Cannabis Grow

In addition, to validate the pupillography as a medicolegal proof, studies with a larger sample are needed as well as pupillographic analysis in subjects who have taken poli-drugs or drugs and alcohol together. Road traffic injuries are the leading cause of death among people aged between 15 and 29 years and it will rise to become the fifth leading cause of death by 2030. These subjects represent the main prevention target of this method.Neuropathic pain is initiated by a damage to the nervous system which might be attributed to infectious agents such as human immuno deficiency virus , metabolic disease, neurodegenerative disease, multiple sclerosis  and physical trauma . Regardless of the cause, damage to the nervous system and subsequent neuropathic pain can be accompanied by dysesthesia or allodynia. As the pathophysiology of neuropathic pain is complex , the current therapeutic modalities are still limited. Hence, it is imperative to find a new therapeutic agent that helps treat or minimize the symptoms associated with neuropathic pain disorder. Cannabis is a promising plant-based medicine that has garnered much attention of late for the treatment of various conditions associated with pain and inflammation. The potential health implications of cannabis are accredited to Δ-9-tetrahydrocannabinol  and cannabidiol. In the majority of studies todate, THC and CBD alone or in combination have been examined for the treatment of various disorders, such as pain and inflammation. However, few studies have investigated the biological benefits of full spectrum cannabis plant extract. Given that cannabis is known to produce a large number of cannabinoids along with numerous other biologically relevant products including terpenes and others, it stands to reason that studies involving purified THC and/or CBD may not accurately reflect the potential biological benefits of the full-spectrum cannabis extract especially with regard to their crucial role in the treatment of neuropathic pain and inflammation.

Therefore, the goal of this review is to discuss the current knowledge about the potential beneficial effects of full-spectrum cannabis extract in pre-clinical studies involving rodents with neuropathic pain and inflammation.In 1964, Dr. Raphael Mechoulam discovered THC, which was the first identified cannabinoid. This groundbreaking work paved the way for the discovery of the endogenous cannabinoid system of which anandamide and 2-arachidonoylglycerol are considered the main endogenous cannabinoids in higher order mammals,vertical grow system including humans. Both anadamide and 2-arachidonoylglycerol regulate the sensitivity of serotonin, dopamine, gamma-aminobutyric acid  and glutamate in the central nervous system, thus demonstrating how these endogenous cannabinoids regulate many physiological and pathological processes such as pain, immune response, appetite, thermoregulation, energy metabolism, depression, memory and fertility.Anandamide was the first endocannabinoid isolated and it is chemically characterized as N-arachidonoylethanolamine. The name of anandamide originates from Sanskrit term ananda, which refers to “bliss”. Bliss is defined as euphoria that involves physiologic and psychologic harmony. Anandamide is synthesized from the precursor N-arachidonoyl phosphatidylethanolamine by phosphodiesterase phospholipase D enzyme. Once anadamide is synthesized, it is released from the neuronal terminal in a calcium ion-dependent manner and binds to presynaptic cannabinoid receptors. Anandamide is then rapidly up taken by neurons and astrocytes where it is degraded by fatty acid amide hydrolase  into ethanolamine and arachidonic acid. The other endogenous cannabinoid is 2- arachidonoylglycerol, which is synthesized by the hydrolysis of an inositol-1,2-diacylglycerol by phospholipase C. Similar to anadamide, 2-arachidonoylglycerol binds to CB receptors and undergoes rapid biological degradation and catalytic hydrolysis, which is mediated by monaoacylglycerol lipase. Of importance, MGL along with FAAH are considered potential therapeutic targets that can regulate endocannabinoid levels.The most well characterized phytocannabinoids are THC, CBD, cannabinol , cannabigerol  and cannabichromene . These botanical cannabinoids exist as inactive monocarboxylic acids containing precursors referred to as tetrahydrocannabinoic acid , cannabidiolic acid , cannabigerolic acid , and cannabichromenic acid , respectively. The presence of a carboxylic acid moiety on these chemicals precludes cannabinoids, particularly THC, from being bioavailable and binding to either CB receptors or other biological targets. Thus, the conversion of THCA, CBDA, CBGA, and CBCA to THC, CBD, CBN, CBG, and CBG, respectively, through decarboxylation is necessarily before any biological effect can be observed.

Decarboxylation of these carboxylic acids can be promoted by heating the plant above 105 °C, which can be achieved during the smoking or baking process .THC is the primary psychoactive component of Cannabis sativa and chemically analogous to N-arachidonoylethanolamine. THC is a euphoric agent that has anti-nociceptive, anti-inflflammatory, sedative and muscle relaxant effects. Additionally, THC increases appetite, dilates bronchial muscle and it has anti-emetic, anti-spasmodic, neuroprotective and anti-oxidant properties. Mechanistically, the physiological effect of THC is mediated primarily through the activation of CB1 and CB2 receptors with preferential binding to CB1 receptors.This effect might be attributed to the ability of THC to inhibit N-methylD-aspartate  receptor activity in addition to the decrease in the hippocampal acetylcholine release . The decrease in acetylcholine release may be due to the activation of the CB1 receptor on parasympathetic neurons. Intriguingly, it has recently been shown that a low dose of THC reversed the age-related decline in cognitive performance in aged but not young mice. This effect was associated with increased expression of synaptic marker proteins and enhanced hippocampal spine density through glutamatergic CB1 receptors-dependent mechanism . Thus, this study raises the possibility that THC or full-spectrum cannabis extracts may have the potential to reverse cognitive decline in the elderly and suggests an agedependent effect of THC.CBD is the primary non-psychoactive component of Cannabis sativa and possesses sedative, anti-inflammatory, anti-convulsive and anti-psychotic actions, but does not have the typical THC side effects. Of importance, the powerful anti-convulsant effect of CBD appears to be mediated through a CB receptor-independent mechanism. Indeed, CBD mediates neuronal inhibition and anti-epileptic effects through gamma-aminobutyric acid A  and adenosine A1 receptors dependent mechanisms. In addition, CBD has anti-psychotic and neuroprotective effects that are mediated via increasing the effect of dopamine and norepinephrine, activating the 5-hydroxytryptamine 1A  receptor, inhibiting adenosine transporter, blocking T-type voltage-gated calcium channels and reducing glutamate induced-neurotoxicity. Numerous additional effects of CBD have also been reported. For instance, in the heart, CBD inhibits THC-induced tachycardia through the activation of adenosine A1 receptor.

Moreover, it has been reported that CBD protects against cardiac dysfunction, fibrosis, oxidative stress, and cell death signaling pathways in diabetic cardiomyopathy and doxorubicin-induced cardiotoxicity. In addition to the cardiac effects, CBD has recently been shown to be cytotoxic in estrogen receptor-positive and triple negative breast cancer cells through the induction of apoptosis aswell as it increases the uptake of the chemotherapeutic agent, doxorubicin, to induce apoptosis in these cells through transient receptor potential vanilloid type-2 -dependent mechanism. Thus, the potential benefits of CBD are extensive, even independent from the classical endocannabinoid system involving CB receptors.CBN is an oxidized by-product of THC produced in trace amounts by aged cannabis upon long exposure to air. Studies have shown that while CBN is inactive when administered alone to healthy volunteers, it still can potentiate the sedative effect of THC. Given that CBN is closely related to CBD in terms of the chemical structure, it shares the anti-convulsant and anti-inflammatory effects with CBD. The physiological effect of CBN is attributed to the modulation of the CB2 receptor with lower affinity for the CB1 receptor in comparison to THC .CBC is one of the main phytocannabinoids and appears to have no affinity to CB1 and CB2 receptors. Similar to CBD and THC, CBC possesses anti-inflammatory and anti-nociceptive effects through the inhibition of the cyclooxygenase enzyme and its associated prostaglandins. In contrast to CBD, CBC neither has an anti-convulsant effect nor inhibits the activity of cytochrome P450.CBG is the precursor phytocannabinoid compound of THC, CBD and CBC and is only produced in trace amounts in cannabis. Although CBG has low affinity to CB receptors, it is still capable of reducing pain, erythema and inflammation through the inhibition of peripheral lipooxygenase enzyme and the activation of central α2-adrenergic receptor. Furthermore, CBG has an anti-depressant effect because it is a potent anadamide uptake inhibitor as well as a moderate 5- HT1a antagonist.diate their pharmacological actions by binding to CB1 and CB2 receptors and through the regulation of the production and the degradation of endogenous endocannabinoids. Both CB1 and CB2 receptors are 7-domain Gi/o-protein coupled receptors decreasing the level of cyclic-AMP by suppressing adenylate cyclase. CB1 receptors are abundant and widely expressed throughout the CNS  and they are responsible for the psychopharmacological and analgesic effects of THC. Of particular interest, CB1 receptors have high expression level in areas of the brain that are implicated in nociceptive perception, such as the thalamus and amygdala,cannabis grow equipment the midbrain periaqueductal grey matter cells, and the substantia gelatinosa of the spinal cord.

The presynaptic localization of CB1 receptors enables cannabinoids to modulate neurotransmitter release such as dopamine, noradrenaline, glutamate, GABA, serotonin and acetylcholine. The activation of the CB1 receptors in the aforementioned brain areas modulates nociceptive thresholds and produces multiple biological effects by regulating the balance between excitatory and inhibitory neurotransmitters. While the CB2 receptor has limited expression in sensory and CNS cells, it is mainly expressed in peripheral tissues, including keratinocytes and tissues of the immune system such as the lymphatic system. The CB2 receptor was shown to contribute to analgesia through suppressing the release of inflammatory mediators by cells located adjacent to nociceptive nerve terminals. In addition, activation of peripheral CB2 receptors blocks the transduction of pain signals into the CNS. Given that CB2 receptors are expressed in several types of inflammatory cells and immunocompetent cells, it is reasonable to assume that the activation of peripheral CB2 receptors may contribute to analgesic effect in conditions of inflammatory hyperalgesia and neuropathic pain such as MS. Consistent with this notion, increased numbers of microglia/macrophage cells expressing CB2 receptor have been reported in spinal cords derived from MS patients relative to controls, suggesting the involvement of CB2 receptor in the regulation of pain and inflammation in MS patients. Based on these findings, it was proposed that cannabinoid-based pharmacotherapies might be effective therapies for the reduction of pain due to MS.The anti-nociceptive effect of cannabinoids might not necessarily be due entirely to the activation of CB1 and CB2 receptors. Indeed, the analgesic effects may be due to the modulation of the transient receptor potential vanilloid 1 . The evidence supporting this is based on the observation that the anti-nociceptive effect of CBD in neuropathic rats was completely reversed by capsazepine, a known TRPV1 activator. Other receptor sites implicated in the action of CBD include the suppression of putative novel cannabinoid G protein coupled receptor GPR55, NMDAR and α1-adrenoreceptors and the activation of 5HT1A, adenosine A2, and the peroxisome proliferator-activated gamma  receptors. In addition, THC and CBD are positive allosteric modulators of the μ- and δ-opioid receptors, suggesting the involvement of these receptors in the anti-nociceptive effect of THC and CBD. Moreover, CBD has been shown to block low-voltage-activated  Ca+2 channels, stimulate the glycine-receptor, and modulate the activity of FAAH.

The action of CBD via these pathways may be responsible for the suppression of neuronal excitability and pain perception. In addition, there is evidence that CBD inhibits synaptosomal uptake of dopamine, noradrenaline, GABA, serotonin in addition to cellular uptake of anandamide. The modulation of these neurotransmitters might explain the neuroprotective and the anti-nociceptive effects of CBD. Moreover, CBD and THC have been shown to inhibit the cycloxygenase-2 enzyme and the production of arachidonic acid metabolites, prostaglandins, suggesting anti-inflammatory effects. Of note, the inhibition of cycloxygenase-2 was associated with an increase in the level of endocannabinoids, anandamide and 2-AG. This observation suggests that the suppression of cycloxygenase-2 enzyme by CBD and THC may not only decrease nociceptive and inflammatory prostaglandins but it may produce an indirect increase in the level of endocannabinoids, anandamide and 2-AG .Another important biological system that is affected by cannabinoids, at least when consumed orally, is the gastrointestinal microbiota. The gut microbiota is known to produce various metabolites resulting from the fermentation of molecules of either exogenous source  or from endogenous origin. These metabolites can act as signals that can contribute to the maintenance of host immunity and physiology . For example, the gut bacteria Lactobacillus acidophilus, metabolizes tryptophan from dietary sources such as eggs, milk, red meat, and vegetables into diverse metabolites, including indole propionic acid, which can signal through the aryl hydrocarbon receptor.

Illicit drugs are substances that either stimulate or inhibit the central nervous system or cause hallucinogenic effects  to the effect that their nonmedical use has been prohibited globally . For some substances, like cannabis, the prohibition or legalization status varies widely over time and over different countries and states . In the present paper we focus on illicit drugs in a broad sense, including cannabis, ecstasy, stimulants, opioids. We do not consider substances that are legal in the Netherlands, such as nicotine and alcohol. Cannabis is one of the most widely consumed drugs worldwide, with 192.2 million past-year users in 2016, corresponding to 3.9 per cent of the global population aged 15–64 years . Despite the increasing use of cannabis for medicinal purpose and an ongoing debate about medicalization and decriminalization, associations with adverse health effects have been reported. These adverse health effects include development of dependence, cardiovascular disease, impaired respiratory function and mental health problems . Another increasingly popular drug is ecstasy, a psychoactive drug that consists of MDMA. The prevalence in the global population aged 15–64 years is estimated to be 0.4 % . In Europe, approximately 1.7 % of young adults  have used ecstasy, with estimates ranging from 0.3%–5.5% between countries . Other relatively popular illicit drugs include amphetamine and cocaine , with worldwide past year estimated prevalences of 0.77 %, and 0.35 % respectively . The past year prevalence of opioids  was 0.37 % worldwide in 2017 . For all illicit drug use together, the overall disease burden was estimated to be 27.8 million attributable disability-adjusted life-years  in 2017. DALYs reflect the number of years lost due to ill-health, disability or early death. The mortality rate due to illicit drugs was 6.9 deaths per 100,000 people in 2017 . Substance use, including cannabis use, is moderate to highly heritable ; Verweij et al., 2010, 2017. The largest genomewide association  study for cannabis use to date has successfully identified 35 genes  associated with lifetime cannabis use .

Two other genome-wide association studies identified genes for cannabis dependence and cannabis disorder . In the current study we have information on use , and will therefore use the GWA for cannabis use  as discovery sample. Epidemiological studies have consistently shown correlations between use of different substances, such that individuals that use one substance are more likely to also use another . The phenotypic correlations between substances are partly explained by common genetic influences . Many genetic variants, each with a small effect size, contribute to complex behaviors, such as substance use. With methodological advances in molecular genetics and increased sample sizes in GWA studies it has become viable to use many measured genetic variations in individuals to estimate their genetic vulnerability for a certain trait. To do this, polygenic scores  in individuals from a target dataset can be calculated based on their genome-wide genetic data and the genetic effect sizes estimated in large GWA studies . If the PGS in the target set, for example reflecting the genetic vulnerability for cannabis use, is associated with drug use, for example ecstasy, this would suggest that there is overlap in the genes underlying grow cannabis in containers and ecstasy use. In the present study, we used summary-level data from the largest GWA study for lifetime cannabis use to date  to generate PGSs in an independent sample of 8348 individuals registered at the Netherlands Twin Register . We tested the association of the PGS for lifetime cannabis use with ecstasy, stimulants  and a broad category of drug use, including stimulants, opioids and hallucinogens.A significant association  may indicate that there are common underlying genetic predispositions to the use of these substances, or can be the result of a causal association  between the use of the different substances. In that last case, use of cannabis may lead to use of ecstasy or other drugs, and therefore genes associated with cannabis use will also –indirectly- be associated with use of other drugs. The different explanations are not mutually exclusive and are difficult to distinguish.

If a significant association is found between the cannabis PGS and use of other drugs, we will explore the nature of this relationship by repeating the same analyses separately in cannabis users and non-users. If the association between the polygenic risk for cannabis and drug use is only significant in cannabis users and not in never users, this might indicate that causal effects play a role , although other explanations  are still possible. To further explore the causal role of cannabis in other drug use, we also explored drug use in monozygotic twins discordant for cannabis use.Prediction analyses were carried out using generalized estimation equations with a logit link function. To account for familial relatedness, this method uses an exchangeable covariance matrix, allowing for correlated residuals between family members. Analyses were run using robust standard errors for the parameter estimates. Sex, age, and 10 genetic principal components were included as covariates in all analyses. Principal components were included to correct for effects of population stratification. Age was negatively correlated with the outcome measures  and males had a higher prevalence of drug use than females. To explore possible sex differences we tested the interaction between the cannabis PGS and sex for ecstasy, stimulants and any illicit drug use . Estimates of the explained variance  were obtained from logistic regressions by subtracting the pseudo-R2 estimates of the model with only covariates from the model including both the PRSs and covariates. Odds ratios were also obtained through the regression analyses.To inspect how drug use varied with increasing cannabis PRS we used quintile plots. The cannabis PRS was divided in quintiles, and we calculated the odds ratio for respectively ecstasy use, stimulants and any illicit drug use within each quintile. For the twin analyses, we compared the prevalence of drug use in the cannabis using twins to that of their non-using co-twins with a McNemar test . In this design, genetic and common environmental influences are controlled for because MZ twins share all their genetic material and their  home environment. If the association between cannabis use and other drug use is solely explained by genes and/or shared environmental factors, then the twins who have used cannabis and their co-twins who have not should be equal in their use of other drugs. In contrast, if the association is to some extent causal or explained by environmental factors for which twin pairs are discordant, we would expect to find significantly higher prevalences in the cannabis users compared to their unaffected MZ co-twins.We showed that the genetic liability underlying cannabis use significantly explained variability in ecstasy, stimulant, and any illicit drug use.

When the sample was stratified for lifetime cannabis use, this association seemed to be stronger in cannabis users compared to nonusers for ecstasy and stimulants, but not for any drug use. However, this trend was not significant after correction for multiple testing. The observation that the PGSs for cannabis use were significantly associated with the examined drug use variables , suggests genetic overlap between the traits. The explained variance ranged between 0.5 and 1.2 %, which is quite low but consistent with other PGS studies of addictive phenotypes . As far as we know this is the first study exploring the genetic overlap of the genetic vulnerability for cannabis with other illicit drug use. Only a few studies explored genetic overlap across substances using a PGS method. A previous study showed genetic overlap between PGS for cigarettes per day with glasses of alcohol per week and cannabis initiation as well as between PGS for age at onset of smoking and age at regular drinking. However the PGSs for smoking initiation and smoking cessation did not significantly predict alcohol or cannabis use, possibly due to limited power . Demontis et al. showed that a PGS-for lifetime smoking was associated with cannabis use disorder . Recently, Chang et al. tested the association between 5 PGSs for licit substances  with 22 target phenotypes for illicit substance use and substance use disorders. Only 9 of the 110 tested associations were significant. Interestingly, the stimulants  showed some significant results, while associations with sedatives or pain killers were not significant. In particular, the PGS for smoking initiation significantly explained variation in the risk of cocaine, amphetamine, hallucinogens, ecstasy and pot for cannabis initiation, as well as DSM-5 alcohol use disorder . The PGS for drinks per week significantly explained variation in cocaine, amphetamine and ecstasy initiation . Taken together, these results indicate genetic overlap between the use of different substances, although in previous studies not all tested associations were significant. As explained in the introduction, genetic overlap may indicate that there are common underlying genetic predispositions to the use of these substances . In case of drug use, this could be genes involved in the vulnerability for reward , but could also reflect genetic vulnerability for more general personality traits, such as impulsivity, risk-taking behavior or sensation seeking which are also often associated with drug use  or educational attainment . On the other hand, genetic overlap can also be the result of a causal association . To explore whether cannabis use itself caused the use of ecstasy, stimulants or any drugs we tested the association between the PGS for cannabis and the outcome variables in cannabis users and never users separately. The association of the cannabis PGS with ecstasy and stimulant use seemed stronger in cannabis users compared to never users which could point to a causal relationship . This effect was only observed in people born after 1968, but given the fact that the prevalence is higher in this younger group there is probably more power to detect an association than in the older group. Since the association was not significant after correction for multiple testing we must be cautious with drawing conclusions. In addition, we explored the differences in drug use prevalence in MZ twin pairs discordant for cannabis use.

The twins who used cannabis had more often used drugs, compared to their MZ co-twins who never used cannabis. This is in accordance with previous research using the co-twin control methodology . This finding suggest that the differences in illicitit drug use between twins who used cannabis and their unaffected co-twins cannot solely be explained by genetic influences  but that individualspecific environmental factors such as cannabis use play a role. Together, this suggested that cannabis use could be a causal factor for other drug use. Future studies should explore causality with more advanced methods such as Mendelian Randomization , but larger samples sizes are needed than available in the current study to obtain enough power. In previous studies using two-sample bi-directional Mendelian Randomization analyses, no evidence was found for causal relationships between smoking, alcohol, caffeine, and cannabis  but these studies did not includeother illicit drugs. There might not be a sequential order of use for initiation of smoking, alcohol use or caffeine consumption since these substances are widely available and some people start with smoking while others start with drinking first. A gateway from licit substance use to illicit drug use or from one drug  to other drugs  might be more plausible. Ideally, causality should be tested in two directions, because some studies have also found evidence supporting a reverse-gateway hypothesis . For example, cannabis could influence ethanol  levels, although existing findings are inconclusive , and a recent MR study did not find evidence for a causal relationship ). A limitation of the PGS approach is that currently only large GWA studies are available for lifetime cannabis   and opiod use disorder , but not for other illicit drugs such as ecstasy. Large genome wide association studies for illicit drugs are needed as input to calculate reliable PGSs. A strength of the current study is the large discovery sample for cannabis . It is known that a larger discovery sample leads to a more reliable  PGS in the target sample. In the present study we showed as a proof of concept that the PGS for lifetime cannabis use was significantly associated with cannabis use in the target sample.

Two herb spice tobacco grinders were purchased from commercial retailers. The Cannabis research program at the National Institute of Standards and Technology  provided 20 cannabis samples, all of which had the % total THC and % total CBD previously determined through Liquid Chromatography-Photodiode Array .First, 10 µL of the plant extract or reference standards were pipetted onto the PSPME substrate. Next, 10 uL of 0.1% FBBB solution was then pipetted onto the substrate followed by 10 uL of 0.1 N NaOH.The solvents evaporate within 1–2 min as the color develops. A red color is indicative of THC and an orange color is indicative of CBD. The FBBB test was performed in 5 replicates per extract. Each substrate was photographed with a Dino-Lite AM4115ZT digital microscope . A Dino-Lite AM4115T-GRFBY Digital Microscope was used to capture fluorescence images of the substrates. The Dino-Lite AM4115T-GRFBY uses a 480 nm excitation light source and contains emission filters for 510 nm and 610 nm. These images were taken in the absence of ambient light to remove interference from outside sources of light. The visible and fluorescence images are analyzed using the ImageJ software using the RGB measure plugin to obtain the average RGB numerical code across each substrate. All five blue ridge hemp samples and 20 cannabis samples of known cannabinoid concentrations  were evaluated using the FBBB reagent with 5 replicates. For each replicate, a color image, a fluorescence image, and fluorescence spectra were obtained. The color results for all samples are summarized in Table 5. All of the hemp samples formed an orange color when reacted with FBBB, except for Sample 11 and Sample 12, which did not have any reaction. Of the 13 samples that are marijuana , 6 of them produced an orange color instead of the red color indicative of THC.

These six were Sample 6, Sample 9, Sample 10, Sample 18, Sample 19, and Sample 20. For samples 6, 9, 10, and 20 the total CBD was at a higher concentration than total THC, all containing a THC:CBD ratio below 1. Samples 18 and 19 had THC:CBD ratios of 1.0 and 1.4 respectively. The other marijuana type samples had a THC:CBD ratio much higher than 2 and formed a red color. Samples that had a THC:CBD ratio below 2  did not fluoresce brightly under the Dino-Lite microscope at 480 nm excitation. Importantly, the marijuana-type samples that either had no CBD or a high THC:CBD ratio did fluoresce brightly under the Dino-Lite at the same excitation. These results suggest that when there is more CBD than THC in the cannabis grow set up plant, or if the concentrations are similar, the FBBB will produce an orange color indicative of hemp rather than a red color indicative in marijuana. In addition, when the THC:CBD ratio is low, the fluorescence of the chromophore will also be low. The fluorescence spectra from the VSC2000 for hemp-type samples showed a low % intensity at 655 nm, typically between 10% and 20%, and a higher intensity at 695 nm, between 15% and 40%. The exception to this were samples 11 and 12 whose extracts did not react with FBBB and had similar spectra to the blank. The marijuana-type samples with a low THC:CBD  showed similar spectra to the hemp samples, with fluorescence intensities at or below 20% at 655 nm for those with THC:CBD significantly lower than 1. Samples 18, 19, and 20, which have THC:CBD from 0.48 to 1.4, all showed slightly higher intensities at 655 nm than the hemp samples . For marijuana-type samples with a THC:CBD above 2, the intensity of fluorescence increases between 40 and 70% at 655 nm and 695 nm. Low fluorescence intensity for hemp samples at 655 nm is expected since there is very little THC in these samples. For samples 6, 9, and 10 there was much more CBD than THC in the cannabis plant leading FBBB + CBD to form over FBBB + THC. Samples 18, 19, and 20 showed a slightly more intense band at 655 nm. This increase could be attributed to the fact that there is a similar concentration of CBD and THC in these samples and allowed for FBBB to react with both THC and CBD.

In addition, all cannabis extracts contain a band at 695 nm. This interference is likely due to chlorophyll and other pigments from the plant material, however, even with this interference, the difference in fluorescence intensity between hemp and marijuana-type cannabis with a high THC:CBD is noticeable. When the THC:CBD ratio is below 2, the fluorescence intensity decreases. This is consistent with the results obtained from the color images and fluorescence images using the Dino-Lite microscopes. A comparison of a marijuana-type sample and a hemp-type sample through color images, fluorescence images, and the fluorescence spectra is shown in Fig. 7. Linear Discriminant Analysis  was used as a supervised technique to determine whether FBBB can be used to correctly classify hemptype cannabis and marijuana-type cannabis. Each sample described in Table 5 was evaluated in 5 replicates. For each replicate RGB of the color image, RGB of the fluorescence image, and the % intensity at 655 nm and 695 nm in the fluorescence spectra were recorded. The LDA analysis was performed using the JMP software. The first LDA model was constructed using % intensity at 655 nm and % intensity at 695 nm values as the variables. The resulting model had an R2 of 0.61 and misclassified samples 6, 9, 10, 14, 15, 18, 19, 20. Samples 6, 9, 10, and 18–20 are marijuana-type samples with THC:CBD below 2, showing similar fluorescence spectra to hemp samples leading to their misclassification. Samples 6, 9, 10, and 18–20 were removed from the data set and LDA was performed again using the data from the 7 remaining marijuana-type samples  and the 12 hemp-type samples. This analysis resulted in an R2 of 0.999 and no misclassifications. LDA was also performed using the R, G, and B codes for each color image and fluorescence image. LDA of all the samples using RGB for the color images produced an R2 of 0.51 and misclassified samples 3,4, 10, 18–20, and one replicate of 16 and 17 each. To improve the model, all marijuana type samples with THC:CBD below 2 were removed from the data set. Samples 11 and 12 were removed as well since they did not produce a color as they were likely the cause of the misclassification of samples 3 and 4, which produced a light red color.

This did improve the model with the R2 value of 0.95 and only misclassifying one replicate of sample 3. This indicates when using only RGB of the visible image, one should exclude samples that do not form a color as it may cause misclassification. An LDA model of all samples using RGB of the fluorescence images taken for each replicate was also made. This LDA model misclassified multiple hemp-type and marijuana-type samples resulting in an R2 of 0.46. When the marijuana type samples with THC:CBD below 2 were removed from the data set, there were no misclassifications and R2 was 0.995. Finally, an LDA model was made to classify the marijuana-type samples with a high THC:CBD and all the hemp-type samples using the R,G and B  from the color images and R-F,G-F and B-F  from the fluorescence images for a total of 6 variables. This model resulted in a clear separation between hemp-type and marijuanatype cannabis resulting in an R2 of 1.0  with G  providing the highest correlation to hemp  and R-F  providing the highest correlation to THCrich cannabis . A Receiver Operating Characteristic  of the model showed that the area under the curve for both hemp and marijuana are 1, displaying excellent selectivity and sensitivity when combining color and fluorescence to discriminate from hemp-type cannabis  and marijuana type cannabis. The FBBB test was used to evaluate 6 different cannabinoids, 5 commercial hemp strains, 20 cannabis samples, and various herbs and spices. It was determined that when FBBB reacts with THC, it forms a red chromophore that fluoresces under 480 nm light. Conversely, when reacted with CBD or CBD-rich products, such as outdoor cannabis grow, an orange chromophore is formed, and this chromophore does not fluoresce. This is the first time, to the author’s knowledge, that the fluorescence of the FBBB + THC chromophore/fluorophore is reported for a colorimetric test. This fluorescence is easily visualized using a portable Dino-Lite microscope and its spectra obtained with a VSC2000 spectrometer. The intensity and wavelength of the fluorescence for the chromophore combined with the distinct red color it displays makes for a more selective and sensitive test to differentiate between marijuana and hemp. The structure for FBBB + THC has been previously determined by the Almirall lab, as shown in Fig. 1. The chromophore results from an extended conjugation of π-bonds decreasing the distance between energy transitions between the ground state and excited state.

This extended conjugation causes a “red shift” of the FBBB chromophore, which is responsible for the red color and the fluorescence that is observed when THC reacts with FBBB. One theory for CBD + FBBB lacking fluorescence intensity is that CBD has a less rigid structure than THC. It is known that structure rigidity and a fused ring structure increases the quantum efficiency, and therefore fluorescence of a molecule. Since CBD is less rigid than THC and does not have a fused ring structure, it is prone to relaxation through internal conversion rather than through radiative means. Therefore, FBBB + CBD likely relaxes through nonradiative mechanisms, which decreases overall fluorescence. The difference in both color and fluorescence that is observed for FBBB + THC and FBBB + CBD is an advantage that the FBBB test has compared to other tests for presumptive analysis of cannabis, which only use color. The selectivity of the FBBB test was evaluated by analyzing 5 other cannabinoids, herbs, spices, essential oils, tobacco, and hops. None of these substances produced color like that of FBBB + THC nor fluorescence observed. For the colorimetric calibration experiments, it was shown that when the ratio of THC:CBD is above 1, a red color forms indicating that there is marijuana present. These experiments also found that the absolute LODs for THC on the PSPME substrates was as low as 500 ng, which is significantly lower than the LOD for the D-L test . The THC LOD for the 4-AP test is not currently known but expected to be >500 ng. This study demonstrates that the FBBB test is very selective and sensitive for THC, forming a red color and an intense fluorescence that can be distinguished from other chromophores. In addition, this chromophore is long lasting, allowing the color and fluorescence to be observed long after the test is performed. This longlasting color is attributed to the nature of the FBBB being a diazonium salt, which are known to be stable and even used to form dyes in textiles. One limitation that was discovered for the FBBB test is that the reagent is not stable at room temperatures over more than a few days, losing its color and producing no reaction with THC or CBD. The FBBB reagent and the preloaded FBBB substrate were stable in the refrigerator/cooler for at least 45 days. The temperature instability is not ideal for field work since a kit using the Fast Blue BB test would likely be exposed to temperatures above 4 ◦C. For this reason, future work will focus on determining a method to maintain the FBBB stable at ambient temperatures. The analysis of the Blue Ridge Hemp and NIST samples demonstrate that FBBB is very effective at discriminating between hemp-type samples with THC content <0.3%  and marijuana-type samples with a high THC content or THC:CBD ratios. Marijuana-type cannabis containing >0.3% THC and high CBD could be misclassified as hemp but these types of samples are uncommon in seized drugs. The results of these LDA models using RGB inputs support the observed findings of the visual evaluation of the Blue Ridge and NIST samples with FBBB.

We further sought to determine whether gender, dose, cannabinoid content of cannabis used and/or cannabis use sessions across time would predict changes in symptom severity. Results revealed that, on average, respondents self-identifying as having PTSD reported a 62% reduction in the severity of intrusive thoughts, a 51% reduction in flashbacks, a 67% reduction in irritability, and a 57% reduction in the severity of anxiety, from before to after inhaling cannabis. Moreover, these symptom reductions were reported in the majority of cannabis use sessions for intrusive thoughts , flashbacks , irritability , and anxiety . While inhaled cannabis resulted in significant and substantial reductions in ratings of all four of the PTSD symptoms that we assessed, it is important to note that we detected significant heterogeneity in these effects across individuals, indicating that cannabis may not uniformly reduce PTSD symptoms for everyone. Concretely, while the four baseline LCS models confirmed that the reported symptom reductions were statistically significant, the variance estimates for all four models revealed significant individual differences in the rates of change among participants for each symptom. Taken together, these results provide strong evidence that cannabis can provide temporary relief from symptoms of PTSD, but that the magnitude of these effects varies across individuals. One source of this heterogeneity may have stemmed from differences in baseline ratings of the symptoms. The LCS models revealed significant covariance estimates between symptom severity before cannabis use and the latent change factor for each symptom, which indicates that those with more severe symptoms reported greater reductions in their symptoms after cannabis use. This may indicate that cannabis is more effective for more severe symptoms. Alternatively, this finding could also simply reflect the fact that there is more room for improvement of more severe symptoms. While the LCS models indicated that gender did not predict changes in symptom severity from before to after cannabis use ,cannabis drying racks comparisons of men and women’s mean severity ratings before and after cannabis use revealed small but statistically significant gender differences.

Specifically, women reported significantly greater symptom severity before cannabis use for all four PTSD symptoms we assessed. Women also reported significantly greater post-cannabis use severity for intrusions, flashbacks, and anxiety. This finding that women reported more severe symptoms of PTSD than did men is consistent with previous research indicating women are more likely to meet criteria for PTSD and to demonstrate worse symptom severity . The results further revealed that women reported significantly more cannabis use sessions during which flashback and anxiety severity were reduced than did men. In contrast, men reported significantly more sessions during which irritability was reduced than women. Nevertheless, while these differences were statistically significant, they were small in size and rather trivial . Both genders reported that their symptoms were reduced in the vast majority of cannabis use sessions. Concentrations of THC, CBD, and interactions between THC and CBD appeared to have no influence on changes in any of the four symptoms assessed. Cannabis can contain up to 120 cannabinoids, over 250 terpenes, around 50 flavonoids, as well as a number of other molecules that may exert biological action  and therefore it may be one of these other constituents or an entourage effect that is responsible for the therapeutic effects of cannabis on these PTSD symptoms. Unfortunately, information on these other constituents was too sparse in the obtained data to permit for meaningful analyses. Clinical trials are needed where THC, CBD, minor phytocannabinoids and/or terpenes are directly manipulated by investigators to determine the concentrations of these constituents that provide the greatest relief from PTSD symptoms. Results pertaining to the time/cannabis use session predictor in the LCS models revealed no changes in the efficacy of cannabis in reducing anxiety or flashback severity across cannabis use sessions over time. In contrast, time was a significant predictor of reductions in intrusions and irritability, with later cannabis use sessions predicting greater symptom relief than earlier cannabis use sessions. These findings may indicate that cannabis becomes a more effective treatment for intrusions and irritability as it continues to be used to manage these symptoms over time.

Alternatively, this finding may represent a statistical artifact, such that individuals who obtain the greatest relief in intrusions and irritability from cannabis may simply be the most likely to use cannabis for longer periods of time. Further longitudinal studies are required to better establish the direction of this effect. Moreover, results of multilevel models further revealed that the dose of cannabis used increased significantly across time/cannabis use sessions for anxiety, which may be an indicator of tolerance. Collectively these two sets of results indicate that people are using consistent doses to achieve larger reductions in intrusions over time and higher doses to achieve larger reductions in anxiety over time. The escalations in dose for anxiety adds credence to concerns of individuals with PTSD developing cannabis dependence , especially given that excess cannabis use has been associated with more negative long-term outcomes in individuals with PTSD . Interestingly, the severity of baseline symptom ratings did not change significantly across time/cannabis use sessions. This may suggest that while acute use of cannabis leads to perceived reductions in acute symptom severity, these effects may not extend beyond the period of intoxication and regular use of cannabis may simply maintain the disorder over time. In other words, while cannabis intoxication can provide transient relief from PTSD symptoms, long-term cannabis use may not ultimately improve the severity of this disorder. These findings, however, contradict longitudinal data demonstrating long-term benefit of THC on PTSD symptoms and diagnosis over the course of one year  as well as previous research demonstrating that cannabis/cannabinoids impair retrieval of emotionally aversive memories and promote the extinction of fear memories . Alternatively, it is possible that the present finding of consistent baseline symptoms over time simply reflects a tendency for people to self-medicate with cannabis once their symptoms reach a specific threshold.

More controlled longitudinal research is clearly needed to disentangle these complex bi-direction temporal associations.The present study has a number of limitations that should be noted. First, respondents self-identified as having PTSD and it was not possible to verify these diagnoses. As such, some of the individuals in the present sample may have been experiencing sub-clinical PTSD. Further, not all clinically recognized symptoms of PTSD were assessed.The evidence for individual differences in the efficacy of cannabis in reducing symptoms further supports this idea that not all individuals will find cannabis equally effective at reducing their symptoms. Finally, it was not possible for us to include a placebo control group. In the absence of this group, it is likely that some of the reported effects were driven by expectations about the therapeutic potential of cannabis for reducing symptoms of PTSD. Finally, because the app was created for industry, rather than research, purposes only a single item was used to assess each symptom and standard definitions of these symptoms were not provided for users. While single item indicators of constructs such as stress have been demonstrated to possess content, criterion, and construct validity , it is unclear whether this would generalize to indicators of intrusions, flashbacks, irritability, and anxiety. Further, users may have varied in what they considered an intrusion vs. a flashback. Thus, future research should attempt to replicate these findings with a larger sample of patients with clinician-verified diagnoses of PTSD, using a double-blind placebo controlled clinical trial, and standardized measures of the symptoms being assessed. These limitations are offset by numerous strengths of the study. First, this study utilized a large sample of over 400 medical cannabis users who tracked over 11,000 cannabis use sessions over a 31-month period of time. These medical users were able to use a large variety of cannabis products in their own natural environment, affording our study very high ecological validity. We also limited analyses to sessions during which lab-verified THC and CBD data were obtained in order to increase confidence in the THC and CBD concentrations. Thus, the present study has excellent ecological validity, and threats to internal validity are more likely to be implicit, in the form of expectancy effects.Cannabis is currently legal for adult use  in 11 US states, the District of Columbia, Canada, and several other countries, and retailer licensing laws vary widely . California legalized cannabis for medicinal use in 1996 and for adult use in 2016 . The 2016 Control, Regulate, and Tax Adult Use of Marijuana Act allows the state, counties, and cities to regulate commercial medicinal and adult-use retail cannabis grow tray sales. effective January 1, 2018, cannabis retailers must obtain a state license from the California Bureau of Cannabis Control  as well as local authorization .

State law grants cities and counties the right to allow, prohibit, or choose not to regulate cannabis businesses in their jurisdictions . Incorporated cities may have their own local ordinances for regulating commercial cannabis activities that are separate from county regulations. The BCC began accepting applications for retail licenses in December 2017. To obtain a license, retailers must document acceptable procedures for transportation, inventory, quality control, and security, provide the business formation and ownership documents, demonstrate compliance with environmental and labor laws, and prove that they own or lease a location that is not near schools or on Tribal land. Licensed retailers were allowed to open on January 1, 2018. Washington State established a similar retailer licensing process in 2012. Individual counties and cities implemented various temporary and permanent restrictions on retail cannabis sales, resulting in a patchwork of local ordinances throughout the state.  Furthermore, numerous unlicensed retailers appeared during the two years following legalization of adult-use cannabis, but prior to the issuance of cannabis retail licenses . This sequence of events appears to be repeating in California. Numerous unlicensed cannabis retailers opened throughout the state following the law’s passage in November 2016 but before the licensing application process began in December 2017 . Even after licensing began, the number of applications quickly outpaced the BCC’s ability to review them, creating a backlog of pending applications. Enforcement efforts to close unlicensed retailers also lagged; local regulators stated whenever they closed an unlicensed retailer, several more appeared . Therefore, in 2018–2019, a combination of licensed and unlicensed retailers operated throughout California . This illustrates some of the challenges faced by state and local governments in regulating adult-use retail cannabis. The high prevalence of unlicensed cannabis retailers might thwart municipalities’ efforts to prevent youth access to cannabis and cannabis-related health emergencies such as acute psychosis . A comparison of 37 licensed and 92 unlicensed cannabis retailers in Los Angeles County  found that unlicensed dispensaries were more likely to sell high potency cannabis products, allow onsite consumption, sell products designed to be attractive to children, and sell products without child-resistant packaging. As of 2019, only 108  of California’s 485 municipalities allow any type of cannabis business to operate in their jurisdictions, and 18 of the 58 counties permit cannabis businesses in their unincorporated areas  ; these numbers have fluctuated throughout 2018 and 2019 as municipalities without regulations began to pass new ordinances . The licensing process has been slower than expected  because of the high cost of establishing a cannabis business, as well as public safety concerns associated with cannabis operations in a community. Meanwhile, unlicensed retailers have proliferated . Studies in several states have found that both licensed and unlicensed cannabis retailers tend to locate in areas with more racial and ethnic minority residents, more poverty, and more alcohol outlets . This is similar to alcohol and tobacco retailers, which are more concentrated in areas with more racial and ethnic minorities, more low-income households, and lower social capital . A high concentration of unlicensed retailers in disadvantaged communities could exacerbate health disparities in chronic respiratory diseases, acute respiratory distress from contaminated THC, motor vehicle accidents, and unintentional overdoses of mislabeled products . Research is needed to understand the disparities created by locations of unlicensed vs. licensed cannabis retailers.

NEET categorization identifies youth who are disconnected from employment and education structures, i.e., not engaged in any form of employment, education, or training structures . Precarious/institutional housing status included any participants who indicated living in a rooming or boarding house, group home, foster care, supportive/transitional housing, treatment facility, or shelter, or who were couch surfing or living on the street . The items in the GAIN-SS are endorsed based on recency of symptoms, i.e. 0 , 1 , 2  and 3 . For the purposes of the current analyses, past month and 2–12 months were combined to indicate past year symptom endorsement. Each scale score is based on the number of symptoms endorsed in the past year, with scores ranging from 0 to 5. Based on scale standards, a youth is considered to have a high probability for a diagnosis if three or more items in a subscale are endorsed in the past year. In the current study, the GAIN-SS domains were analyzed as continuous scores  rather than cutoff scores for the likelihood of a diagnosis due to a ceiling effect. With permission from Chestnut Health Systems to the project leads at [BLIND], the GAIN-SS was modified by adding seven items to create a 27 item version that was used in this project. The seven additional items screen for traumatic stress , distorted thinking , excessive internet or videogame use , gambling issues  and eating concerns .The Trauma History Screen  was used . It asks respondents to endorse whether they have ever experienced any of 13 specific forms of traumatic events, including accidents, natural disasters, sexual trauma, bullying, etc., plus one item referring to any “other” type of traumatic event. The test-retest reliability of the exposure to the assessed stressors has been found to be .93 for the total scale score . To tailor the tool to an adolescent sample, our team removed an item referring to military trauma and added two items referring to experiencing bullying ; other minor adjustments were made to adapt to a youth population . The resulting scale had 14 specific items and one ‘other’ item. For the purposes of this study, the trauma variable is defined as the sum of the number of types of trauma to which the participant has been exposed ; analyses were rerun excluding the bullying items given that they are newly added, unvalidated items.

The exposure variable of interest in the current analyses is the age of first use of cannabis, dichotomized as < 14 years of age versus 14+, followed by age of onset as a continuous variable in the final analyses. Using descriptive statistics, we characterized the sample on demographic characteristics using chi-square analyses. We conducted the subsequent analyses controlling for sex and duration of use, given that there were duration of use differences between the two groups in association with age and that sex differences are consistently found in cannabis use behaviours . We then conducted multiple logistic regression analyses, vertical grow system controlling for sex and duration of use, independently for individual exposure variables, which included each substance use variable . Crosstabulations described proportions by age group. We conducted ANCOVAs to analyze the association between cannabis age of onset groups and GAIN-SS domains, controlling for duration of use and sex, with logistic regressions for the GAIN-SS extension items. We then identified the substances that participants reported most often as the substance first used based on the AADIS-age of onset variable; since almost all participants reported their youngest age of onset for cannabis, tobacco, and/or alcohol, these three substances were further explored. Venn diagrams were drawn using EulerAPE software  to characterize the age of the first substance of use  in the 14+ and < 14 groups, examining age-of-first use percentages for each substance and then age-of-first use percentages for co-occurring substances when there was overlap, with chi-square tests for significance. Based on the exploratory findings and to expand on the findings for the two dichotomous age groupings, we conducted multiple regression analyses to identify factors uniquely associated with age of first cannabis use as a continuous variable; entered into the model were sex and duration of use in Block 1 as control variables, then in Block 2, each of the primary variables identified as significant in the between-group comparisons . Collinearity diagnoses demonstrated that none of the variables were highly correlated. Pairwise deletion was used. For multiple comparisons, the False Discovery Rate  correction was used . Statistical analyses were conducted with using SPSS 24.0 . This study characterized clinical risk profiles for those initiating cannabis use in early adolescence , i.e., prior to the transition to secondary school and in an age range rarely considered in research, in comparison to those initiating cannabis use in mid-to-late adolescence through to early adulthood , through direct comparison of patterns of substance use behaviours and co-occurring concerns. Nearly 30% of service-seeking youth reported initiating cannabis use before the age of 14. Results support distinct and clinically meaningful differences between these age groups, with earlier cannabis use initiation serving as an important marker for more problematic concurrent mental health and substance use concerns.

The under 14 and 14+ groups had similar sociodemographic profiles, with some important differences: the under 14 group was more likely to be NEET, precariously housed, and involved in the legal system. The under 14 group also reported more frequent  polysubstance use, with an earlier age of onset for all substances. Youth initiating cannabis use at under age 14 endorsed more externalizing disorder symptoms, more crime/violence-related behaviors, and more co-occurring concerns. This finding did not hold up in the current study; however, internalizing disorder symptoms were high across both early and later onset cannabis users in the current study, suggesting a ceiling effect. The association between early cannabis use and externalizing disorders found in the current study has been previously demonstrated . Co-occurring challenges were highly endorsed: those in the under 14 group in the current sample were more likely to endorse symptoms from all four domains of the GAIN-SS . Higher trauma exposure is an additional notable finding, given the demonstrated association between trauma, mental health challenges, and self-medication via substance use . Further research is required to better understand the role of a diversity of risk factors — including mental health, concurrent disorders, trauma, environmental and social risk factors — and how they may influence each other leading to varying levels of risk for early age of cannabis use onset. In terms of polysubstance use profiles, participants who began using cannabis under the age of 14 were more likely to begin their substance use trajectories with cannabis rather than alcohol, which differed from the profiles of those who initiated cannabis at a later age; early cannabis initiators also initiated other substances at a younger age. Behrendt et al.  found that alcohol use preceded cannabis use for a vast majority of young people and that only 4.4% reported initiating cannabis and alcohol use in the same year; given changes in the social acceptability of cannabis, the increased rate of cannabis as a first substance of use and of concurrent onset of cannabis and alcohol may be a cohort effect that requires further attention in research. Previous literature has supported an association between early cannabis initiation and the development of CUD . However, little guiding research is available to shed light on the trajectories of the earliest cannabis initiators. Previous research has pointed to the role of polysubstance use in ongoing substance use trajectories ), a finding that was supported in the current study. There are a number of possible risk and protective factors that may potentially mediate and moderate progression to CUDs, such as resilience, substance use among peers, polysubstance use, legal system involvement, and mental health service. An important future research direction will be to investigate the role of diverse risk factors and protective factors in the progression towards and away from CUD. A secondary and more exploratory goal of the current study was to identify which of the sociodemographic, substance use and mental health variables would hold as unique associations with earlier onset cannabis use. Legal system involvement and crime/ violence behaviors were most strongly associated with early cannabis initiation. The relation between early cannabis use onset and factors such as crime/violence and externalizing disorders have previously been demonstrated . Youth with legal system involvement are often found to have social networks that consist of peers with behavioral issues and substance use , and are often characterized as having risk associated with particular personality profiles. 

Legal system involvement may reflect social and/or personality risk factors that may be more predictive of early initiation of cannabis grow equipment. Combined with the higher externalizing disorder symptoms, criminal justice involvement may point to behavioral concerns that may lead to both early cannabis use and crime and violence challenges, although the directionality is unclear. However, it should be noted that most of the data was collected while cannabis use was illegal in Canada for people of all ages locally; further work should explore how these findings might change with changes in legislation. Nevertheless, previous research has suggested that youth with cannabis use who are referred by the criminal justice system may stay in treatment longer than those who were not referred , and that they can benefit substantially from substance use treatment . Additional supports for navigating the criminal justice system may be warranted for some youth in this group. These findings have important implications for cannabis-use prevention, early intervention, and treatment initiatives. Findings are mixed on whether prevention initiatives are effective in younger or older adolescents. A meta-analysis of cannabis prevention interventions for adolescents  found interventions are more effective among high school students  than among younger students, a finding that was attributed to developmental factors . However, another systematic review found programs designed to prevent cannabis use among adolescents and young adults  to be more effective when targeted towards a younger age group , since the program would potentially precede the onset of cannabis use . Based on the findings of the current study, interventions aiming to prevent or delay the first use of cannabis should start early, particularly for more vulnerable children and youth. Targeting older students may be too late for some youth. Optimal prevention and early intervention efforts should be developed with the knowledge that some youth will have already tried cannabis, even during childhood; as these may be the most vulnerable youth, it should be kept in mind that they may also have considerably more concurrent issues. Preventionists and early interventionists are encouraged to continue working to optimize cannabis prevention programs in age-appropriate ways for children and youth at different ages and with different levels of cannabis experience or non-experience. Overall, these findings have meaningful clinical implications for treatment among youth seeking services across sectors. Notably, the results highlight the importance of taking early cannabis initiation into account to understand the vulnerabilities and concurrent mental health, behavioral, substance use, and other concerns of youth. However, as about half of youth among both early cannabis initiators and later initiators began using cannabis, alcohol, and tobacco at about the same time, the use of any of these substances should be taken into account as markers that may suggest the need for further assessment of substance use. Given the comorbidity of mental health and substance use problems in youth, especially youth with higher levels of vulnerability, like those who initiate cannabis use early, youth-focused service providers are encouraged to consider youth substance use as part of routine youth mental health and wellness services. For youth presenting with substance-related problems, service providers should consider current and previous substance use, particularly age of cannabis use onset, as part of the assessment and service planning process. For treatment services, asking the age of onset of cannabis use may provide insight into historical and current vulnerabilities, as the duration of use is strongly associated with multiple outcomes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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