Cannabis Grow

The highly infectious nature of the COVID-19 virus, its rapid spread throughout the world, and the significant mortality associated with it have greatly changed many peoples’ lives . Often, individuals are encouraged to limit transmission by restricting time out of the home to necessary activities, such as obtaining food or medical care, working, and exercise, depending on the locality . This policy of limiting contact with others is often termed “social distancing” , though some prefer “physical distancing” to encourage ongoing social interaction . Evidence from across the population in China, which was first impacted by COVID-19, suggests that levels of anxiety, depressive, and post-traumatic stress symptoms were higher than expected after the outbreak of COVID-19, with poorer sleep quality as well . Also, this evidence suggests that young adults, 21–30 years, may be most affected . Xiong et al.  reviewed the literature on mental health symptoms from eight countries after the COVID-19 outbreak and found high rates of anxiety, depressive, and post-traumatic stress symptoms, with elevations in stress and psychological distress as well; those 40 years and younger and who were students were more affected than older age groups and non-students . Evidence also suggests that substance use has increased, with the best evidence for increased alcohol use , though increases are not always found  and vary around the strictness and timing of COVID-related lockdowns . These inconsistencies may also result from the population studied and levels of preexisting use, with greater increases in alcohol use among adults with higher pre-existing levels of alcohol use . Cannabis use changes related to COVID-19 are virtually unstudied, with only one study finding decreased use prevalence but increased use frequency among Canadian adolescents .

The effects of COVID-19 are largely unstudied in college students, but they are already a group at elevated risk for substance use and mental health symptoms , Young adults, 18–25 years of age, have the highest marijuana grow system, illicit drug, and prescription drug misuse rates of any cohort, with alcohol use prevalence rates that only slightly trail those of adults aged 26–39 years . Among young adults, college students have higher rates of problematic alcohol use than non-college students , with increasing rates of cannabis use and alcohol-cannabis co-use . The typical college years coincide with the peak age period for incidence of many mental health conditions , with high rates of depressive disorders, anxiety disorders , and poor sleep . Significant substance use and mental health symptoms are each linked to poorer academic performance, college dropout, and other poor outcomes , yet the vast majority of affected students do not receive treatment, likely due to inadequate campus resources . College students have experienced many significant COVID-related stressors, including the transition to distance learning, unstable housing situations and/or unexpected moves back to the parental home, cancelled or delayed graduation ceremonies, and disrupted access to campus-based mental health treatment . Self-report of changes in mental health symptoms suggest increases in stress and mood disorder symptoms , but these are limited by smaller samples.On the one hand, two studies suggested increases in alcohol use , while three studies found decreases in alcohol use following university closures . A sixth found a complex pattern of changes, with increases in frequency of use that were counteracted by declines in quantity of use and binge drinking, all of which was moderated by pre-COVID use patterns . While college students are an important population in which to examine changes in mental health and substance use related to COVID- 19, the findings are limited by conflicting data on alcohol use changes. Also, studies to date have not assessed cannabis use in U.S. college students. Given this limited evidence on college student mental health and substance use related to university closures, outstanding questions remain about cannabis use changes and the degree of change and direction of mental health and alcohol use changes. To address these outstanding questions, we used data from the U.S. college-based Stimulant Norms and Prevalence  study. This cross-sectional study collected data from college students on mental health symptoms, alcohol, and cannabis use from September 2019 to May 2020, allowing for examination of differences in psychopathology symptoms from before to after outbreaks of COVID-19 in the students’ communities. Our primary aim was to examine differences related to university COVID-19 closure announcements  in mental health and substance use in U.S. college students. To evaluate differences in average symptom levels before and after COVID-19 closure announcements , zero-inflated negative binomial  regression was used for count outcomes . Thus, the main independent variable in all analyses was pre- or post-CCA survey completion status.

Substance use outcomes are likely to be characterized by long-term abstinence in some participants, while other participants are not abstinent. The ZINB models account for both kinds of substance patterns simultaneously via a 2-part model, with a binary part of the model seeking to identify complete abstinence , and a second count part of the model accounting forsubstance use rates via negative binomial regression. This approach to modeling accounts for dual processes that can occur during substance use, where some participants might have zero use during a period of time but still potentially engage in use at other times , while some participants might never engage in any substance use . Of note, the binary part of the model addressing binge drinking prevented model convergence, likely as a consequence of limited variance explained. Linear regression  was used for continuous outcomes.In addition to hypothesis tests, we evaluated effect sizes in terms of incidence risk ratios  for the count process part of ZINB models testing for days of use, odds ratios  for the binomial process part of ZINB models testing for any 30-day use, and raw unit differences in LR . To further support model choice, we ran overdispersion tests following approaches suggested by Venables and Ripley  and we tested for zero-inflation and improvements in model fit compared to simpler models using Vuong non-nested model tests . Both overdispersion and zero-inflation were consistently detected across models. We also evaluated moderation by SES, sex at birth, and race/ ethnicity through statistical interactions with the COVID cancellation announcement . Moderators were dummy coded with reference groups of “poor” for SES, “male” for sex, and “White, non-Hispanic/ Latino” for race/ethnicity. Per Benjamini and Hochberg , all moderator hypothesis tests were adjusted using false discovery rate procedures , such that each single predictor/outcome was considered as a separate family of hypotheses for evaluation . To account for site-based clustering of participants, university/site served as a fixed effects covariate . Missing data were very limited  except for binge drinking . To address missing data, multiple imputation was employed using predictive mean matching and the fully conditional specification . All analyzed variables  were included in the imputation model, and 40 imputations were employed . The R statistical software language version 4.0.2  was used for all analyses, including the “mice” package for multiple imputation  and the “pscl” package for ZINB regression . These results provided evidence of generally greater levels of substance use and psychopathology in students completing the survey after their university’s COVID closure announcement , though changes were generally modest and not seen for all outcomes. On the one hand, depressive symptoms and anger were greater in students who completed the survey after their CCA, though anxiety symptoms did not vary.

In the model with all participants, sleep interference was non-significant, though in the model without participants whose data straddled their university’s CCA, post-CCA participants had significantly greater sleep interference. This was a very small effect, though. Furthermore, most alcohol and cannabis vertical farming use indicators were higher in those taking the survey post-CCA, yet binge drinking days were lower in those assessed after closings. None of the pre-to post-CCA differences in substance use or mental health symptoms were moderated by sex at birth, race/ ethnicity, or SES. Together, these findings suggest a picture of modestly higher levels of substance use, depressive symptoms, and anger among U.S. college students from pre-through a two-month period post-university closure. These results, however, do not correspond with research in other countries about COVID-related mental health among young adults and students. That research suggested much larger differences in mental health symptoms than found here . One reason could be that our methodology compared two separate groups of college students, while the predominant measure in other studies has been for the participant to self-report change in symptoms after the spread of COVID-19 in their community, which is likely to suffer from retrospective bias. Alternatively, U.S. college students may perceive lesser threat from COVID-19 than non-U.S. samples. For alcohol use, our results add to the conflicting literature in U.S. college students by suggesting increases in frequency of use and level of consequences but decreases in binge use. Our findings are most similar to those of Jackson et al. , as they found increases in frequency but decreases in indices of heavy use. Clinically, these results suggest that universities and care providers for college students need to carefully screen for alcohol and cannabis use and for depressive symptoms and problematic anger in students. Sleep problems also may warrant examination. While the overall level of pre-to post-CCA difference in outcomes was modest, that does not mean that a specific individual’s change related to university closings will necessarily be modest. Data from across Australian adults found the greatest increases in alcohol use among those with greater pre-COVID levels of use , and providers should be aware of the possibility for greater increases among those with pre-existing substance use and mental health problems. Furthermore, these data only examine the first two months post-CCA, and substance use and mental health symptoms are likely to continue changing in college students. Ensuring continuity of care to those already enrolled in treatment could be crucial in preventing significant problems in the most vulnerable students. In addition, universities may need to increase availability of substance use and mental health treatment services, but given that most college students who need treatment do not receive it while in college , college health professionals may need to consider innovative screening, outreach, and broad use of self-help materials and/or technology-aided treatment solutions to reach a broad and dispersed population of students . First, participants are younger, four-year undergraduate students at public universities and are not a representative sample of all college students.

These include older students, private school students, and those attending two-year schools. Second, these results cannot be generalized to non-college young adults, who differ in significant ways from young adults in college. Another limitation comes from the measures employed: while they have strong psychometrics, they also were brief screening measures, and post-traumatic stress symptoms were not assessed. Also, the measures captured retrospective ratings of 30-day substance use and 14-day mental health symptoms. As such, students who completed the survey within 30 or 14 days of their university’s CCA were reporting on both pre- and post-CCA experiences for substance use and/or psychopathology symptoms, respectively. Those who were in the pre-CCA sample were reporting on only pre-CCA dates. This means that the post-CCA ratings should be interpreted in light of the inclusion of limited pre-CCA data. With that said, we performed sensitivity analyses  that suggested only one change in significance when participants were excluded if they had data including both pre- and post-CCA days. Furthermore, these data are cross-sectional, which prevents tests of within-participant change and reduces the strength causal inference in the relationships of COVID-19/CCAs and mental health or substance use changes. Finally, the data are subject to both self-report and selfselection bias, given the nature of the data and that some eligible students opted to participate in other research studies for course credit. These weaknesses, however, were balanced by the large and diverse sample from seven universities/colleges across the U.S. the valid and reliable measures of alcohol use, cannabis use, and psychopathology, and the robust analytic plan.In a broad sample of U.S. college students, days of alcohol and cannabis use, prevalence of alcohol use and alcohol use consequences, depressive symptoms, and anger were all significantly higher in participants who provided data in the two months post-university closing, versus pre-closing.

Many patients also opt for medical cannabis, which can be easier to access than prescription drugs and has been legalized in more than half of the states in the U.S. . However, medical cannabis has not undergone the U.S. FDA approval process, and is not under the same supply chain controls as other prescribed pharmaceuticals. With the increase in popularity of cannabis and cannabis‐derived products, more attention is given to toxicology and human health risk of cannabis contaminants . Several cannabis product recalls have been issued in the U.S. due to contamination of insecticides  and fungicides. Additionally, there are reports of pesticide spiking in illegal synthetic products, including brodifacoum  and paraquat. Pesticide use in agricultural commodities is regulated under the Federal Insecticide, Fungicide and Rodenticide Act. Yet, due to the federal status of cannabis as a Schedule I substance , the U.S. Environmental Protection Agency  has not issued any guideline on pesticide applications in cannabis. Following the wave of legalization of medical or recreational cannabis across the U.S., there is an expectation of the general public that cannabis legalization also results in regulation to ensure safety in cannabis consumption . In many states, cannabis is recommended by physicians for therapeutic use in various medical conditions. At the same time, there are no federal regulations in place to standardize cannabis as a pharmaceutical. The potential for contamination of cannabis with pesticides is an area of ongoing analysis , and has been observed in medical cannabis samples . The inconsistent regulation of medical cannabis, together with potential exposure to harmful pesticides, can result in adverse health outcomes in patients with susceptible conditions. Here, we examine the state‐level regulations, publicly available pesticide residue testing reports, and curated biological interactions in the Comparative Toxicogenomics Database  to evaluate the potential neurological hazards of pesticide exposure in medical cannabis.

We surveyed the online information provided by the public health agencies and agriculture departments of 50 states and Washington, D. C. between September 15 and November 29, 2020. We first determined whether medical and/or recreational cannabis was legalized in each jurisdiction. If medical cannabis was found legal in a jurisdiction, we would categorize the qualifying conditions with reference to the 2017 National Research Council report, “The Health Effects of Cannabis and Cannabinoids”, which described 21 cannabis treatable diseases with different levels of therapeutic evidence . An earlier study took a similar approach to evaluate the prevalence of qualifying conditions in the U.S. . Here, we mainly focused on neurological diseases in our analysis. We next compared the action levels published by each jurisdiction to regulate pesticide residues in cannabis. If no action level was published online,trim tray we would submit a direct inquiry to the cannabis program. We also checked with ISO/IEC 17025‐ certified laboratories in the state with legalized cannabis . With the passage of the 2018 Farm Bill, pesticide applications in hemp are now regulated by the U.S. Department of Agriculture  under FIFRA . Thus, we excluded the states that only allowed the use of cannabidiol oil in our analysis.We evaluated the potential connections between insecticides, cannabinoids, and seizure using CTD . We searched CTD for specific insecticides and cannabinoids to build sets of computational constructed information blocks  that related a chemical‐gene interaction with a phenotype and seizure, following the methodology previously described . Briefly, five independently curated data sets  were integrated and used as lines of supporting evidence to connect and computationally construct CGPD‐tetramers. Each CGPD‐tetramer represented a potential chemical‐to‐seizure connection that met all five lines of evidence. We also compared the gene connections of the insecticide and cannabinoid CGPD‐tetramers to the 38 gene variants listed in the 2016 and 2018 reports of the International League Against Epilepsy Genetics Commission .We calculated the medians and ranges of pesticide action levels in different jurisdictions. We compared those figures with the tolerances  set for food commodities by the U.S. EPA  and the reported values of pesticide residues in cannabis.

Using Tableau Desktop , we created layered plots that encoded the range of the action levels as gray horizontal lines, and plotted key values as colored circles. In the first chart, the lines served as paths between two values: the minimum and maximum action levels set by each jurisdiction in our data collection. The second chart used a “barbell” style plot, where horizontal lines also served as paths, but these paths connected two different values: the lowest U.S. EPA tolerance levels for food commodities and the median of the action levels. The third chart showed the highest reported values of pesticide residues in cannabis from an open literature search. The action levels, tolerances, and reported values were plotted on a log scale. Using the CTD CGPD‐tetramers, we produced a list of relationships between chemicals and genes, with each relationship weighted by the number of tetramers in the database mentioning the interaction between a chemical and a gene. This produced a weighted edge list that we passed into Gephi, a network analysis and visualization application . Using Gephi, we calculated weighted degree centrality, and used the biological functions of genes as node categories. The result was a bimodal network of chemicals × genes, with each gene and their connections to the chemicals color‐coded by the gene’s biological function. Functional annotation of the genes used the NIH/NIAID  Database for Annotation, Visualization and Integrated Discovery  version 6.8 . Nodes and edges are sized by weighted degree centrality. Larger nodes indicate chemicals and genes that receive more attention in the CTD curated literature.We began by surveying the status of cannabis legalization in 50 states and Washington, D.C. Thirty‐four states and D.C. permitted cannabis use for medical purpose. Since South Dakota legalized both medical and recreational cannabis on November 3, 2020 , the qualifying conditions for medical use were not yet available. The other 16 states allowed the use of cannabidiol oil only.Three of the jurisdictions had specialized programs for adults and a separate restricted list of qualifying conditions for pediatric use of medical cannabis. Three jurisdictions did not list any explicit condition to qualify medical use. Ten jurisdictions gave physicians full discretion to prescribe outside of the listed conditions. Another 11 jurisdictions allowed petitioning on a case‐by‐case basis or adding a new qualifying condition at any time, and two jurisdictions allowed public petitioning during legislation changes.

Table 1 shows a total of 56 qualifying conditions related to neurological dysfunction , psychological conditions , and pain and injuries  as listed by 31 jurisdictions as well as 2 conditions listed in the NRC report that no jurisdictions explicitly allowed. The average number of enumerated conditions per jurisdiction was 17. One jurisdiction listed as broadly as 53 conditions, while another listed only 9 for non‐pediatric prescription. Some of the qualifying conditions were described in language with limited specificity. For instance, many jurisdictions listed “Multiple Sclerosis” as a qualifying condition, but the majority also listed “Severe or Persistent Muscle Spasms”, often in the same sentence. Multiple sclerosis was mentioned together with muscle spasms in 15 jurisdictions. It was mentioned alone in 10 jurisdictions. An additional two jurisdictions listed muscle spasms as a qualifying condition without mentioning multiple sclerosis. Depression and schizophrenia – both of which were reviewed in the 2017 National Research Council report  – were not listed by any jurisdiction. We next examined the listing of 11 neurological categories across the 31 jurisdictions. “Movement Disorders” was the most common neurological category and all 31 jurisdictions listed at least one movement disorder as a qualifying condition . These conditions included epilepsy, certain symptoms of multiple sclerosis, Parkinson’s Disease, mobile vertical rack and any cause of symptoms leading to seizures or spasticity. This was consistent with earlier reports that epilepsy and seizure disorders were the two common conditions qualified for medical use in the U.S. . Based on the language used by these 31 jurisdictions, the authorized use of medical cannabis appeared to be intended to address the movement related symptoms rather than the etiologies of the disorders. “Pain‐Related Conditions” was the second most common category , followed by “Anorexia and Weight Loss”  and “Psychiatric Conditions” . Many of the qualifying conditions were comorbid such as cachexia/wasting syndrome and HIV/AIDS, cancer, or other causes of majorweight loss. Notably, 46 conditions were qualified for medical use by just one jurisdiction .Medical cannabis is a potential route of pesticide exposure to patients with neurological diseases. Instead of alleviating a patient’s condition, the use of cannabis may harm the patient if it is contaminated by pesticides. We investigated the pesticide testing requirement of cannabis in the state‐level jurisdictions with legalized medical use. We found that 24 states and D.C. were posting the pesticide testing requirements and action levels online. We contacted the cannabis programs in the remaining nine states and found that pesticide testing was not required in three states. Also, three states provided no clear response to our inquiries. By the end of this study, we were able to obtain the action levels in 27 states and D.C. In all 28 jurisdictions, pesticide testing of cannabis was required at both the raw agricultural commodity level and the final product level.

Six states – Connecticut, Illinois, Louisiana, Maine, North Dakota, and Ohio – adopted the U.S. EPA tolerances for food commodities as the action levels of pesticide residues in cannabis . In these states, a cannabis sample would pass the pesticide residue test if it satisfied the most stringent tolerance levels for up to 400 pesticides. Maine also banned the use of 195 pesticides in cannabis that were federally prohibited for use on organic produce . Minnesota adopted the pesticide testing guideline for articles of botanical origin provided by the U.S. Pharmacopeia Convention . Twenty states and D.C. took a different approach to assess each pesticide and develop action levels individually.Pesticide exposure can result in adverse neurological effects in humans. For instance, acute poisoning of organophosphate and carbamate insecticides results in cholinergic symptoms . We reviewed the 155 pesticides regulated by the 20 states and D.C. . Insecticides  and fungicides  were the most two regulated classes of pesticides, followed by plant growth regulators , herbicides , and rodenticides . These 155 pesticides also included 16 organophosphate and 8 N‐methyl carbamate insecticides listed in the 2006 and 2007 U.S. EPA reports on cumulative risk assessment . The large number of insecticides and fungicides under regulation reflected the industrial practice of using chemical measures to control mite infestation and powdery mildew . Most of these 21 jurisdictions had action levels for 40–60 pesticides. Abamectin, bifenazate, etoxazole, and imidacloprid were regulated by 20 of the 21 jurisdictions. These four pesticides were also regulated by the six states that adapted the U.S. EPA tolerances. In contrast, 84 pesticides were regulated in only one of the 21 jurisdictions with specified action levels, and only 17 of those were also covered by the U.S. EPA tolerances for food commodities. Lastly, the 155 pesticides regulated by the 20 states and D.C. did not include a number of pesticides previously found in illegal samples, such as brodifacoum, naphthalene, and paraquat . Fig. 3 shows the top 50 pesticides with the largest variation of action levels in 20 states and D.C. On average, the action levels of these 50 pesticides were 32‐fold higher than the most stringent tolerances for food commodities by the U.S. EPA . Sixteen out of the 17 reported values of pesticide residues in cannabis plant matter were above the U.S. EPA tolerances for food commodities . Dimethomorph, a fungicide, showed the largest variation in the action levels, ranging from 0.1 to 60 ppm in 5 states. Azoxystrobin  and chlorantraniliprole  both showed a 4,000‐ fold difference in action levels. The action levels of these two pesticides ranged from 0.01 to 40 ppm in 17 and 12 jurisdictions, respectively. Ethephon, a plant growth regulator, was regulated by nine states for applications in cannabis. Six of these nine states adopted the U.S. EPA tolerance at 0.002 ppm . Two states set their action levels at 1 ppm. The remaining state set its action level at 0 ppm  with a target limit of quantitation of 0.005 ppm. In this state, the laboratories were required to detect at least 0.005 ppm of ethephon using their analytical instrument. If their instrument allowed them to detect smaller quantities of ethephon, any amount detected would cause the sample to fail the testing process.

Limiting consideration to respondents who reported past-30-day marijuana use and adjusting for complex survey design, sampleweighted multivariable logistic regressions estimated associations between a binary indicator for respondents’ selection of vaping as their primary mode of use and indicators for state marijuana policies  at the respondent’s interview date. Covariates adjusted for interview year to capture national time trends in product choice/availability, census region to capture time-invariant regional differences in attitudes towards marijuana use and access, and respondent sociodemographics . Sensitivity checks added three binary covariates for MM-only laws that allowed home cultivation, that had operational dispensaries, and that forbade smoking as a mode of use. Robustness checks repeated these analyses with a vaping-or-dabbing indicator as the outcome variable.Yale University’s IRB deemed this study exempt from review . This study is the first to show a relationship between MM policy attributes and EVALI. It also replicates prior findings on the relationship between RM and EVALI : states that legalized RM by August 1, 2019 had a lower EVALI incidence. Given that EVALI cases stemmed primarily from informally-sourced vaporizable marijuana concentrates, these results are consistent with crowd-out, whereby introduction of one market  displaces utilization of another . Simply put, if the public can obtain products legally from reputable sources, there is less demand for illicit market products. Thus, RM legalization could have dampened market penetration of tainted marijuana concentrates by reducing consumption of informally-sourced marijuana products more generally.

Findings for MM legalization, however, were more nuanced: among states with MM only, laws allowing home cultivation were associated with fewer EVALI cases relative to those prohibiting it. This might be expected if home cultivation increases the availability of marijuana flower while decreasing reliance on commercial marijuana markets, reducing exposure when tainted marijuana concentrates are introduced. Specifically, patients and caregivers who can grow their entire grow cannabis supply at home would be less likely to consume illicit market products. The resulting reduction in demand for marijuana flower on the illicit market should depress its price, such that individuals who continue to rely on the illicit market face financial incentives to consume flower over vaping concentrates, based on the change in their relative prices. Both of these effects—directly on MM patients’ and caregivers’ likelihood of exposure to tainted products and, via price, on product choice among consumers who remain in the informal market—should reduce exposure to tainted marijuana concentrates. An additional policy attribute, prohibitions on smoking as a mode of MM use, was also associated with increases in hospitalized EVALI cases when excluding states that had this policy attribute but allowed sales of marijuana flower, effectively enabling combustible use. This might be explained by impacts on mode of use. Specifically, given that vaping is the second most popular mode of marijuana consumption after smoking , restrictions on combustible use could lead to increased use of vaporizable marijuana. For MM patients, this could occur via both new MM users initiating with vaporizable marijuanaproducts and established MM users switching from smoking to vaping. Effects could also extend to non-medical users if consumers interpret the prohibition as a signal that vaporizable marijuana products are safer or switch to vaping as a means to evade detection of illicit use. Indeed, devices used to vaporize marijuana concentrates are often indistinguishable from nicotine e-cigarettes and produce less odor than smoking marijuana, making them easier to conceal . Consequent increases in the share of people who vape concentrates would be expected to increase the number of EVALI cases when a contaminated product enters the informal market.

Among those living in MM-only states, allowing home cultivation was associated with reduced odds of reporting vaping as one’s primary mode of use, consistent with increased reliance on home cultivation and/or reduced prices of marijuana flower. Concurrently, operational dispensaries were associated with increased odds of vaping as the primary mode of use, consistent with increased access to marijuana concentrates as well as potential effects on perceptions of vaping marijuana as a safe mode of use. Further adjusting for MM-only states that prohibited combustible use found a positive but statistically non-significant association between this restriction and marijuana vaping , although limited power may have influenced the ability to detect a significant finding. Variation in MM policy attributes’ associations with both EVALI case counts and adults’ mode of marijuana use suggests that understanding the implications of such policy details is critical for informing marijuana regulatory decisions. Indeed, analyses suggest different relationships when using a single yes-no indicator of MM legalization versus adjusting for the laws’ policy attributes. These policy details may also be politically malleable: they can be modified via legislative amendments without requiring the full repeal of existing MM laws, which are often quite popular with the electorate . This study improves upon prior analyses of states’ marijuana policies and the prevalence of EVALI in three important ways. First, while others relied on binned case data , we used exact case counts, removing a potential source of bias . Second, we assessed the role of MM policy attributes in this relationship, revealing greater nuance in the MM-EVALI relationship by identifying specific policy details that may be consequential for EVALI and subject to amendment in established legislation. Third, we considered how these attributes related to adults’ self-reported mode of marijuana use to clarify the mechanism behind the MM-EVALI relationship. This study’s primary limitation was related to available data on marijuana vaping. BRFSS, the US’s only annual, state-representative adult dataset that asks about mode of marijuana use, did not field thisquestion in all states. Moreover, its wording did not clearly differentiate vaping indoor cannabis grow system concentrates from vaporizing marijuana flower . This distinction is critical: while vaping marijuana concentrates was implicated in EVALI, vaping flower was not. As even preEVALI analyses suggest that vaping marijuana flower poses lower health risks than vaping marijuana concentrates , future research will require nationally representative data that clearly distinguishes these modes of use. A second limitation was the potential for differences in case detection between states. Reassuringly, findings held when limiting consideration to hospitalized EVALI case counts, which state and local health departments regularly reported to the CDC over December 2019 and January 2020. Moreover, to drive this study’s results, case detection would have to have been systematically lower in states that legalized recreational marijuana use or medical use with home cultivation. It is not clear why that would be so. A third limitation was our inability to assess variation in recreational marijuana policy attributes.

Specifically, among the 10 states that implemented recreational marijuana legalization prior to 2020—excluding Washington DC, which was not in our data—none prohibited combustible use, only one forbade home cultivation , and three  lacked recreational retailers  as of August 1, 2019. Beyond concerns about generalizability and limited statistical power with variation based on so few states, none of those four states fielded the BRFSS marijuana module between 2016 and 2019, precluding estimation of RM policy attributes’ associations with mode of use. Thus, we leave consideration of recreational marijuana policy attributes to future work. Although this study’s findings are not causal, they provide direction to states that have passed or are considering MM legalization. Specifically, to the extent that such policies affect licit and illicit marijuana use, policymaking not only must ensure the safety of legal products but also should consider potential impacts on illicit market offerings. In particular, incentivizing or restricting a particular mode of marijuana use based on presumed or demonstrated health effects with unadulterated product may have unexpected consequences if the proposed “less harmful” mode of use involves a product that is more vulnerable to adulteration, as was likely the case for vaporizable marijuana concentrates during the 2019 EVALI outbreak. This is relevant to RM states as well, since youth who cannot purchase marijuana legally may turn to informal sources. To the extent that these findings reflect causal relationships, carefully-crafted marijuana legalization policies may provide a means to reduce the scope of future EVALI outbreaks, whether due to vitamin E acetate or other additives. More work is needed in this area, as the stakes for getting these policy details right are high: with over 17% of Americans ages 12-and-up reporting past-year marijuana use , population health depends on it. While rates of adolescent combustible tobacco product  use have continued to decline in recent years , rates of noncombustible tobacco product ) use have risen in U.S. high school youth . In 2019, e-cigarettes were the most commonly used tobacco product by high school students with 27.5% reporting past 30-day  use behavior . Rates of adolescent lifetime and current use of marijuana are also increasing among youth in the U.S. , with reported annual use rates of 36% in 12th grade students and 29% in 10th grade students in 2019 . Adolescents who use marijuana are at increased risk to initiate use of e-cigarettes and to be dual users of e-cigarettes and marijuana . Tobacco product use is a leading preventable cause of morbidity and mortality. There are known adverse health effects associated with ecigarette use including nicotine addiction, respiratory symptoms, asthma exacerbations, and e-cigarette or vaping product use associated lung injury. There is also concern that similar to individuals who smoke combustible tobacco products, individuals who use e-cigarettes may be at increased risk for cardiovascular disease . Further, individuals who use marijuana are at increased risk for respiratory illnesses such as asthma  and also at increased risk for cardiovascular disease .

Obesity is also another leading preventable cause of morbidity and mortality. Rates of obesity in adolescents are 20.6% . Obesity in adolescents is also associated with adverse health consequences, some of which overlap with tobacco product- and marijuanarelated morbidity, including type 2 diabetes, hypertension, cardiovascular disease, and metabolic syndrome . Previous research indicates that adolescent males who are obese are at increased odds of using e-cigarettes compared to peers who are not obese , and that female adolescents who use substances including marijuana are at increased odds to be overweight or obese . Even though the association of substance use and obesity is complex,  they share common risk factors. Particularly, use of e-cigarettes or marijuana is individually linked to increased appetite,  reduced physical activity, and increased screen time,  all of which contribute to excess weight. In addition, research evidence suggests dual use of ecigarettes and marijuana exacerbates the likelihood for risk behaviors compared to single or non-users.Given the rising rates of e-cigarette and marijuana dual use in adolescents and the potential associations with obesity, it is important to identify behaviors that may contribute to obesity in adolescents who use e-cigarettes and/ or marijuana. These behaviors include unhealthy diet and inadequate exercise patterns during childhood and adolescence which may continue throughout adulthood if not modified early . To evaluate this, we examined data from adolescents who participated in the 2017 Youth Risk Behavior Survey. To assess risk factors of obesity, we examined the associations of exclusive and dual use of ecigarettes and marijuana and the attainment of the “Let’s Go! 5–2-1–0” obesity prevention guidelines from the Maine Youth Collaborative . The ‘5–2-1-0’ recommendations have been used to screen and evaluate healthy behaviors in children in various settings and in research . These daily guidelines recommend that youth eat at least five servings of fruits and vegetables , view two hours or less of screen time , participate in at least one hour of physical activity , and consume zero sugar-sweetened beverages . We also assessed the associations between current e-cigarette and marijuana use and perceptions of weight status among adolescents. We hypothesized that compared to non-users of e-cigarettes and marijuana, exclusive e-cigarette users, exclusive marijuana users, and dual users of e-cigarettes and marijuana would be at reduced odds of meeting the ‘5–2-1-0’ recommendations and of perceiving themselves as slightly/ very overweight. Specific to current users only, we also hypothesized that dual users would be at decreased odds to meet these recommendations and perceive themselves as slightly/very overweight than exclusive users of either e-cigarettes or marijuana.

A 23 year old gentleman presented to hospital with complaints of acute onset left sided chest pain and heaviness for 6 hours. This was associated with profuse sweating and shortness of breath. He did not have a family history of premature coronary artery disease. Clinical examination revealed normal blood pressure and normal heart sounds with no murmurs. ECG showed sinus rhythm with ST elevation in leads V2–V5,1 and avL suggestive of extensive anterior wall myocardial infarction . Reciprocal ST depression was noted in inferior leads. Echocardiography revealed mild left ventricular dysfunction with ejection fraction 47%, regional wall motion abnormalities were noted in anterior segments with severe hypokinesia of apical segment. The patient was thrombolysed with streptokinase . Post thrombolysis, his chest pain subsided. ECG taken at 90 min post lysis showed <50% resolution in ST segment height as compared to baseline. Initial troponin T and N-Terminal pro B-natriuretic peptide  levels were 4.3 ng/ml and 5370 pg/ml respectively. Hemogram, liver and renal function tests were normal. Considering a pro-thrombotic state, thrombotic panel was done which turned out to be negative. Patient was subsequently referred to our hospital where he underwent coronary angiogram. CAG showed normal left main coronary artery bifurcating into LAD and LCX. LAD was a type III vessel with 60% hazy lesion in the mid LAD and no other lesions and had TIMI III flow distally. LCX and RCA were normal . In view of borderline stenosis in the setting of acute coronary syndrome, intravascular imaging was performed to determine the culprit lesion morphology. Optical coherence tomography run showed presence of red thrombus at the site of lesion which obscures the underlying vessel wall due to its characteristic high attenuation . No thin cap fibroatheroma/macrophages/micro-channels were noted. Plaque burden was insignificant. Minimum lesional area measured was 6mm2 . In view of satisfactory minimum lumen area and TIMI III flow distally, intervention was deferred. Patient was started on anticoagulation . Follow up CAG after 2 weeks showed normal coronaries without any lesions . OCT was repeated which showed complete resolution of red thrombus . Mild lipidic plaque was noted at the site of previous lesion with a thick intact fibrous cap which points to plaque erosion as the cause of acute coronary syndrome. On further enquiry, the patient admitted to recreational use of marijuana 12 hours prior to the onset of chest pain. He had been a regular marijuana user for the last 5 years and used to smoker once or twice every week. He was discharged on dual antiplatelets  and warfarin.

Patient is on regular follow up and is otherwise asymptomatic. He was counselled regarding the adverse effects of Marijuana smoking at discharge. Substance abuse is an uncommon cause associated with acute myocardial infarction. The World Health Organization estimates that about 2.5% of the total world population uses cannabis, ten times more than cocaine or opiates.1 The cardiovascular effects of marijuana are well documented. It stimulates the sympathetic nervous system causing elevation in heart rate as well as systolic and diastolic blood pressure.It reduces the exercise time to angina due to increased cardiac workload and relative reduction in oxygen delivery to tissues due to carboxyhaemoglobin formation. Marijuana is postulated to act via CB1 and CB2 receptors. CB1 receptor has a pro-atherogenic action as it increases reactive oxygen species and promotes endothelial injury. On the other hand, CB2 has an anti-atherogenic action. CB1 expression is abundant on vascular smooth muscle cell where upregulation is induced by oxidised LDL which leads to activation of pro-atherogenic pathways.3 Marijuana intake is also a known trigger for acute coronary syndrome. Mittleman et al. showed that 3.2% of patients were marijuana users in a cohort of 3882 acute myocardial infarction patients. These patients were predominantly males who were current smokers. The risk of AMI was substantially increased  in the 1st hour after marijuana smoking, with gradual reduction in risk with time.Our patient had history of marijuana smoking within 24 hours of myocardial infarction. Heightened sympathetic stimulation after marijuana intake can lead to atherosclerotic plaque rupture.It has also been proposed that marijuana can lead to prothrombotic states by increasing Factor VII activity leading to thrombus formation.Our patient demonstrated significant thrombus burden on OCT without any underlying plaque which indicates de-novo coronary thrombosis. Patients with thrombus who have an intact fibrous plaque cap  as well as those without any underlying plaque may not require a stent as they have reasonable luminal area. EROSION study has shown that these lesions can be managed conservatively, with use of dual antiplatelet agents,vertical grow system leading to near complete resolution of thrombus on follow up. Majority of these patients are free of adverse cardiovascular events on follow up.6 Thus, dual antiplatelet therapy is an attractive option in such circumstances. Our patient had good luminal area inspite of significant thrombus burden with minimal underlying plaque burden. Thus, he was managed with dual antiplatelets and anti-coagulants, without stenting. This patient was investigated for conventional pro-thrombotic markers and all turned out to be negative. This highlights the prothrombotic milieu associated with marijuana use and its adverse cardiovascular effects. Road traffic injuries  are among the leading causes of emergency care in many low- and middle-income countries. Currently, Africa has the world’s highest road traffic fatality rates, with motorcyclists being disproportionately over represented . Also, RTIs among motorcyclists often go unreported  and consequently, the official statistics tend to be an underestimation of the true magnitude of the problem. Tanzania is one of the countries in Africa with a high burden of motorcycle-related RTIs.

A study conducted at six public hospitals in Tanzania showed more than half of all injury-related admissions were due to motorcycle RTIs . A large proportion of injured motorcyclists were commercial motorcycle riders . The high rate of road traffic crashes in this group was documented elsewhere in Tanzania  where, half of the riders reported to be involved in crashes and more than 80% experienced near-crash events within the past month before the interview . Commercial motorcycle riders in Tanzania are mostly men with limited formal motorcycle training . A report by Bishop et al. showed that only 23% of commercial motorcycle riders had received formal motorcycle training . Furthermore, there is no standardised curriculum in Tanzania for training motorcycle riders, and when available, it is mostly theory-based as opposed to practical skills training . Studies have shown that risky driving behaviours are common among commercial motorcycle riders . Even though it is illegal to drive without a license in Tanzania , however, many commercial motorcycle riders tend to disobey the law . For example, a study found that only 29% of commercial motorcycle riders reported having a driving license . . Additionally, profitability among riders depends on the number of trips they can complete during the working day, which incentivises commercial motorcycle riders to work for long hours and ride at higher speeds to maximise the number of trips . Regarding the use of protective safety measures, motorcycle helmet usage has been reported to be about 75% to 80%; however, the quality of helmets differs, and they are often not fastened correctly . Evidence suggests that helmet use is associated with reduction of mortality and the risk of head injuries among motorcycle riders . Studies have also indicated that alcohol consumption and psychoactive drug use are common among commercial motorcycle riders . The consumption of alcohol, even in small doses is associated with an increased risk of being involved in a crashes and RTIs . Alcohol intake affects judgment, slows down visual information processing and the ability to discriminate traffic signs, impairs psychomotor skills, and prolongs reaction time . Moreover, the influence of alcohol has been shown to be a stronger risk factor for crashes among motorcycle riders than for other motorists . Epidemiological studies have reported lower mean Blood Alcohol Concentration  among motorcycle riders who were involved in road crashes relative to car drivers, evincing the need for greater physical coordination and balance when driving a motorcycle . Simulation experiments of alcohol’s effect on driving show increased reaction time and errors for motorcycle riders compared to car drivers . Other documented effects of alcohol include excessive or inappropriate speed, inattention, failure to navigate curves, and increased probability of running off the road . The risk of RTIs related to alcohol consumption is linked to both the amount and drinking pattern. Two studies conducted in sub-Saharan Africa found that alcohol consumption was associated with an increased risk of RTIs among commercial motorcycle riders .

Studies have shown that motorcycle riders with a hazardous pattern of alcohol consumption are more likely to drink and drive . High-risk drinking has also been shown to be associated with other unsafe driving behaviours including the use of mobile phone while driving, speeding, not wearing a helmet and other protective gear . Risky drinkers have also been shown to be less compliant to traffic rules and road signs as well as driving without a driving license . Recently,mobile grow systems there has been increasing recognition of the effect of psychoactive drugs on RTIs . The potential psychoactive drugs reported to be associated with the risk of RTIs are including marijuana/cannabis, amphetamines, cocaine, heroin and opiates . These substances impair driving performance by altering the perception of external stimuli, and consequently, their response to them . A cohort study conducted among trauma patients in Tanzania indicated that more than third of patients were tested positive for psychoactive drugs, and the most of the patients were motorcycle drivers.Moreover, the combination of psychoactive drugs and alcohol has been shown to compound the impairment and further increase the risk of RTIs . Motorcycle riders who operate commercially are a distinct population in the traffic environment. They are exposed to a greater risk of road crashes and injuries as they spend more hours on the road and have different incentives for taking risks than other road users. There is limited evidence on the role of alcohol consumption and marijuana on RTIs among this group of riders in sub-Saharan Africa. Therefore, this study aimed to determine the association between alcohol consumption, marijuana use and RTIs among commercial motorcycle riders in the city of Dar es Salaam, taking into consideration sociodemographic, driver’s and work-related factors.Cases were identified and recruited from two tertiary hospitals of Muhimbili National Hospital  and Muhimbili Orthopedic Institute , and three main regional referral hospitals of Mwanayamala, Temeke, and Amana located in Dar es Salaam. These hospitals were purposefully selected because they are major public hospitals that provide care to RTI victims in Dar es Salaam. The three regional hospitals represent the second-highest level of hospital care next to the tertiary hospitals, and the majority of RTIs victims with moderate and severe injuries would eventually end up at these hospitals. This approach ensured the capture of the majority of injured commercial motorcycle riders who sought hospital-level of care. At the tertiary hospitals, cases were identified retrospectively from patient admission records at the Emergency Department  by a research assistant on a weekly basis. The information such as hospital registration number, name, phone number, date of the crash, and mechanism of injury that were recorded in the hospital patient registration system was extracted to assist tracing of commercial motorcycle riders who admitted due to RTIs at MNH and MOI wards. Once the cases were identified at the wards, they were informed about the study and, after informed consent, interviewed by our trained research assistant. At the regional hospitals, cases were identified by a triage nurse at the outpatient/surgery department daily. The triage nurse then alerted our research assistant to interview without hindering or delaying the care or diagnostic services. Cases visiting during the weekend or nighttime were recorded in a logbook and invited for an interview the next day at the hospital. Cases that were discharged before the interview could take place were tracked by phone number and then invited for an interview at the hospital when they came in for clinical check-up, at homes or at the parking stages.

Approval of law opened the window for scientific community to conduct research and cultivate hemp. Since then, 33 US states and more than 47 countries around the world have been growing hemp for research and industrial use . On the other hand, Marijuana research and legalization have been expanding at a comparatively slower rate and till now only 16 countries have legalized medicinal cannabis . Furthermore, a detailed study would be desirable to understand the gene function, the genetic composition, and the underlying mechanisms regulating the diversity of cannabinoids in both major varieties. Availability of the regeneration protocol  and transformation  studies could be utilized for the expression studies to unravel the mystery of these mechanisms, especially in trichomes. Glandular trichomes are the primary site for cannabinoid biosynthesis and accumulation  in C. sativa. The biosynthesis of cannabinoids  starts from the plastidial localized methylerythritol 4-phosphate  pathway resulting in the formation of geranylpyrophosphate   and the fatty acid pathway leading to the production of olivetolicacid.GPP and OA in the presence of olivetolic acid cyclase   and an aromatic prenyltransferase catalyze the reaction to form the cannabigerolicacid, which is the centralprecursor for cannabinoids biosynthesis. van Bakel et al., 2011 analyzed the transcriptomic and genomic data and described the exclusive presence of the THCAS and CBDAS in the drug and hemp typeplant, respectively . It is suggested that the activation of respective enzymes from the central precursor CBGA is responsible for regulating the THC and CBD concentration for eachchemotype. However, the precise regulatory mechanism is still unknown.Besides biosynthesis, understanding the trichome physiology is also vital to elucidate the trafficking and localization of metabolites. For cannabinoid biosynthesis, there exist three major reactions:  biosynthesis of monoterpene precursor  via MEP and fatty acid intermediate  from polyketide pathway, prenylation of the precursors, and  cyclization. The MEP pathway in plastid prenylation is localized in the chloroplast membrane, where the C-prenylated CBGA synthase is membrane-bound.

The integration of the enzyme in the membrane seems essential, and the folding pattern is crucial for its functioning. Therefore,simple cloning and functional expression of this enzyme in a heterologous host such as yeast to generate the desired cannabinoids is challenging. Terpenoid cyclization reactions are the most complex reactions found in nature and the biotransformation from CBGA to THCA by the THCA synthase is assumed to occur in the cytosol. This hypothetical model is under ongoing debate and it might be likely that biocatalysis occurs in the extracellular oil container under a non-aqueous environment .In 1992, Mahlberg and Kim postulated that THCA synthase is located in the outer membrane of the head cells or even attached on the outer membrane surface extending into the essential oil . In recent studies, LC-MS/MS was used to detect a functional active THCA and CBGA synthase in the exudates from glandular trichomes of cannabis . Zirpel et al.,described the need for an excellent understanding of protein chemistry and folding of these enzymes to produce the cannabinoid using a heterologous host . Detailed knowledge of genetic regulatory mechanisms underlying cannabinoid biosynthesis is a future challenge. Identification of regulatory elements such as transcription factors  and micro RNAs  could be utilized to understand the mechanistic insights of trichomes initiation, development, and densities. An in-depth understanding could be applied toward the breeding of genetically improved cannabis varieties with enhanced cannabinoids concentration in trichomes. Drug- and fiber-type plants differ in biosynthesis, concentration, and composition of metabolites . To determine the genetic variations regulating plant-specific differences, it is essential to compare the genomes. Advanced sequencing technologies combined with continuously improving bioinformaticstools have allowed rapid sequencing and analysis of multiple genomes and transcriptomes. The very first draft genome of C. sativa was released in 2011 by Bakel et al. . They sequenced cannabis grow racks cultivar Purple Kush by using Illumina short reads and assembled them in combination with 454 reads. They also sequenced fiber-type hemp cultivar Finola for a genome-level comparison. In addition to whole genome, the first complete mitochondrial reference genome was also obtained in 2016from the cannabis hemp variety Carmagnola.Later in July 2016, two complete chloroplastgenomes of marijuana African variety Yoruba Nigerian and Korean hemp non-drugvariety Cheungsam were sequenced and used to determine the phylogenetic position of C. sativa relative to other members in the order Rosales.

Furthermore, in September 2016released complete chloroplast genomes of two Cannabis hemp varieties, the Carmagnola and Dagestani , to determine their genetic distance compared with the closest cannabaceae chloroplast of Humulus lupulus variety Saazer .Increasingly growing support for open-data policy by multiple industries is improving transparency in cannabis agriculture. In 2016, the industrial lead in cannabis research from Courtagen Life Sciences and Phylos Bioscience independently generated the genomes of hybrid marijuana strain Chemdog91  and marijuana strain  Cannatonic , respectively.Phylos Bioscience also released genomic data of 850 Cannabis strains as a part of ‘‘Open Cannabis Project’’ for plant breeding programs. With an objective to explore Cannabis population genetics, PhylosBio science developed three-dimensional interactive map of nearly 1000 cannabis strains . In 2017, the genome of hybrid marijuana cultivar Pineapple Banana Bubba Kush was released as part of Cannabis Genomic Research Initiative. In 2018, Grassa et al. generated the first chromosome-level assembly for the genome of CBDRx, a high CBD cultivar of C. sativa by using advanced long-read Oxford Nanopore Technology  and PacBio Single-Molecule Real-Time  sequencing. Later in 2019, Laverty et al., improved the initial draft assemblies of drug-type Purple Kush and hemp-type Finola to chromosome-level by using ultra-long PacBio reads. In addition to genomes of high CBD and THC marijuana and hemp cultivars, a medicinal Cannabis strain with a balanced THC/CBD ratio was sequenced by Shivraj et al. .Until 2020, nearly all Cannabis genomes had been obtained from the hemp and marijuana cultivars, selectively bred for generations. However, cultivars lose genetic diversity owing to domestication and successive plant breeding for selected traits. In contrast, the wild-type genomes exhibit relatively high heterozygosity and genetic diversity, which might provide unique evolutionary insights into the cannabis genome. Therefore,in 2020, Gao et al. sequenced the first samples of C. sativa wild-type ‘‘Jamaican Lion’’ variety growing in the geographically isolated Himalayan region in Tibet. Because these wild-type plants retained theancestral genetic make-up, therefore, the data generated from this study was used as a tool to determine the inheritance patterns and evolutionary inference of cannabis .The published genomes of high THC, high CBD marijuana cultivars, and hemp varieties, exhibited inconsistent chromosomal nomenclature, arrangement, and varying degree of gaps. Therefore, by end of 2020,Shivraj Braich et al. generated a relatively complete draft genome assembly for Cannbio-2, the medicinal cannabis strain with a balanced THC/CBD ratio .

To present date, only 13 Cannabis genomes are publicly available at National Center for Biotechnological Information.Of which 3 assemblies are at chromosome-level, 7 at contig-level, and one at scaffold-level. However, by March 2021, the 1000 Cannabis Genomes Project comprises of genomic data of nearly 1000 samples from multiple cannabis strains. These datasets were the first genome data released on Google Cloud Big Query database.Continuously expanding the list of cannabis genomes needs collaborative efforts toward curating the information.Therefore, academic and industry experts in diverse fields formed the International Cannabis Research Consortium  during the annual PAG meeting in 2020. Despite several cannabis genome assemblies, the selection of single standard reference genome is still a huge challenge for the scientific community, especially plant breeders. Therefore, ICRC proposed CBDRX Cs10 assembly as the most complete reference for use in cannabis genome research . Additionally, a member genomics company, NRGene, generated an integrated Cannabis, and Hemp Genomic Database  optimized and curated for the genomics-based breeding of cannabis varieties. Finally, in 2021, the first-ever open-access and comprehensive database of cannabis genome Cannabis GDB  were released  with integrated bio-informatic tools for the analysis of datasets.Overall, the genomic data of diverse cannabis genotypes are the untapped reservoirs of genetic information which could be applied toward pan-genomic understanding of cannabis evolution and determining the effect of genetic variations upon the pathways, development, and concentration of cannabis derivatives.Detailed genetic atlas would facilitate the designing and further breeding of cannabis varieties forpreferred metabolic yields. The availability of several high-quality cannabis grow system genomes made it easier to apply the transcriptome sequencing to elucidate detailed expression dynamics in time-, tissue-, stage-, and chemotype-dependent manner. Furthermore, the differential expression analysis provides in-depth insight into co-related genenet works. In 2011, Bakel et al. sequenced and compared the transcriptomes of marijuana variety PurpleKush  and hemp cultivars Finola  and USO-31. Gene expression analysis revealed preferential expression of cannabinoid and precursor pathway-associated genes in marijuana . Expression ofTHCA synthase in the PK and cannabidiolic acid synthase in FN was found to be consistent with the exclusive production of psychoactive THC in marijuana. In a recent study, transcriptomics of hemp-type plants was analyzed to determine the expression profile of the fiber-type plant at the various developmental stages . Similarly, the transcriptome of marijuana flowers at different stages was captured and sequenced and found the gene expression pattern consistent with the cannabinoid contents.As glandular trichomes are the central reservoir for cannabinoids ,therefore, the trichome transcriptome could yield valuable insight to determine the variation in cannabinoid biosynthesis, composition, and concentration between the drug and fiber-type plants. Importantly,the identification of the differentially expressed genes could unravel the underlying molecular mechanisms of natural genetic and metabolic variation. The gene expression in trichomes of female plant strain Cannobio-2 was compared with genome-wide transcriptomics of female floral tissues at different stages of development as well as other tissues including female and male flowers, leaves, and roots .

The extensive-expression atlas was applied toward the identification of genes expressed preferentially in various tissues at different developmental stages. Interestingly, the majority of genes involved in terpenoidand cannabinoids synthesis were significantly over-expressed in trichomes. In 2021, Grassa et al. usedgenomic, and expression associated expression of THCAS and CBDAS with THC:CBD ratio by Quantitativetrait Loci  analysis of Cannabis cultivars .Datasets from similar genomics, transcriptomics, microbiome, and metagenomics studies of various cannabis strains are currently accessible from the Sequence Read Archive  repository at NCBI. In the past 3 years, there has been unprecedented growth in Cannabis genome and transcriptome studies and corresponding SRA entries. To date, there are over 4571 Bio Samples from multiple studies related to Cannabis of which 2871 public Bio Samples are exclusively for C. sativa with 2546 DNA and 325 RNASeqdatasets in SRA. The SRA data for transcriptomics and metagenomics have reportedly procured from various tissues including seeds , flowers , leaves , shoot  stem , root , and trichomes, while genomic data lacks tissue-specific information. In-depth transcriptomic studies will be required in the future to improve the understanding of regulatory genetic networks. One of the fundamental aspects of patents, especially in medical science or biotechnology, is to involve industrial partners in investing in research and development .Cannabis-related patents have been issued by the US-patent office since 1942. More than 1,500 applications have been filed only in the US patent office. Among them, approximately 500 applications got patent protection rights  and most of them were from the last decade.Here, we analyzed the patentsspatiotemporally and categorized them into four main classes:  patents related to cannabinoids as constituents, pharmaceutical applications,endocannabinoid pharmacology, and  genome and gene related. Among the suggested four categories, the patents related to the pharmaceutical application were the most significant category with 73 patents registered. These are further sub-grouped into the preparation of the drugs,  treatment,  delivery technology, and  detection method each with 14,33, 13, and 13 patents, respectively. Endocannabinoids-related patents comprised of the CB1/2 receptor, TRPV1 , and GPR119  reviewed in . The category of cannabinoids consists of cannabinoid isolation,  extraction, and  synthesis or biosynthesis-related patents each with 6, 6, and 12patents granted, respectively. For the division of the sequences, 15 patents are from enzyme inhibition followed by the gene and the protein each with two patents. Most of the patents are from the US  followed by the GB  and the other European countries Figure 2 . In addition, 25 patents for fiber/textile, 10 for foodstuff, 5 for the paper industry, 3 for architecture,1 for biofuel, and 3 for plant breeding have been registered. Also, four patents each in the category of oil,extracts, and cosmetics each with four have been filed.

The aim of this paper is to 1)conduct a comparative analysis of the SMILE Google Earth Engine machine learning classifiers and analyze their performance in crop type classification over a semi-arid irrigated heterogeneous landscape; 2) classify cannabis fields in the Bekaa region for the years 2016, 2017 and 2018 using multi-temporal multispectral imagery and GEE built-in classifiers; 3) evaluate the added value of the increase in temporal frequency due to the use of Landsat-Sentinel-2-Sentinel-1 in crop type classification. We use data from Sentinel 2, Sentinel 1, and Landsat 8. We evaluate the accuracy of four different SMILE classifiers: Random Forest, Support Vector Machine, Classification and Regression Trees, and Gradient Tree Boosting, which was recently introduced to GEE classifiers. We perform multi-temporal image classification using three different imagery combinations. Each combination consists of two or three sensors. The first combination consists of Sentinel 2 and Sentinel 1 imagery , the second combination consists of Landsat 8 and Sentinel 2 imagery , and the third combination consists of Landsat 8, Sentinel 2, and Sentinel 1 imagery . Sentinel-1 mission  is composed of two polar-orbiting sun-synchronized satellites, each carrying an imaging C-band synthetic aperture radar  instrument. The Sentinel-1 mission supports four imaging modes providing different resolutions: Interferometric Wide Swath , Extra Wide Swath , Strip Map , and Wave . We use the IW mode which supports operation in dual-polarization and has a ground range resolution of 5 × 20 m. Sentinel-2 mission  provides high spatial resolution multispectral satellite imagery with a high revisit frequency  and a wide field of view. The spatial resolution varies from 10 m to 60 m depending on the spectral band. Sentinel-2 carries the Multispectral Imager  sensor with 13 spectral bands in the visible, near-infrared, and shortwave infrared part of the electromagnetic spectrum.

Landsat 8 , a NASA and USGS collaboration, provides global moderate-resolution imagery  in the visible, near-infrared, shortwave infrared, and thermal infrared part of the electromagnetic spectrum. Fig. 2 represents the flow of work and the three main stages for crop classification. Sentinel 2 TOA reflectance dataset was obtained from GEE platform, it was then refined to remove cloudy pixels by cloud masking using QA band and pre-filtered at 5% or fewer clouds. We used the Normalized Difference Vegetation Index  and the Enhanced Difference Vegetation Index , the two most widely used vegetation indices as additional bands in the classification process. EVI provides improved sensitivity in high biomass regions while minimizing soil and atmosphere influences but requires a blue band. EVI2 performs similar to EVI but without the blue band . Other vegetation indices such as the soil adjusted vegetation index  were tested but did not offer an advantage. The images were filtered over six months, from March to August and one image from each month was chosen. This resulted in six images from each dataset each containing 13 bands . A median, maximum, and minimum composite were created from the image collection. All bands were then combined to form a single image containing median, maximum, and minimum values for each band . The calibrated and ortho-corrected Sentinel-1 product was accessed from within the GEE platform. It was then filtered to get images with VV and VH polarization. The images were filtered over the same period as Sentinel 2 images. A mean composite was obtained of all Sentinel 1 images which resulted in a total number of 12 bands . Three texture metrics  were also added for both bands . The images were converted to bands and added to the Sentinel 2 bands which resulted in a total of 282 raster features for the six months. Finally, cannabis grow system the GEE Landsat 8 Surface Reflectance dataset was filtered to mask clouds and cloud shadows over the study area over the same period as the previous two datasets. A median and variance composite of all Landsat 8 images was created. The result is six Landsat 8 images with 8 bands each . We combine Landsat 8 bands with Sentinel 2 bands for the L8S2 dataset, resulting in a total of 330 raster features. The third and final dataset combination contains a total of 378 raster features for S1, S2 and L8 combined over the full-time series. Table 1 summarizes the datasets used. The training data used includes ground-truth crop fields collected in 2016 & 2018 as polygons. After delineating surveyed crops, they were imported to GEE as assets and grouped into six crop groups. For the 2016 classification: wheat and barley fields were combined; beans, broccoli, cabbage, celery, coriander, corn, cucumber, lentil, lettuce, melon, mint, onions, parsley, pepper, radish, tomato, carrot, watermelon fields were combined into one crop group; and stone fruits and vineyards were combined.

For the 2018 classification: wheat, barley, alfalfa, and vetch fields were grouped into one crop group; onion, cucumber, eggplant, squash, pepper, tomato, lettuce, parsley, mint, cabbage, carrot, chickpea, peas, beans, corn, and melon fields were grouped into another; and apple, apricot, peach, plum trees, and vineyards were grouped. Cannabis fields were filtered into irrigated and rain-fed or not irrigated, and only the irrigated fields were used in this classification. We use irrigated cannabis fields for the training of the classifiers because tobacco and cannabis share a similar growing cycle . Since tobacco fields are rain-fed or non-irrigated, confusion between detection of tobacco and cannabis occurred. Thus, we decided to use only irrigated cannabis fields to avoid this type of confusion. The other two crop groups were potato and tobacco. Urban and fallow polygons were selected based on their NDVI being less than 0.18 over the whole year. Finally, each crop group and the urban/fallow group were given a label  starting from 0 until 6. Fig. 3 shows the spatial distribution of surveyed crops over the Bekaa plain in 2016. We use the GEE “sample Regions” as the sampling method to select training data. The method requires inputs that are a Feature Collection, a class property, and a scale. We provide the polygons in a form of a Feature Collection as an input. The class property in our classification is ‘Croplabel’. Hence, a ‘Croplabel’  is assigned to each class of the sampling data, and the scale size used is 50 m. The number of training pixels used for the 2016 classification and 2018 classification is shown in Table 3. The number of pixels used was obtained by taking the average number of pixels of 10 GEE permutations. The total number of pixels was divided such that and 70% of the pixels  were used for training and 30% of the pixels were used for validation. The number of training and testing pixels for each crop type  is also shown in Table 3. Classifying different crop types requires knowledge of their spectral characteristics. Spectral characteristics of different crops are an important factor that affects the accuracy of crop identification. Each crop type has a unique spectral signature that changes with time depending on crop phenology and crop growth stage . Thus, making the temporal feature essential in crop identification. Defining the best “identification time” provides opportunities to identify spectral differences during specific phenological stages. To enhance spectral discrimination among different crops, vegetation indices can be used to take advantage of the difference in reflectance between different wavelengths . In this study, we aimed to identify a period where the spectral differences are suitable to maximize classification accuracy. To achieve this, we analyzed the spectral characteristics of the crops in the study  during a whole year. We calculated the mean NDVI, using Sentinel 2 imagery, over 15 different surveyed fields from each crop group for the year 2016.

The most spectral differences among these crop groups were observed between April to August in both years. However, we applied the classification on different periods and found that the highest classification accuracy was between March to September, which was adopted in this analysis. Random Forest classifier is an ensemble-based tree-structured classification method that grows several classifiers instead of one classifier to perform better classification . It creates several decision trees  and aggregates their results, thus predicting a response from several decision trees. The variation between individual trees results in more diversification and better accuracy. The classifier requires two user-defined parameters: the number of decision trees to be grown and the number of features used at each node. In our classification, the number of trees is chosen to be 20 trees based on parameter tuning to minimize computational timeouts, reduce run times, and avoid over-fitting . We performed hyper-parameter tuning by training the classifier using a different number of trees  and plotting the overall accuracy results for each number of trees. The number of splits at each node is set by default to the square root of several variables. Gradient tree boosting , similar to the random forest, is a tree-based ensemble learning algorithm that uses gradient descent as its optimization algorithm to minimize the loss function. It works by sequentially training several weak learners, which are shallow decision trees. During the training process, the mis-classified pixels – due to the weak learners that classified them earlier – are assigned to stronger weights and thus classified correctly . A loss function is associated with the model such that, each time a new tree is added it minimizes the loss function. With every addition of a new tree, the overall prediction error will decrease . Classification performance of gradient boosting is highly influenced by parameter tuning. Parameters include the number of trees, shrinkage, learning rate, loss function, sampling rate, and maximum node size. In this classification, we use 15 trees and the default parameters set by GEE: “Least Absolute Deviation” as loss function, shrinkage of 0.005, and sampling rate of 0.007. Classification and regression tree  is a tree-based machine learning algorithm . It can be used to classify numerical and categorical  data. The algorithm behind CART classifier builds a decision tree starting from the roots, which will split at each node. The training data will pass down the tree through the splits and a decision is made at each node to decide the next direction of the data. The decision is made to reduce the impurity at each node, which depends on the splitting rule. The splitting rule metrics for classification trees include, but are not limited to, misclassification error, Gini index, Entropy index, and Twoing . The splitting will continue until there is only one sample left, and a final decision is made at the terminal node . The inputs for CART classifier are a feature collection which is the 70% training data, a class property which is ‘Croplabel’, and input properties which are all the bands being used for classification. We classify crops in 2017 using the previously created models from 2016. We prepare a composite, similar to Section 2.2, for the year 2017. We extract the spectral, spatial, and textural characteristics of the 2016 training polygons using different dataset combinations. We train the classifiers using the 2016 training data and we classify the 2017 composite using the trained classifiers in 2016. We choose the year 2017 because we have obtained marijuana grow system fields’ survey data during 2017. We perform an accuracy assessment as described earlier. The surveyed fields were all used as a testing data set since this data was not used for training. We perform several permutations, using the most robust classifiers, in order to decide which model classifies cannabis in 2017 better than the other.

The most accurate model in classifying cannabis is presented below. The application of machine learning algorithms in crop-type and land cover classification has gained popularity in the past decade. However, the identification of cannabis plantation areas has not received enough attention. Our study provides a crop classification method to identify cannabis fields using freely available medium resolution imagery  and machine learning algorithms. We perform several permutations using different dataset combinations and different metrics for each satellite data. The highest accuracy was achieved using the mean composite of Sentinel-1 and median and variance composite of Landsat 8, however, for Sentinel-2, using min, max and median composite achieved better results. Our results are in agreement with Carrasco et al.  who evaluated the use of different metrics  derived from Sentinel-1, Sentinel-2, and Landsat 8 as a time series for land cover mapping, and classification was achieved using the mean composite of Sentinel-1, the median composite of Sentinel-2, and the median and variance composite of Landsat 8. Previous studies have reported the improvement in classification accuracy after combining radar sensors such as Sentinel-1 to optical sensors . Our classification results show a slight improvement in classification accuracy when Sentinel-1 is added using RF and SVM classifiers.This marginal improvement in accuracy was also observed earlier by Boryan et al.  when identifying winter wheat in two different locations using optical and SAR data. The authors suggest that texture features from SAR are not necessarily important when cloudless optical data is available.

Multivariate modeling indicated that adolescents with a history of cannabis use had lower perceived risk of harm compared with those who had a history of using other substances. This is an important finding as studies have found that cannabis consumption decreases perceived risk of harm from using cannabis . Limitations to consider when interpreting results of this study include the inability to conclude causal relation, limited generalizability, and response biases. Although complex sampling was used to have the most generalizable adolescent population to the US, some differences in perceived risk of harm from cannabis use and history of substance use were detected between the full sample of US adolescents from the NSDUH dataset and the analytic subsample that had complete responses to the pertinent survey questions, thereby limiting generalizability. Furthermore, there is potential for response biases with retrospective, self-report questions that may result in under reporting or recall bias. For example, reported perception of peer use has been linked to a respondent’s own substance use . Recommendations for future research are to conduct prospective studies to confirm the findings of the current study. Furthermore, research analyzing longitudinal data to monitor trends in risk perceptions and consumption, differentiating by state is essential as cannabis laws differ by state. Additionally, research examining the effects of interactions between age or sex and peer influence on cannabis risk perception will be useful for adapting prevention services tailored for age and sex. Understanding the effects of legalization of cannabis on adolescent use can better inform state officials on ways to implement programs to educate adolescents about the risk of harms associated with its use. Adolescent cannabis use prevention programs might include age-appropriate messaging about the risk of harm from using cannabis and elements that bolster the protective influences of peers and schools.

The current study adds further evidence to justify capitalizing on the potency of peer and social influences in substance use prevention interventions. Adolescents in this study who perceived risk of harm from monthly cannabis use had lower odds of believing their peers used outdoor cannabis grow, higher odds of perceiving their peers disapproved of using cannabis, higher odds of their parents limiting their time out with friends during school nights, higher odds of regarding school as important, and higher odds of reporting participation in extracurricular activities. This study further emphasizes the need for effective, multidimensional programs that target adolescent attitudes and beliefs about cannabis use through peer education, enhancing school engagement, and promoting youth clubs, athletics and other school-based or community social events. Tobacco companies have long employed numerous tactics to advertise their products to youth and young adults , and young people who report viewing tobacco advertisements are at greater risk for tobacco use initiation, progression to regular use, and development of nicotine dependence.As a result, the 1998 Tobacco Master Settlement Agreement  limited the marketing of tobacco products in ways that might entice under aged youth to use them and movies, use of cartoon characters such as “Joe Camel”. However, following passage of the MSA, more subtle product placement strategies continued to be used in TV and movie productions with tobacco products featured as a part of the plot or character development. Similar to direct tobacco advertising, viewing tobacco products on TV/movies is also positively associated with youth tobacco use . Several U.S. states, including California, legalized the sale, possession, and use of cannabis recreationally among adults, beginning in 2012.However, unlike with tobacco, there are relatively few restrictions on cannabis marketing, in part because cannabis is classified federally as a schedule I drug despite legal sales of recreational cannabis in 11 states and the District of Columbia . Consistent with studies that show viewing tobacco marketing increases risk for tobacco use, a small body of cross-sectional work has also shown that viewing cannabis advertisements is associated with higher odds of cannabis use , as is seeing cannabis use in TV/movies . The tobacco and emerging cannabis marketplaces have changed considerably over the past decade, resulting in a proliferation of new tobacco and cannabis products , which have become increasingly popular among YA . There is also evidence that tobacco and cannabis companies are marketing these products in new  ways – through online platforms such as social media , which may disproportionately impact YA who rely on the Internet more heavily than older adults .

For example, a recent study examining JUUL’s  marketing campaign revealed that thousands of Instagram posts, emails, and other advertisements were targeted to youth, and non-smoking populations.Similarly,Medmen recently initiated a well-funded national advertising campaign, including advertisements on the Howard Stern and Adam Carolla shows, YouTube videos, billboards, and social media advertisements . Given the increasing array of tobacco and cannabis products and methods for marketing them , it is important to identify the extent to which YA recall seeing marketing, for which products, and through which channels . Marginalized populations, including sexual and gender minorities, racial/ethnic minorities, and populations of lower socioeconomic status, use tobacco and cannabis products at higher rates, relative to the national average . Historically, these groups have also been disproportionately exposed to cigarette advertisements targeted specifically to minority populations . However, little is known about whether, or how viewing of marketing for new and emerging tobacco and cannabis products differs across sociodemographic characteristics, such as race/ ethnicity, gender identity, sexual identity, and socioeconomic status in YA. This study had two aims. First, we assessed prevalence of recalling online advertisements, as well as seeing product use in TV/movies, for a wide range of tobacco and cannabis products among a diverse sample of YA  cannabis products in California from Southern California. Second, we assessed sociodemographic differences in recalling online advertisements and seeing product use in TV/movies for any tobacco products and any cannabis products. All analyses were limited to never-users of tobacco and cannabis products, respectively. Sociodemographic characteristics were first calculated, separately among never users of tobacco and cannabis products . Then, prevalence estimates were calculated for recalling seeing tobacco and cannabis marketing. Unadjusted estimates are reported for both Internet- and TV/movie-based marketing, and F-tests assessed whether prevalence differed significantly by marketing source for each product. Finally, sociodemographic differences were assessed for recall of marketing for any tobacco  or cannabis  products, separately for Internet- and TV/movie-based marketing channels.Multivariable logistic regressions also assessed associations between all sociodemographic characteristics, in combination, on recalling any tobacco and any cannabis marketing. All analyses were limited to never-users of tobacco  and cannabis  and were conducted in 2020 using Stata SE version 15. Sample characteristics of users vs. never-users of tobacco and cannabis were compared in Supplemental Table 1. This study assessed prevalence of, and sociodemographic differences in recalling tobacco and cannabis grow equipment product marketing among a sample of Southern California YA reporting no history of tobacco and cannabis use, respectively. For the majority of products – all combustible tobacco products, combustible cannabis, and edible cannabis – respondents had higher odds of seeing use of those products on TV/movies than they did seeing online advertisements. Portraying tobacco use in TV/movies has been an effective – and profitable – way to advertise tobacco products , increasing risk for tobacco use initiation among youth .

While considerably less research has assessed the role of seeing cannabis products in TV/movies on initiation of cannabis use among young people, cannabis brands have been successful in negotiating product placements with entertainment studios, and with almost no regulation . While it is impossible to discern the degree to which respondents saw tobacco and cannabis products in TV/movies through intentional product placements and/or through the artistic discretion of the TV/filmmakers, our results highlight that shows and movies reaching young people include a considerable amount of tobacco and cannabis product use. Effective measures to reduce exposure to this form of marketing may include giving programs that display tobacco or cannabis use R  and TV-MA  ratings and prohibiting the display of recognizable brand names, among other actions. Consult the Truth Initiative  for a full list of measures endorsed by the organization . While there is ample evidence that JUUL and other e-cigarette brands are indeed promoted on TV/movies , respondents in this sample had higher odds of recalling seeing online advertisements  for these products. Given that youth and YA remain the largest demographic group of Internet users , and that the proportion of young people using e-cigarettes has risen , online advertisements for e-cigarettes may disproportionately influence underaged youth and YA to experiment with, and become regular users of e-cigarettes. A future direction for effective tobacco regulation might include limiting online marketing for e-cigarette products. While logistically challenging, online marketing should ideally be regulated in such a way that ensures first amendment protections to e-cigarette companies, while also limiting exposure among YA never users  . A number of sociodemographic differences were also found with regard to viewing tobacco and cannabis marketing. For example, women reported seeing online cannabis advertisements at higher rates than men. Compared to men, greater percentages of women also reported seeing tobacco and cannabis products on TV/movies. It is possible these findings stem from documented gender differences in processing and recall of advertising details, with women recalling details of advertisements more clearly than men . However, it is also plausible that young women  who recall seeing tobacco and cannabis products in TV/movies may be at especially high risk for using those products themselves. In prior longitudinal work among non-smokers, young women who watched a favorite actor smoke on screen had a nearly twofold increase in risk for smoking themselves. This association was not significant among young men . In multivariable analysis, LGB-identified YA also recalled seeing online cannabis advertisements at a higher rate than straight respondents, and prior research has shown that LGB youth have a greater willingness to use cannabis products than their straight peers . Together, these findings highlight that young women and LGB people may be priority populations for public health efforts to prevent tobacco and cannabis use. Several racial/ethnic differences were found. Interestingly, racial/ ethnic minority respondents had lower odds of recalling marketing, across a number of comparisons. For example, Asian YA had about half the odds of recalling seeing online cannabis advertisements and seeing use of cannabis products in TV/movies, compared to White respondents. Further, compared to White respondents, Black respondents had about 40% odds of recalling both tobacco and cannabis use in TV/movies, and respondents reporting an “other” race/ethnicity had about 50% odds of recalling cannabis use in TV/movies. While cigarette companies have a long history of targeting advertisements to Black populations , our results suggest that among never-users, White YA are more likely to see online advertisements for tobacco and cannabis, or to see those products used in TV/movies.

However, it should be noted that while this analysis was focused on identifying sociodemographic disparities in seeing marketing, all groups did recall seeing some degree of marketing . Several steps have been taken at the state and federal levels to regulate underage exposure to tobacco marketing . However, while many U.S. states have legalized the sale of cannabis products, they remain illegal federally. This limits the ability to effectively regulate accessibility to cannabis products for adults and those who may benefit from them , while also limiting exposure to those most vulnerable to misuse . Respondents in this sample were below the legal purchasing age for cannabis products in California, yet a large proportion of them – all of whom reported never using cannabis in the past – reported seeing online advertisements and use of these products in TV/movies. These results highlight a need for more research related to cannabis marketing exposure and subsequent use among YA, and the role of regulations to limit exposure. While individual states may be unable regulate online advertisements, they would be able to regulate local advertisement , should they be shown to deliberately and effectively target underage youth. More work is still needed to understand how to effectively regulate pro-use messages online and in TV/movies. First, our main outcome measure was self-reported recall of tobacco and cannabis marketing, which may not reflect actual marketing efforts to place ads where YA will see them. Instead, our measure signifies where YA were most likely to notice tobacco and cannabis advertisements. Second, these analyses were cross-sectional; we were unable to assess whether recalling marketing was associated with tobacco and cannabis use initiation. Third, this study assessed whether respondents recalled seeing marketing both online and in TV/movies, though there exist a host of other ways in which tobacco and cannabis products are marketed .

Approximately one-third of people with epilepsy will experience treatment-resistance which is defined as failure of adequatetrials of two tolerated, appropriately chosen antiepileptic drugs to achieve seizure-freedom.Children unresponsive to conventional treatments face an increased risk of cognitive, behavioral,and psychosocial dysfunction that can have a negative impact on their health and development . This prognosis has led to strong consumer interest in and uptake of alternative treatments such as artisanal ‘cannabidiol -rich’ products as a way to manage seizures in children with epilepsy . However, such products are typically of unknown quality, composition, and safety,and their use may conceivably pose unpredictable health risks to these children.Despite increasing access to legal pharmaceutical-grade cannabis grow facility products globally, many consumers continue to use artisanal cannabis preparations.

This may be done for various reasons including lower cost relative to the prescribed product, lack of awareness or knowledge of the patient access pathways, bias against pharmaceutical products, or perceived superior effectiveness and/or tolerability of artisanal products relative to the This current analysis of ‘artisanal’ cannabis samples administered to children with epilepsy in the Australian community found potentially unsafe levels of residual solvents, mainly ethanol, in approximately one quarter of the cannabis samples tested. In the manufacture of artisanal cannabis preparations, the incomplete evaporation of ethanol and other solvents prior to reconstitution with an oil-based diluent can lead to consumers ingesting higher amounts of residual solvents than anticipated, particularly if products are taken at high doses and/or for prolonged periods of time.There are legitimate concerns around the potential harmful effects of ethanol on the developing brain, as well as the fact that alcohol consumption, particularly chronic and/or acute use of considerably large amounts of alcohol , and sudden alcohol withdrawal, can increase the risk of seizures .

Other alcohol-related factors for increased seizure risk include impaired sleep quality and interactions with antiepileptic drugs. Despite these concerns, ethanol is commonly used as a solventin many oral liquid preparations for pediatric populations to improve drug solubility and/or as a diluent . According to ICH guidelines, ethanol and isopropanol are ‘Class 3 solvents’ which are regarded as less toxic and of lower risk to human health.Such solvents may be administered in concentrations higher than the toxicity limit  provided this is underpinned by good manufacturing practice or other quality-based requirements.The exact implications of this observation are unclear, but it suggests that pesticide contamination is a legitimate concern which requires further investigation across a larger set of samples.At the time the ‘PELICAN’ study was collecting samples from participants , legal pathways to accessing medical cannabis grow system in Australia were still evolving and highly bureaucratic,time-consuming, and expensive for patients . This represents a time in history when consumers had few alternatives to accessing medicinal cannabis and, artisanal ‘black market’ cannabis products,by comparison, were cheaper and easier to access. There are now better legal options available for accessing medicinal cannabis that avoid the concerns identified with unregulated products.

In Australia, Epidiolex is now a registered and government-subsidized medicine for the treatment of Dravet syndrome and Lennox–Gastaut syndrome  and an array of other CBD-containing products are available on prescription via schemes overseen by the Therapeutic Goods Administration . Despite this, the use of artisanal cannabis products will undoubtedly continue because of the perception that artisanal products are more effective and/or better tolerated than pharmaceutical-grade cannabis products, and that the addition of D9-THC and minor-cannabinoids may harness a supposed ‘entourage effect’ that enhances overall efficacy . To-date, no randomized, controlled studies have compared pharmaceutical-grade CBD against artisanal cannabis preparations in apopulation with epilepsy, although preclinical studies are starting to shed light on the pharmacological interactions between cannabis constituents . Meanwhile, in North America, concerns continue around an overall lack of mandatory testing of cannabis products to ensure patients are obtaining safe, quality-controlled product from licensed producers. Several recent reports have described cannabis-derived products contaminated with microbes, heavy metals, pesticides,and other toxins .

A developing brain subject to chronic cannabis-exposure may to a greater extent affect these underlying mechanisms, and hence result in increased severity of diminished expression. Apathy, on the other hand, is linked to reward expectancy and cost-benefit-computation . It is possible that these mechanisms have other determinants, and therefore present as associations to male sex and depressive symptoms. A previous study by Strauss et al.  also found male sex to be linked to the apathy-dimension. And depression shares many features with the apathy-dimension of negative symptoms, such as anhedonia. Especially anticipatory anhedonia is affected in schizophrenia , but is also found as a feature in depression,vertical grow system and may therefore contribute to drive this association. The frequency of cannabis use at baseline was also associated with the severity of diminished expression after 12 months.

Our interpretation is that higher frequency of cannabis use before baseline predicts less improvement in symptom severity over the first year of treatment. In contrast to Sabe et al.’s findings of less severe negative symptoms in recent cannabis abstainers, we found no difference in symptom severity in either dimension when comparing abstainers to continued-users and non-users. And continued use did not contribute to symptom severity at 12-month follow-up. A possible explanation for this is the abstaining groups’ heterogeneity with regards to amount of intake, i.e. that both heavy and more recreational users are included in the abstainer group, with consequences for the effect of abstaining. In our sample, the “abstainer”-group was too small to do further sub-categorization. We could, however, speculate that abstaining from heavy continued use  would have beneficial effects on the development of negative symptoms.However, both the effect of different classes of drugs, and the severity of substance misuse will vary significantly. We consider it unlikely that the intake of drugs of abuse protect against or reduce negative symptoms.

Rather, it may suggest that individuals with a heterogenous intake of illicit drugs constitute a subgroup with lower levels of primary negative symptoms. The main strength of this study is the use of a validated two-dimensional model of negative symptoms in a large sample of FEP participants. This enabled us to counteract some of the limitations found in previous studies, and provides a more differentiated investigation of negative symptoms in line with the current theoretical understanding of its phenomenology. We also used a more differentiated measure of cannabis use, encompassing the frequency and recency of use and thus enabling study of potential dose-response effects. The sample size and the inclusion of relevant clinical and sociodemographic characteristics enabled statistical control for potential confounding group differences associated with both cannabis grow equipment use and negative symptoms. There are also important limitations. First, the chemical composition of cannabis may vary significantly, especially with regards to the THC content. We could not correct for this in the analyses. From police confiscate in Norway, THC content has been estimated to vary from 30 to 45% .

In line with this, the assessment of “instances of use” as a proxy for the amount of cannabis used is no measure of the actual amount of cannabis consumption, or the effect of other illicit drugs that may have been used simultaneously. Second, it is widely accepted that side-effects of antipsychotics, such as sedation and extrapyramidal symptoms, may constitute sources of secondary negative symptoms . Clinical measures of these were not included in the analyses. Different antipsychotics display different side-effect profiles , and this variation is not fully captured by the measure of DDD. It may be that the dose dependent effects are less relevant than the receptor profile of the different antipsychotics. Since this is a naturalistic study, the treating clinicians may also have adjusted the dose or changed medication to reduce side-effects. The absence of an association in our data does not contradict antipsychotics’ potential to cause secondary negative symptoms. Finally, there was a substantial loss to follow-up. However, there were no significant differences between the drop-outs and those who completed follow-up.  The use of cannabis for medical purposes is increasing worldwide . With the changing public and political opinion, more countries are implementing medical cannabis legalization. Although approved in many regions, safety data from clinical trials are not as robust for medical cannabis as for other pharmacotherapies.

Brief individual interventions addressing substance use motivations and expectancies have been successful in reducing adolescent cannabis use ; however, research on preventing initiation through brief intervention and among JIY is nascent. Extension of expectancies research with JIY samples is necessary, particularly using prospective data and examining the role of positive expectancies and cannabis use outside detention when there is greater opportunity for use. Studies of school-based and general adolescent samples have also demonstrated the importance of understanding reasons for and protective factors against cannabis grow lights use. Data from the Monitoring the Future Survey examining past 10-year trends demonstrates adolescents cite more coping-related reasons than any other motivations for use . Individual factors that positively influence social cognition and behaviors  appear to buffer against substance use among early adolescents in public school , and higher self-esteem is associated with less substance use  among Black adolescents exposed to community violence and with high family stress.

Enhanced emotion regulation skills, which are influenced by social cognitive factors , are also protective against cannabis use initiation among Black adolescents . Justice-involved youth, who experience high rates of trauma, poverty, stigma and discrimination, may cite multiple reasons to use cannabis as a coping strategy, however, research in this area is lacking. Understanding how individual level, substance-related attitudes, beliefs and social cognitions influence JIY’s cannabis use, while accounting for known factors associated with increased likelihood of use, such as psychiatric symptoms , other substance use , and externalizing behaviors, is key to shaping the development of feasible systems-embedded brief substance use prevention interventions. Identifying individual social cognitive factors that might protect against cannabis use initiation in first-time JIY allows incorporation of a strengths versus deficit framework; a theoretical approach still largely lacking in the study of cannabis use and juvenile justice. In this prospective cohort study of first-time JIY, we aimed to understand rates of early onset cannabis use  and individual level factors associated with early onset use and new initiation in the 12 months after first court contact.

We hypothesized more psychiatric symptoms, other substance use, pro-cannabis use beliefs, attitudes and intentions, and lower self-concept and less self-control would be associated with early onset use and new initiation over follow-up. Three variables were derived from youth baseline self-report  and 3 follow-up assessments  over a 12-month period . Youth who had no lifetime use at baseline but reported cannabis use during the 12-month follow-up period were coded as new initiation. Descriptive statistics were examined at baseline. Next, we determined factors associated with lifetime cannabis use reported at baseline using bivariable measures of association . Third, among youth who reported lifetime cannabis grow tent use at baseline, we compared those who did and did not report early onset use at baseline. Fourth, we conducted modified Poisson regression to determine the independent associations between baseline factors and two primary outcomes:  lifetime cannabis use  reported in the entire sample, and  early onset use  in the subset of participants who reported baseline lifetime cannabis use.  Covariates were selected for inclusion in the multivariable models based on the standard cut-off rule of p < 0.05 in bivariable analyses except age, gender, and race/ethnicity, which were included in all models.

We created final multivariable models using a sequential backwards selection approach, in which variables with the largest p-values were removed sequentially, with the final model having the lowest AIC. Next, among youth who did not report lifetime cannabis use at baseline , we compared baseline factors associated with cannabis use initiation over follow-up using the same methods as described above. We then conducted a survival analysis using Cox proportional hazards regression to determine baseline factors associated with time to cannabis use initiation among youth who reported no lifetime cannabis use at baseline. We estimated the length of follow-up by calculating the difference between the interview date during which the first instance of cannabis use was reported and the interview date of the baseline survey.All variables significant at p < 0.05 in bivariable survival analyses were included in the multivariable Cox proportional hazards regression model; we also included age, gender, and race/ethnicity and obtained a final model using a sequential backwards selection procedure, as above.