemented by an experienced health sciences librarian. In addition, the references of included publications were hand searched for potentially relevant studies that were not captured in the search and for gray literature sources. Two reviewers screened the titles and abstracts of identified publications for eligibility. Citations considered potentially eligible by either reviewer were retrieved for full-text screening and screened independently by two reviewers, with disagreements resolved by consensus or a third reviewer.Methodological, demographic, and prevalence data were extracted independently by two reviewers, with disagreements resolved by consensus or a third reviewer. Extracted data included first author, publication year, data collection year, data source, study design, recruitment strategy, location, sample characteristics, sample size, cannabis use point or period prevalence, and prevalence period used . Data on variables associated with cannabis use prevalence were extracted independently by one reviewer and verified by a second. Data were extracted for all investigated variables, regardless of the significance of their association with cannabis use or statistical tests used.

Both multivariate and bivariate data were extracted. Results pertaining to substance use in general were not extracted. Data directly comparing cannabis use prevalence in Indigenous and non-Indigenous populations were also extracted.We used a descriptive analytical approach to synthesize the prevalence of and factors associated with non-medical cannabis use among Indigenous populations in Canada. Cannabis use prevalence findings were grouped for synthesis first by age group  and then by Indigenous identity . The synthesis of factors associated with cannabis use began with a thematic analysis of variables investigated by included publications. The analysis was performed by one reviewer and verified by a second, and resulted in eleven major themes: sex, age, use of other substances, mental health, physical health, socioeconomic status , Indigenous-specific factors, school, sexual behavior, life events, and other. These themes, along with the aforementioned groups, were used to guide the reporting of associated factors. Due to substantial heterogeneity between included publications in terms of sample characteristics and years of data collection, it was not meaningful to meta analyze extracted data.Factors associated with cannabis use among adults were reported for 9 samples of on-reserve First Nations and Inuit populations. Results suggest associations between cannabis use and male sex, younger age, and use of other substances . There was also evidence of an association between cannabis use and riskier sexual behavior, higher incidence of injury, and single marital status. There was no clear association between cannabis use and physical health, mental health, or socioeconomic status , and no evidence of an association with childhood trauma. Regarding Indigenous-specific factors, there was evidence of an association between cannabis use and lower cultural involvement and intergenerational trauma; others  were not found to be associated.Factors associated with cannabis use among youth were reported for 10 samples of all Indigenous, on-reserve First Nations, and Inuit populations . Results suggest an association between cannabis use and older age, use of other substances, poorer mental health, poorer physical health, and a poorer relationship with school. There was limited evidence for an association with low SES . In addition, there was evidence for associations with Indigenous-specific factors , life events, permissive attitudes to substance use among friends, lower family connectedness and social support, poorer relationship with parents, exposure to second-hand smoke, and smaller households. Cannabis use had no association with sex.This systematic review was designed to synthesize the evidence on the prevalence of non-medical cannabis grow supplies use and factors associated with its use among Indigenous Canadians. The most recent available estimates of prevalence of use in the past year ranged from 30% among on reserve First Nations adults to 60% among Nunavik Inuit adults , and direct comparisons indicated a 1.2–15 times higher prevalence of use in Indigenous compared to non-Indigenous youth.

The available evidence indicates that the prevalence of cannabis use is higher among Indigenous Canadians than in the general population. Although no publications directly compared Indigenous and non Indigenous adults, the most recent estimate of past year cannabis use in on-reserve First Nations adults   is twice that of the general Canadian population  ; among Indigenous adults, prevalence was lowest in on-reserve First Nations compared to other groups. In adults, cannabis use was associated with male sex, whereas in youth, males and females had a similar prevalence of use. In addition, cannabis use among youth was associated with poorer mental health, poorer physical health, a poorer relationship with school, and lower neighbourhood income.Overall, our systematic review reveals that the literature on cannabis use in Indigenous Canadians is limited. Data are largely restricted to the on-reserve First Nations population, which represents around a fifth of the total Indigenous population . The paucity of data specific to off-reserve First Nations, Inuit, and Métis is an important gap in the literature, and future research is needed on cannabis use in these populations. Another notable gap in the literature is the lack of direct comparisons of cannabis use between Indigenous communities; studies have either focused on one community, or have grouped data for all participants. Future, and especially large surveys should move away from grouping data for all Indigenous populations, and towards stratification by Indigenous identity and by community/region where possible. This will enable the identification of community-specific cannabis use patterns, and is a key future research direction. Finally, the available data on cannabis use in Indigenous Canadians is largely cross-sectional, which limits in particular the interpretation of factors associated with cannabis use. Rigorous, longitudinal studies are needed. The factors associated with cannabis use identified in our systematic review are largely consistent with those previously described in the general Canadian population. Cross-Canadian surveys have indicated a higher prevalence of cannabis use among younger and male adults . Multiple publications have described an association between cannabis use and low SES among Canadian youth , and among British Columbian youth, an association was reported between cannabis use and poorer mental health, as well as use of other substances . The higher prevalence of cannabis use in Indigenous compared to non-Indigenous groups in Canada may be explained by a higher prevalence of these risk factors in the Indigenous population; the mean age of the Indigenous population is almost a decade younger than that of the general Canadian population , and lower SES  and poor youth mental health  are much more prevalent. Indigenous-specific factors such as intergenerational trauma, for which an association with cannabis use was described in both adults and youth, may also play a role. However, future research is needed to better understand the factors underlying cannabis use in this population.Canada’s cannabis legalization framework is rooted in a public health approach to cannabis regulation, which, among other objectives, involves the implementation of targeted interventions for high-risk individuals . Moving forward, there is an urgent need for culturally-appropriate interventions in the Indigenous population, particularly in youth; the protection of young Canadians was one of the principle objectives outlined in the government’s prelegalization discussion paper . Prevention interventions targeted towards Indigenous youth should be developed and implemented by or in partnership with Indigenous communities. As older youth and younger male adults are at increased risk of cannabis use, adolescence and early adulthood will be key intervention points, particularly for males. Interventions for the adult population should similarly be developed and implemented in a community engaged fashion.

Activation of AhR halts inflammation notably through induction of interleukin-22 . Of interest, activation of AhR, dependent or independent of the gut microbiota, has been reported to limit the production of microglial pro-inflammatory mediators such as transforming growth factor alpha  and vascular endothelial growth factor B  in a mouse model of MS. Interestingly, oral administration of Lactobacillus acidophilus was shown to combat in- flammation and nociception through increasing the expression of the CB2 receptor in intestinal epithelial cells, suggesting that probiotics and cannabinoids might work together to halt inflammation and nociception. In support of this, it has recently been shown that THC reduces inflammation and adiposity in mice by increasing the accumulation of mucin-degrading bacteria, Akkermansia municiphila. Of relevance, Akkermansia municiphila supplementation was shown to reduce systemic inflammation in mice, further supporting the notion that microbiota contributes to the anti-inflammatory and analgesic effects of oral cannabinoids.Terpenes are volatile unsaturated hydrocarbons that represent the largest group of plant organic chemicals with around 20,000 fully characterized compounds. Terpenes account for a unique aroma of cannabis, but there is evidence that they may play more of a role in biology than simply affecting taste/aroma of cannabis. Indeed, while > 200 terpenes have been identified in cannabis, 3 monoterpenes, , and sesquiterpenoid, , have been shown to have biological importance. Indeed, sedation and a decrease in motility have been observed in mice upon inhalation exposure to terpenoids in a concentrations equivalent to 0.05% v/w for 1 h. In addition to having their own independent effects, these cannabis-derived terpenoids have beenpostulated to modulate the effects of cannabinoids via what has been termed the “entourage effect”. However, it is important to note that many terpenoids appear to modulate molecular/biological functions only when the concentration of the terpene in full-spectrum cannabis extract is above 0.05% v/w. β-caryophyllene is the most common sesquiterpenoid, a class of terpenes that contain three isoprene units, and one of the most predominant terpenoids in cannabis extracts.

There is evidence that β-caryophyllene may synergize the anti-inflammatory and the analgesic effect of THC through the inhibition of prostaglandin E1 and the activation of the CB2 receptor, respectively. Therefore, the synergistic effect of β-caryophyllene with THC strongly suggests that the potential health benefits of complete cannabis extracts may be more evident in the treatment of inflammation compared to THC alone. Of interest, mobile grow system the anti-inflammatory activity of β-caryophyllene is comparable to the potency of the nonsteroidal anti-inflammatory drug, phenylbutazone, suggesting that this terpene is a potent anti-in- flammatory molecule. β-caryophyllene also inhibits lipopolysaccharide -induced proinflammatory cytokine expression through the activation of the CB2 receptor. On the other hand, β-caryophyllene lacks any anti-inflammatory or anti-nociceptive activities in mice lacking CB2 receptors suggesting that β-caryophyllene exhibits cannabimimetic-dependent effects. In support of this, β-caryophyllene has been identified as a natural selective agonist of the peripherally expressed CB2 receptor. Of importance, oral administration of β-caryophyllene attenuates thermal hyperalgesia, mechanical allodynia and spinal neuroinflammation in a neuropathic pain model and suppresses neuroinflammation in an animal model of MS suggesting that β-caryophyllene is an effective anti-inflammatory molecule for the treatment of MS.D-limonene is the second most widely distributed terpenes found in nature. While D-limonene has a low affinity for cannabinoid receptors, it synergizes the anxiolytic, anti-stress and sedative effects of CBD by increasing serotonin and dopamine in the prefrontal cortex and hippocampus through 5-HT1A receptor. Similar to myrcene, D-limonene suppresses the metabolism of Aflatoxin B1 to its carcinogenic active metabolite and thus it may act as a chemopreventive agent. Furthermore, D-limonene was shown to induce apoptosis in human breast cancer cells, and this effect has been postulated to potentiate the antitumor activity of CBD in advanced stages of breast cancer.Cannabis is commonly administered via inhalation  or orally in the form of syrup , oromucosal aerosol  or capsule. Since inhaled therapies involving cannabis may pose certain health risks, alternative routes of delivery such as oral administration have been studied. Perhaps one of the largest challenges associated with the use of cannabinoids as an orally available medical therapy is their low bioavailability. Although cannabinoids are highly absorbed when administered orally, they have a very low bioavailability due to the first pass metabolism .

Of interest, this issue might be less pronounced in the full extract, as it has been suggested that other components present in the full-spectrum cannabis extract modulate the bioavailability of THC and CBD.The high absorption rate of THC upon inhalation either by smoking or vaporization results in a time to peak plasma levels of 6–10 min and a bioavailability of 10–35%. In contrast, when THC is administered orally, the time to peak plasma levels is between 2 and 6 h and the bioavailability is very low. Similar to THC, CBD has poor oral bioavailability but CBD has a higher absorption rate than THC with a time to peak plasma level of 2 h. Notably, the absorption of both THC and CBD can be improved using oil vehicle such as sesame oil or a glycocholate solution, suggesting that the administration of THC and CBD using oil based formulation might be the best existing oral dosage form for THC and CBD . The poor oral bioavailability of cannabinoids is largely attributed to the first pass metabolism in hepatic tissue. CYP2C9 and CYP2C19 are the main enzymes responsible for the metabolism of THC into 11-OH-THC. While 11-OH-THC is psychoactive like THC, it is oxidized in the liver into an inactive metabolite THC-COOH which is eventually conjugated to glucuronic acid by UDP-glucuronosyltransferase . The presence of the glucuronide group increases the polarity and thus water solubility of this metabolite, which facilitates its excretion in the urine. Thus, the THC-COOH glucuronide conjugate in the urine is considered to be a good biomarker for THC-containing cannabis use. Interestingly, while 25% of THC metabolites are excreted in the urine, > 65% of these metabolites are eliminated in the feces. Similar to THC, orally administered CBD is extensively metabolized into an inactive metabolite, 7-OH-CBD, by the CYP2C19, CYP3A4, CYP1A1, CYP1A2 and CYP2D6 enzymes in the liver. CBD and 7-OH-CBD are then eliminated in the feces, with a minor amount being excreted in the urine. Interestingly, CBD has been shown to bind to the catalytic site of CYP2C9 and CYP2C19 and competitively suppress the activity of these enzymes. Thus, CBD has been reported to inhibit CYP2C9 mediated hydroxylation of THC to 11-OHTHC providing a molecular mechanism that may explain why CBD can improve the oral bioavailability of THC. Intriguingly, β-myrecene and other terpenoids in cannabis have been shown to enhance the effect of CBD on hepatic CYP enzymes. Importantly, the effect of CBD on CYP enzymes could have implications for the metabolism of other drugs and thus may be either additive or contraindicated for specific existing pharmacotherapies.

Indeed, CBD was shown to decrease the metabolism and the excretion of anti-convulsant drugs, hexobarbital and clobazam in human subjects, which results in an increase in the plasma level of the aforementioned drugs and subsequently their side effects. Thus, care should be taken when CBD is co-administered with hexobarbital and clobazam.Epidiolex is an oil formulation of purified, plant-derived CBD that has been recently approved by FDA for the treatment of certain rare and catastrophic forms of childhood-onset epilepsy such as Lennox–Gastaut Syndrome and Dravet Syndrome. Another clinically approved product derived from cannabis extract is Sativex. Sativex is an oromucosal spray containing a full-spectrum cannabis extract with a standardized ratio of THC and CBD in addition to other cannabinoids and terpenes in an aromatized water-ethanol solution. Sativex has been approved in many countries such as Canada, UK, Spain and Germany for the treatment of symptoms associated with MS. In addition to Epidiolex and Sativex, Cannador is an oral capsule containing a full plant extract, mobile vertical rack with a standardized ratio of THC and CBD that has been used in several clinical trials for the treatment of symptoms associated with MS.While numerous studies have investigated the effects of cannabis extracts in multiple disease conditions, some of the most impactful effects appear to occur in the treatment of inflammation and neuropathic pain. For instance, the role of full-spectrum cannabis extract in the treatment of spasticity and neuropathic pain has been investigated in a mouse model of amyotrophic lateral sclerosis . In a mouse model of ALS, mice treated daily with 20 mg/kg Sativex for 20 weeks displayed significantly reduced progression of neurological deficits and had improved survival, particularly in females. The protective effect of Sativex has also been confirmed in a mouse model of MS. This study utilized the Biozzi ABH mice with chronic relapsing experimental allergic encephalomyelitis as a model of MS. These mice were treated with a full-spectrum cannabis extract, Sativex, baclofen  or vehicle and the stiffness of limbs of the mice were evaluated by measuring the force required to flex the hind limb. In a manner similar to baclofen, Sativex significantly reduced neuropathy and spasticity by approximately 40% compared to control. Overall, the study proposes that Sativex is as effective as baclofen in the treatment of symptoms associated with MS. The neuroprotective effect of Sativex has also been studied in the Theiler’s murine encephalomyelitis virus-induced demyelinating disease model of MS.

Treatment of these mice with 10 mg/kg i.p. of Sativex significantly improved the neurological deficits associated with MS . Specifically, Sativex significantly improved motor activity, reduced axonal damage and restored myelin morphology in MS mice. Mechanistically, Sativex was shown to act as an anti-in- flammatory agent as it suppressed microglial reactivity, the expression of proinflammatory cytokine IL-1β and adhesion molecules, and it upregulated the anti-inflammatory cytokines, arginase-1 and IL-10. Furthermore, Sativex was able to decrease the accumulation of chondroitin sulfate proteoglycans and astrogliosis in the spinal cord of MS mice and in astrocyte culture in vitro. In order to explore whether or not the protective effects observed with Sativex were due to extracted CBD, THC or both, MS mice were treated with extracted CBD or THC alone. In a manner similar to Sativex, extracted CBD was sufficient to significantly lessen the motor deterioration and axonal damage whereas extracted THC induced much weaker effects. Together, these findings suggest that the neuroprotective and anti-inflammatory effects of Sativex are mainly due to CBD. Contrary to the previous study , another study found evidence that the neuroprotective effect of Sativex was due to THC. In that study, female C57BL/6 mice with a bacteria-induced experimental autoimmune encephalitis and MS were treated with Sativex, extracted THC, or extracted CBD. Notably, while administration of Sativex, extracted THC, and extracted CBD all produced beneficial effects in neurological function, only Sativex and extracted THC maintained improvement of the neurological function along with reduced cell in- filtrates in the spinal cord and thus slowed disease progression. Importantly, the beneficial effect of extracted THC was abolished by the treatment of mice with the CB1 receptor antagonist, rimonabant, suggesting a CB1-dependent mechanism.Although the discrepancy between the two studies discussed above is unknown, it is possible that the differences may be attributed to different environmental conditions for each study and/or sex differences For instance, the first study was performed on male mice using virus-induced demyelinating disease model of MS, while the later study was conducted on female mice with bacteria-induced experimental autoimmune encephalitis disease-related MS. Thus, it is possible that the effects of Sativex, THC and/or CBD are dependent on sex, species, or pathogenesis of MS. Nevertheless, given that the beneficial effect of fullspectrum cannabis extract was consistent in both studies, this would highlight a crucial role of full-spectrum cannabis extract, in this case Sativex, in the treatment of neuropathic pain and inflammation associated with MS. As mentioned, in addition to MS, cannabis extracts have also shown promise in the treatment of neuropathic pain associated with other conditions. Indeed, an important role of full-spectrum cannabis extract in the treatment of neuropathic pain compared to purified THC or purified CBD has been studied in a rat model of chronic constriction injury of the sciatic nerve. In this study, rats were treated with full-spectrum cannabis extract, purified CBD or purified THC. Of note, the full-spectrum cannabis extract used in this study contained 64.5% CBD, 4% THC, < 4% of other cannbinoids  and additional minor components.

A limitation is that the sample size of the target sample was not large enough to carry out more advanced methods such as one-sample Mendelian Randomization to further explore causality between cannabis and other drug use. However, as far as we know this is the first study exploring genetic overlap between cannabis and ecstasy, stimulants and any other drugs and power was sufficient to detect these associations. In summary, PGS for cannabis use was significantly associated with use of ecstasy, stimulants, and any illicit drugs. An exploratory followup analyses indicated that this association was slightly stronger in cannabis users compared to non-users for ecstasy and stimulant use, but only in people born after 1968. The results of the MZ discordant twin analyses were in line with the suggestion that cannabis could be a causal factor for other drug use. Given the exploratory nature of this study, the present findings must be considered as preliminary rather than conclusive. Further unravelling the nature of the co-occurrence between substances will have implications for public health and intervention research. If there is no causal relationship then interventions that target reductions in one drug may not necessarily also lead to any change in use of another drug, and interventions that seek to target both drugs will need to incorporate active ingredients for each substance.Tobacco and cannabis use during adolescence, when the brain is still developing and undergoing considerable structural and function changes , is a major public health concern. The association between adolescent tobacco and cannabis use and subsequent cognitive functioning has received particular attention because certain cognitive functions  do not peak until early adulthood  in parallel with maturation of the prefrontal cortex . Due to the prolonged neurodevelopmental period and the potential for the endocannabinoid and nicotinic cholinergic signalling systems to be involved in altering development , it is plausible that tobacco and cannabis use during this potentially critical period could play a role in disrupting normal brain development . Nonetheless, there is still uncertainty regarding the nature of the association between tobacco and cannabis use and neurocognitive function.

A recent review of prospective studies of the association between cannabis use and cognition in young people  highlighted an association between cannabis use and neuropsychological decline . However, studies often fail to control for neurocognitive measures prior to cannabis use  and associations were largely found for the heaviest cannabis users and were often attenuated when potential confounders  were included . A recent study , using a co-twin design , assessed IQ prior to cannabis initiation and found insufficient evidence to suggest pot for growing cannabis use was associated with decline in general IQ. Findings from two recent longitudinal studies of adolescents  using a repeated measures design suggest that the association between cognitive functioning and cannabis use could be bidirectional. The direction of association between tobacco and cognitive functioning is also unclear as there is a lack of epidemiological studies that have prospectively examined this relationship. Evidence from animal studies suggests that nicotine exposure may have more deleterious developmental effects during adolescence, when the brain is thought to be more vulnerable . Furthermore, human studies suggest that nicotine has a more potent effect when consumed in late adolescence compared to in adulthood . One small prospective study  found that current smokers performed worse than non-smokers on a variety of cognitive assessments including language related IQ and working memory while controlling for earlier cognitive measures and other substance use . Finally, one large study  on Israeli male soldiers  found a dose-response relationship between number of cigarettes smoked and lower general cognitive ability compared to non-smokers. They also found diminished cognitive functioning in individuals who started starting smoking after 18 years of age. The literature is further complicated by the differential effects of acute, chronic, and withdrawal from chronic nicotine on cognitive functioning. Studies have reported beneficial effects of acute nicotine , negative effects of nicotine withdrawal on cognitive functioning , and the reduction of beneficial effects with nonacute nicotine consumption as tolerance develops . In an effort to strengthen the evidence, we used data from the Avon Longitudinal Study of Parents and Children , a large UK prospective birth cohort, to investigate whether patterns of adolescent tobacco and cannabis use were prospectively associated with cognitive functioning at 24 years of age. Separate measures of tobacco and cannabis use were assessed on six occasions across adolescence allowing distinct classes of tobacco and cannabis use to be established. As young people do not initiate tobacco or cannabis at the same time , we used longitudinal latent class analysis to identify heterogenous classes of individuals with different tobacco and cannabis use profiles across adolescence . As a next step we used genetic variants that are separately associated with smoking initiation and lifetime cannabis use to perform Mendelian randomization  to improve causal inference .

The aims were to investigate  whether separate patterns of tobacco smoking and cannabis use  were associated with working memory, response inhibition, and emotion recognition assessed at age 24, and  whether tobacco use and cannabis use were associated with these cognitive outcomes using MR. The Stop Signal Task  was used to assess response inhibition – the ability to prevent an ongoing motor response. The task consisted of 256 trials, which included a 4:1 ratio of trials without stop signals to trials with stop signals. Mean response times were calculated. An estimate of stop signal reaction time  was calculated using the median of the inhibition function approach . SSRT used as the primary outcome as it is a reliable measure of inhibitory control, with shorter reaction times indicating faster inhibition. SSRT data were available for 3201 participants. Individual Stop Signal indices  were examined as secondary outcomes. Emotion recognition was assessed using a six alternative forced choice  emotion recognition task  comprising of 96 trials  which measures the ability to identify emotions in facial expressions that vary in intensity. In each trial, participants were presented with a face displaying one of six emotions: anger, disgust, fear, happiness, sadness, or surprise. Participants were required to select the descriptor that best described the emotion that was present in the face, using the computer mouse. Emotion intensity varied across 8 levels within each emotion from the prototypical emotion to an almost neutral face. Each individual stimulus was presented twice, giving a total of 96 trials. An overall measure of emotion recognition  was used as the primary outcome. Emotion recognition data were available for n = 3368 participants. Each of the individual emotions were examined as secondary outcomes. Confounders comprised of established risk factors for cognitive functioning that could plausibly have a relationship with earlier substance use. Potential confounders included: income, maternal education, socioeconomic position, housing tenure, sex, and maternal smoking during first trimester in pregnancy. Working memory at approximately 11 years and experience of a head injury/unconsciousness up to 11 years were included to control for cognitive functioning prior to baseline measures of substance use. Finally, a measure of alcohol use asking whether they had ever had a whole drink of alcohol was collected at age 13 years . Further information is presented in Supplementary Material.This approach uses the weights derived from the latent classes to reflect measurement error in the latent class variable. Linear regression was used to examine the association between the cognitive outcomes and latent class membership controlling for the confounding variables. Results are reported as unstandardized beta coefficients with 95 % confidence intervals.

Analyses were carried out using Mplus 8.4 . Missing data was dealt with in three steps. First, full information maximum likelihood  was used to derive trajectories tobacco  and cannabis  based on individuals who had information on at least one timepoint between 13 and 18 years. For a detailed description of missingness at each timepoint see Tables S2a and S2b. Next, multiple imputation was based on 3232 participants  who had information on at least one of the cognitive outcomes. The imputation model  contained performance on all of the cognitive tasks, all measures of tobacco and cannabis use, and potential confounding variables, as well as a number of auxiliary variables known to be related to missingness . Finally, inverse probability weighting was used where estimates of prevalence and associations were weighted to account for probabilities of non-response to attending the clinic. See Table S3 for a detailed description of attrition for completing the cognitive assessments at age 24 years. See Tables S4a and S4b for a detailed description of confounding factors associated with tobacco and cannabis use class membership. See Table S5 for a detailed description of sample characteristics. Our aim was to triangulate the findings from the observational analyses with one- and two-sample MR analyses. However, due to insufficient power in the two-sample MR analyses, we will primarily focus on the one-sample MR results. Two-sample MR are still included as a set of sensitivity analyses as they allow us to conduct some of the pleiotropy robust methods , but must be interpreted with caution. Information on genotyping and quality control are presented in the Supplementary Material. This observational study provided evidence to suggest an association between tobacco and cannabis use across adolescence and subsequent cognitive functioning. Early- and late-onset regular tobacco smokers demonstrated poorer working memory and poorer ability to recognise emotions; while, early-onset regular tobacco smokers had slower ability to inhibit responses compared to non-tobacco smokers. Early-onset regular cannabis users had poorer working memory performance and slower ability to inhibit responses compared to non-cannabis users. Our results remained largely consistent when controlling for prior measures of substance use and cognition allowing for clear temporality between exposure and outcomes. Genetic analyses were imprecise and did not provide sufficient evidence for a possible causal association between smoking initiation and lifetime cannabis use and cognitive functioning in the ALSPAC sample. It is likely that these analyses were underpowered. To our knowledge, this is the first study to assess the relationship between separate tobacco and cannabis use in adolescents, and subsequent cognitive functioning using a combination of observational and genetic epidemiological approaches. Overall, we found an adverse association between tobacco/cannabis use and working memory, response inhibition, and emotion recognition in ALSPAC. Those who initiated regular use at earlier and later ages demonstrated poorer performance on the cognitive tasks. There was some evidence to suggest cannabis use with associated with emotion-specific impairments in emotion recognition. This is in line with previous research suggesting container for growing weed cannabis users may have poorer recognition of negative emotions . Our results also tentatively suggest that recognition deficits may be related to specific patterns of cannabis use, with different patterns in early- and late-onset use. The observational findings contribute to a literature of mixed findings regarding the direction of association between tobacco and cannabis exposure and subsequent cognition by suggesting that adolescent tobacco and cannabis use precede observed reductions in cognitive function. These findings support studies that have demonstrated effects may depend on the frequency, duration, and age at onset of use .

Our study extends previous findings in a number of ways. First, the observational study was better powered than most of the previous studies as it used data from over 3200 participants providing information spanning birth to 24 years of age. Second, identifying heterogeneous patterns of tobacco and cannabis use across this crucial period allows individuals who follow markedly different developmental trajectories to be captured . Third, the cognitive measures were assessed at a time when they are expected to have reached maturity in some individuals , in comparison to previous studies which have examined cognitive functioning at earlier ages while they are still maturing. Examining mature levels of cognitive functioning reduces the possibility that cognitive functioning is influencing earlier tobacco and cannabis use, effects that cannot be disentangled in purely cross-sectional studies. Further, our ability to control for earlier measures of cognitive functioning and substance use, prior to the baseline measures of tobacco and cannabis use helps to rule out the possibility of reverse causation. Fourth, our study sought to examine specificity in cognitive functioning, by using well-validated tests to probe different domains of cognitive functioning instead of focusing on general intelligence. Finally, we sought to triangulate our results by using one- and two-sample MR approaches to assess tobacco and cannabis use as causal risk factors for cognitive functioning.

The models show when FBBB is used to classify for marijuana, which has a low THC:CBD ratio, there is a decrease in specificity that causes these marijuana-type samples to be misclassified as hemp. When marijuana-type samples with THC:CBD <2 were removed from the LDA models, FBBB has high sensitivity and specificity for marijuana-type cannabis with a high THC:CBD ratio and shows a clear separation from hemp samples. In addition, the combination of RGB values from the fluorescence images and color images provided the most reliable model that correctly classified all 7 marijuana samples and 12 hemp samples. This study has demonstrated the specificity and sensitivity of the FBBB reaction with THC compared with other cannabinoids. The combination of the red color and fluorescence of the FBBB + THC chromophore/fluorophore allows THC-rich cannabis to be distinguished from CBD rich cannabis. ElSohly et. al. analyzed confiscated cannabis in the US between 2009 and 2019 and found that the average THC:CBD ratio of the cannabis plants was found to be above 20 across the decade. Although false negative results can be obtained for samples with a low THC:CBD ratio, FBBB is useful in discriminating between marijuanatype cannabis with a high THC:CBD ratio from hemp-type cannabis. Since most illicit cannabis grow equipment in the US contains a high THC:CBD ratio, FBBB is applicable to field use as a presumptive test to distinguish between cannabis types. When compared to the other field tests on the market, FBBB is more selective as well, producing less false positive results among herbs, spices, and hops. This test uses a small volume of reagents and can be performed on a 3.5 mm PSPME substrate, which simplifies the analysis while allowing for portability.

Finally, the observation time window for the FBBB + cannabinoids is longer than for other competing techniques such as the 4-AP reaction that has an observation window of a few minutes. Future work will include validating the FBBB test by conducting an interlaboratory study with several operational laboratories and increasing the number of authentic cannabis samples of known cannabinoid concentrations. Future studies will also focus on better defining the analytical figures of merit for the reaction including LOD and the THC:CBD range in which ambiguous or false negative results are obtained using this test and the potential to conduct a concentration determination of the THC is some samples. FBBB will also be validated for field use, assessing operational parameters such as chemical stability of the reactants, storage limitations and the possibility of incorporating a portable spectrometer to determine the fluorescence spectra of the chromophore/fluorophore in the field. Additional studies will be conducted to determine how the FBBB test performs in comparison with, and in combination with, existing presumptive cannabis tests, such as the 4-AP test. As of April 2021, 36 states and the District of Columbia have passed laws that allow for the medical use of marijuana and 16 states and the District of Columbia have passed laws that legalize recreational use of marijuana; more states appear to be heading toward similar legislation. Even though the debate on marijuana’s safety and benefits continues to be fiercely debated , public opinion on the legalization of marijuana has grown more favorable. In 1969 only 12% of the adult population supported legalization compared to 66% in 2019. Among the 18-34-year-old subgroup, support was even higher with 81% favoring legalization in 2019. With this data, it is not surprising that marijuana use has also gradually increased with college age adults, 18–25 years, experiencing the greatest growth from 17.3% in 2002 to 22.1% in 2018. Furthermore, in their examination of national survey results on drug use from 1975 to 2016, Schulenberg and colleagues noted historical trends of marijuana use increased at different levels and for different lengths of time across younger ages, 19–20 years through 29–30 years, with almost all age groups reporting increasing prevalence from 2010 onward.

Earlier studies have shown numerous negativeassociations with marijuana use and academic success indicators such as lower GPA, reduced studying time, late assignments, missing class and discontinuous enrollment in college. Furthermore, the heavier the marijuana use, the more likely negative outcomes were reported. Importantly, the majority of the studies investigating negative impacts and marijuana use in college students are prior to legalization of recreational marijuana, and even less of this data are available on a diverse student population. Adverse high-risk behaviors, including tobacco use, binge drinking and use of other illicit drugs have also been associated with marijuana use in college students . Using marijuana and tobacco at the same time may lead to increased exposure to harmful chemicals, causing greater risks to the lungs, and the cardiovascular system. According to the National Survey on Drug Use and Health, alcohol and marijuana were substances most frequently used by college students. In addition, among users of both substances, alcohol and marijuana have been shown to be more concurrently used rather than alone. Another area of concern is sexual risk behaviors. Marijuana use, among adolescents and young adults, has been associated with sexual risk behaviors and outcomes, including inconsistent condom use, multiple sexual partners, and STI diagnoses. Bryan and colleagues showed that marijuana use increased the likelihood of intercourse due to reduced inhibitions and effects on cognitive ability. In addition, they noted a decreasing ability to negotiate and carry out condom use. Furthermore, Metrik and colleagues found that both alcohol and marijuana use were independently correlated with greater odds of having casual sexual intercourse. Data from the National Survey on Drug Use and Health also showed that among 18-24-year-old respondents, Blacks and Hispanics experienced more marijuana use disorder than other ethnic groups. Keyes and her colleagues found similar results when examining race/ethnicity in Monitoring the Future data from 2006 to 2015, pre-legalization. However, they note the importance of a deeper look into diverse populations as they also found confounding factors of class size for Black high school seniors and urban setting for Hispanic seniors. With legalization legislation in the United States beginning only a decade ago and in California as recently as 2016, there is limited data available on high-risk health behaviors and marijuana use postlegalization in college-aged students.

Even more limited are those that focus on ethnically diverse college groups . Therefore, this study examined health behaviors in marijuana-users post legalization in a diverse, urban college population in Southern California. Specifically, we investigated alcohol and tobacco use, as well as sexual behaviors among marijuana-users compared with non-users post legalization in a racially/ethnically diverse college group. We hypothesize that marijuana-users will partake in increased high-risk behaviors compared with non-users, and that use will vary by race/ethnicity. Our findings post legalization of marijuana for recreational use clearly demonstrated a higher prevalence of high-risk behaviors such as alcohol use, tobacco use, drinking and driving and sexual activities among college students who use marijuana compared to those who do not. Specifically, adjusting for all covariates in the logistic regression model, younger respondents were found to be more likely to be marijuana users; similarly, students earning D grades were 1.68 times more likely to use marijuana compared to those earning A grades . Compared to APIs, Whites were 53% more likely and the Other race/ethnicity category were twice as likely to be marijuana users. These findings are consistent with similar studies examining risktaking behaviors among marijuana-users for recreational use pre legalization. This study is one of the first to assess marijuana use in an ethnically diverse student population. We found marijuana use was higher among White college students which mirrors most recent national data for 18- 25-year-olds. Interestingly, when examining marijuana-use disorders Pacek and colleagues found African Americans twice as likely to be diagnosed with this condition versus both Whites and Hispanics. Due to small sample size of certain ethnic groups we did not specifically examine marijuana use among Blacks and Native American students. However, we found a strong relationship between the Other race/ethnicity category, , and marijuana use, suggesting higher use of marijuana in this respective population. Nonetheless, further studies are needed with larger samples of African Americans and Native Americans. While we found marijuana use slightly higher among males than females, it was not a statistically significant difference. National data shows men age 18 to 25 are more likely to use marijuana at least once a month compared to women. However, when analyzing a 15-year trend, Johnson and colleagues found that the male-female differences in marijuana use decreased over time; with the most recent SAMSA data indicating among 18-25 year-olds, males are only slightly higher in reported marijuana use than females. In addition, we found an inverse relationship between academic performance and marijuana use; specifically, adjusting for all covariates in the logistic regression model, younger respondents were found to be more likely to use marijuana; similarly, students earning D grades were 1.68 times more likely to use marijuana compared to those earning A grades. This is similar to the findings from Arria and colleagues. Their research found that not only did marijuana-users have lower grade point averages, but also skipped classes more and took longer to graduate college. Whether these negative outcomes are related to poor academic behaviors or poor cognitive function or both continues to be researched. In a review of the literature, Crane and colleagues found that numerous studies continue to demonstrate the negative effect of cannabis on learning and memory, in addition to deficits in attention, concentration, and abstract reasoning. With regard to alcohol use, our findings indicate that increased alcohol use is also associated with an increase in marijuana use. Recent studies exploring the role of marijuana policies on alcohol and marijuana use, vertical grow system show varying results. Some hypothesize that alcohol use may decrease as marijuana becomes legalized for recreation use as it would substitute for alcohol.

Others posit that with more liberal marijuana policies, both marijuana and alcohol use will increase as they may complement each other. A critical review of the literature provides some evidence of both. The researchers of these studies note the issue is complex, suggesting likely factors such as how long the policy has been in place, how it is implemented as well as the age of users, play a role. Tobacco was another substance we found marijuana smokers more likely to use. This was a significant finding as tobacco users were four times more likely to be marijuana users compared with non-tobacco users. Interestingly, some researchers have found that using nicotine with cannabis together can intensify the effects of cannabis. In their study Ream and colleagues found participants reported smoking cigarettes directly after using marijuana to enhance the intoxication. This may be due to the close overlapping in the distribution of brain receptors for nicotine and cannabis.The relationship of smoking and marijuana use is supported by findings that show marijuana-users who also smoke cigarettes have increased risk of marijuana relapse when they are trying to quit. As is the case with many substances, marijuana impairs judgement; therefore, we found a positive association between high-risk sexual behavior and marijuana use. Our univariate analyses also showed that marijuana-users in this study indicated they never use a condom during vaginal intercourse 25% of the time compared to only 14% of the time for non-users. These findings are similar to data showing marijuana use associated with non-use of condoms and having a higher number of sexual partners . Some researchers assert this association may be due to marijuana use potentially increasing sexual desire or sensation while others believe decreased cognition and increased disinhibition are contributing factors. Since the outbreak of the Coronavirus Disease 2019 , individuals and societies have faced various and serious health, social and economic challenges and uncertainties. By early September 2021, WHO reports that there had been more than 218 million confirmed COVID-19 cases and 4.5 million related deaths globally. For Germany, the figures to date are 3.9 million confirmed COVID-19 cases and over 92,000 deaths. The economic impact of the COVID-19 pandemic cannot yet be quantified conclusively. However, there were significant declines in gross domestic product , particularly at the beginning of 2020. GDP in Germany, for example, fell by 4.9% compared with the previous year. In addition, the German government has approved 7.3 billion Euros of COVID-19-related emergency economic aid and 130 billion Euros for economic stimulus measures.

This article extends research in other states by investigating the landscape of licensed and unlicensed cannabis retailers in California as of October 2018, providing a descriptive snapshot of California’s cannabis retail landscape at one point in time. We hypothesized that neighborhoods with cannabis retailers—particularly unlicensed retailers—would show more socioeconomic disadvantage  than communities without cannabis retailers. We also tested the hypotheses that unlicensed facilities would be more likely than licensed facilities to be in unincorporated areas .We also analyzed the locations of licensed and unlicensed facilities relative to whether medicinal, adult-use, or both types of cannabis retail businesses are allowed or not allowed in all unincorporated and incorporated jurisdictions throughout California. We obtained data on local cannabis ordinances as of October 2018 from local news stories and the websites of local jurisdictions. We took state data on population by jurisdiction  and calculated the proportion of the state population living in incorporated areas versus unincorporated areas and the proportion living in localities that allow  versus localities do not allow adult-use retail .We estimated the expected values of facility locations by apportioning the total number of licensed or unlicensed facilities according to the population, and then used chi-square statistics to test whether facility locations varied significantly from the expected values.Licensed retailers can benefit public health by ensuring that cannabis products are uncontaminated, accurately labeled, and sold only to adults . Our findings show that neighborhoods with only licensed retailers contain a disproportionately high proportion of non-Hispanic whites, compared to neighborhoods with unlicensed retailers or a mix of licensed and unlicensed retailers.

Unlicensed dispensaries are problematic because they have been reported to engage in illegal business practices that can compromise public health and encourage underage use, including selling products that exceed the legal THC limit, selling counterfeit products that contain pesticides, allowing consumption of cannabis in retail stores, not imposing daily limits on purchases, staying open late at night, and selling products that are attractive to youth and lack child-resistant packaging . From a social justice perspective, it is important that African American communities now benefit from the safety precautions, employment opportunities, and revenue afforded by the retailer licensing process. For this to occur, it is important to prevent unlicensed retailers from competing with licensed retailers in African American and Hispanic neighborhoods. Retailers in unincorporated areas were more likely to be unlicensed, relative to retailers in incorporated areas. Enforcement could be difficult in unincorporated areas because these areas lack the representation of a centralized local government, which can provide local control over community services such as law enforcement and regulatory oversight for cannabis retail stores. Increased county-level enforcement resources are needed to eliminate unlicensed cannabis retailers in areas that are outside the jurisdictions of city governments. California currently has more unlicensed cannabis grow tray retailers than it can control with existing enforcement resources . Enforcement by the state has been hampered by a lack of resources and a decision to give new businesses time to comply with complex regulations. At the same time, lack of enforcement has created an environment for a thriving unregulated ‘underground market.’ Major depressive disorder  is a potentially debilitating psychiatric disorder with an estimated worldwide prevalence in emerging adults of 16–18% . Cannabis is the most commonly used recreational drug after alcohol and the highest prevalence of use is in teens and young adults . A recent study of Canadian middle school age youth showed that cannabis use was strongly associated with internalizing mental health problems  with an odds ratio of approximately 6.5 . There is some overlap in symptomatology between MDD and heavy cannabis use including anhedonia, changes in weight, sleep disturbance and psychomotor problems . A recent meta-analysis also found that adolescent cannabis use predicted depression and suicidal behaviour later in life . The link between mood disorders and cannabis use is complex, especially with respect to directionality; cannabis use is predictive of the onset of mood disorders in youth , even while some individuals use cannabis in an attempt to regulate the symptoms of depression .

The likelihood of developing MDD in heavy cannabis users who began at a young age has been estimated to be up to 8.3 times higher than in individuals who do not use cannabis . Emotion regulation, or the ability to modify one’s emotional experience to produce an appropriate response, has been shown to be maladaptive in teenagers and young adults with MDD and who use cannabis . For example, suppression is a maladaptive regulation style in which an individual inhibits expressing emotions, and is correlated with greater depressive symptoms in youth and adults . In contrast, reappraisal is an adaptive regulation style in which an individual changes their interpretation of a situation to alter the emotional impact, and is underutilized in emerging adults with MDD  and in those who are cannabis users . In the context of MDD, studies have shown lower activity in brain areas involved in emotional processing when compared to healthy controls in the dorsolateral prefrontal cortex , ventrolateral prefrontal cortex , anterior cingulate cortex, as well as the basal ganglia . These findings fit well with models of emotion regulation and of MDD. Emotion regulation is thought to occur through a network of regions, beginning with affective arousal in the amygdala and basal ganglia, then projecting to frontal regions including the vlPFC and the insula, as well as other regions such as the superior temporal gyrus and angular gyrus . The vlPDC then begins the process of emotional appraisal, indicating the need for regulation to the dlPFC. From there, the dlPFC regulates the emotion and feeds forward to the angular gyrus, STG, and back to the amygdala and basal ganglia, all of which create a regulated emotional state . Disruption of the communication among these areas in individuals with MDD has been observed both in measures of resting state connectivity  and in the suppression of activity within these frontal regions in association with over-activation of temporal regions such as the insula and hippocampus . The prevalence of depressive symptoms in frequent cannabis users suggests that brain regions involved in emotion regulation may overlap with those affected by cannabis use. A study showing emotion regulation deficits in young, regular recreational cannabis users compared to nonusers bolsters this hypothesis . Indeed, a meta-analysis showed that cannabis use was linked to brain activity abnormalities in the vlPFC, dlPFC, and dmPFC, orbital frontal cortex, ventral striatum, and thalamus . A recent review of the imaging literature indicated that adolescent cannabis users showed differences in frontal-parietal networks that mediate cognitive control .

Further, emotion regulation deficits in frequent cannabis users were associated with abnormal neural activity in bilateral frontal networks as well as decreased amygdala-dorsolateral prefrontal cortex functional connectivity . Suppressed inferior frontal and medial PFC activation has been found in cannabis users during positive and negative emotional evaluation , as has suppressed activity levels in the amygdala . The overlap in these brain regions, combined with weakened emotional regulation in people with both MDD and cannabis use, suggests that there may be an interaction between MDD and cannabis use on human brain function in the context of emotion regulation. The aim of the present study was to examine the combined effect of MDD and cannabis use on the brain during emotion regulation in emerging adults, as well as how specific characteristics, such as degree of depressive symptoms and age of cannabis use onset, affect emotion processing. To address these questions, we employed an emotion regulation task while participants underwent functional magnetic resonance imaging . We recruited individuals either with or without MDD, who either did or did not use cannabis frequently, and used a mixed effects approach to identify the unique contributions of each factor on emotion processing. Because both MDD and cannabis use have been shown to suppress activation within frontal regions during emotion regulation, we predicted that combined MDD and cannabis use would interact with emotion regulation within the vlPFC, dlPFC, and dmPFC, above and beyond the contribution of each factor alone. In contrast, we predicted that we would see a dissociation between MDD and cannabis use in temporal regions, with MDD showing increased activity levels and cannabis use showing suppression of activity during emotion processing. Finally, we predicted that severity of depressive symptoms, emotion regulation style, and age of cannabis use onset would each uniquely interact with emotion regulation, further elucidating the relationship between MDD, cannabis use, and the brain. The emotion regulation fMRI task, adopted from Greening et al. , was designed to have participants actively alter their feelings elicited by sad  and happy  emotional scenes. Twenty negative and 20 positive emotional scenes were taken from the International Affective Picture System for this study. The task involved viewing both negative and positive emotional scenes while being instructed to either simply view the scene or actively alter their feelings while viewing the scene . The four task conditions were therefore attend negative, reduce-negative, attend-positive, and enhance-positive. During the reduce-negative task condition participants were instructed to ‘acknowledge that the scene is negative. However, it does not affect you, things do not stay this bad, and the scene does not reflect the whole world’ and during the enhance-positive task condition participants were instructed to ‘acknowledge that the scene is positive. Further, that it does affect you, things can and do get even better and the scene does reflect the real world’ . This paradigm attempts to target and modify the negative thought tendencies about self, the world, and the future that are typical for depressed patients . Participants were trained and practiced the paradigm before being scanned. During 4 imaging runs each participant completed 20 trials of each task condition . The 20 negative and 20 positive emotional scenes were displayed twice, once during the attend condition and again during the regulate condition. Participants never saw the same picture twice in the same run. To help mitigate any order affects,vertical grow systems for sale the trial order in each run was set as 4 independent runs and these were counterbalanced across subjects. The functional data were also preprocessed according to the fMRIPrep pipeline. For each of the BOLD runs per subject, the following preprocessing was performed. First, a reference volume and its skull stripped version were generated using a custom methodology of fMRIPrep. The BOLD reference was then co-registered to the T1w reference using bb register which implements boundary-based registration .

Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference  are estimated before any spatiotemporal filtering using mcflirt. BOLD runs were slice-time corrected using 3dTshift from AFNI v16.2.07. The BOLD time-series, were resampled to surfaces on the following spaces: fsaverage5. The BOLD time series  were resampled onto their original, native space by applying a single, composite transform to correct for head-motion and susceptibility distortions. These resampled BOLD time-series will be referred to as preprocessed BOLD in original space, or just preprocessed BOLD. The BOLD time series were resampled to MNI152NLin2009cAsym standard space, generating a preprocessed BOLD run in MNI152NLin2009cAsym space. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIP rep. Several confounding time series were calculated based on the preprocessed BOLD: frame wise displacement , DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in Nipype . The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction. Principal components are estimated after high pass filtering the preprocessed BOLD time-series  for the two CompCor variants: temporal  and anatomical . Six tCompCor components are then calculated from the top 5% variable voxels within a mask covering the sub-cortical regions. This sub-cortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, six components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run .

Since cannabis initiation can begin early, it is essential to have early interventions in place to prevent adverse outcomes in later adolescence and adulthood for those youth who do initiate early . Effective early interventions include multidimensional family therapy, motivational enhancement therapy, and cognitive behavior therapy . However, despite the potentially detrimental effects of cannabis use during early adolescence, very few young people receive treatment . Barriers to treatment include a lack of treatment seeking due to embarrassment or due to a lack of felt need to discuss these issues or motivation to work on them . Youth might be more inclined to seek treatment when substance use is interfering with their mental health, educational attainment, or family, peer and romantic relationships . Since parental encouragement and support can be a key factor in youth treatment seeking , awareness and promotion interventions should target parents. In addition, adolescents may be open to discussing cannabis use with health care practitioners , but many may choose not to discuss it without probing; thus the onus of early identification among adolescents may therefore fall on service providers, making systematic screening important. Several limitations should be kept in mind when interpreting the findings. Notably, the data were self-reported, with retrospective estimates of the age of onset, which may have affected their validity. In addition, the youth in this current sample only reported past year symptom endorsement on the GAIN-SS. It is unknown whether these symptoms existed at age of initiation or whether symptoms progressed or improved from age of initiation. In the absence of a time course analyses , the directionality or bidirectionality of these associations is unclear, i.e., it remains to be determined whether early cannabis use represents a unique risk factor for developing problematic mental health and substance use behavior or whether is a marker of shared  or common underlying risk etiology . These findings support the conclusion that early cannabis initiation is clinically meaningful marker of risk across mental and behavioral health. The initiation of cannabis use at an earlier age than typically considered is associated with particular risk for more complex, concurrent mental and behavioral health concerns.

Cannabis use and other substance use, as well as broader mental health challenges should be systematically assessed from an early age, while prevention initiatives designed for younger youth are also called for. Service providers and researchers are encouraged to consider the potential complex comorbidities and polysubstance use patterns of youth who initiate cannabis use early, mobile vertical rack as well as ways to address factors such as trauma and their role in the early initiation. Individuals suffering from schizophrenia and related disorders have a high prevalence of substance use disorders . Epidemiological studies consistently report a higher lifetime prevalence rate of smoking  and cannabis use in patients with schizophrenia compared to the general population . Patients with schizophrenia who use cannabis present higher relapse rates, longer hospital admissions, and more severe positive symptoms than individuals who discontinue cannabis use and those who are nonusers . The association of comorbid substance use with negative symptoms has received less interest, although negative symptoms have a strong impact on functional outcome and remain difficult to treat . The negative symptoms of schizophrenia include blunted affect, alogia, asociality, avolition, and anhedonia , which can be organized along the two dimensions diminished expression and apathy . Negative symptoms be classified as primary or secondary . Primary negative symptoms are thought to be intrinsic to schizophrenia, while secondary negative symptoms can be caused by positive symptoms, depression, side effects and substance abuse. The hypothesis of cannabis as a potential cause for secondary negative symptoms was initially based on observations of an amotivational syndrome in otherwise healthy individuals who are chronic cannabis users . Cannabis may also exert an amotivational effect in patients with schizophrenia and thus be a cause for secondary negative symptoms . Following this argument, one would expect more prominent negative symptoms in cannabis-using patients with schizophrenia than in nonusers. This pattern has yet to be confirmed by the literature. An earlier meta-analysis by Potvin and colleagues studied the association between substance use disorders and negative symptoms  and found a lower severity of negative symptoms across different substances of abuse. In a subgroup analysis, fewer negative symptoms were found in patients with cannabis use disorders than in those without cannabis use disorders. However, in a more recent meta-analysis including more studies and excluding patients with previous substance use, Large and colleagues did not find an association between current substance use  and negative symptoms . In summary, the existing meta-analyses do not suggest a higher level of negative symptoms in patients with schizophrenia who use cannabis, but they were based on a limited number of available studies that did not allow us to distinguish between patients using cannabis only and patients using cannabis as a main drug of choice. Moreover, while data suggest a limited effect of cannabis discontinuation on negative symptoms , to the best of our knowledge, there is no systematic review of negative symptoms in patients recently abstaining from cannabis use. It is important to note that individuals with schizophrenia consume cannabis almost exclusively in the context of nicotine use .

Most research on the association between symptoms and nicotine use has not focused on negative symptoms but on cognitive dysfunction . One recent metaanalysis reported more severe positive symptoms but similar levels of negative symptoms in smokers compared to nonsmokers . Of importance, no distinction was made in this study between patients using only nicotine and patients using nicotine along with other drugs. It has been suggested that negative symptoms are associated with reduced dopaminergic neurotransmission in the mesolimbic and mesocortical systems . The acute administration of cannabis, nicotine and other drugs of abuse can increase dopaminergic neurotransmission in these systems and could thus improve negative symptoms in the short term . However, chronic substance abuse has been suggested to impair dopaminergic neurotransmission and could have deleterious effects on negative symptoms . To our knowledge, no joint meta-analysis of the association of cannabis and nicotine with negative symptoms has been conducted thus far. The previous meta-analyses examining substance use disorder applied restrictive criteria, and therefore, only a few studies specific on cannabis use were retained . Furthermore, in these previous studies, no distinction could be made between populations consuming only cannabis and/or nicotine and populations using other substances such as alcohol, stimulants and opioids. Therefore, this systematic review investigates the relationship of cannabis and nicotine, used alone or mixed with other substances, with negative and positive symptoms of schizophrenia. In addition, we aimed to perform an analysis of negative symptom severity in persons recently abstaining from cannabis or nicotine use. Studies that met all the following criteria were included: a) available in English; b) included inpatients or outpatients aged greater than 18 years with a DSM  or ICD  diagnosis of schizophrenia, schizoaffective disorder, or related psychotic disorder; patients considered stable ; c) an observational study  or a clinical trial reporting a baseline measurement; d) reported current nicotine or cannabis use either alone or as main drug of choice; e) specified whether patients were abstinent at the time of evaluation; and f) reported a baseline negative symptom score. Studies that met any of the following criteria were excluded: a) meta-analyses, reviews, case reports, posters; b) reported populations with other diagnoses  and patients with first-episode psychosis; c) articles with overlapping datasets ; and d) articles with nonexploitable datasets . In case of exclusion, reasons were reported. Two authors  independently applied the inclusion criteria to the identified studies.To achieve a high standard of reporting and to assess study comparability, we performed a full-text review with a detailed analysis of each included study . Two authors  independently performed data extraction. Discrepancies between the two authors were resolved upon a joint full-text analysis with the third author . All extracted data were independently crosschecked by two authors  before the calculation of outcome variables. If necessary, additional or missing information was obtained from the study authors. For the primary outcome, we extracted the mean value and standard deviation at baseline  of negative symptom severity.

A broad range of assessment instruments was considered: the negative subscale of the Positive and Negative Syndrome Scale  , the Scale for Assessment of Negative Symptoms  ,vertical grow rack the withdrawal/retardation factor of the Brief Psychiatric Rating Scale   or any other validated scale for the assessment of negative symptoms. In a secondary analysis, we considered the negative symptom dimensions amotivation and diminished expression separately; amotivation was calculated as the sum of the global ratings of avolition/apathy and anhedonia/asociality, and diminished expression was calculated as the sum of blunted affect and alogia . The SANS dimension scores were imputed using the correlation matrix for the SANS global domain scores from a dataset reported in a previous study . We considered positive symptoms at baseline  as secondary outcomes. Positive symptoms were assessed with the positive subscale of the PANSS , the Scale for the Assessment of Positive Symptoms  , or the sum of the thinking/disturbance and hostile/suspiciousness factors of the BPRS. Additionally, we extracted all baseline values for scales reporting depression scores and specific variables such as demographic information , disease information , study design , and information on substance use . The included studies were assessed for methodological quality using the Newcastle-Ottawa Scale   modified version for each type of observational study retrieved . The NOS is based on three subscales concerning the selection of cases, their comparability with the controls and the ascertainment of the exposure. A star system is used to assess each quality item; the highest-quality studies are awarded up to nine stars. For each study type, we defined the mean score of all included studies as a cutoff to identify high-quality studies. Two authors  independently performed assessments. All statistical analyses were performed using Cochrane Collaboration software, Review Manager  and Stata software . Our primary goal was to examine the association between cannabis and nicotine use and negative symptoms. We distinguished studies where nicotine alone  or cannabis and nicotine were used by patients from studies where other drugs were used or combined with these drugs , as these drugs have different effects on the brain. Accordingly, we defined four different subgroups of drug use: ‘cannabis and nicotine’, ‘cannabis as a main drug of choice’, ‘nicotine only’ and ‘nicotine as a main drug of choice’.

We defined the ‘main drug of choice’ as either the drug the user reported as preferred or as the drug considered to be the main drug by the clinician rater. In addition, in almost all studies dependence criteria were fulfilled only for the main drug of choice. In the few studies including patients fulfilling dependence criteria for more than one substance, only a minority of patients was concerned. For a detailed description of drug use patterns in each study see supplementary table S1. An additional subgroup consisted of studies including patients with ‘recent cannabis abstinence’, which corresponds to the absence of cannabis consumption for at least the past 21 days . We intended to create an equivalent group for recent nicotine abstinence, but only two studies addressed the question, and the available data did not allow us to perform a meta-analysis . In a first main analysis, we focused on cannabis use and included the subgroups ‘cannabis and nicotine’ and ‘cannabis as a main drug of choice’; then, we compared these results with those of the ‘recent cannabis abstinence’ group. In a second main analysis, we focused on nicotine use and included the subgroups ‘nicotine only’ and ‘nicotine as a main drug of choice’. Since all included studies reported a baseline or a single score for our outcomes of interest, we did not separate studies on the basis of their design. For the primary outcomes, different scales or versions of scales were used across studies, and we therefore used standardized mean differences of cross-sectional measurement . There were clear a priori reasons for assuming heterogeneity because studies differed in design, population and substance use. Therefore, we used a random-effects model for all analyses.

Only binge drinking days were significantly lower in the post-CCA sample, and the groups did not differ on anxiety symptoms, with evidence of greater sleep interference in the post-CCA sample when participants with data that straddled the CCA were excluded. All of these pre-to post-CCA differences were relatively modest with small to moderate effect sizes for the substance use outcomes and small effect sizes for psychopathology. Given the large size of the U.S. college population, though, these modest effects can have large overall impacts. Universities should continue to consider innovative ways to screen for substance use and mental health symptoms and to initiate treatment, and those with pre-existing problems might have the greatest treatment needs. As more states have legalized both recreational and medical marijuana use, the number of first-time users has risen dramatically. The perception that marijuana is an innocuous drug has led to significant increases in cannabis consumption both for its recreational properties and for its alleged medicinal properties.As a result, emergency departments and psychiatrists are treating a growing number of patients with medical and psychiatric complications of cannabis use.Cannabis users are being exposed to higher concentrations of delta 9-tetrahydrocannabinol  than in previous years. In the 1960s and 1970s, confifiscated marijuana samples contained anywhere from 1% to 3% THC. However, the average concentration of THC found in illicit samples has grown to approximately 12% in 2014.3 On the other hand, the content of cannabidiol  has shown a general decline over the last decade, going from approximately 0.5% in 2004 to less than 0.2% in 2014.3 THC is the primary compound implicated in the psychoactive effects of marijuana. The effects of THC are primarily mediated through two G protein-coupled receptors, cannabidiol receptor 1 , and cannabidiol receptor 2 .

CB1 receptors in the brain are primarily expressed in the neocortex, basal ganglia, and hippocampus. CB2 receptors are primarily in the immune system but also in the central nervous system at lower concentrations than CB1. CBD is a compound produced by the grow cannabis in containers plant that is believed to block or reduce many side effects of THC. CBD lacks significant affifinity for the CB1 receptor but has been shown to displace THC at low concentrations. It is postulated that CBD may act antagonistically against THC at CB1 via a nonorthosteric binding site. When CBD is coadministered with THC, it significantly reduces tachycardia and anxiety associated with THC.Cannabis containing high CBD content has also been associated with fewer symptoms of psychosis in recreational cannabis users.Furthermore, synthesis of high-potency resin/hash oils through solvent extraction has resulted in THC concentrations reaching as high as 80%. Resin oil is commonly created using butane and aptly named “butane hash oil,” “oil,” “dabs,” “honeycomb,” and “wax.” “Dabbing” is a common term to describe the inhalation of vaporized resin oil or wax. Marketing of resin oil has focused on superior pain relief, better utilization of the cannabis plant, and less product needed for the same effect. Resin oils can be used with a classic “oil rig” water pipe or e-cigarette  device.6 Vaping devices, or “e-cigarettes,” are used as a delivery system for nicotine, THC, and other chemicals. Advertisements focus on flflavoring, being “safer” than traditional cigarettes, not leaving behind a stench, and being easier to hide.7 There is growing concern for the use of vaping products especially among teenagers. For the first time, the National Institute on Drug Abuse’s Monitoring the Future Survey of 2019 included reports on daily cannabis vaping, with 0.8% of eighth graders, 3.0% of tenth graders, and 3.5% of twelfth gradersreporting daily use.

National Institute on Drug Abuse data also indicate that 14% of twelfth graders had engaged in THC vaping in the past month. Moreover, the number of students answering “Yes” to the question of “ever vaping” more than doubled from 2018 to 2019: 7% of eighth graders, 19.4% of tenth graders, and 20.8% of twelfth graders. This is the second largest 1- year jump ever tracked for any substance in the history of the 45-year-old survey. Of additional interest, the largest jump in the survey’s history was from 2017 to 2018 regarding nicotine vaping among twelfth graders.8 We present a case report of a patient who presented with symptoms of catatonia, mania, and psychosis in the setting of recent cannabis vaping and are concerned this severe presentation could be related to increased psychiatric toxicity associated with the high concentration of THC present in cannabis oils.Mr. R was a 20-year-old man with previous diagnoses of mania, cannabis use, and generalized anxiety disorder who originally presented to our psychiatric triage department with a chief complaint of “severe anxiety” and was admitted to our free-standing psychiatric hospital for evaluation and management. On the initial mental status examination after arrival on the unit, he exhibited severe poverty of thought content and appeared to be responding to internal stimuli. Most of his limited speech was repetitive and nonsensical with repeating phrases such as “shoes and socks.” He was also seen making bizarre postures in the hall, standing on one foot, holding his hands above his head, or standing with his head between his knees without moving for extended periods of time. He also avoided eating or drinking.

A Bush-Francis catatonia rating scale was administered at that time and was positive with a score of 19 for symptoms of mutism, posturing, stupor, staring, grimacing, verbigeration, stereotypy, and withdrawal. He exhibited paranoia in not wanting to take medications and fearfulness of being outside of his room. He was given a 2 mg intramuscular lorazepam challenge with some improvement of his symptoms, with a lowered Bush-Francis score of 10. At that time, the diagnoses of catatonia and psychosis were made. Owing to communication difficulties related to profound thought blocking and internal preoccupation, history taking from him was difficult. Laboratory studies were ordered including a complete blood count, complete metabolic panel, thyroid stimulating hormone, HIV, and urine drug screen. These were unremarkable aside from a urine drug screen immunoassay that was positive for cannabinoids and negative for cocaine, opiates, benzodiazepines, amphetamines, and barbiturates. We were unable to characterize the amount or pattern of cannabis use at that time because of his inability to provide information himself. From this, we learned he had one other admission six months before the first admission, with no reported manic or psychotic symptoms before this. He had also been diagnosed with generalized anxiety disorder and prescribed paroxetine by his primary care provider for several years before presenting to our hospital for the first time. He had taken a prescription stimulant brieflfly a year before admission to help with poor attention during college courses but was not taking this before either admission. During the first admission, he was found to have mania and psychosis with symptoms of grandiosity, decreased need for sleep, and psychomotor agitation. The psychiatrist postulated his symptoms were due to either his new onset use of vaping cannabis  or underlying bipolar disorder . A urine drug screen immunoassay during this admission was also positive only for cannabinoids. He suggested that his pot for cannabis use had preceded the onset of the symptoms of mania and psychosis. At that time, he was started on olanzapine and lithium, and his acute psychiatric symptoms resolved before discharge. His outpatient psychiatrist later questioned the diagnosis of BPD and favored a substance-induced etiology for his symptoms. He was tapered off of lithium and olanzapine and then started on sertraline for generalized anxiety disorder. He continued on this regimen for 6 months and according to notes by his outpatient therapist was functioning well without recurrence of psychosis or manic symptoms. His parents were heavily involved in his care and acted as surrogate decision makers when needed owing to his lack of capacity.

They corroborated the history from his past hospitalization but were unaware of his substance use before the current admission.During his most recent hospitalization, he was started on 2 mg of lorazepam  every four hours. Owing to the diagnosis of catatonia, further laboratory studies were performed, including creatine phosphokinase , which was elevated to a peak of more than 700 U/L. He also continued to refuse to eat or drink. For these reasons, he was sent by an ambulance to an associated medical hospital owing to the need for intravenous hydration that could not be provided at our free-standing psychiatric facility. He was seen by the medical hospital’s consultation-liaison psychiatry service and continued on lorazepam given intravenously and titrated to a total daily dose of 5 mg. He was restarted on lithium at this time as he had responded well to this medication during his prior hospitalization, and there was concern that his symptoms could be related to an underlying BPD. He was not started back on olanzapine as in his prior admission owing to concerns this could exacerbate his catatonic state. After three days, he had stabilized enough to transfer him back to our psychiatric facility. From there, we continued treatment with lorazepam, and his symptoms of catatonia continued to improve: his speech became more flfluid and he was able to engage in meaningful clinical discussions, was active on the unit, behaved appropriately with staff, and was able to meet his physical needs by eating and drinking. Lithium was titrated to a dose of 900 mg daily, and he was continued on sertraline 100 mg daily owing to history of generalized anxiety disorder. At the time of his discharge, his CK level had returned to within the normal limits, his lithium level was 0.4, and his Bush Francis score was 0. After the catatonic symptoms resolved, he was more capable of providing a detailed history of the events leading up to his admission. The history surrounding this episode was similar to his prior hospitalization: he once again reported symptoms of grandiosity, increased activity, decreased need for sleep, hallucinations, and paranoia in the days leading up to admission and again reported daily cannabis oil vaping for 14–21 days before his presentation. He felt that his symptoms were again related to heavy use of vaping cannabis just before onset of symptoms of mania and psychosis. Although he did report a long history of anxiety, the symptoms of mania, psychosis, and catatonia were only present during periods of cannabis use. He denied a history of major depressive episodes and did not believe he had BPD. He reported first use of cannabis one year prior to his first admission, with use increased in the month before his first admission after he obtained a vaping device. He also reported experimentation once with alkyl nitrate “poppers,” but use of these were not associated with his admissions. He reported a family history of anxiety symptoms in his mother and obsessive compulsive disorder in his brother. After a total hospitalization of eleven days, he was discharged home on lorazepam, sertraline, and lithium with close outpatient follow-up with our residency clinic.

Over a period of 4 months, he was slowly tapered off of lorazepam, decreasing by 1 mg per month. He was continued on lithium 900 mg daily and sertraline 100 mg daily. After eight months of no significant mood symptoms, he was tapered off of lithium. No further mood symptoms have been noted since that time. He reported no further use of cannabis since hospital discharge.Catatonia is a neuropsychiatric condition commonly characterized by striking behavioral abnormalities and can progress to life-threatening symptoms in severe cases. Common symptoms include immobility, mutism, refusal to eat or drink, staring, and negativism. A typical laboratory evaluation includes tests to rule out underlying medical conditions and substance use. In addition, serum creatine phosphokinase, white blood cell count, and serum transaminases can be elevated in patients with catatonia. Because catatonia impairs the ability to care for oneself, hospitalization is usually required. Fluid and nutrient intake must be maintained, often with intravenous lines or feeding tubes as patients with this syndrome often do not eat or drink. Benzodiazepines such as lorazepam are considered the firstline treatment for catatonia.9 Before 1970, catatonia was primarily linked with schizophrenia. Literature since the 1970s indicates catatonia is common among psychiatric inpatients and occurs most often in mood disorders such as bipolar disorder  with mixed or manic symptoms .