Hemp Growing

In this study, five distinct hemp seed protein concentrates , produced at industrial scale using dry or wet protein enrichment technologies , were comparatively investigated in terms of their functionality, structuring potential during wet extrusion, and the molecular properties of their most abundant component, protein. In addition, their macro-nutrient composition , mineral profile, and phenolic fractions  were comparatively investigated to elaborate on the underlying mechanisms for protein molecular properties and structuring behaviour of hemp protein concentrates during processing. Finally, selected hemp protein concentrates were subjected to wet extrusion and the resultant High Moisture Meat Analogues  were investigted for anisotropy, viscoelasticity, and water mobility  as a proxy for mouthfeel. Field pea protein, one of the most common plant-based protein alternatives, was also included in this study for comparative purposes. Pea protein is also made up of albumins and globulins, it is abundant and cheap, and pea plants are grow in moderate climates . Dietary pea and hemp proteins are less connected to GMO questions and are not listed as allergenic. Nevertheless, compared to other seeds, the content of phytic acid in the hemp seed cotyledon is reported to be higher , with values as high as 22.5 mg/g in hemp meal . The presence of phytic acid could further reduce the bioavailability of multivalent cations and its separation from the plant tissue is challenging due to its low solubility in water. Although phytic acid can be vastly reduced during pilot scale AE-IP , this would not be the case for dry fractionated hemp protein concentrates, which could limit their nutritional interest. Therefore, in this work, phytic acid content was measured in all flour samples and HMMA prototypes. Protein powders were analysed for moisture, ash, protein, and fat,drying rack cannabis and then the total carbohydrate content was obtained by weight difference with the other components. Moisture was analysed using an automated moisture analyser  following AACC method 44–15.02 .

Ash content was analysed according to AOAC 923.03 method  using a muffle furnace at 550 ◦C for 4.5 h. Protein content was determined using AACC method 46–30.01  with an automated Dumatherm N Pro protein analysis system . The released nitrogen was converted into protein by multiplying the value of nitrogen by a factor of 6.25. Lipid content was determined by Low-Field Proton Nuclear Magnetic Resonance  following the AOAC method 2008.06 . All analyses were run in triplicate. Since hemp seeds are very low in starch , total dietary fibre  was analysed using the AOAC official method 991.43 . The extraction and analysis of extractable polyphenols  and non-extractable polyphenols , the latter consisting of non-extractable hydrolysable polyphenols and non-extractable proanthocyanidins , were performed as described by Pico et al. , with modifications.Specifically,the quantification of EPP and HPP in the solutions was performed using the highly phenolic selective Fast Blue BB  reaction, developed by,to account for the lack of specificity of the Folin–Ciocalteu assay due to numerous interferences . NEPA content in the solution obtained by depolymerisation by butanolysis was measured at 555 and 450 nm to detect anthocyanins and xanthylium compounds, respectively . EPP and HPP fractions were expressed as mg of gallic acid equivalents /100 g dry matter, while the anthocyanins-NEPA fraction was expressed as mg of delphinidin equivalents /100 g dry matter. All analyses were performed at least in triplicate. The main processing steps used to obtain the hemp seed protein concentrates are detailed in Table 1. All hemp protein materials were first subjected to cold pressing-expelling  for lipid extraction, although they differed in their protein enrichment steps . Hemp 1 and Hemp 3 were subjected to dry fractionation steps, with Hemp 3 being the only sample coming from dehulled seeds. Meanwhile, Hemp 1, Hemp 4 and Hemp 5 were fractionated through AE-IP. The proximate composition of the resulting hemp protein concentrates as well as of pea protein is shown in Table 2, which was very similar to the data provided by the manufacturers.

The oil , carbohydrate  and protein  content of the hemp samples were distinctly different from each other. Compared to the pea sample used in this study, hemp protein concentrates were significantly richer in oil, carbohydrates and ash, and they exhibited a lower protein content. In terms of macro-nutrient abundance, Hemp 1, Hemp 2, and Hemp 5 were characterized by a lower lipid content  than Hemp 3 and Hemp 4 . The lower lipid content of Hemp 1 compared to Hemp 3 , and of Hemp 2 versus Hemp 4 , confirms a very efficient oil extraction performed by the manufacturer of Hemp 1 and Hemp 2. Hemp 1 contained the highest amount of carbohydrates , which can be attributed to the choice of using whole seeds and dry fractionation. Conversely, Hemp 5 presented the lowest carbohydrate and the highest protein content, which is explained by the double AE-IP. The ash content was rather similar for the Hemp 1–4 samples, ranging from 7.3 to 8.8% d.b. Hemp 5 exhibited a significantly lower ash content than the rest , which could suggest that minerals were removed during the isoelectric precipitation step , which was performed twice for Hemp 5. The mineral profile revealed that all hemp samples were generally rich in the macro-elements potassium , phosphorous  and magnesium , which are needed in the amount of >50 mg/day in the human diet. Generally, hemp samples were also richer in the macro-elements calcium and the micro-elements  manganese, copper, and zinc than the pea counterpart. Hemp samples had a significantly lower sodium content  than pea . Interestingly, lower sodium content for dry fractionated hemps, Hemp 1 and 3, was observed  compared to wet-fractionated hemps and pea, suggesting that the sodium in is coming from the alkaline extraction step, normally done with sodium hydroxide. The high amount of potassium along with a relatively low sodium content leads to a high K/Na ratio, which is believed to be related to cardio protective effects as it promotes a high K intake that is considered to be inversely related to blood platelet aggregation and stroke incidence . Nonetheless, compared to other seeds, the content of phytic acid in hemp seeds is reported to be higher , which could further reduce the bioavailability of multivalent cations, including Zn2+, Fe2+/3+, Ca2+, Mg2+, Mn2+, and Cu2+. Hemp 3 was significantly richer in magnesium and phosphorus than the other hemp samples, which could be due to differences in genotype, environmental and soil conditions as well as higher ash content as a consequence of the protein enrichment processing .

Since phytic acid naturally stores phosphorous in plants , the high phosphorous content in dry-fractionated samples  might be indicative of high levels of phytates in these particular samples. In fact, the phytic acid content of Hemp 1  and Hemp 3  was significantly higher than that of pea  and wet-fractionated hemp samples , as shown in Table 2. These values are similar to those reported previously for hemp protein concentrates  and pea . The higher values of Hemp 3 compared to Hemp 1, both being dry-fractionated samples, could be explained by the fact that only Hemp 3 seeds were hulled and that the majority of phytate is found in the cotyledon fraction . Therefore, pearling, or mechanical fractionation to remove coarse fractions, would get rid of those fractions low in phytic acid  and, therefore, increase the weight percentage of phytic acid in protein concentrates. In contrast, a significant reduction of phytic acid during the precipitation step was already reported for wet-fractionated hemp, which was partially explained by a potential activation of plant phytases at the isoelectric pH of proteins  or by the higher solubility of inositol phosphates during acidic precipitation of proteins. To the best of our knowledge, there are no regulations in the European Union regarding acceptable levels of phytic acid in hemp proteins. The total content of phenolics  and the phenolic fractions  of the pea and hemp protein flours were also studied, and the results are summarized in Table 2. Hemp protein concentrates possessed higher content of TP  than pea protein concentrate . Our values are higher than those reported by Izzo et al.  in hemp inflorescences using high resolution mass spectroscopy. However, it is noted that our study also focused on those phenolics not extracted during the aqueous-organic treatments commonly performed to analyse polyphenol content in foods. In fact, HPP  was the main phenolic fraction found in all samples. Phenolic acids and monomeric flavanols are usually reported to remain mostly un-extracted , which could represent an important fraction of HPP since hemp inflorescences are rich in both . Several studies showed that hemp seeds are rich in various phenolic acids, lignanamides, phenolic amides and flavonoids . The comparison between samples revealed that Hemp 1 was significantly richer in EPP and HPP than the rest of samples, which suggest that many of these compounds remain attached to the abundant carbohydrate fraction in Hemp 1, which was not removed during processing.

Conversely, Hemp 3 exhibited the lowest EPP to HPP ratio, followed by Hemp 5, indicating that even more phenolics were present in bound form. Interestingly, these two hemp samples presented the highest protein content and relatively low presence of carbohydrates, which may have contributed to the loss of EPP during processing. However, further studies are needed to understand why free phenolics may have been lost during the pearling used for Hemp 3 dehulling or dual AE-IP processing. It should also be noted that differences found between phenolic fractions in the samples could also be attributed to other factors different to protein enrichment processing, commercial greenhouse supplies including but not limited to phenotype, seed maturity, growth and post harvest environmental conditions.The proanthocyanidins in hemp seed cake/meal have been identified to be essentially catechin polymers, i.e., procyanidins, with total values of 245–262 mg proanthocyanidins/100 g in non-treated hemp seed cake , which generally aligns with the reported results in this work. Nonetheless, some proanthocyanidins could be extracted as EPP fraction, as reported by P´erez-Jim´enez and Saura-Calixto  with fruit peels. Hemp 1 and Hemp 2, followed by Hemp 5, displayed significantly lower NEPA than the rest of the hemp samples, suggesting that NEPA could have been co-extracted during the extraction of oil in Hemp 1 and Hemp 2 and during the more aggressive aqueous protein extraction of Hemp 5 . Hemp 4 followed by Hemp 3 possessed the highest NEPA content. Similar to flavan-3-ols, anthocyanins are highly susceptible to degradation during hydrothermal treatment . Hence, any protein enrichment technology that did not elevate the temperature of the samples could have partially protected some NEPAs from thermal degradation. The hemp protein fraction is comprised mainly by globular proteins in the form of globulins and albumins, whose relative abundance might impart different functionality. Reversed-Phase Chromatography has confirmed the quantitative dominance of the edestin fraction, accounting for up to 70% total hemp protein content . The edestin molecule is composed of six identical subunits, and each subunit consists of acidic subunits  and basic subunits  linked by one disulphide bond . Fig. 1 depicts the SDS-PAGE  and Native-PAGE profiles of pea and hemp proteins. In reducing SDS-PAGE , edestin appeared to dissociate into its acidic and basic subunits, corresponding to bands at around 35 and 20 kDa, respectively. These results are consistent with previous studies by Shen et al.  and Wang and Xiong  which found roughly the same molecular weights for these fractions. Besides the bands of edestin acidic and basic subunits, three visible but less abundant bands appeared between 50 and 70 kDa. Tang, Ten, Wang, and Yang  reported that the band at about 48.0 kDa was similar to the β-subunit of the trimeric β-conglycinin found in soybean, suggesting the presence of a 7S-vicilin-like protein. Nevertheless, it was a minor fraction compared to edestin  and the albumins seen at <18 kDa. These findings are corroborated by the results from Mamone et al.  using proteomic analyses, indicating that hemp protein isolate consisted of essentially three major storage proteins, 11S edestin, 7S vicilin-like protein, and albumin. In the absence of reducing agents, the disulfide bonds between AS and BS of edestin are not disrupted and an intense higher molecular weight band of ~55–60 kDa was instead visible .

Responses collected during the most recent interview were used for these analyses.To examine the dimensionality of the 13 subjective experiences examined for each substance, we conducted Mokken Scale Analysis  using the statistical software STATA.Mokken scaling analysis extends traditional factor analysis by systematically hierarchically ordering items that are highly correlated. Mokken analysis provides a nonparametric, iterative scale-building technique that identifies the smallest set of internally consistent scales from a given item pool. This model assumes the presence of one or more latent traits that can be measured by subject responses to a set of items.MSA is probabilistic and hierarchical, meaning that the items can be ordered by a degree of “difficulty”;individuals who agree with a more difficult item will tend to agree with less difficult items.Scales from MSA are formed by taking pairs of items with the highest correlation and including other items until there is no further improvement.Loevinger’s H coefficients, which indicate the fit of an item to the scale, are computed for each item  within a scale and for the scale as a whole.H coefficients ranging between 0.3 and 0.4 indicate a weak scale, 0.4–0.5 a medium scale, and 0.5–0.9 a strong scale. In MSA, an item can remain “unscaled” because it could not be added to one of the alternative scales without weakening the scale’s homogeneity. Based on our previous analyses,we used the scaling derived from the CADD sample. Positive and negative scales were standardized by age, sex, and clinical status with two groups.Pairwise correlations between the resulting two scales were then determined for alcohol, tobacco,pot drying and marijuana.In the current report, we examined subjective experiences to three commonly used drugs of abuse among young adults from the general community and an area treatment program.

In these data, we obtained results that supported previous observations indicating positive and negative subjective experiences for a particular drug were predictive of problem use of that same drug. We then extended this relationship in two ways. First, we obtained results that supported the notion that positive and negative experiences to one drug are similar to those experienced for another drug and second, that subjective experiences to a drug are predictive of the risk for problem use of other drugs. We interpret these findings to suggest that subjective experiences may be a useful indicator of a common liability towards use and problem use of multiple substances. Following on our previous work on marijuana and subjective experiences,we used Mokken scaling to simultaneously examine whether subjective experiences to three drugs are associated with drug use outcomes. From these analyses we observed that the subjective experience scales for each of the three drugs were comparable to those found in previous studies despite using a different methodology.We observed differences in item means and hierarchical ordering of the items by substance suggesting that subjects are reporting drug specific subjective experiences. This interpretation is consistent with findings from laboratory based studies which have shown that subjects can differentiate between a placebo and a drug or between different drugs based on subjective experiences.As different combinations of alcohol, tobacco, and marijuana use are commonly reported in epidemiological studies, we investigated the relationship between subjective experiences to different drugs in poly-substance users. We observed that subjective experiences to one drug were significantly correlated with experiences to another drug, though the strength of the relationship varied for different drug combinations. The strongest relationships were between alcohol and marijuana, replicating two previous studies,and between alcohol and tobacco. These particular drug combinations target similar neuronal receptor systems and are reported to enhance the overall drug experience when taken together.Further, as subjective experiences are thought to reflect the underlying physiology of a drug’s actions,these cross-substance relationships may provide a closer approximation of a common risk factor suitable for molecular genetic investigation. In this sample of community and clinical subjects, subjective experiences for one drug were associated with outcomes related to a different drug. Though our results replicate findings that relate positive experiences with greater use of other drugs, we also identified that negative experiences were predictive of abuse and dependence status of a different drug. In particular, negative effects of alcohol and marijuana were associated with misuse of these same drugs as well as tobacco. Although this may appear counter-intuitive, a possible explanation could be that subjects who needed greater amounts of a drug in order to feel its effects drove the observed association.

Findings from laboratory-based drug discrimination studies suggest that some subjects are unable to differentiate between drug and placebo at a standard training dose.Differences between the two conditions could, however, be reported as non-discriminators were exposed to greater doses of a drug. Interestingly, those who were able to discriminate between non-exposure and exposure to a drug reported stronger positive and negative subjective experiences, often simultaneously,at greater doses. This underscores the importance of dose in determining individuals’ drug sensitivity as assessed by subjective experiences. The relationship between drug dose, the resulting subjective experiences, and problem drug use has also been examined using self-ratings to the effects of alcohol [SRE; 28]. The SRE primarily assesses negative experiences to alcohol such as dizziness and passing out as related to the dosing levels needed to feel the sedative effects of alcohol. Among adolescent and adult samples of both sexes and family-history positive studies of alcoholics,low levels of response, as measured by the SRE have been implicated as a risk factor for alcohol use disorders. This notion that some drinkers need to ingest greater amounts of alcohol to feel its sedative effects and that this effect is related to greater drinking quantities has been recently supported  and extended to include the observation that this relationship is also relevant to those reporting lower levels of stimulant effects during the first five drinks. Our finding that negative alcohol experiences were predictive of problem alcohol use is consistent with this research, despite using a different questionnaire, and extends it to include the potential prediction of other drug use problem behaviors.The prevelance of PVCs is increased in patients diagnosed with hypertension especially when it is associated with left ventricular hypertrophy, dilated cardiomyopathy and heart failure,post acute myocardial infarction,and congenital heart disease. PVCs often cause no symptoms. In many patients, the presence of PVCs could result in the sensation of fluttering, pounding, skipped beats, palpitations, dizziness, and/or near syncope. The etiology of PVCs is not well known. Many mechanisms may explain the origin of PVCs, including enhanced normal or abnormal automaticity inside the heart, triggered activity in Purkinje cells of the ventricular myocardium, or reentry. Anxiety, alcohol, caffeine, tobacco, exercise, illicit drugs, hypokalemia, HTN, ischemia, infarction, excessive calcium, drug toxicity,or an underlying heart disease could result in PVCs through previously mentioned mechanisms.

ECG is the mainstay of diagnosis of PVCs. This includes standard ECG, exercise stress ECG, holter monitor, and event recorder depending primarily on the frequency of PVCs which helps to decide the best way to detect them. Only in symptomatic patients do PVCs need to be diagnosed and treated. Beside eliminating previously mentioned possible triggers, beta blockers and calcium channel blockers are recommended as first-line therapy for symptomatic PVCs, especially with outflow tract morphology in a structurally normal heart. Antiarrhythmic medications, such as amiodarone can sometimes be tried but with caution because of its side effects. Frequent PVCs may be associated with worsening of systolic heart failure in patients with a dilated cardiomyopathy. Small studies have suggested that in selected patients, radio frequency ablation of ectopic ventricular foci is associated with an improvement in left ventricular function and clinical improvement in symptoms.The 2006 American College of Cardiology/American Heart Association/European Society of Cardiology guidelines for the management of ventricular arrhythmias included suggestions regarding ablation therapy for PVCs. They note that ablation therapy of PVCs may be useful if they are frequent, symptomatic, and monomorphic, if they are refractory to medical therapy, if the patient chooses to avoid long-term medical therapy, or if they consistently provoke ventricular arrhythmia storm of a similar morphology. SCFP, as a separate entity, has a widely diverse presentation including chest discomfort, unstable angina, non ST elevation MI, ST elevation MI or non-sustained ventricular tachycardia. It usually presents with recurrent rest pain requiring urgent admission. The etiology of SCFP is not completely understood. It is speculated that it is caused by acute but recurrent perturbations of microvascular function. Histopathological examination  of left and right ventricular endomyocardial biopsies taken from some patients showed fibromuscular hyperplasia, myofibrilar hypertrophy, endothelial degeneration with swollen endothelial cells encroaching on the lumen, luminal size reduction, mitochondrial abnormalities, lipofuscin deposition, and glycogen content reduction, which can cause the elevation in resting coronary artery resistances, especially toward microvasculature beds, found in SCFP. Normal and pathological zones often coexisted in the same specimen.Thus, in some patients with slow coronary flow and patent coronary arteries, functional obstruction of microvessels seems to be implicated, as it is relieved by dipyridamole infusion.

This shows also that small-vessel CAD can cause classic angina pectoris. The diagnosis can be suspected when the coronary angiogram shows large patent arteries with slow flow of the angiographic contrast medium and it can be confirmed by endomyocardial biopsy. Another study suggested that elevation in plasma homocysteine, evenifitismild,may play a role in the pathogenesis of SCFP by severely disturbing vascular endothelial function and subsequently impairing coronary blood flow, and showed that patients with SCFP have statistically significant raised level of plasma homocysteine compared to control subjects with normal coronary flow.As a treatment, dipyridamole, which has dilatator properties on coronary microvessels, cannabis drying proved to be useful in most patients with SCFP. It abolishes functional obstruction in coronary arteries with diameters less than 200 m and is considered far superior in treating SCFP as compared to nitroglycerine. Other therapies proved to be effective also include simvastatin, atorvastatin, nebivolol, and mibefradil although the use of the latter is limited because of drug interactions caused by its inhibition of the cytochrome P450 3A4 pathway. Beside mean arterial pressure, blood flow in coronary arteries depends on heart contractility. During systole, and because of the big muscular mass of the left ventricle,extravascular compression prevents any antegrade blood flow in left coronary artery  particularly at the end of isovolumetric contraction. Conversely, and during diastole and especially early diastole which represents isovolumetric relaxation, coronary blood flow in LCA becomes maximal.In right coronary artery,blood flow is maximal during peak systole, because extravascular compression within the right ventricle is less than its counterpart in the LV preserving antegrade blood flow in RCA in both systole and diastole. The fact that the highest blood flow in LCA occurs throughout different stages of diastole, which is not the case in RCA, assumes that any PVC happening through diastole will have a bigger impact on blood flow in LCA compared to RCA. This, in turn, will be reflected as a slow blood flow  in LCA more than in RCA. In patients with SCFP, resting coronary artery resistances, as mentioned above, are abnormally elevated, however, these resistances respond normally to vasodilator stimuli such as papaverine and adenosine and during exercise. Marijuana affects the heart in different ways. The acute cardiovascular effects of marijuana, which are palpitations, tachycardia, elevated blood pressure, and a greater myocardial oxygen demand, are mainly caused by increased release of catecholamines. Chronic use of marijuana can worsen any underlying disease through prolonged vasoconstriction and causes digital clubbing.After a thorough review of the literature, it was noted that there was a lack of description of any specific time frame, after which marijuana consumption will have effects on the heart, resulting in myocardial ischemia or heart failure. Patient’s age and the presence of underlying CAD are key players in determining how fastthe results of smoking marijuana will take effect on the heart, especially when it comes to chronic use. The mechanisms by which marijuana affects the heart are diverse including increased cardiac work through elevated catecholamines and carboxyhemoglobin levels, as well as possible episodes of intense postural hypotension. Moreover, smoking cannabis was rarely found to trigger MI. Among cannabis users who sustained an acute MI, the risk was nearly five times higher within the first hour after smoking compared to periods of nonuse.

Alcohol likely has similar deleterious consequences on the brain. The present dose-dependent associations are consistent with our previous findings, as Squeglia et al., found decreases in cortical thickness estimates associated with heavy episodic alcohol use in males,and accelerated declining brain volume trajectories in a large prospective investigation examining individuals  who transitioned to heavy drinking .Alcohol likely interferes with neural development of the cerebral cortex, and thinner cortices observed with more cumulative use reported may represent non-beneficial pruning and/or inhibition of cell generation or cell death.Limitations of the present study include self-report of substance use, which can introduce measurement error. Further, while this study was prospective, participants were not assessed prior to initiation of substance use. However, previous work in our laboratory finds marijuana-related associations with white matter integrity in a sample of individuals assessed pre- and post-initiation of substance use.Nevertheless, future work should determine the influence of pre-existing differences on cortical metrics. The current investigation included users of both marijuana and alcohol, and despite controlling for alcohol use, it remains unclear what is precisely the result of marijuana as compared to the combination of co-occurring marijuana and alcohol use. Our sample was predominately male,however gender should be evaluated and future studies will focus on differential gender effects on brain morphometry in adolescent marijuana users. Group did not statistically differ on days since last use of cannabis square pot and alcohol use,likely influenced by the monitored abstinenceb period,therefore acute effects may not have been captured in our reported findings.

Astatistically significant within-subjects effect was not widely observed,which may be attributed to the smaller sample size combined with a more restricted age range. We tried to reduce the number of correlational analysis that were conducted, however given that effects were modest, future work should replicate findings. Studies should continue to follow existing adolescent cohorts to understand neural and behavioral changes that occur into young adulthood. Understanding how co-occurring marijuana and alcohol use influences both macrostructural and microstructural brain development, along with structural and functional connectivity, will help clinical interventions target neural vulnerabilities to develop novel and effective interventions to reduce marijuana misuse as prevalence rates of marijuana continue to increase .To guide recruitment, the Adolescent Brain Cognitive Development  Study required a method for identifying children at high risk for early-onset substance use that may be utilized during the recruitment process.In this context, childhood risk refers to characteristics identified at ages 9 or 10 years that predict adverse outcomes in adolescence, and “high risk” refers to a categorical classification of some children as having increased risk compared to others. The construction of a brief measure for childhood substance use risk involves the identification of characteristics that predict early-onset substance use in mid to late adolescence. The identification and evaluation of optimal items for a brief childhood measure to serve as a high-risk screener ideally involves data from several large prospective studies with assessments initiated prior to the typical age of onset of substance use. To inform ABCD Study recruitment, secondary analyses are needed with data-sets collected prior to ABCD Study initiation.

In this context, a set of analyses with available data focused on a specific substance use outcome was determined to be most likely to be informative and feasible. While other substance use outcomes are also important, early-onset marijuana use is a relevant target.Marijuana is the most commonly used illicit drug by adolescents, and regular marijuana use identifies youth likely to develop cannabis use disorder.In these secondary data analyses, the definition of early-onset marijuana use was defined by the initiation of regular use as indicated in the available data-sets.The studies contributing data-sets were the Center for Education and Drug Abuse Research,the Pittsburgh Youth Study,the Pittsburgh Girls Study,and the Michigan Longitudinal Study.In the studies contributing data to the secondary analyses described here, the definitions of regular marijuana use differed by sample due to measurement variations. The variations in the definitions of regular marijuana use were as follows:five or more use occasions in the past year  and;six or more occasions in the past year.By efficiently identifying children at high risk for early-onset marijuana use,a brief and effective measure of childhood risk measure could be utilized as a screen to identify high risk children in prevention research, primary medical care, and mental health clinic settings. The present analyses were specifically undertaken to inform the development a childhood high risk screen for use in the ABCD Study.The ABCD Study is the National Institute of Healths’ large-scale prospective population study of the biological and environmental factors that influence young people’s ability to successfully navigate adolescence. The study has a special emphasis on the risk and protective factors that influence marijuana and other substance use, and subsequent health problems including substance use disorders. Utilizing data from previously conducted studies, the present study was thus undertaken to develop and establish the efficiency of a short measure  to identify youth at high risk for early-onset marijuana use with optimal features for use in the ABCD Study.

To achieve this goal, the risk level of a potential participant needs to be determined at the time of recruitment and prior to their scheduling for the extensive ABCD Study assessment protocol. Consequently, the optimal ABCD Study high risk screen has several characteristics: extreme brevity, including less than ten items;lack of sensitive items that may raise confidentiality concerns at this early stage of considering participation; consistency with prior research. These characteristics were taken into consideration in the analyses that follow. Historically, studies focusing on mental disorders such as schizophrenia, alcohol use disorder, and major depressive disorder, have used positive family history as a risk marker.Family history has been demonstrated to identify children at high risk of later substance use disorders in many prospective studies.However, a detailed family history may involve the parent being asked to disclose their own socially undesirable, embarrassing or, in some cases, illegal behavior. There have been alternative strategies to acquire this information, such as the use of publicly available records of drunk driving or other drug offenses, or the use of hospital records to identify parental diagnosis.Obtaining such records would not be feasible in the initial recruitment phase of the ABCD Study. Regardless of the method for obtaining this information, requesting this information at the point of introducing the ABCD Study raises the real possibility that the parent  will decline study involvement. Few longitudinal studies have formulated and tested measures for identifying high risk children likely to exhibit early-onset marijuana use. There have been several approaches developed for predicting substance use disorders, but relatively few have targeted the adolescent developmental period. One of the risk measures developed to identify high risk children is the SUD Transmissible Liability Index  developed by Vanyukov, Tarter, Clark and colleagues,using longitudinal data from the CEDAR study. Although the TLI is sophisticated in its development, it is long,uses different portions of existing instruments, and is under copyright. In addition, the TLI did not focus on the age 15 outcome of marijuana use, and the publications did not use Receiver Operating Characteristic  Area Under the Curve  analyses to determine an optimal threshold score.

Another screening instrument, the DSM Guided Cannabis Screen  has unknown predictive value because it was constructed using cross-sectional data from a small clinical sample aged 14–59. Therefore, the current study fills a significant gap in the empirical literature. This report describes the process and results of secondary data analyses to prospectively identify a brief screening measure applicable to age 9–10-year-old children that would predict early-onset marijuana use in the 5–7 years following the initial screening measurement. To acquire data useful for developing this screening measure, we needed to identify population-based prospective studies which  began assessments in late childhood,  had been continued at least through ages 14–17,included marijuana use variables at both age periods, measured domains previously identified in the literature as predictive of adolescent substance use disorder outcomes, and  had a sufficient number of measures in these domains that were shared across these studies so that screening validation could be replicated across different demographic groups .The objectives of these secondary data analyses were as follows:To develop a brief screener for 9–10-year-old boys and girls to predict early-onset marijuana and other substance use in mid adolescence with demonstrated predictive utility across four longitudinal data sets; To dichotomize the outcome variable, which will reduce shrinkage,improve replicability and practical utility.;  To replicate findings across construction and validation samples.The advantage of this dual analysis approach is that we could construct a screener that considers shrinkage  that typically happens between construction of a screener and subsequent validation in another sample. In summary,trim tray the objective was to develop a brief and feasible approach to the identification of children at increased risk  for early onset  marijuana use that may inform the ABCD Study recruitment procedures.The potential items for analyses were identified by examining prior research,prior analyses with the available data-sets, particularly the extensive analyses with CEDAR data,identifying pertinent items available in the four longitudinal projects used in these secondary analyses, and deliberations on the acceptability of areas of inquiry for potential participants during the recruitment process. Based on these considerations, the constructs represented by the pool of items to be considered included child externalizing behaviors, child internalizing behaviors, and parent tobacco smoking. Child externalizing behaviors. In the case of the ABCD Study design, we are projecting from ages 9–10, when marijuana use typically is minimal and not a viable risk item for screening purposes. Therefore, for candidate items on child externalizing behaviors, we considered non-substance use characteristics that other studies have found to predict early-onset substance use in mid adolescence, particularly child externalizing behaviors.

Potential externalizing behaviors considered were vandalism, lying, and disobedience at school.Child internalizing behaviors. In addition, we examined whether selected internalizing behaviors augmented predictions. After examining potential internalizing items’ correlations with both the tentative screener  and with the outcome variable, we initially focused on the following items :  unhappy, sad or depressed;  too fearful or anxious;  secretive or keep things to oneself;  self-conscious or easily embarrassed. After considering which internalizing items correlated with the externalizing screener at that point, we finally focused on:  unhappy, sad or depressed;  too fearful or anxious.Parent smoking. For candidate items on parent behaviors, parent smoking  was also considered a viable candidate. This candidate item for the screener  was available in the 4 study data sets.We searched for equivalent predictor items of interest in each data-set. This is very important because we needed construct convergence among the four longitudinal data-sets. We used prorating in cases where there were missing items  so that we would maximize the numbers of participants. Note that sample sizes varied somewhat due to missing cases for each analysis. In the PYS data-set, we combined parent and child information on child predictor variables to obtain a best estimate of the child behavior. For example, a behavior was counted when either the parent or child reported the behavior. Item scores were recoded as “Yes” or “No” where necessary to make them uniform across studies. For example, the Child Behavior Checklist [CBCL] has response options of 0, 1 or 2  then item scores were recoded as Yes or No. We undertook separate analyses for each gender. We first determined which items were predictive of the outcome. We next summed significant items into an index, examined AUC, and computed sensitivity, specificity, and positive predictive power for the summary screening score. If the variance accounted for by these indicators proved too low, we repeated the procedure for “new items”. In the final analyses, three of the studies used CBCL items,and one study  used data based on self-reported antisocial behavior,MFQ,and the Child Symptom Inventory.The items from the CBCL, the MFQ, and the CSI were highly comparable.The intercorrelation results of the predictor items showed that some items were significantly negatively correlated with the outcome variable, and other items correlated with the outcome non-significantly across all three data-sets. This reduced the number of viable items in the Pittsburgh data-sets to 14. The Michigan group derived their own scale of 9 items.In brief, a procedure very similar to that described here for the three Pittsburgh data-sets was used. We intercorrelated available predictor variables that overlapped with those originally identified across externalizing, hyperactivity/impulsivity, internalizing, and temperament items  with the outcome variable. This method was used to reduce the item pool, based on predictive accuracy.

Additionally, we found that activation in the occipital cortex to negative emotional stimuli mediated the relationship between marijuana use and later resiliency. Specifically, activation in the cortical region surrounding the calcarine fissure, including portions ofthe right cuneus and lingual gyrus, was lower in heavy marijuana users than controls. This activation was further associated with decreased resiliency at follow-up, when controlling for resiliency at the time of scan. Although the cuneus and lingual gyrus are classically considered as visual processing and integration regions, there is a large literature associating both regions with aspects of emotion functioning, including the processing of emotional faces,high-arousal emotional words,and emotional film clips,as well as the evaluation of one’s own emotional state.Cuneus activation has also been associated with the ability to attribute mental states to others, termed “theory of mind”. A recent study reported that adult marijuana users had differences in brain activation compared with controls during a ToM task, including lower activation in the right cuneus.Therefore, an impact of heavy marijuana use during adolescence on the functioning of occipital regions involved in the evaluation of emotional stimuli with respect to oneself and to others may impair self-regulation of emotional processes.In addition to the regions found to mediate later outcome, heavy marijuana users had less activation than controls in the insula to negative words. These findings are consistent with previous work showing adolescent marijuana users had reduced cerebral blood flow in the insula compared with controls.Furthermore, studies of adult cannabis grow racks users found less activation in the insula to loss outcomes during a monetary incentive task  and to errors in an inhibitory control task  compared with controls.

The insula is critical to the integration of emotional and homeostatic information, and may be involved in translating interoceptive signals into conscious feelings.For example, the magnitude of insula activation while participants evaluated their own emotional and bodily states was found to be associated with social anxiety and neuroticism.Insula activation has also been associated with self-report measures of anxiety  and anticipation of aversive exposure in anxiety-prone individuals. Here we found less insula activity to negative words in heavy marijuana users compared with controls, which was further associated with more negative emotionality at the time of scan. Together, this evidence suggests that heavy marijuana use may lead to impairment in the integration of emotional experience.Along with the insula, the amygdala is part of a network involved in translating interoceptive responses to emotional stimuli into emotional experience.Blunted amygdala response has been observed in individuals with difficulties experiencing and processing emotions.Acutely, cannabidiol, a psychoactive component of cannabis, has been shown to decrease amygdala activation to anxiety-inducing emotional stimuli; this effect was further associated with a reduction in electrodermal activity,supporting links among marijuana, amygdala functioning, and interoceptive response to emotion. Furthermore, prior evidence indicates that the impact of marijuana use on amygdala-mediated emotional responding is not restricted to negative stimuli. Gruber et al. reported less amygdala activation in adult heavy marijuana smokers compared with controls to both happy and angry faces presented below the level of conscious processing. Here we found less amygdala activation to both positive and negative words in heavy marijuana users compared with controls, which further correlated with negative emotionality. Therefore, marijuana may have an impact on amygdala functioning that impairs general emotional arousal and integration. The finding of an association between negative emotionality and reduced activation of the insula and amygdala is opposite to effects described in the depression and anxiety literature, which reports enhanced activation to negative stimuli.

However, a longitudinal study of individuals with comorbid major depression and marijuana dependence found that greater marijuana use was associated with reduced amygdala activation to emotional stimuli.This suggests that the mechanism through which marijuana impacts negative emotionality differs from the mechanism underlying depression and anxiety. For example, the associations between insula and amygdala functioning and negative emotionality in the current study may be more pertinent to differences in the experience and processing of emotions  rather than depression and anxiety. Finally, heavy marijuana users showed reduced activity in the right inferior parietal lobule and greater activation in the right dlPFC during the viewing of positive words. The inferior parietal cortex is part of an attentional system involved in the automatic allocation of attention to task-relevant information,whereas the dlPFC is involved in more effortful attentional control.Thus, the current results suggest a decrease in automatic attention to positive words in heavy users with a corresponding increase in effortful attentional control necessary to attend to the task. This is consistent with prior work demonstrating heightened activation of right-hemisphere prefrontal attentional control circuitry in adolescent marijuana users,which may reflect the need for increased effort in attending to task-related stimuli.This study extends our prior work by providing evidence of reliability and generalizability of a surveillance tool for assessing the marketing practices and socio-contextual characteristics of recreational marijuana retailers. While this study is limited by its focus on a convenience sample of 25 retailers in Seattle chosen from Weed maps, this data builds on prior tool utilization among 20 Denver retailers. This study also helped to identify distinct variables relevant within differing policy contexts.In the current and previous studies,high compliance with age requirement/verification practices, as well as use of security measures, was documented. In terms of promotion, novel products  were frequently advertised, likely in an attempt to familiarize customers with newer products.Unlike the Colorado study,however, bud was also frequently advertised among Seattle retailers. Loyalty club memberships and daily/weekly deals were prevalent, similar to the Denver study.

However, using social media was not as common in this sample of Seattle retailers compared to the sample of Denver retailers,which may reflect more conservative policies regarding online promotion and sales in Washington  relative to Colorado.Similar to our prior work,this study also documented little product and price variability among the shops. This lack of variability in product offerings and price suggests that other shop characteristics  might be used to differentiate retailers from one another. Indeed, unlike the tobacco and alcohol industry, building strong brand affiliation with shops rather than products may be strategic in the marijuana industry, potentially given limited variability in product offerings and price across marijuana retailers.This study highlighted that assessments of the marijuana retail environment should be informed by policies and regulations given activities that may be differentially prohibited or restricted in differing jurisdictions. For example, while marijuana retailers are allowed to sell clones and seeds in Colorado, retail sale of clones and seeds is prohibited in Washington. Similarly, Washington retailers are prohibited from selling branded apparel or other merchandise  in the retail store.However, the Colorado market is not restricted in this way. This is particularly relevant given that this study noted violations of these regulations, specifically in relation to the sale of branded apparel in Seattle. Furthermore, this study documented retailers being proximal  to schools, parks, and playgrounds, despite regulations limiting them to further than 1000 ft ; however, our assessment tool lacked the specificity to capture if retailers were indeed within 1000 ft. Attempts to circumvent policies are also noteworthy. This study noted that some Seattle retailers had separate storefronts adjacent to the marijuana shop where they could sell branded apparel and justify larger exterior signage. Our previous study in Denver also noted other attempts to circumvent policies; for example, publicizing “private” parties where marijuana use would be allowed despite prohibition of marijuana in public places.Surveillance of such activities is critical to informing regulatory and enforcement efforts. The MRST demonstrated perfect inter-rater reliability in two-thirds of items and ≥0.73 congruence in the remaining items. Incongruence occurred in assessments of the external environment, marketing and promotion, and price.

Greater rigor in training regarding the use of the MRST, including standardized protocols that include examples and practice assessments,cannabis grow system is needed. Additionally, in assessing products, complexities in mode of consumption, tetrahydrocannabinol versus cannabidiol, and strain  make thoroughly assessing each product category cumbersome and complicated. Moreover, assessing lowest price across marijuana product categories is complex given the diversity of product offerings in any single product category  and the ranges in volume, potency, strain, etc. Thus, this approach will need to be further refined over time and adapted as differing policy contexts may prohibit certain types of products  or regulate how they are packaged.Finally, studies involving larger sample sizes could examine differences between recreational only retailers versus those with a medicinal endorsement.A 2015 conference on medical use of marijuana indicated that 23 US states have legalised medical marijuana with some also legalising marijuana for recreational use.Although there is moderate evidence for efficacy of cannabinoids for chronic pain and spasticity, and some evidence for Multiple Sclerosis and treatment-resistant epilepsy, there is not good evidence for its use to treat nausea and vomiting associated with chemotherapy,perhaps the best known indication for medical marijuana. Some pregnant women report using marijuana to alleviate nausea and vomiting in pregnancy with success but evidence for its efficacy is mostly anecdotal. However, reports of adverse events for non-pregnant populations using medical marijuana raise concerns for pregnant marijuana users. According to the National Drug Strategy Household Survey in Australia, 7.6% of females aged ≥14 years used marijuana during 2010,with 34.8% of the female population having used marijuana at least once in their lifetime. A similar trend has also been observed in New Zealand and Europe, with 47.2% of women aged ≥16 years in NZ, 24.6% in the United Kingdom and 17.5% in Ireland having used marijuana at least once. Apart from reported negative impacts on fetal growth and brain development, marijuana has been associated with adverse pregnancy outcomes, including preterm birth,small for gestational age,placental abruption and antepartum haemorrhage. Specifically, studies have shown that using marijuana during pregnancy is associated with low birthweight and increases the risk of PTB and SGA, with an odds ratio of at least 1.5 when adjusted for age, BMI and smoking.

The association between marijuana use and pregnancy outcomes is often confounded by other known risk factors including cigarette smoking, body mass index,and socioeconomic index. Women who use marijuana also tend to smoke cigarettes and are more likely to use other drugs and alcohol, for whom national statistics have shown that amongst Australian women aged ≥14years who used marijuana in 2010, 82.7% also consumed alcohol, and 68.5% were cigarette smokers, with similar patterns of prevalence in New Zealand. There have been inconsistent results reported from American prospective cohort studies, in which associations of marijuana use with adverse pregnancy outcomes were either found or not found. Hence, this study aimed to examine the association of maternal marijuana use  in a multi-centre cohort with major pregnancy complications, amongst both cigarette smokers and non-smokers, controlling for well-known risk factors including age, SEI and BMI, as well as its effects on length of gestation.Data from this analysis were obtained from the SCreening fOr Pregnancy Endpoints  study, which aimed to build a clinical database and pregnancy biobank to screen candidate markers of pregnancy complications. The SCOPE study recruited nulliparous women with singleton pregnancies between November 2004 and February 2011 from one centre in each of Australia, New Zealand, and Ireland, and three centres in the United Kingdom. Ethical approval was obtained from local ethics committees and all women provided written informed consent. Women were invited to participate prior to 15 weeks’ gestation when attending hospital antenatal clinics, obstetricians, general practitioners or community midwives, and were interviewed and examined by a research midwife at 15 ± 1 and 20 ± 1 weeks of gestation. The exclusion criteria included women who were considered to be at high risk of PE, SGA or PTB due to underlying medical conditions,previous cervical knife cone biopsy, three terminations or three miscarriages or if their pregnancy was complicated by a known major fetal anomaly or abnormal karyotype, or if they received interventions that may modify pregnancy outcome.Details of maternal age, BMI and socioeconomic index1,medical and family history, along with dietary and lifestyle questionnaires with self-reported marijuana and cigarette smoking were recorded at 15 weeks’ and 20 weeks’ gestation and entered into an internet-accessed, password-protected centralised database with a complete audit trail.

Given a high rate of overlap between marijuana and tobacco use among the participants, our study findings provide further support for implementing comprehensive tobacco control programs and underscore the importance of target interventions among high risk populations, including those using marijuana, in order to enhance the reach and effectiveness of prevention. Third, multiple unhealthy behaviors tend to co-occur but they are amenable to concurrent or sequential interventions. A successful change in one unhealthy behavior may lead to increased self-efficacy to modify other co-occurring unhealthy behaviors for which individuals may have low motivation to change.Finally, the difference in the prevalence of unhealthy behaviors  across a number of sociodemographic subgroups highlights the need for evidence-based research to identify interdisciplinary intervention strategies that integrate science, practice, and policy to address health disparities among the population. Our study results also have several important clinical implications. SRH is an assessment tool for Patient-Reported Outcomes Measurement Information System  that measures patient–reported health status for physical, mental, and social well-being.In light of the legalization of medical and recreational marijuana use in some US states, patients may be more likely to ask their healthcare providers about potential health effects of marijuana use. Many of the proposed health benefits and unintended consequences of marijuana use have not been fully explored. The findings of our study suggest that, in addition to receiving counseling about marijuana, patients with a history of marijuana use should obtain firm advice and support for not using tobacco. For those who plan to quit, evidence suggests that simultaneously quitting both tobacco and marijuana may yield important psychological and neurobiological benefits.Until more results from experimental research are forthcoming to provide guidance, it is important to encourage dual cessation. Our study has several limitations.

It was cross-sectional and cannot establish cause and effect. With the ongoing changes in medical cannabis grow setup law in a growing number of states, people with certain health conditions might be drawn into marijuana use. It might be possible that poor perceived health resulted in marijuana use rather than marijuana use caused poor perceived health. Research shows that the predictive power of SRH for mortality is robust across many subgroups of country of origin even after extensively controlling for numerous covariates. However, validation study has not been conducted among the population of US adult ever users of marijuana. We adopted serum cotinine as a biomarker to define current tobacco use, occasional tobacco users who had used tobacco beyond 3–5 days prior to examination may be included as non-current tobacco users. In addition, data on marijuana use were self-reported and were subject to potential recall bias or under-reporting of less socially desirable behaviors. Our study did not identify the use of specific tobacco products, including electronic cigarettes or electronic nicotine delivery systems that have been gaining popularity in recent years. Due to data constraints, we were unable to account for several potential confounders such as medication use, drug abuse, and other co-morbid conditions, as well as the difference in patterns of recreational versus medical marijuana use that might have had an effect on suboptimal SRH.In the present study, we assessed the impact of recent 30-day and 60-day regular marijuana use on suboptimal SRH among regular marijuana smokers. Because of the limited analytical sample size, we could not explore additional harmful marijuana and tobacco usage patterns related to quantity, frequency, timing, and duration of usage. It is worth noting that although several subgroups of the adult population  were not evaluated in the current study due to a limited sample size, such adults are especially vulnerable to a multitude of health consequences associated with unhealthy behaviors even at low threshold levels for exposure. Long-term change of unhealthy behaviors is challenging and may require multifaceted efforts to effectively address the interplay of behaviors with biological, health, and social factors across various subgroups and environmental settings over persons’ life course .

Previous epidemiological studies have revealed strong negative impacts of marijuana use, suggesting that marijuana has similar potential for abuse as other illicit substances,is associated with respiratory illnesses, and leads to cognitive impairment.However, several focused empirical studies have countered these results, finding instead no significant effect of marijuana use on subcortical brain morphometry and only an uncertain effect on cognition.The past two decades have seen shifts in legal and societal attitudes toward marijuana use, with 23 states and the District of Columbia legalizing medical marijuana and four states legalizing recreational marijuana ; moreover, perceptions of the risk of regular marijuana use have decreased, even amongst adolescents, particularly in Colorado, recreational marijuana is now legal.As increases in the potency of marijuana have accompanied these shifts in attitudes,it is becoming increasingly important to understand the precise neural effects of long-term marijuana use and the impact of the age of first use. Adolescence is a sensitive period for brain development with white matter myelination and gray matter pruning, and, critically, an increase in the number of cannabinoid receptors that respond to marijuana.While preliminary studies of the effects of marijuana use on white matter integrity showed no significant effects in adolescents or adults,a growing body of research suggests that an adolescent onset of heavy marijuana use can have neurotoxic effects on developing white matter, reflected in decreased white matter coherence as assessed by measures of diffusivity, e.g., fractional anisotropy  and radial diffusivity.Importantly, these effects have been observed longitudinally, suggesting a causation between marijuana use and white matter changes.However, most of these studies have relied on small sample sizes,so their ability to generalize to a broader population is limited. Moreover, the majority of these studies all examined the effects of heavy use,and much less is known about the effects of casual marijuana use on white matter integrity. As many white matter tracts continue to develop in adolescence and young adulthood,with maximal change in such development during this time frame,it is important to understand how the age of onset of marijuana use impacts neurodevelopment not only in heavy users but more casual users, especially considering that adolescence is often a time of experimentation with substances of abuse.

Studies of the effects of marijuana use on cortical and subcortical morphometrics in humans have typically focused on the amygdala and hippocampus  and, to a lesser extent, the nucleus accumbens and orbitofrontal cortex. These structures are known to have important roles in reward processing and their function/structure is known to be disrupted by drugs of abuse.At least some, but far from all, of the evidence suggests an influence of marijuana on brain structure. For example, marijuana users compared to nonusers have been found to have reduced amygdala volume,and amygdala volume reductions have been correlated with increased levels of self-reported craving and relapse in consumption after 6-months from detoxification from alcohol dependence.On the other hand, a recent meta analysis of 14 studies of marijuana users compared to nonusers found no summary changes in amygdala volume, but did observe a consistent pattern of reduced hippocampal volume.In addition, a large number of studies with animals and humans have shown that marijuana affects the structure of the nucleus accumbens.Hence, there is evidence in the existing literature to suggest the possibility that marijuana influences the structure of these regions, all of which are known to be affected in addiction.Nonetheless, a recent well-controlled study by Weiland et al.  found no evidence of an effect of marijuana on the morphometry of these structures. They compared morphometry in a sample of adult and adolescent daily users of marijuana to nonusers,while controlling for other confounding variables of tobacco use, depression, impulsivity, age, and gender. Importantly, they found no group differences in measures of brain morphometry for the nucleus accumbens, amygdala, hippocampus, cerebellum, or 35 cortical regions in each hemisphere. Interestingly, when they simply controlled for the amount of alcohol use, rather than matching users and nonusers, they replicated several findings of Gilman and colleagues. Furthermore, when examining effect size across previous studies, they found that the literature demonstrates a mean lack of effect. Given the discrepancies in the literature, we wanted to re-examine this issue using a large representative sample.

To this end, we analyzed extremely high-quality multi-modal neuroimaging data from 466 participants in the Human Connectome Project  who reported using marijuana at least once during their lives.The participants in this sample consist of twins and their non-twin siblings who have no history of major psychiatric illness, but vary greatly in terms of race, education, income, BMI, and the degree of recreational drug use. A recent study used this HCP data-set to disentangle causal effects of marijuana use on regional brain volume from shared genetic effects and found that it was mainly shared genetic effects explained differences in bran volumes.However, this study did not investigate the effects of marijuana use on white matter integrity or the shape of subcortical regions, which was the focus of the current study. Rather than investigating extremes of marijuana use  like most previous studies, we leveraged the large sample size to take a parametric approach, examining marijuana use along a spectrum, so as to search more specifically for dose-dependent effects. Nevertheless, a comparison of users and nonusers was also performed as a replication of prior work.Analyses of voxelwise gray matter morphometry were carried out with FSL-VBM  an optimized VBM protocol carried out with FSL tools.First, structural images were brain-extracted and gray matter-segmented before being registered to the MNI 152 standard space using non-linear registration.The resulting images were averaged and flipped along the x-axis to create a left-right symmetric, study-specific gray matter template. Second, all native gray matter images were non-linearly registered to this study-specific template and “modulated” to correct for local expansion  due to the non-linear component of the spatial transformation. The modulated gray matter images were then smoothed with an isotropic Gaussian kernel with a sigma of 3 mm. Finally, voxelwise GLM was applied using permutation-based non-parametric testing, correcting for multiple comparisons across space, using Threshold-Free Cluster Enhancement.Following Weiland et al.,we also performed a multivariate analysis on the effects of outdoor cannabis grow use on subcortical and cortical volumes and cortical thickness extracted with Free Surfer.

Rather than analyzing whether marijuana showed a multivariate effect across all 35 cortical regions contained in this table as did Weiland et al.,we chose an a priori approach, focusing on prefrontal regions and subcortical regions where marijuana has been shown to have significant effects.Regions of interest included 15 prefrontal cortical regions : medial and lateral orbitofrontal cortex, caudal anterior cingulate, caudal middle frontal, inferior frontal gyrus,rostral middle frontal, superior frontal, and frontal pole. Subcortical regions included nucleus accumbens, hippocampus, cerebellum cortex and white matter, thalamus, and amygdala.White matter volumes were included for the anterior and mid anterior corpus callosum.We then examined the effects of marijuana use on white matter diffusion parameters.The group comparison showed no significant effects, possibly suggesting that the frequency of marijuana use in the HCP sample is not severe enough to replicate previous studies, which largely focused on comparisons of non-users and daily marijuana users. In line with this possibility, there were no linear effects of the number of times used on white matter coherence in users.As shown in Fig. 2, age of first use had a positive association with FA as well as a negative association with RD, such that an earlier age of first use was associated with lower FA and greater RD in a large cluster of right hemisphere white matter. These tracts primarily subsisted of the Superior Longitudinal Fasciculus,Inferior Longitudinal Fasciculus,and Forceps Major and Minor. The SLF connects the prefrontal cortex and parietal cortex and is involved in executive functions,and the ILF connects the temporal and occipital cortices, has been shown to affected by adolescent marijuana abuse.The Forceps Major and Minor are extensions of the corpus callosum connecting the left and right occipital and frontal lobes, respectively. Thus, even though most of the effects on FA and RD were found in the right hemisphere, communication between the left and right hemispheres may be impacted by marijuana age of onset. When examining the subset of unrelated marijuana users, we confirmed the negative effect of an earlier age of first use on FA and RD in the SLF, as shown in Fig. 3.

Participants were also told that some decks were worse than others and were asked to treat the money in the game as real money. Following card selection, participants were given feedback about monetary gain or loss displayed on the computer screen. Participants began the task with $2000 in their bank. After card selection, participants could win $100 in decks A and B or $50 in decks C and D. In some instances, however, participants were credited with money, but were required to pay a penalty. For each card chosen, there was either an immediate gain or an immediate gain followed by a penalty.Unknown to participants, card selections in decks A and B were classified as disadvantageous decisions because although larger winnings were possible by selecting cards from these decks, selection from these decks was also associated with larger losses, decreasing net earnings during the task. Card selections in decks C and D were classified as advantageous because although smaller winnings were possible by selecting cards from these decks, selection from these decks was also associated with smaller losses, increasing net earnings during the task. Participants completed 100 trials without interruption or caps on deck selections. At the end of administration, the net earnings were displayed on the computer screen. Total net scores were derived by subtracting the total number of cards selected from disadvantageous decks A and B from the total number of cards selected from advantageous decks C and D. The majority of studies in MJ users have used net IGT scores to examine decision making between and within groups across the task .This strategy of analysis allows researchers to compare decision-making differences between and within groups across the task by examining differences of advantageous and disadvantageous card selections.

Additional analyses for the IGT include comparing the total amount of money lost by each group,or examining net earnings at the end of the task,as well as measuring the amount of time needed to complete each task administration for multiple IGT sessions.However, these strategies do not account for the possibility of detecting between group differences across time. Therefore, we chose to focus the analyses on net IGT scores using a mixed-model analysis of covariance,cannabis grow supplies as outlined below .This study examined the relationship between frequent MJ use and risky decision-making in young adult college students using the IGT. To our knowledge, only one other study has examined risky decision making using the IGT in a similar and narrow age range of young adults.In the current study, MJ+ were older and had significantly lower IQ scores relative to HC. As both age and IQ were related to IGT performance, they were included as covariates in the analyses.Although MJ+ made advantageous card selections as indicated by the positive net IGT scores, they made less advantageous choices compared to HC. This effect is consistent with prior research examining group differences between MJ users and healthy controls in young adults.Research suggests that MJ users are more likely to make risky judgments despite subsequent monetary punishment than healthy controls  and exhibit increased impulsive decision-making by selecting more disadvantageous cards than healthy controls.Additionally, the current findings support prior research that found young adult MJ users made more selections from disadvantageous decks A and B compared to healthy controls.However, in the current study, MJ+ also made fewer card selections than HC from deck C, an advantageous deck, but one that is associated with frequent punishments relative to deck D.This could suggest MJ users may prefer decks that are associated with frequent rewards and infrequent losses, which could drive reward-driven behavior.

This observed performance difference in reward-driven behavior may be attributed to differences in utilization of the prefrontal cortex during strategy and choice selection. Future studies that utilize the IGT in young adults during fMRI are needed to explore this question. Furthermore, we found that the effect of group on net IGT scores was significant when including sex as a factor in the model. Overall, MJ + had lower net IGT scores compared with HC.Additionally, there was a trend for female participants to have lower net IGT scores than male participants.In the current study, the trend towards poorer net IGT performance in female relative to male participants appears to be driven by females tending to make more disadvantageous selections from deck B, where rewards are frequent and losses are infrequent, while at the same selecting fewer cards from advantageous deck C in which loss frequency is equal to gain frequency. Females may also be performing worse than males due to differences in the time needed to develop decision-making strategies towards advantageous choices. Male participants may be better at suppressing reward-driven behaviors due to activity in the right dorsolateral prefrontal cortex activity that has been shown in males but not females completing the IGT.A previous study that examined sex differences between young adult male MJ and female MJ users found that lifetime MJ use was associated with poorer decision-making performance in male but not female participants.However, this study did not perform an interaction between group and sex on net IGT scores due to the absence of healthy controls. Thus, it is unknown whether similar findings would have also been seen if female and male non-MJ users had been included. The observed trend for sex differences on the IGT may also be attributed to the possible influence of sex hormones on executive functioning. A study examining the interactive effects of dopamine base levels and cycle phase on executive functions found that women were significantly faster on the Stroop during the luteal phase compared to menses and pre-ovulatory phases.

This suggests women have improved verbal skills during the luteal phase when levels of progesterone and estradiol are high. Another study found that women ovulating were more likely to choose risky options than men.In the current study, females may have performed worse on the IGT because we may have unknowingly sampled a high percentage of women in a stage of their menstrual cycle where they are more likely to take risks. However, since we did not ask female participants to report menstrual cycle stage at the time of the study visit, we are unable to confirm whether hormone levels may have influenced IGT performance. No differences were observed between MJ+ and HC mean reaction times during the IGT, which is inconsistent with our initial hypothesis. To our knowledge, no studies in MJ users have examined mean reaction times on the IGT. While risky decision-making may be related to impulsivity, it may be important to utilize other neurocognitive measures that assess motor impulsivity and response inhibition. In a fMRI study investigating the relationship between MJ use and inhibitory control processing, MJ users tended to have faster reaction times than healthy controls.Additionally, brain activity differences were observed in the dorsal anterior cingulate cortex, a region of the brain thought to be involved in impulse control. In the present study, as mean reaction time was not significantly related to IGT performance, MJ+ took the same amount of time as HC to make decisions during card selection. This finding suggests that lower net IGT scores in MJ+ relative to HC may be related to maladaptive decisions that are not associated with motor impulsivity during card selection. Although age at first MJ use, 30 day MJ use and lifetime MJ use were not significantly related to IGT performance among MJ+, between group differences on the IGT suggests there may be potential differences between MJ+ and HC that could be related to pre-morbid vulnerability for risk-taking tendencies and/or the effects of substance use itself.For example, a previous study showed that early-onset frequent marijuana users had a thicker prefrontal cortex than late-onset frequent MJ users, which could indicate reductions in normative grey matter pruning in the prefrontal cortex in participants who begin using MJ at a younger age.While previous studies have found associations between early adolescent MJ use and impairments in executive functioning,we did not find a relationship between age at first MJ use and risky decision-making.

In the current study, we asked participants to report their age at first MJ use instead of age at regular MJ use, which may be more closely associated with patterns of MJ use that could predict neurotoxic consequences of use. Age at first use can be a difficult variable to assess, especially in young adults aged 18–22 years, since age at first MJ use may have occurred very recently in this population and thus, participants may have only had a year or two of substance use prior to the study visit.One limitation of the current study is the modest sample size. Although our sample was relatively well matched in the number of participants in each group, our findings may not be readily generalizable to young adult college students. Another related issue is the overrepresentation of males in the MJ group. Although the prevalence of MJ use is higher in males than females,our findings may not be generalizable to female MJ users. Although onset of cannabis grow facility withdrawal symptoms typically occur in frequent MJ users after 24 h of abstinence, and peak 2–6 days post cannabis abstinence,we cannot confirm whether or not participants were in active withdrawal during the study visit. Future studies should administer the Marijuana Withdrawal Symptoms checklist  to assess withdrawal symptoms in participants at the time of the study visit. In addition, the potency of MJ is not standard and our study design does not take into account dose-response associations in MJ+. Future studies will need to assess other indicators of MJ use, such as asking participants to report THC content of the MJ they typically use. Another limitation is that we utilized a laboratory task of decision making and provided participants with hypothetical earnings rather than tangible incentives. In future studies, it will be important to use other real-life decision-making measures to determine if our findings are specific to the IGT, are associated with non-monetary risk-taking behaviors, or are associated with decision-making in general.

As we only used one task of decision-making, our findings may not generalize across a wide range of decision-making tasks. Future studies may want to utilize additional tasks to assess risky decision-making, such as the Balloon Analogue Risk Task  or Cambridge Risk Task.Additionally, as most MJ users are also alcohol users, alcohol was not used as exclusionary criteria for MJ+. While post-hoc analyses suggested alcohol use was not related to IGT performance, we cannot rule out the possibility that the neurotoxic effects of alcohol may play a role in the observed group differences on decision-making performance. In models examining the effects of both MJ use and alcohol use on net IGT scores, neither significantly predicted decision-making performance in MJ+, which may be due to lack of refined measure to assess frequency of these substances and premorbid characteristics that distinguish MJ+ from HC. Other studies that reported group differences on the IGT between MJ users and healthy controls either did not examine relationships between marijuana use variables and IGT performance,only examined other substance use variables in relation to IGT performance,or did not find associations between substance use variables and IGT performance.One study by Verdejo-Garcia et al.  reported greater joints smoked/week was associated with lower net IGT scores in abstinent marijuana users, but did not examine other substance use characteristics in relation to IGT scores within the same model. We believe future studies should consider the relationship between MJ use and decision-making performance, while accounting for poly-substance use. Finally, while we observed a trend for MJ+ to report greater recent anxiety on the Beck Anxiety Inventory,compared with HC,the main effect of group remained significant when controlling for BAI scores in the ANCOVA models with  and without sex  included as a factor. As anxiety levels may affect decision-making, future studies should ascertain that anxiety levels in MJ users are not driving any observed decision-making differences between MJ users and healthy controls. In summary, the current study examined the effects of frequent MJ on risky decision-making in college-aged young adults.

Bryson also concluded based on his review of the literature, that the pulmonary complications in the chronic marijuana smoker are equivalent to those seen in the chronic tobacco smoker, while Wu et al. estimated that 3e4 cannabis cigarettes daily equates to about 20 tobacco cigarettes in terms of bronchial tissue damage. Cannabis use has also been linked to a higher risk in cancers, possibly due to the increased carcinogens entering the airway. Similar to tobacco use, marijuana use plays a significant role in the development of lung cancer. In a case control study performed in New Zealand, young adults  had an 8% increase in lung cancer risk for each joint year of cannabis smoking after adjusting for the cofounders, such as age, sex, ethnicity and family history. Berthiller et al. pooled data from a multitude of institutions across multiple countries, comprising of over 1200 patients, and reported an increased risk of lung cancer for every marijuana use. In addition, a 40 year cohort study  with about 48,000 patients reported an increased risk of lung cancer in young men  who had smoked marijuana more than 50 times. This study was limited however by the nature of patient self-reporting. Head and neck cancers have also been theorized to be at a higher risk similar to that of tobacco smoking. However, a pooled analysis performed by Berthiller et al. found that infrequent marijuana smoking did not confer a greater risk after adjusting for cofounders. The authors did note that due to the low prevalence of frequent smoking within the study population, that a moderately increased risk could not be ruled out. In another population based case control study, there was an increased incidence of head and neck cancers in patients with a 30 joint-year history, yet the association did not exist when accounting for tobacco smoking suggesting the risk is greater with tobacco than marijuana grow system.

In a crosssectional study conducted by Mills et al., the rate of marijuana use via patient self-reporting was found to be 14% amongst surgical patients in 2003. This led the authors to conclude that questions about illicit drug use should be a routine part of the preanesthetic assessment, especially in patients that the anesthesiologist finds hard to settle, due to anxiety or other psychologic manifestations, because of the potential anesthetic complications that may occur. In a series of case reports, Guarisco presented three patients who suffered from significant respiratory distress due to isolated uvulitis, a disease of low incidence typically associated with infection or traumatic irritation from instruments used in the airway. Investigating further, all three patients were found to have inhaled large quantities of cannabis within six to twelve hours of the onset of symptoms leading to the conclusion of a possible correlation with inhaled irritants such as cannabis. Due to known cases of isolated uvulitis and the possible link with marijuana, the authors suggest that toxicology urine and blood studies for THC should be performed in cases where marijuana use is suspected but not confirmed by history taking. Multiple other cases have also been reported with similar findings. In a case series by Sloan, three adolescents suffered acute uvular inflammation post the heavy use of marijuana, having smoking at least three marijuana cigarettes, despite negative throat cultures. In 1971, a cohort study was performed in which a large quantity of marijuana, over 100 grams, was smoked over several months. Of the 31 subjects, almost half suffered from recurrent rhinopharyngitis as well as developed acute uvular edema after the heavy marijuana inhalation which lasted approximately 12e24 hours. These findings stress the importance in the maintenance of the airway during anesthesia following acute marijuana use due to the potential airway obstruction that may occur. In fact, in presenting a case of uvular edema and airway obstruction with cannabis inhalation 4 hours prior to surgery, Pertwee recommended that elective operations should be avoided altogether if a patient was recently exposed to cannabis smoke. This recommendation seems reasonable when taking into consideration the life-threatening bronchospasm leading to asphyxia, brain damage or death resulting from tracheal intubation in patients with obstructive airways.

One proposed course of action has been the therapeutic use of steroids. In Guarisco’s study, he theorized that steroids should help inhaled irritant uvulitis.As steroids increase endotracheal permeability, decrease mucosal edema and stabilize lysosomal membranes, thus decreasing the inflammatory response, the theory has scientific basis. In a prospective, randomized, double-blind study, Silvanus et al. found that the addition of methylprednisolone to salbutamol in patients with a partially reversible airway obstruction helped in the diminution of the reflex bronchoconstriction that can result from tracheal intubation. This led to Hawkins et al.’s recommendation that at the first signs of airway obstruction, dexamethasone should be used as the drug of choice, 1 mg/kg every 6e12 hours over the course of one to two days.This recommendation gained credence in the dramatic relief that dexamethasone provided in the post-traumatic cases. However, Mallat et al. concluded that although marijuana-induced uvular edema is a serious postoperative complication that has a potential for simple treatment, in the case of an elective surgical procedure with an acute history of cannabis exposure, surgery should be cancelled as prophylactic treatment may not be efficient.The complications of the airway are not limited to intubation however. The inhalation of toxic chemicals as well as smoke can cause laryngospasm by chemoreceptor stimulation. In addition, the inhalation of hot gasses can trigger laryngospasm via thermoreceptor stimulation, especially in the case of lowered sensory afferent neuron threshold potentials such as in light anesthesia. In line with this, White presented a case in which a known cannabis smoker suffered severe laryngospasm following extubation.As found within the reviews, multiple observations have been made showing crosstolerance between marijuana and barbiturates, opioids, prostaglandins, chlorpromazine and alcohol. In addition, animal studies have shown additive effects amongst them all except for alcohol. These drug interactions have led to further exploration of its reactions to other medication groups. As a result of fat sequestration and subsequent slow elimination from the tissues,cannabinoids may be present to interact with multiple anesthetic agents.

In Symons’s case report, the patient required multiple boluses of propofol and two additional doses of midazolam to achieve appropriate sedation.In a prospective, randomized, single-blind study of 60 patients, chronic marijuana users required significantly increased doses of propofol to facilitate successful insertion of the laryngeal mask and thus suggesting that the increased doses, in chronic marijuana users, may be a requirement for appropriate loss of consciousness as well as jaw relaxation and airway reflex depression. The authors theorized that the variations in the level of delta9-THC can explain variations in propofol responses. In a review written in the American Association of Nurse Anesthetists Journal, Dickerson reported the synergistic effects of cannabis to include: potentiation of nondepolarizing muscle relaxants, potentiation of norepinephrine, the augmentation of any drug causing respiratory or cardiac depression, as well as a more profound response to inhaled anesthetics sensitization of the myocardium to catecholamines due to the increased level of epinephrine. On the subject of muscle relaxants, THC depletes acetylcholine stores and exerts an anticholinergic effect and thus creates a potentiation of the nondepolarizing muscle relaxants. A review by Hall et al. explored THC’s interaction with drugs affecting heart rate and arterial pressure and found that due to cannabis’s own cardiovascular effects,it may interact with medications such as beta-blockers, anticholinergics and cholinesterase inhibitors.Due to these potential autonomic reactions, as well as theoretical psychiatric complications, such as withdrawal effects and their interference with anesthetic induction or postoperative recovery, there has been a stress made to inquiring about drug history or avoiding elective operations altogether. Dickerson, in his review, recommended that, due to all potential effects and interactions, not only should an extensive history of drug use be elicited at the time of the preoperative assessment, including the frequency of use and time of last use, but that anesthesia should be avoided in any patient with cannabis use within the past 72 hours. This gained further credibility in a randomized, double-blind trial, in which an apparent drug interaction was observed in the patient population who underwent general anesthesia within 72 hours of marijuana use leading to a sustained postoperative tachycardia, a finding potentially due to an interaction between cannabinol metabolites and atropine administration during anesthesia.

One of the most researched and known risk factor for perior postoperative complications, increased hospitals costs and resource usage is smoking, specifically tobacco smoke. In fact, the rates of perioperative respiratory events, such as reintubation, hypoventilation, hypoxemia, laryngospasm, bronchospasm, and aspiration, have a total incidence of 5.5% in smokers compared to 3.1% in nonsmokers, making these events 70% more prevalent with smoking. In addition, in a randomized controlled trial out of Denmark, orthopedic surgery patients who smoked were compared to those who underwent cessation counseling and nicotine replacement therapy. In the study, they found an overall complication rate of 18% compared to the 52% found in the smoking group, including a cardiac event rate of 0% compared to 10%. A similarly designed study found a significant relative risk reduction of 49% for not only systemic complications but that of wounds as well. These call into question the role of marijuana on perioperative complications, especially when taking into consideration that the pulmonary complications in the chronic cannabis smoker are equivalents to that of a chronic tobacco smoker, probably due to the cannabis vertical farming smoke products.One such pulmonary complication is airway obstruction, extensively linked to marijuana use, in which Warner et al. found that untreated, such as a lack of smoking cessation in the case of marijuana, leaves patients at a high risk for perioperative complications.When it comes to the case of cardiovascular maintenance in the perioperative period, marijuana presents complications. As mentioned previously, cannabis use can create a series of ECG changes that must be considered and monitored such as PVCs, atrial fibrillation, AV block,or Brugada-like changes. As a worst case scenario, cannabis use has been linked to plaque rupture and resultant myocardial infarction. These are all causes for concern considering that Gregg et al. reported, in conducting a series of 55 clinical trials in patients medicated with THC, that peak heart rate increased by 24.1% in surgical patients compared to the non-surgical. The authors concluded that THC may have a synergistic cardiovascular relationship with surgical stress. This tachycardia gave credence to Bryson’s recommendation that ketamine, pancuronium, atropine and epinephrine, all drugs known to affect heart rate, should be avoided in patients with history of acute marijuana use, while the bradycardia and hypotension that results from high doses of marijuana called into question the amount of atropine and vasopressors needed. Despite the impetus behind these recommendations, 1 trial showed epinephrine to have no synergistic effect with marijuana when it comes to cardiovascular effects, showing more research is needed on the potential interactions of marijuana and perioperative medications.

Field visualization plays a key role in any operation. Marijuana, however, may affect this. In a literature review published in Poland, Zakrzeska et al. explored how cannabinoids and their metabolites and their effects on the receptors CB1, CB2, CBPT and CBED as well as other systems may impact hemostasis.The authors concluded that despite the studies that have shown contradictory effects, based on the physiology, it is reasonable to conclude that marijuana could have an anti-hemostatic effect. Multiple studies have backed up that conclusion. In 1979, Schaeffer et al. reported that cannabis users had a diminished ability for platelet aggregation.This led to further investigations and in 1989, Formukong et al. looked at cannabinoids’ effect on platelet aggregation. The authors found that in both rabbit and human platelet aggregation that was induced by adenosine diphosphate or epinephrine was inhibited by cannabinoids in a dose-dependent manner and with cannabidiol more potent than THC in this effect. Then in 2007, an in vitro coagulation study showed that marijuana and two of the major cannabinoids, including THC, had an anticoagulant property and even more so, an antithrombotic effect. In the in vivo model testing clotting times of lean and obese rats, those treated with cannabis had clotting times 1.5 to 2 times greater than the controls, thus supporting the results of the in vitro study.

Furthermore, the complexity of cannabis increases geometrically under the ‘entourage effect’, which postulates that cannabinoids interact to modulate their therapeutic effects. An experimental basis for the entourage effect is provided by murine studies, which have demonstrated that binary combinations with acidic cannabinoids increase bioavailability, potency and efficacy of neutral cannabinoids in epilepsy models.Clinical evidence is also mounting, with a recent meta-analysis on observational studies of epileptic patients concluding that crude cannabis extracts yielded a greater reduction in seizure frequency and had fewer side-effects than equivalent doses of purified CBD. However, as most extracts were only characterised to the extent of standardising the CBD dose, information about other cannabinoids was absent or based on inference. Consequently, the authors’ attribution of the differences between the extracts and purified CBD to the entourage effect was speculative. It was not possible to evaluate if the effects of the other cannabinoids added together, comparable to merely increasing the dose of CBD, or if they magnified the effect to surpass what CBD could achieve alone. Evidently, to progress beyond studies of binary combinations or poorly characterised extracts, routine analyses capable of quantifying panels of cannabinoids could help to better inform the design and interpretation of future studies that investigate the entourage effect. A clinical understanding of this effect might subsequently inform the extent to which cannabinoids are screened during cannabis product quality control. Several published methods are available for the separation and quantification of cannabinoids, with a variety of limitations which constrain their routine use. For the analysis of neutral cannabinoids, GC is simple, sensitive, and provides acceptable resolution. However, GC is not immediately suitable for acidic cannabinoids, weed trimming tray as they are poorly volatilised and rapidly undergo thermal decarboxylation into neutral cannabinoids.

Fortunately, this limitation can be surmounted by trimethylsilyl derivatisation of the labile acid group. Alternatively, some analysts have adopted LC for the separation of cannabinoids in medicinal cannabis. Following separation by LC, detection can be achieved by MS or by PDA. The MS detector enables the peak identity confirmation from their fragmentation patterns and relative ratios, and is sufficiently specific to recognise coeluting impurities in complex matrices. However, the required technical expertise, operation, and maintenance costs prohibit the use of MS for the routine analysis of cannabinoids. The UV-Vis PDA detectors are much cheaper, require less operator expertise, and are widely available. Since cannabinoids contain UV chromophores, they are amenable to PDA detection. Moreover, the UV spectra may assist with compound identity confirmation and the measurement of peak purity, which aids in quantification. Whichever detector is used, the elevated cost and limited availability of certified analytical reference standards for some cannabinoids remain impediments to their analysis. The cost can exceed $200 AUD per mg, and newly identified pharmacological leads in cannabis are possibly more expensive with significantly longer shipping times. To surmount this, some analysts have performed stereoselective microscales syntheses to obtain cannabinoids in a timelier manner, but this is beyond the remit of a typical QC lab. If the analysis of such cannabinoids is to become routine, the cost for their quantification must be mitigated. To this end, this study aimed to develop and validate a HPLC-PDA method for the determination of ten cannabinoids in medicinal cannabis inflorescence and oil and to explore the feasibility of using RRT for peak identification and RRF for their quantification. By this approach, an initial once-off purchase of all the standards was required to establish the RRT and RRF between the cannabinoids and the reference compounds: CBD as a reference for neutral cannabinoids, and CBDA as a reference for acidic cannabinoids; chosen as they are cheaper and available in many jurisdictions. Subsequently, the method may be routinely used in QC laboratories for the quantification of a panel of ten cannabinoids, requiring only sparing amounts of the reference compounds.

To optimise sample preparation, a variety of extraction solvents were tested with duplicate extractions. The solvents trailed were methanol, ethanol, acetonitrile, ethyl acetate, methanol:water , and acetonitrile:methanol . Cannabinoid peak areas were maximised by ethyl acetate and acetonitrile:methanol. However, due to markedly mismatching the initial mobile phase condition, ethyl acetate gave rise to significant band broadening. Thus, acetonitrile:methanol was selected as the extraction solvent, which is consistent with other extraction optimisation reports. The CV contribution of the method preparation procedure to the total uncertainty was determined by performing six replicate extractions and analysis of a single cannabis inflorescence sample. Grinding the inflorescence to pass through a < 710 µm sieve before sub-sampling achieved a CV range of 1.2 to 3.6%. When sub-sampling without grinding, the CV unacceptably ranged from 7.6 to 23.6%, thus indicating the importance of preparing a homogeneous sample. To optimise the chromatographic conditions, the method was iteratively developed. Baseline separation was achieved for eight of the ten cannabinoid standards , however, the CBD and CBG standard peaks overlapped slightly , as shown in Fig. 1A. Likewise, acceptable separation of cannabinoids in the extracts of cannabis inflorescence and cannabis oil were demonstrated in Fig. 1B and C, respectively. Whilst most matrix components eluted before the cannabinoids, a compound in inflorescence samples was observed to elute between CBDA and CBGA. This peak was identified to be tetrahydrocannabivarin by comparison with the UV spectrum and retention time obtained for the THCV standard. THCV was not included in the present method validation study as it was not part of the original selected set of analytes. When the analytes were sufficiently abundant in the sample, the UV spectra of their peaks were compared to that of the standard. As shown in Fig. 2, spectra superimposed closely, indicating good peak purity. To optimise PDA detection, wavelengths corresponding to the λmax of the different cannabinoids, specifically 210, 232, and 270 nm, were considered. Whilst 210 nm has been used in other studies, it produced a sloping baseline in the present study due to the use of methanol rather than exclusively using acetonitrile as the organic component of the mobile phase. Instead, it was found that visualising the chromatogram at 232 nm gave the best compromise between sensitivity and baseline noise. Some studies used 270 nm to improve sensitivity for the acidic cannabinoids, but this higher sensitivity is not required due to their relatively high abundance in the inflorescence samples. This high abundance was anticipated as acidic cannabinoids are the secondary metabolites synthesised in cannabis, whereas the neutral forms are produced by spontaneous decarboxylation. Retention times pooled from the three analysts are reported in Table 3. The CV in the retention times for each cannabinoid ranged from 0.18% to 0.56%, demonstrating an excellent inter-batch repeatability.

For the cannabinoids detected in the available inflorescence samples, the retention times observed for the sample peaks deviated by <1% from the standard retention times. To formalise the peak identification, and to demonstrate further gains in the inter-batch repeatability, the RRT were also pooled from the three analysts and were appended to Table 3. RRT should correct for inter-batch variabilities in retention times, provided that the variation in conditions proportionally affected all of the closely related analytes being studied. As anticipated, the pooled RRT values for each cannabinoid had CV which ranged from 0.04 to 0.34%. This represents a modest gain in repeatability, which should be maintained even if the retention times start to shift by >1%. Critically, it was also shown that the range of RRT values for each cannabinoid did not overlap. This means that analysts reported comparable values for the RRT, and that these values were unique for each cannabinoid. Thus, cannabinoid peaks in samples may be identified from their RRT values relative to the retention time of the CBD or CBDA from the working standard tested in the same batch of analysis. Detection and quantification limits for the cannabinoids are presented in Table 4. The LoD ranged from 20 to 78 µg/g and the LoQ ranged from 60 to 238 µg/g, relative to the inflorescence sample preparation. These limits are sufficiently low to enable the quantification of the studied cannabinoids in cannabis biomass and, observing that even relatively small amounts in crude biomass can be extracted and concentrated to therapeutically relevant concentrations in final products, these limits are suitable for quality control throughout the supply chain. However, with the quantification limits in the determined order of magnitude, it is unlikely that this method could be adapted for the analysis of the recently identified trace cannabinoids with heptyl sidechains. This includes THCP, which, by a published MS method, was identified in the inflorescences of THC dominant chemovars at concentrations routinely less than 140 µg/g and was undetected in CBD dominant chemovars. Accuracy of the method was evaluated from the recoveries of analytes spiked onto surrogate matrices, as presented in Table 7. For the cannabis inflorescence and oil, the spike recoveries from the surrogate matrices ranged from 90.1 to 109.3% and from 95.4 to 103.1% , respectively. Most recoveries were within 5% of the nominal concentration and the only two recoveries which were outside of this criterion had been spiked at the quantification limit, so their recoveries within 10% were acceptable. The precision of the recoveries was also acceptable, except at the LoQ of Δ8 -THC and CBDV which were only precise to 12%. Therefore, the method for the quantification of cannabinoids has acceptable accuracy. In this study, chamomile was selected as surrogate matrix for cannabis inflorescence as it was floral, available at little cost and, vertical grow system with the exception of the cannabinoids, shared phytochemical classes such as fragrant terpenes and flavonoids. Other published articles have used Urtica dioica or Humulus lupulus, with justifications based on tracing their phylogenies relative to Cannabis sativa.

Whilst sharing botanical orders or even families does not necessarily provide better matrix matching, it may be a reasonable approximation. Likewise, for cannabis oil, the choice of olive oil as a surrogate matrix had precedent from previous publications. Indeed, some cannabis oil products contain refined resins or even crude inflorescence extracted into an olive oil base, making its choice as the surrogate matric reasonable for such products. The appearance of publications employing surrogate matrices is being increasingly accepted as a cost-reduction strategy during method development, which is a clear advantage over articles which did not conduct recovery studies at all. Analysts in some jurisdictions may also find it pertinent to consider the use of surrogate matrices if licencing requirements preclude the use of the amount of cannabis material which would be required for the complete spike-recovery protocol on the true matrices. Cannabinoid concentrations in six different inflorescence samples were determined by conventional multipoint calibrations and the RRF method, as reported in Fig. 3. For cannabinoids above the order of magnitude of the LoQ, concentrations determined by the two methods agreed satisfactorily . The only cannabinoid above the LoQ which differed between quantifications by more than 5% was CBC but, relative to its low concentrations, the absolute differences was always acceptably less than 80 µg/g. The good agreement between the results obtained using the two different quantification methods applied to real samples demonstrates that the use of RRF for quantification is a valid alternative with its concomitant cost saving. Considering the cannabinoid profiles of the inflorescence samples, the high ratios of acidic to neutral cannabinoids were indicative of good drying and storage conditions. Furthermore, samples A and B were classified as having moderately high total THC and low total CBD , whilst samples C to F had moderate amounts of both . Beyond these observed concentrations, the proposed method is appropriate to analyse most samples with even greater levels of cannabinoids, as very few inflorescences exceed 200 mg/g total THC. Other cannabinoids such as CBC and CBN were also quantifiable, but Δ8 -THC was not detected in any sample. However, other authors have reportedly identified inflorescence samples with Δ8 -THC concentrations up to 4.9 mg/g, well above the LoQ of the present method. Accordingly, the present method has sufficient dynamic range to quantify cannabinoids at their various native concentrations. Public opinion toward cannabis, particularly for medicinal uses, has shifted in a more positive direction since the 1990’s. The perception of cannabis from the public is informed by a number of factors, and each individual may have a different view based on personal needs or experience.

Together, these findings support advocacy for policies that support patient access to MC. This study has several limitations. First, MC use remains controversial, and this may limit our patients’ willingness to report MC use and provide honest opinions on MC. We attempted to minimize this bias through collecting data anonymously, but this bias may still be present. The controversy behind MC may have impacted which patients responded to our survey, and thus, despite our favorable response rate of 72.5%, we cannot rule out nonresponse bias affecting our findings. Additionally, this study is conducted with patients presenting to outpatient hand and upperextremity clinics in 2 states in which MC has been legalized for at least 4 years, therefore limiting the generalizability of study findings for patients in states where MC has been recently legalized or where it remains illegal. We defined MC as any legal MC product in our study survey , but investigating patient responses to specific MC products could be explored further in future studies. Further, our patient population consists of predominantly patients with health insurance, which limits the generalizability of study findings. Lastly, our study is limited in that we do not collect information on the patients’ current pain levels, chronicity of symptoms, or RC use status, which could affect patient willingness to use MC. These variables may act as confounders of patient perception of MC, and these relationships should be explored further in future studies. This study found that most hand and upper-extremity orthopedic patients presenting to outpatient offices would consider using MC, and most perceive it as a safe treatment option for common orthopedic conditions. Moreover, 10% of survey participants were already using MC. One of the major barriers to MC use is the financial cost. Most patients support insurance coverage of MC, suggesting that in the future insurance coverage could potentially offset the cost barrier to MC use.

Further studies are necessary to evaluate the effectiveness of MC for the treatment of common hand conditions, as well as to better define the long-term safety and side effects of MC in this patient population.Under suppressive antiretroviral therapies , infection with Human Immuno deficiency Virus remains a challenge, both due to the maintenance of cellular reservoirs and to chronic inflammation driven by low viral replication and dysregulated immune mechanisms . In end organs such as the brain, indoor grow tent where the majority of the HIV-1 targets and reservoirs are of myeloid origin , the remaining inflammatory environment contributes to co-morbidities , including neurological and cognitive problems , particularly if ART is not introduced sufficiently early . Substance use disorders are frequent among the HIV-infected population, further contributing to cognitive impairment . Nonetheless, the mechanisms by which addictive substances and HIV interact are multfactorial and poorly understood. Drugs of abuse impact the brain reward system, by modifying levels and balance of neurotransmitters . The HIV target cells, macrophages and microglia, as well as CD4 T cells, express receptors to neurotransmitters, so SUDs are likely to impact mechanisms of immune and inflammatory, and anti-viral responses . Biomarkers that detect the effect of SUDs, and distinguish HIV in that context, may clarify how drugs affect HIV and inflammation. Cannabis is one of the most prevalent substances among HIVþ subjects, compared to the non-infected population , either prescribed for ameliorating symptoms associated with the virus or with ART , or used recreationally, as well as a component of polysubstance use , which in itself is a risk factor for HIV infection. The effects of cannabis may drastically differ from the effects of stimulant drugs such as Methamphetamine , particularly in the context of HIV infection . Yet, similar to other drugs of abuse, cannabis may be a confounder shifting the expression of biomarkers of inflammation and cognition, masking our ability to clearly measure the impact of virus, ART or other treatments in the immune status and brain pathogenesis, or may be altogether beneficial. In terms of cognition, cannabis exposure has been linked to lower odds of impairment in people living with HIV. On the other hand, impaired verbal learning and memory, may be negatively impacted by cannabis use .

Other studies report no differences, or detrimental effects in HIV-negative populations, suggesting that the observed effects of cannabis, including its benefits, may be largely domain and context-dependent. It has been reported that cannabis use improves biomarkers of inflammation in the CSF and plasma of HIVþ subjects and decreases the number of circulating inflammatory cells . We have tested the value of a large panel of transcripts associated with inflammation and neurological disorders, digitally multiplexed and detectable in peripheral blood cells from HIV-positive and HIV-negative subjects, users of cannabis or not . The differences between groups were analyzed using a systems biology approach that identified associated gene networks based on pathways and molecular interfaces, for identifying and visualizing orchestrated transcriptional patterns consistent with HIV infection, CAN exposure, and their interactions. Trends in the behaviors of gene clusters and their predicted regulators revealed that effects of cannabis differ between HIVand HIVþ groups. Moreover, mixed statistical models have pinpointed genes that are further influenced by cannabis in the context of polysubstance use. These context-dependent effects of cannabis indicate the complexity of its molecular actions and properties, and the challenges of biomarker discovery in the context of SUDs. At the same time, the results suggest that cannabis in the context of HIV infection may drive benefits by promoting a decrease of pro-inflammatory and neurotoxic transcriptional patterns, changes and changes in gene clusters associated with leukocyte transmigration and neurological disorders.Molecular markers of neuroinflammation, activation and leukocyte transmigration were measured in the peripheral blood cells under the hypothesis that cannabis use has an effect by itself and on modulating the effects of HIV. A panel of 784 markers relevant to neurological disorders and inflammation were tested by Nanostring. Of these 381 did not produce any signal in any of the specimens and were excluded from the analysis. The expression of genes with significant signal over noise in more than arbitrarily 10% of the samples was normalized by an average of 8 housekeeping genes.

Hierarchical clustering performed using average normalization method applied to digital gene expression data has revealed similarities between HIV-/CANþ, HIVþ/CAN- and HIVþ/ CANþ, but all these groups were distinct from HIV-/CAN-. Clustering also allowed to identify individual specimens that showed patterns distinct from the majority within groups . Systems biology strategies were used to identify defining expression patterns in transcriptional data, and gene clusters exhibiting orchestrated behaviors perturbed by HIV infection, by the use of cannabis, or by their interaction. We have identified significant trends in a number of gene clusters functionally annotated to biological processes and pathways of relevance to the neuropathogenesis of HIV. Overall, the analysis indicates context-dependent effects of cannabis. The majority of the digitally multiplexed genes exhibited detectable and overlapping interactions based on pathway, as indicated in Fig. 4. The visual inspection of the cluster in Fig. 4 shows that both HIV and cannabis alone increase the expression of a number of genes indicated by nodes with orange color . In cells from HIVþ/CANþ individuals, a number of genes showed decreased expression compared to HIV-/CAN- . HIV infection in the context of cannabis, revealed by the comparison of HIVþ/CANþ and HIV-/CANþ , was characterized by stronger upregulation of genes, but also several genes with decreased expression. The effects of cannabis in the context of HIV measured by the ratio between HIVþ/CANþ and HIVþ/CAN-, were characterized by a higher number of down regulated genes, and a more modest upregulation, as suggested by overall lighter orange shades. A complete list of the genes in this network and T ratio in indicated comparisons can be found in Supplementary Materials 1. Pathway-based interactions were subdivided for identification of embedded functional annotations impacted by HIV and/or cannabis, identified by DAVID Bioinformatics Resources with a gene list input. Individual functional annotations were then assembled in GeneMania for visualization of effects. A complete list of significant pathways and functional annotations can be found in Supplementary Materials 1. The pathways selected for visualization were curated based on the expression of inflammatory genes, significance to neurological disorders in the context of HIV, viral infection, pathogenesis and networks with interventional value.

For instance, a gene network functionally annotated to viral host interactions was identified , where the ratio between HIVþ/CAN- and HIV-/CAN- indicated that HIV increased a number of genes annotated to that function. The ratio between HIV-/ CANþ and HIV-/CAN- , as well as between HIVþ/CANþ subjects were compared to HIV-/CANþ , indicated that both cannabis alone and HIV in the context of cannabis use increased a large number of genes in this cluster, but several genes were also decreased in both conditions, including the Ras homolog gene family GTPase RhoA, the Proteasome 20S Subunit Beta 8 , indoor hydroponics grow tent the intracellular cholesterol transporter , the E1A Binding Protein P300 and the histone deacetylase Sirtuin 1 . The ratio between HIVþ/CANþ and HIVþ/CAN- indicated that cannabis in the context of HIV was associated with a mild increase of genes in viral host interaction function , and a decrease in the general transcription factor IIB and the ubiquitin protein ligase 3A were characteristic of this comparison. Apoptosis was also identified as a relevant functional annotation , showing differential effects of HIV and/or cannabis. HIV alone decreased Caspase 7 CASP7, but increased CASP9 and the apoptosis regulator BCL2 . The effect of cannabis, on the other hand , indicated decrease in BCL2 . Likewise, HIV in the context of cannabis had a decrease in BCL2 . On the other hand, the ratio between HIVþ/CANþ and HIVþ/CAN- indicated that cannabis decreased or had mild effects on the expression of genes associated with apoptotic functions detectable in peripheral leukocytes . Neurodegeneration and inflammation were functional annotations identified in BIOCARTA. Given the large degree of overlap between these networks , we applied a merge network function in Cytoscape, which is shown in Fig. 7. The visualization of this gene network indicates that both HIV and cannabis increase genes with functions in neurodegeneration and inflammation , but cannabis decreased key contributors to the inflammatory process such as IL1b, TLR2, MyD88 and PARK7, as well as RASGRP1 . HIV infection in the context of cannabis indicated patterns that were similar to cannabis alone, with decreased expression in the same genes. Moreover, cannabis in the context of HIV elevated TLR2, TLR4 and MyD88, but had no or mild effects, or decreased a number of genes in this network .

Functional annotations associated with leukocyte-vascular adhesion and transmigration capacity were also sorted from pathway interactions. These functions were affected by HIV and cannabis.Yet cannabis lowered the expression of a large number of genes with cytoskeleton and signaling properties, including RHOA, AKT3, RAC1, BRAF and BCL2 . HIV in the context of cannabis had also lower MAPK1 and CTNNB1 compared to uninfected cannabis users . HIVþ cannabis users had a high number of genes that were lower or mildly changed compared to HIV non-cannabis users . Inflammation is highly regulated by a kinases. HIV and cannabis affected the expression of a number of kinases and genes involved in kinase regulation. The effects were differential and context-dependent. All the conditions showed decrease in CAMK4, in comparison to respective controls . HIV alone decreased mTOR, CSF1R, EPHA4, PDPK1 and DGKE . Cannabis alone, as well as HIV in the context of cannabis , decreased ATK3 and MAPKPK2. Cannabis alone decreased CALM1 . HIV in the context of cannabis decreased the expression of PGK1 and RAF1 . Cannabis in the context of HIV decreased several genes in this network that were either not modified or increased by the other conditions . These included MAP2K1, MAPK9, MAPK3, PRKCA and PDPK1 .Networks analyzed above have shown distinct effects of cannabis, which differed between cannabis alone and in the context of HIV. We used iRegulon to make predictions on transcription factors usage associated with these context-dependent patterns, in order to identify regulatory and co-regulatory elements. Fig. 11 shows the same gene network assembled based on pathway interactions in Fig. 3, but now reorganized based on the expression of transcription factor motifs in these genes’ promoters. The table legend in Fig. 11 shows the transcription factors mostly associated with the genes in the network.

The intent of this research was not to provide a definitive description of how and how often Canadian adolescents are exposed to cannabis marketing, but to provide and test a tool that could facilitate such future research. With this tool, we also aim to provide preliminary data demonstrating the existence and potential impacts of cannabis advertising on youth. Data collection occurred between March 2020 and February 2021. We recruited participants through print and digital advertising including Facebook, Instagram and other social media sites. Recruitment materials contained no information about cannabis; prospective participants responded to advertisements with a general goal of using cell phones to study advertising. Research assistants screened individuals over email or Facebook messenger to determine eligibility. Eligible individuals attended a virtual baseline session with a parent or legal guardian where they provided written informed consent and assent if they were under the age of 18. Participants who were 18 years of age provided written informed consent. All adolescents completed a baseline questionnaire assessing demographics, social determinants of cannabis use, and cannabis use history . Parents/guardians were informed that they would not have access to their child’s study data. Participants were trained to download and use the Expiwell app to photograph/screenshot and describe each individual cannabis advertising exposure that occurred during the 9-day study period through a brief questionnaire. The questionnaire assessed advertising channel ,grow tent complete kit message, and context , followed by participants’ real-time ratings of cannabis use expectancies and intentions.

Participants also responded to two daily randomly issued prompts, which also included questions about cannabis use expectancies and future cannabis use risk. Participants who completed the study received $75 or 5 hours towards 40 hours of volunteer service required for a high school diploma in Ontario. Participants received full compensation if they completed at least 70% of the device-issued random prompts within 5 min of the notification; those who completed <70% of random prompts within the 5-minute window received $50 or 3.5 hours of high school volunteer time. All procedures were reviewed and approved by Lakehead University’s Research Ethics Board. Historical research on other legalized, recreational drugs suggests that Canadian youth are likely cannabis marketing targets, but to date, almost no data exists regarding the scope and impact of cannabis marketing on Canadian youth in a post legalization context . This study presents some of the very first data that demonstrate that cannabis marketing to youth is actively occurring in Canada, and that researchers and policymakers must begin to take action on the issue in order to protect youth and public health. To our knowledge, this is the very first study to use EMA to capture adolescent cannabis marketing exposures, in Canada or elsewhere. A particular strength of the EMA approach is that it enables detailed data collection about each exposure , and its immediate impacts, that are otherwise obscured or blurred in retrospective self-report. . Thus, the current study provides novel evidence for a powerful tool that researchers and policymakers can use to obtain detailed information about cannabis marketing exposure characteristics , and strategies for assessing their subsequent effect on adolescents’ cannabis-related expectancies and intentions. We recognize that EMA methods overall are not new, and that Shiffman and others have been advancing the science of EMA for more than 30 years.

The newness of cannabis legalization in Canada and the associated taboo of cannabis in some communities , however, necessitated this extensive pilot work, not only to demonstrate the concept of the work , but also that our recruitment procedures, messaging, and protections were acceptable to adolescents and their guardians, as all of these components of the study are essential to effectively conduct the research. Overall, our results show that the protocol is feasible. Although overall rates of compliance were slightly lower than previous EMA studies of adolescents, rates among the participants whose app was working well were directly in-line with other work . Unexpectedly, a major task of this pilot research was to resolve software compatibility issues between the Expiwell app and older versions of Android platforms, including identifying device setting issues such as “do not disturb” or “battery saver” mode on individuals’ phones that interfered with participants receiving notifications from the study app. Participant compliance rates were much better among those with newer phones whose platforms were more compatible with the app. Researchers replicating or extending this research will need to consider the pros and cons of allowing participants to use their own devices for data capture as compared to using a study-issued device. Despite it’s feasibility design, this study also provides new, albeit very preliminary knowledge regarding the quantity and characteristics of cannabis marketing currently reaching Canadian adolescents; information that has previously only been described in aggregate, retrospectively, and by self-report. Overall, data showed that nearly all participating adolescents had cannabis marketing exposures during the study period. This included an average of about two cannabis-related marketing exposures per week, substantiating previous research . This finding demonstrates cannabis companies’ success in skirting current cannabis-related marketing laws which categorically prohibit marketing of cannabis products to youth.

Our data also showed that most cannabis-related exposures occurred through promotion by public figures and through ads on the internet. This finding is likely influenced by the COVID-19 context in which data were collected, and it is consistent with a significant increase among youth in the use of social media, streaming services, gaming sites and apps . At the same time, previous research has demonstrated that cannabis has an established and sophisticated presence specific to the internet based on creative advertisements designed for social media platforms , regardless of legality or media company policies . Indeed, while alcohol and tobacco industries developed their original marketing campaigns decades ago using traditional media channels , Canada’s sale and legalization of cannabis began in the digital age, and as a result, cannabis companies rely mostly on social media to market their products . Information shared through social media and the internet may also be viewed as more relevant or persuasive to youth, with the social endorsement by trusted celebrities or peers . This is concerning, as an increase in social media use and novel potential for social engagement and peer network integration could increase youth vulnerability to cannabis marketing through social medical channels . Youth exposure to online cannabis marketing is especially concerning when it is accompanied by dispensary practices facilitating easy access to cannabis. Altogether, if replicated in a larger, more representative sample and during less unusual times, data showing that most exposures occur through online formats may suggest the need to better describe and reinforce online cannabis-related marketing to mitigate harms to youth. We also found that the timing and social context for cannabis-related marketing exposures occurred consistently throughout the week, mostly in the afternoon and the evening, while youth were alone and at home. This finding makes sense given that the majority of exposures occurred through the internet or public figures while youth were browsing social media online.

As the majority of exposures also occurred through the internet or public figures, it is possible that cannabis advertisements influenced adolescents’ view of injunctive norms by suggesting high levels of peer approval of cannabis use and/or demonstrating or reinforcing positive outcome expectations related to cannabis use; this is particularly alarming because exposures generally occurred in the absence of family member who could buffer these effects . Research that identifies clusters in the context of cannabis-related marketing exposures is also important as it can inform cannabis marketing regulations, such as the need for tighter restrictions on marketing channels that frequently reach youth, especially in vulnerable contexts . We also found that youth described cannabis marketing as relatively visually engaging. This is consistent with research demonstrating that companies marketing age-restricted substances create designs that likely appeal to youth, including bright colours, cheerful messages, cartoon and/or animal characters, and other features explicitly prohibited by legislation . Unfortunately, we do not have sufficient data to conclude whether the vividness of ads impact adolescents’ cannabis expectancies or intentions to use cannabis. Policymakers and public health officials will likely benefit from larger and more detailed analyses of the features and content of cannabis ads that put them at the greatest risk for future cannabis use. Limitations of this feasibility study include its small sample size and its geographically unique sample of convenience. In Northern Ontario, adolescent cannabis use rates are high compared to major centres of the province . This smartphone owning sample may have been more willing or able to utilize EMA effectively than youth in the general population, although data now show that more than 85% of Canadian youth own and operate a smartphone . Second, information related to youths’ exposure to educational cannabis-related information and anti-cannabis information, and its subsequent effect on cannabis-related cognitions and cannabis use was not collected as part of this protocol, although it could be in the future. Lastly, this small sample size did not support meaningful statistical comparisons of random prompt and exposure occurrences,grow tent kit and assessment of differences according to demographic or SES factors.

Larger studies, conducted beyond the immediate impacts of the COVID-19 pandemic, are needed to verify the type and impact on cognitions and cannabis use for cannabis-related marketing exposures. In conclusion, to our knowledge, we have provided the first example of an EMA protocol that adolescents can use to systematically demonstrate whether Canadian cannabis marketing regulatory efforts are comprehensive, effective, and the extent to which Canadian adolescents are exposed to cannabis marketing. Policymakers, educators, families and communities need to know the nature and extent of Canadian adolescents’ exposure to cannabis marketing and its impact on their attitudes, beliefs, and ultimately their decisions to use cannabis. With data from larger, more diverse samples, this information could be used to hold companies accountable, to validate and enhance current regulations, and to minimize public harm of early cannabis use among youth. Cannabis sativa L. is one of the earliest known cultivated plants since agricultural farming started around 10,000 years ago . It is a multi-purpose crop plant with diverse agricultural and industrial applications ranging from the production of paper, wood, and fiber, to potential use in the medicinal and pharmaceutical industries. The first-ever report to reveal the prospects of C. sativa L. as a medicinal plant was already published in 1843 and described the use of plant extracts to treat patients suffering from tetanus, hydrophobia, and cholera . However, the first chemical constituent identified was oxy-cannabis , isolated cannabinoid , and fully identified in 1940 was cannabidiol followed by tetrahydrocannabinol  and cannabigerol in 1964, and cannabichromene in 1966 . identification of THC later led to an understanding of the endocannabinoid system followed by the discovery of the first cannabinoid receptor in 1988 . CB1 receptor acts as a homeostatic regulator of neurotransmitters for pain relief mechanisms, but the same mode of action was responsible for intoxicating effects from cannabinoids’ excessive use. Thus, the understanding of mode of action of CB1 receptor raised concerns about the adverse effects of cannabis use. Consequently, the plant was removed from the medicinal category and recategorized exclusively to the category of drug-type plants. Cultivation and use of cannabis plants for recreational, medical, and industrial use were strictly banned and severely limited the scientific research in the field.

Owing to strict legal regulations, the plant remained unexplored for its incredible potential in drug discovery for an extended period until it was legalized for medical use first in California and later in many countries around the globe. Extensive research followed legalization to explore the chemodiversity of cannabinoids for potential clinical value. In total, more than one thousand compounds—278 cannabinoids, 174 terpenes, 221 terpenoids, 19 flavonoids, 63 flavonoid glycosides, 46 polyphenols, 92 steroids—have been identified . Nearly 278 of these compounds are cannabinoids and classified as phytocannabinoids to distinguish them from endocannabinoids . Cannabimimetic drugs binding to CB1-receptors in the endocannabinoid system can also be found in algae, bryophytes, and monilophytes. The major cannabinoids in cannabis include THC, CBD, and CBC, their precursor CBG and cannabinol. To date, 10 CBN-type, 17 CBG-type, 8 CBD-type, and 18 THC-type cannabinoids have been isolated . Cannabigerolic acid , a CBG-type cannabinoid, is the central precursor for the biosynthesis of psychoactive THC, non-psychoactive CBD, and CBC .