Cannabis Grow

Among hemp samples, Hemp 5 displayed the highest WHC and HWHC, which could be attributed to its higher protein content and denaturation/aggregation occurring during its double AE-IP. In fact, Hadnađev et al.  showed that AE-IP hemp protein showed a two-fold higher WHC than their counterparts obtained through micellisation. On the contrary, it seems that pea protein lost its ability to hold water upon heating likely due to the exposure of more hydrophobic groups on the protein surface.This process occurs upon heating and subsequent cooling of a concentrated protein solution. In fact, to create a heat-set gel, proteins should be unfolded upon heating so that both polar and non-polar residues are exposed and able to form hydrogen bonds and hydrophobic interactions during the cooling process. In this process, the rate of protein aggregation and network formation is higher than that of unfolding . Therefore, proteins with a high number of non-polar residues can form the network faster . In this respect, Hemp 2, with the highest H0, exhibited the lowest LGC . Meanwhile, Hemp 5, despite having the highest protein content among hemp samples, showed one of the highest LGC, which could be explained by its significant denaturation/aggregation . The low LGC of Hemp 1 could be explained by its highest content of carbohydrates , sub irrigation cannabis which could have hindered its ability to form a stable gel at lower concentrations. HMMAs were made considering Hemp 3 and Hemp 5 samples as representatives for dry- and wet-fractionated hemp protein samples, respectively.

Low-field nuclear magnetic resonance  has been recently utilized as a fast and non-destructive method to study the distribution and mobility of water in food matrices such as meat and meat analogues . In this technique, a sample is subjected to radio frequency pulses inside a magnetic field and the water or lipid state in the sample can be assessed based on the relaxation time  of proton nuclei of water molecules . The relaxation time of the protons indicates how tightly  or loosely  water molecules  are bounded to the solid phase of the matrix. In general, an NMR decay curve can be fitted to three or more exponentially decaying compartments , showing the water distribution in the sample. Accordingly, a relaxation time curve provides two different parameters for the water state in a food matrix, namely water mobility  and the amount of water distributed within each mobility component . Fig. 5 shows the T2 relaxation time curve for pea, Hemp 3, and Hemp 5 HMMAs, and four distinct peaks roughly centred at 1–2 ms , 10–14 ms , 45–50 ms , and 138–220 ms  were observed. The first water population with the lowest relaxation time  reflects those water molecules closely bound to proteins or polysaccharide molecules . In this respect, Hemp 3 extrudate displayed the lowest absolute area in this region probably due to the higher number of hydrophobic patches, i.e., higher surface hydrophobicity , which could expel water upon protein structuring on fibrous structures. The second two compartments, T21 and T22, represent the immobilized water entrapped in protein or polysaccharide gel structures. As can be seen in Fig. 5 and Table 4, pea extrudate had the highest absolute area in T21 and a small peak in T22. This occurrence indicates that most of the water in this sample is entrapped in a protein network, as opposed to being immobilized in a polysaccharide network, which is consistent with the fact that pea displayed the highest amount of protein  and the lowest amount of TDF . Hemp HMMAs, with lower protein and higher carbohydrate contents, showed shorter relaxation time  and higher absolutes areas of T22 peaks than pea, respectively , indicating more water being entrapped by the polysaccharides.

It is noteworthy that the main peak T21 is sensitive to the homogeneity of the water in the samples , increasing its susceptibility to become broader in compositionally heterogeneous samples . As an example, Aursand, Gallart-Jornet, Erikson, Axelson, and Rustad  observed that the addition of salt to cod and salmon meat leads to peak broadening due to the salt hydration effect. This occurrence would explain the T21 peak broadening of Hemp 3 and Hemp 5, which possessed more ash, lipids, and TDF than pea . Lastly, the third region of relaxation time curve  reflects the free water in the system, which is the fraction that can be lost upon the cooking process of meat. Typically, this water population appears above 100 ms in an animal meat system . Thus, the longer relaxation time of T23 region shown for the HMMAs indicates that water is more prone to leave the plant protein structure, which more closely resembles the process that occurs in animal meat. In this regard, Hemp 3 HMMA would be the best choice from a free water standpoint, since it had the highest absolute area in this region, followed by Hemp 5 and pea . Tensile and small amplitude oscillatory rheological experiments were performed to evaluate the mechanical properties of the plant-based HMMAs and their relationship to their visual appearance and water mobility. HMMA made with hemp exhibited a remarkably greater fibrousness, following the order of EHemp5> EHemp3 >EPea . It was also observed that the extrudates made of EHemp5 presented longer and more defined fibres that the EHemp 3 counterpart. Meanwhile, EPea exhibited a more isotropic structure, where fibres were hardly visible. The visual appearance of the samples was in agreement with the tensile tests performed parallel and perpendicular to the fibre direction, which highlighted a significantly higher anisotropic index for EHemp5 . It is noted, however, that higher anisotropy also resulted in higher sample hardness .

We note that the significantly higher proportion of less interacting water protons with macromolecules of EHemp5 compared to EPea could suggest that the marked phase separation leading to fibrous-like structures could also result in higher proportions of free water . EHemp3 did not follow this rule, which could be likely attributed to peak broadening during LFNMR analysis. Thus, fibrousness, hardness and water mobility should be jointly considered when designing fibrous HMMA. The comparison between SDS-PAGE at reducing and at non-reducing conditions of hemp HMMAs revealed some bands representing edestin that were only present using DTT, in contrast to EPea, whose bands were less intense both with and without DTT. This provides evidence of the higher contribution of S–S bonds in hemp protein aggregation during extrusion than in pea protein. This occurrence aligns with previous reports showing the relative importance of physical and covalent interactions between plant proteins, where S–S bonds were key for anisotropy formation using gluten , and non-hydrophobic and ionic interactions were the main contributors of gel strength using pea . Due to the protein unfolding occurring during extrusion, the hidden non-polar and sulfhydryl groups are exposed to the surrounding aqueous phase. This phenomenon might cause the formation of new assemblies via non-covalent interactions  and disulphide bonds . Hemp protein is richer in sulphur containing amino acids  than pea , which suggests the higher contribution of S–S bonds to form anisotropic structures under laminar flow during cooling of Hemp 5, and could also explain the higher anisotropy of EHemp5, followed by EHemp 3 and EPea. Another explanation could be given by the higher surface hydrophobicity of hemp protein concentrates than the pea counterpart . The combination of highly hydrophobic hemp proteins with the hydrophilic maize starch could have promoted the formation of an optimal multiphasic system during extrusion, where the dispersed phase could have been elongated under tensile and shear stresses into visible fibres in the cooling die. This colloidal approach has been suggested before , although the relative importance of the molecular model  and the colloidal approach are still the subject of debate. The differences in the fibre-forming ability between EHemp3 and EHemp5 should be ascribed to their different protein purity .

The high level of lipids in Hemp 3 protein concentrate could have also played a role in the flowability and shear and tensile stresses of the melt inside the extruder, lowering the SME response during processing from 325 kJ/kg for EHemp5 to 285 kJ/kg for EHemp3. The content of phytic acid in dry-fractionated hemp  was found in the upper part of the typical high valuesreported for oilseeds, vertical grow which was in turn higher than the content typically found in cereals and legumes . For this reason, the content of phytic acid was also measured in HHMAs. The content of phytic acid naturally decreased in HMMA in proportions generally explained by the dilution effect of the protein concentrates with starch and water. Therefore, and as expected, extrusion did not reduce the content of phytic acid due to its known high thermal stability . Likewise, Pontoppidan, Pettersson, and Sandberg  already reported that the degradation of myo-inositol hexakisphosphate in feedstuffs during extrusion is too limited to have any nutritional effect on the availability of phosphorous and minerals. From the phytic acid perspective, if our developed HMMA prototypes were to be consumed as they come out from the extruder, that is, without further water removal  or water addition , a 100 g single portion  would contain a total of 0.6–0.7 g of phytic acid in EPea and EHemp5, and 1.8 g of phytic acid in EHemp3. Remarkably, the consumption of EPea and EHemp5 would not necessarily increase the daily intake of phytic acid in consumers following a plant-based diet, reported as >1000 mg . On the other hand, the consumption of EHemp3 could potentially result in a higher average daily dietary phytic acid intake in humans following a plant-based diet living in certain European countries, such as Sweden . It is noteworthy that the inhibition of the intestinal mineral absorption can be counteracted by many dietary compounds such as organic acids and complexing agent like ascorbic acid, competing with phytic acid for mineral binding. Thus, little evidence exists that in well-nourished population groups dietary phytate may seriously affect the status of iron, zinc, and calcium . On the other hand, phytate may lead to serious deficiencies with unbalanced nutrition or undernourishmentin developing countries. In this case, dry-fractionated protein concentrates from different botanical original than hemp, or wet-fractionated hemp samples, would be preferred. At any event, the consumption of dry-fractionated hemp HMMAs would result in average daily dietary phytic acid intakes within the range found for adults living in developing countries . Industrial hemp  is an annual, bushy, dioeciously or monoecious herbaceous plant which belongs to the family of Cannabaceae, genus Cannabis.

The industrial and medical importance of Cannabis sativa L. is reflected in the increasing market interest for consumables, fiber, medicine and miscellaneous products derived from raw or dried cannabis plant parts. The moisture removal of the harvested goods is one of the most important unit operation, in which the moisture is reduced to a certain balance level that the deterioration and microbial spoilage are minimized. In order for efficient, feasible and properly dimensioned convective drying systems to be deployed, accurate modeling of each raw material behavior during the drying process is required. Several mathematical thin-layer drying models are proposed in the literature for describing the drying kinetics in a range of agricultural products. More sophisticated approaches of drying scheme implementations arise in the scope of improving drying time, cost and energy, since the convective drying is an energy-intensive and slow process when low temperature levels are applied. Non-isothermal drying, implementing a variable mode of drying conditions by modifying as a function of time, one or more drying parameters is a technique followed by many researchers, also applied for herbaceous materials, as a nonstandard drying process aiming to reduce energy consumption, improve the product quality or reduce the required drying duration. In a study presented by Ozguven et al., peppermint was dried at two constant drying temperatures of 35 and 55 ◦C and two incremental temperature rises of 5 and 2.5 ◦C⋅h-1. Both non-stationary temperature profiles had a shorter drying duration a lower final moisture content compared with the lowest constant temperature level applied. A similar study was performed by Tarhan et al. forlemon balm .

Like tobacco smoke, marijuana smoke has been associated with numerous adverse pulmonary effects in humans including airway inflammation, chronic bronchitis, edema, mucus hypersecretion, and the impairment of large airway function and lung efficiency.Moreover, Aldington et al. showed that the impairment of large airway function and lung efficiency is 2.5–5 times greater in marijuana smokers than tobacco smokers.Like tobacco smoke, previous studies have also shown marijuana smoke to be genotoxic both in vitro and in vivo.In addition, it is suspected that marijuana smoke may be carcinogenic.Indeed, some agencies such as the California Environmental Protection Agency have placed marijuana smoke on their list of chemicals known to cause cancer.However, since there is a paucity of marijuana-only smoking populations to complete definitive studies, epidemiological studies conducted to date are limited in scope, and often confounded by concurrent tobacco smoking.Therefore, a clear and widely accepted empirical link between marijuana smoking and cancer does not exist. Information on the pharmacokinetics of marijuana smoke, and the mechanisms by which it may cause adverse effects, is also limited. Several mechanisms have been proposed including genotoxicity,alterations in endocrine function,alterations in cell signaling pathways,and immune suppression. However, many of these findings are based on the testing of individual cannabinoids  found in marijuana smoke, as opposed to the whole smoke or smoke condensate. Genome-wide expression profiling may provide information to permit a better understanding of the toxicological pathways perturbed by exposure to marijuana smoke. Currently, there are no published studies that have used a whole genome toxicogenomics approach to evaluate responses to marijuana smoke.

However, Sarafian et al. employed a targeted stress response gene expression array to evaluate the effects of 9-tetrahydrocannabinol,plant benches the main psychoactive component of marijuana, on human small airway epithelial cells.They observed significant changes in genes related to xenobiotic metabolism,DNA damage response,inflammation  and apoptosis.Microarray technology has been used more extensively to evaluate gene expression changes following exposure to tobacco smoke. For example, Sen et al. reviewed 28 studies examining transcriptional responses to complex mixtures including whole cigarette smoke and cigarette smoke condensate, and included in vivo and in vitro studies using human and rodent tissues.It was determined that the pathways most frequently affected by tobacco smoke were oxidative stress response, xenobiotic metabolism, inflammation/immune response, and matrix degradation. Other microarray studies have noted a DNA damage response leading to cell cycle arrest and apoptosis to be among the top pathways affected by tobacco smoke.The results of this study showed extensive overlap with the affected pathways highlighted in the review by Sen et al..Our study also showed that gene expression is remarkably similar across cigarette brands, and there is limited variation in the genotoxic potency of cigarette smoke condensates. In contrast to these findings, our earlier work revealed that tobacco and marijuana smoke condensates  differ substantially in terms of their genotoxicity.More specifically, MSC were observed to be significantly more cytotoxic and mutagenic than matched tobacco smoke condensates.In addition, TSC appeared to induce chromosomal damage  in a concentration-dependent manner, whereas matched marijuana condensates did not. The mechanisms underlying these differences in toxicity are unclear and warrant further investigation. As an extension of our previous work, the objective of the present study is to employ a toxicogenomics approach to compare and contrast the molecular pathways that are perturbed by MSC and TSC.

A murine pulmonary epithelial cell line was employed for in vitro exposures to both MSC and TSC. The results show that the pathways perturbed by MSC as compared to TSC are largely similar. However, subtle differences in gene expression provide insight into mechanisms underlying the observed differences in toxicities.Hierarchal clustering using all genes that were statistically significant  revealed that the controls and the marijuana high concentration  clustered independently from the rest of the samples. The remaining samples clustered first by concentration ,then by condensate type,with the last branching resulting from time.When cells exposed to TSC and MSC were analyzed separately, samples clustered first by concentration and then by time point, suggesting that concentration has the largest overall effect on gene expression. For MSC, the high concentration samples were on the first main branch, followed by control, low and medium concentrations. The results indicate that the expression profiles of the high concentration MSC exposed cells are quite distinct.For TSC, the controls branched separately from all the treatment groups.The top 10 genes with the largest overall fold changes are listed in Table 2. All of the top 10 genes were significantly up-regulated with the exception of low density lipoprotein receptor,which was down-regulated in MSC exposed cells. Of the top 10 changing genes, five genes  were common to both MSC and TSC. The GO terms  associated with these commongenes included multicellular organismal development, vasculogenesis, regulation of transcription, and regulation of inflammatory response. Ingenuity Pathway Analysis  was used to define the pathways that were significantly altered following exposure to MSC or TSC. Fig. 3 shows the overlap in all the significant pathways between the two condensate types. The top five most significantly altered pathways for cells treated with MSC or TSC are listed in Table 3. NRF2-Mediated Oxidative Stress Response was the most significant pathway for cells exposed to TSC at all concentrations and time points, with the exception of lowest concentration attime 6 + 4 h where LXR/RXR Activation  was the most significant.

For cells exposed to MSC, the most significantly altered pathways were Biosynthesis of Steroids, as well as NRF2-Mediated Oxidative Stress Response, Aminoacyl-tRNA Biosynthesis and HMGB1 Signaling.Some ofthe top five pathways were common to both the MSC and TSC including those related to oxidative stress and xenobiotic metabolism. However, inflammation pathways were more predominant for the MSC, whereas cell cycling and cancer signaling pathways were more predominant for the TSC. To further elucidate differences between the two smoke condensates, the genes that were uniquely expressed following TSC exposure or uniquely expressed following MSC exposure at the highest concentrations for the two separate time points were compared in IPA.The findings confirm the importance of inflammation and steroid biosynthesis pathways in MSC exposed cells and highlight the significance of apoptotic pathways  particularly at the 6 h time point. For cells exposed to TSC,Mphase cell cycle pathways  appear to be of particular importance. Gene Ontology in the Database for Visualization, Annotation and Integrated Discovery  was used to apply functionalannotation to all the significantly differentially expressed genes for each condensate. The full results are shown in Supplementary Tables 1 and 2. For cells exposed to MSC, significant perturbations were associated with steroid/cholesterol/lipid biosynthesis, NODlike receptor signaling,tRNA aminoacylation, transcription regulation, unfolded protein response and DNA binding. Like MSC, cells exposed to TSC had significant perturbations in transcription regulation, unfolded protein response and DNA binding. In addition, perturbations in cell cycle, p53 signaling, oxidative stress, andcancer signaling were alsonoted in TSC exposed cells. Fig. 5 shows the overlap of all the significantly affected ontologies between the two condensate types. Functional annotation clustering in DAVID was used to minimize redundancy in the GO terms. This analysis revealed 19 clusters with enrichment scores greater than 2 for MSC and 19 clusters for TSC.The top clusters for MSC relevant to toxicological processes include lipid/steroid biosynthesis,RNA processing,cellular response to unfolded protein,tRNA aminoacylation,and positive regulation of transcription.The top clusters for TSC relevant to toxicological processes include cellular response to unfolded protein,cell cycle,positive regulation of transcription,response to steroid hormone stimulus,and positive/negative regulation of apoptosis and cell death.To investigate early versus downstream effects, functional annotation was applied to significantly differentially expressed genes at the two separate time points. The results are shown in Supplementary Tables 5–8.

For cells exposed to MSC at the 6 h time point, the analyses revealed 79 significant  terms including those related to transcription activity, DNA binding, and steroid/cholesterol biosynthesis. Four KEGG pathways  and 1 Biocarta pathway  were also deemed significant at this time point. At the 6 + 4 h time point, 76 significant terms were identified. These terms included unfolded protein response, and tRNA aminoacylation, as well as steroid/cholesterol biosynthesis which was found at the 6 h time point. Three KEGG pathways were significant at this time point including Steroid Biosynthesis, Terpenoid Backbone Biosynthesis, and Aminoacyl-tRNA Biosynthesis. Analyses of cells exposed to TSC at the 6 hr time point revealed 67 significant terms including those associated with oxidative stress, cell death, protein unfolding, transcription regulation, DNA binding and cell cycle. In addition, 2 KEGG pathways were significant.At the 6 + 4 h time point, 32 GO terms were identified as significant with oxidative stress being the only relevant toxicological endpoint. In addition, only one KEGG pathway  was significant. Overall for MSC, the DAVID analyses confirmed many of the significant pathways identified by IPA including steroid biosynthesis, tRNA aminoacylation, inflammation and apoptosis. In addition, the analyses highlighted transcription regulation, DNA binding and unfolded protein response as also significant. For TSC, the DAVID analyses confirmed the significance of IPA pathways related to oxidative stress and cell cycle. As with the MSC, the DAVID analyses also further highlighted the importance of transcription regulation, DNA binding and unfolded protein response, as well as cell death. Transcription regulation and DNA binding were significant terms common to both MSC and TSC at the 6 h time point, whereas no common terms existed for the two condensates at the 6 + 4 h time point.In our previous genotoxicity study we showed that MSC and TSC were both cytotoxic and genotoxic.However, quantitatively, MSC was more cytotoxic and mutagenic than TSC, and TSC appeared to induce chromosomal damage in a concentration-dependent manner whereas MSC did not. Our earlier chemical analyses of MSC and TSC noted that aside from the nicotine in tobacco and the cannabinoids in marijuana, rolling bench the two smoke condensates contained mixtures of chemicals that were qualitatively similar though quantitatively different.The similarities in the chemical profiles and some of the toxicity findings suggested that the two smoke condensates might elicit somewhat comparable gene expression profiles. Hierarchal clustering of all the MSC and TSC exposed samples in the present study supported this notion  and samples clustered first by concentration as opposed to smoke type. In addition, analysis of the top ten greatest gene expression changes relative to control revealed that half of the genes were common to both marijuana and tobacco.

A number of previous studies have examined gene expression changes in pulmonary cells following exposure to tobacco smoke.Generally, these studies have shown that tobacco smoke stimulates xenobiotic metabolism, and that metabolized smoke constituents contribute to DNA damage. Following early insult, DNA damage leads to disruptions in the cell cycle such as arrest at the G2 checkpoint to allow time for response. Cellular response can include DNA repair, mutation induction through faulty repair or lack of repair, and programmed cell death of heavily damaged cells. Exposure to tobacco smoke can also trigger an inflammatory response and induce oxidative stress through increased levels of reactive oxygen species. Persistent induction of these processes following repeated exposure contributes to loss of normal growth control mechanisms, which is a key step in cancer development. Our study supports many of these findings, with exposure to TSC inducing the expression of genes involved in xenobiotic metabolism,oxidative stress,and DNA damage response as evidenced by changes in the expression of genes involved in cell cycle arrest, protein unfolding,transcription regulation, and inflammation.These same pathways were also significantly affected following MSC exposure, indicating that, as expected, MSC impacts many of the same molecular processes and functions as TSC. Although the effects of the condensates were largely similar, dose–response analysis indicates that the MSC is substantially more potent than TSC, with BMDs that in many instances are an order of magnitude lower than those for TSC. In addition, the results also highlighted some differences in steroid biosynthesis,apoptosis  and inflammation, which were more significantly affected following MSC exposure, and cell cycle,which was more affected following TSC exposure.IPA canonical pathways related to the metabolism of xenobiotics were significantly affected in both TSC and MSC exposed cells at both time points. These pathways included Xenobiotic Metabolism Signaling, Metabolism of Xenobiotics by CYP450, and AHR Signaling. For both TSC and MSC, the number of genes that were significantly affected increased with increasing concentration and the greatest number of genes changing occurred at the 6 + 4 h time point.

The results and conclusions from this study should be examined within the context of its limitations. First, the data-set examined in this analysis was derived from two larger parent projects,thus, certain variables of interest could not be manipulated or controlled to study more detailed MJ-related effects.In spite of these limitations, MJ users  were homogenous in their MJ use due to study inclusion criteria.In addition, we only had one memory measure common to examine across our combined sample. While the WMS-III story memory paradigm is a well-validated and researched instrument, it may not generalize to learning and memory processes in alternative nonverbal modalities. Further, using a memory measure with a somewhat higher executive loading such as an unstructured word list learning task may elucidate some of the differential effects of MJ versus nicotine on memory functioning. Lastly, the between-group differences observed in this study were characterized by small to-medium effect sizes and future research would be needed to replicate these findings in larger samples.Illicit marijuana use in the United States has been a longstanding public health concern for both adolescents and adults. As many as 44% of college-aged individuals endorse having used marijuana at some point in their life, and 21% of college-aged individuals report marijuana use in the past 30 days.Marijuana intoxication is associated with motor coordination deficits, euphoria, impaired temporal estimation, drying racks and a variety of other psychological phenomena.

Marijuana use has also been associated with more specific cognitive deficits, even after acute intoxication has subsided,and with the development of severe psychopathology.Furthermore, chronic marijuana use has been related to adverse physiological consequences in the cardiovascular and respiratory systems.Adolescence and young adulthood represent periods of the lifespan when increased risk-taking occurs, including the use of illicit substances, such as marijuana. The combination of an innate propensity for risk-taking  and use of a judgment-altering substance is a striking example of the immediate public health concern over marijuana use in young-adults. This concern is particularly pertinent in light of recent efforts in support of marijuana’s legalization in the United States. A challenge for the field is to identify which chemical systems and associated information processing networks are most affected by chronic marijuana use. The main psychoactive component of marijuana, Δ9 -tetrahydrocannabinol,acts as an agonist in central nervous system  cannabinoid  receptors and in other peripheral cell types, primarily immune cells. In the CNS, CB1 receptor density is high in the basal ganglia, particularly in the dorsal striatum.Cannabinoid receptor signaling acts on multiple neurotransmitters through a variety of biochemical cascades, including inhibition of voltage-dependent calcium channels  and by directly inhibiting vesicle release. Both excitatory and inhibitory neurotransmitters, including glutamate,γ-aminobutyric acid  and dopamine, are either directly or indirectly affected by CB1 receptor activation.For marijuana and other drugs of abuse and dependence, the dorsal striatum has been hypothesized to play a key role in the transition from intermittent drug use to compulsive habit-based drug-taking via mechanisms that underlie long-term synaptic plasticity.Exogenous activation of CB1 receptors, as occurs with marijuana intoxication, inhibits the release of glutamate as well as GABA in both the dorsal and ventral striatum.This inhibition facilitates the development of long-term depression  in the striatum, which is a critical component in the altered synaptic plasticity that accompanies drug addiction.Thus, the manner in which corticostriatal functional connectivity is altered in the context of marijuana use is of interest, as is metabolic activity within the chemical systems that contribute to those alterations.

Magnetic resonance spectroscopy is a widely used tool, allowing for in vivo characterizations of various brain metabolites. MRS data is acquired either from single voxel  or multiple voxels. The SVS method typically benefits from high spectral resolution and signal-to-noise ratio.MRSI has better spatial resolution compared to SVS, but typically has a much more limited spectral resolution.To the best of our knowledge, only four other studies utilizing some form of MRS to examine marijuana users have been published, and the methods of these studies are relatively heterogeneous.The existing studies are summarized in Table 1. Individuals ages 16-to-42 years were studied with either SVS or MRSI. In two of the studies, only males were examined.In most cases, marijuana use was reported at 20 or more days per month. Lower levels of Glu, N-acetylaspartate,and myo-inositol were observed in marijuana users compared to controls in regions known to be associated with substance use, including the basal ganglia,thalamus,cingulate cortex,dorsolateral prefrontal cortex,and the striatum as well as posterior cortical regions.The methods, ages of subjects, and extent of current marijuana use in the samples tested vary considerably across studies as summarized in Table 1. As disruptions in glutamate activity have been implicated in the development of addiction,we hypothesized disruptions in glutamate concentrations in marijuana users compared to controls. Several lines of evidence suggest inhibition of glutamate excitotoxicity by marijuana.In addition, based on the MRS literature described above related to the basal ganglia of adult marijuana users and literature describing the inhibitory effects of CB1 receptors on glutamate release, we specifically hypothesized that young-adult MJU subjects would show lower levels of Glu + glutamine in the basal ganglia compared to their non-using counterparts. We did not have a specific hypothesis regarding concentrations of other metabolites given that other researchers have not concentrated their assessments on the striatum. However, the limited available literature suggested the possibility of altered mIns as well as NAA levels in users versus controls.Twenty-seven marijuana users  were recruited into the study through local advertisements on the University of Minnesota-Twin Cities campus. Marijuana users’ ages ranged from 18-to-21 years, with a mean and standard deviation of 19.5 ± 0.6 years.Exclusion criteria are described below.

Twenty-six healthy young adult non-users,who were participants in a large, longitudinal study of normal brain development, served as a control sample. Control participants’ ages ranged from 13-to-24 years, with a mean and standard deviation of 19.3 ± 3.1 years. The recruitment strategy for the control sample has been described elsewhere.Briefly, participants younger than 18 years of age were recruited through a database of research volunteers throughout the Metro community, through post-cards mailed to University of Minnesota civil service employees, and through local advertisements. Participants over the age of 18 years were recruited using on-campus advertisements. During the controls’ third longitudinal follow-up visit, MRS was added to the protocol as time allowed. Thus, the control sample described in this study has a broader age range than the MJU sample, a feature that was considered in the statistical approach described below. A description of the study was initially given to both the MJU and control participants over the phone. Interested participants were then invited to complete a brief phone screening to ascertain study eligibility. Exclusion criteria included major physical, neurological or psychiatric illness, substance use disorders,head injuries resulting in loss of consciousness >20 min, mental retardation, learning disabilities, current use of psychoactive medications, non-native English speaking, vision or hearing that was not normal or corrected to normal, complications at birth, current pregnancy, and MRI contraindications.Inclusion criteria for MJU participants included current use of marijuana at least five times per week for at least one year, and an age of onset of use prior to the age of 17 years. Marijuana users were also excluded if they were daily cigarette smokers, or if their alcohol use exceeded four drinks for females and five drinks for males on more than two occasions per week. Marijuana users were asked to refrain from drug use for at least 12 h prior to their visit  to avoid acute intoxication during study procedures. Participants provided written informed consent  and all study procedures were approved by the University of Minnesota’s Institutional Review Board.After the phone interview, eligible participants were invited to the University of Minnesota’s Center for Neurobehavioral Development for an in-person screening session to further ascertain eligibility and to verify information given over the phone. The Kiddie Schedule for Affective Disorders and Schizophrenia, Present and Lifetime version  was used to assess for current or past Diagnostic and Statistical Manual, Fourth Edition  axis I disorders, including childhood disorders given the relative youth of the sample.

The presence or absence of DSM-IV disorders was confirmed by case consensus meetings with staff members including a license-eligible clinical psychologist. In addition, a two-subtest  version of the Wechsler Abbreviated Scale of Intelligence was administered to yield estimated full scale IQ.Participants who met all inclusion criteria after the in-person interview were invited back for a comprehensive neuropsychological testing battery and an MRI scan. This report focuses on spectroscopy findings.In addition to the K-SADS-PL, the Personal Experience Inventory   was used to further assess alcohol and marijuana use in both the MJU group and in the healthy controls. Briefly, the PEI consists of two main sections, one focused on patterns and severity of substance use, and the other focused on psychosocial consequences of use. In most cases, participants endorse items from the inventory using a four-point Likert response format.Different versions of the PEI have been developed for adolescents versus adults. Participants younger than 18 years of age received the adolescent version and participants older than 18 years of age received the adult version; both versions were computer administered. All MJU participants received the adult version. Scoring was implemented to create comparable metrics across the two versions. Finally, an in-house questionnaire based on guidelines provided by the National Institute on Alcohol Abuse and Alcoholism was implemented to assess detailed daily, weekly, yearly and lifetime use patterns of alcohol and marijuana in the sample, considering frequency and amount of use.This study examined a cohort of college-aged heavy cannabis drying users and a control group of non-using young-adults. Using MR-spectroscopy, it was shown that females, but not males, who used marijuana heavily starting in mid-adolescence and persisting for several years have lower levels of glutamate and glutamine  in the dorsal striatum when compared to controls, even after accounting for age and alcohol use. Similarly, female but not male users differ from controls in their estimated concentrations of myo-inositol, demonstrating higher levels than controls. These patterns are interpreted as pathological in the female users given that male users had comparable levels to controls of both sexes. Female users did not differ from male users in their overall rates of self-reported marijuana use, in their concomitant level of alcohol use,in their numbers of symptoms of marijuana dependence, or presence of other conditions that might impact brain metabolism.These findings have broad parallels in the extant literature, both in relation to the overall patterns observed but also in relation to sex differences.

Decreased glutamate/glutamine concentrations have been reported in two other MRS studies of marijuana users, one that focused on the basal ganglia  and one that targeted the anterior cingulate cortex.First, in an older cohort of marijuana users than is described in the current study, Chang et al.  reported lower glutamate levels in the basal ganglia, suggesting that heavy marijuana use during young adulthood as well as later in life is associated with disruptions in glutamate signaling as has been shown for other drugs of abuse.Recently, Prescot et al.  reported lower glutamate concentrations in the anterior cingulate cortex, which was nonetheless strongest when females were eliminated from the analysis. Interpretation of the current findings is complicated by poor resolution of the glutamate versus glutamine signal. Glutamate is present in all cell types with the largest pools evident in glutamatergic neurons; smaller pools are evident in GABA-ergic neurons and astroglia. Upon release, astroglia convert glutamate to glutamine, which in turn is transferred back to the neuron for conversion once again to glutamate.Glutamine is primarily located in astroglia. Thus, low glutamate levels would be difficult to ascribe to a particular neuronal process. In contrast, if glutamine levels are low, then glial dysfunction may be present, a finding that would be consistent with white matter aberrations in marijuana users.Others have not reported specific metabolic disruptions in female marijuana users; indeed, within young samples, marijuana is more commonly used in males.Although it has been recognized that females are at an increased risk for some behavioral consequences of drug use such as sexual risk-taking  and an increased risk of depression and anxiety following a pattern of daily marijuana use,there are relatively few human studies of brain-based sex differences associated with marijuana. Women have shown slightly more severe neurocogntive deficits related to marijuana use compared to men.

A major strength of this study was its large international multicentre prospective cohort with excellent follow-up and complete data available for this analysis. Women were recruited from a clearly defined population of nulliparous women, with meticulous data monitoring protocols to reduce data entry or transcription errors and ensure the quality of data. While there are other studies that have examined the effect of marijuana use on adverse pregnancy outcomes, interaction tests were not performed. Hence, with complete quality data available from this study, interactions between marijuana use and cigarette smoking status may be examined while also adjusting for potential confounders. It needs to be noted that the number of SPTB cases amongst women who reported marijuana use at 20 weeks’ gestation is small  even in this large cohort. The use of self-reported marijuana use and cigarette smoking status may be a potential limitation, as it may be subject to participant recall bias. Furthermore, this study was undertaken in a nulliparous cohort so it may be the case that our findings apply only to nulliparous women. Although medication for maternal asthma, thyroid disease, and PCOS were recorded, we found no evidence of association with pregnancy outcomes analysed in this study, therefore these were not included in the analysis. Further research is required to confirm these findings, and future studies should include appropriate corrections for the various important confounders .Tobacco smoke can cause numerous health related issues. While cigarette smoking is associated with the development of certain respiratory diseases, the causal link between the onset of asthma and smoking has not been established. To date, studies that examined the association between cigarette smoking and incident asthma have shown mixed results.

Previous work on the topic has reported an increased risk for adults and adolescents but others reported no statistically significant associations. Although what causes the onset of asthma is still relatively unknown,grow cannabis in containers experts in the field have reported that cigarette smoking or exposure to second hand smoke can certainly trigger asthma symptoms and severity. Overall cigarette smoking or second hand smoke can relate to many long term respiratory health issues, but it can also influence more immediate issues for people with asthma including increased coughing and airway inflammation. For adults in California, cigarette smoking was associated with asthma severity, worse asthma-specific quality of life and greater hospitalization for asthma. Furthermore, active cigarette smoking for people with asthma can lead to accelerated loss of lung function and a decreased response to corticosteroids over time. In the past, studies on smoking behaviour in adolescents was focused on cigarette use; however, more recently other types of smoking such as water pipes, marijuana or now electronic cigarettes  have emerged as a concern. water pipes  have become more popular in North America in recent years due to the belief that it is a safer alternative to cigarettes. This is a common misconception in young adults, as a water pipes smoking session can contain over 100 times the amount of smoke in comparison to a single cigarette. In Ontario, the rate of trying water pipes in adolescents has more than doubled from 6% in 2006 to 14% in 2013. Water pipe smoking is linked to several adverse health outcomes such as cancer, cardiovascular disease and decreased lung function. While the causal effect of water pipe smoke and asthma has not been demonstrated, exposure to tobacco smoke was shown to exacerbate asthma symptoms. Since water pipes produce tobacco smoke, it can be assumed that it will be harmful especially for those with asthma. The relationship between marijuana smoking and asthma is somewhat complex. Marijuana has been used as a forbidden medicine to treat asthma symptoms for years as it may have bronchodilator properties,while long term marijuana smoking has also been associated with increased respiratory symptoms.Overall, the relationship between marijuana and lung health is somewhat mixed and the connection may not be the same as tobacco smoke. Previous research has suggested that adolescents with asthma smoked significantly more marijuana than those without asthma.

However, the number of adolescents  who smoked marijuana in Canada has decreased from 32.7% in 2008 to 24.4% in 2013. Electronic cigarettes are battery powered devices that vaporise nicotine and/or other flavouring mixes, but do not burn tobacco. These products have become popular in recent years and they are perceived as a safer alternative to tobacco cigarettes. While preliminary studies suggest that they may be less harmful than cigarettes, the long term health effects and how e-cigarettes relate to asthma symptoms or severity are unknown. The Canadian Tobacco, Alcohol and Drug Survey reported that as many as one in five  adolescents aged 15e19 tried e-cigarettes, however, the absolute trend of usage is still unknown as these products are relatively new. The purpose of this paper is to examine whether adolescents with asthma smoke cigarettes, water pipes, marijuana or e-cigarettes more or less than those without asthma. This study adds to the current literature by examining all smoking habits for youth with asthma, rather than focusing just on cigarettes.The 2013 Ontario Student Drug Use and Health Survey  is a population based survey conducted every two years and completed by grade 7e12 students at publically funded schools in Ontario, Canada. Ontario is the largest province in Canada with a population of over 13 million residents. Ontario includes major urban centres such as Toronto and Ottawa, several smaller cities and an abundance of rural lands. The OSDUHS is designed to collect information about drug use and other health related behaviours among students in Ontario. All parents and students gave consent prior to participation. To examine the association between smoking and asthma, we limit our study sample to high school students  in 109 schools. These schools were selected with probability proportional to size, to obtain a representative sample within the province. The survey included questions that captured information on self-reported doctor diagnosed asthma and data on tobacco, alcohol and drug use. The survey used a random split-ballot design where some of the questions change on each of the surveys. The sample is randomly divided into 2 groups to maximize questions included and minimize burden on students, but it reduces the sample size for some questions. In the OSDUHS, approximately half of the full sample answered questions pertaining to asthma and all types of smoking reducing the sub-sample to 2,840. Data are representative of students in Grades 9 to 12 attending publicly funded schools in Ontario.

Ages for respondents range from 12 to 19 years of age.In Ontario, the majority of children  attended publically funded schools, 5% attended private schools, and another 3% were either home schooled, institutionalized for correctional or health reasons, schooled on a First Nation reserve,military base or lived in remote northern region. This study was approved by the research ethics board at the Research Institute of The Hospital for Sick Children.The primary outcome variables in this study are smoking status with regard to cigarettes, water pipes, marijuana and e-cigarettes. Self-reported frequency and intensity of cigarette, water pipe, marijuana and e-cigarette smoking in the last 12 months and lifetime use were measured in the survey. Cigarette non-smokers were classified as those who never smoked a cigarette or smoked less than  one cigarette in the last 12 months, while cigarette smokers were those who smoked more than one cigarette in the past 12 months. Similarly, smoking status for water pipe was also classified as a binary outcome variable. Respondents were asked how often they smoked a water pipe  in the last 12 months. Those who smoked a few puffs, never smoked, haven’t smoked in the past 12 months or didn’t even know what it was were considered non-water pipe smokers. Those who smoked one or more times were defined as smokers. Marijuana smoking is also defined in a similar manner. Students were asked how often they smoked cannabis  in the past 12 months. If they smoked 1 or more times in the past year they were classified as a marijuana smoker. Respondents who have never or not smoked in the last 12 months were considered to be non-marijuana smokers. Finally, respondents were classified as e-cigarette smokers if they smoked an e-cigarette with or without nicotine in it, pot for cannabis while those who have never smoked or never heard of e-cigarettes were considered non-smokers.The primary risk factor of interest is the presence of asthma which is captured by the response to the question “has a doctor or nurse ever told you that you have asthma”. Other potential confounding variables include: grade,sex and socioeconomic status.SES was measured by a 10-point social ladder. Students were asked to imagine that the ladder represents how Canadian society is set up, where the people at the top of the ladder are the “best off”, meaning they have the best jobs, make the most money and have the highest education. Those at the bottom of the ladder are the “worst off”, with no job, or a job no one wants, little education and the least money. Respondents reported what best represents their family on a 10-point scale, which was further grouped into three levels  based on the interquartile ranges.

While cigarette smoking may aggravate symptoms and severity for adolescents with asthma, some work on the topic suggests that the prevalence of cigarette,water pipe and marijuana smoking was actually higher in adolescents with asthma than those without. These studies, however, are not conclusive as at least one reported that adolescents with asthma were less likely to smoke cigarettes. It is reasonable to suppose that adolescents with asthma will not smoke as it will aggravate their asthma severity and symptoms,but this unfortunately may not be the case. Our study showed that students in grades 9e12 with asthma in Ontario, had a higher odds of smoking e-cigarettes or any substance than their peers who do not have asthma. The odds of smoking ecigarettes for adolescents with asthma, was nearly twice as high as those without asthma after adjusting for age, sex and SES. Given the cross-sectional design of the survey, we cannot infer the causal relationship between smoking and asthma. Previous studies suggest that smoking for adolescents with asthma may relate to the desire to obtain social status among one’s peers, and not wanting asthma to interfere with their social status. Of all demographic characteristics studied, student’s grade was most significantly associated with smoking cigarettes, water pipes and marijuana. A longitudinal study in the United States found that rates of cigarette smoking increased from 1.8% at the age of 9 to 22.5% by age 16. Findings suggest that rates for smoking cigarettes and water pipes among grade 9 students were relatively low,but doubled in grade 10, tripled by grade 11 and quadrupled by grade 12.Cigarette and water pipe smoking became more popular in grade 10 and the trend continued as they aged. E-cigarette smoking on the other hand only marginally increased from grade 9 to 12.For adolescents with asthma, rates of e-cigarette smoking were similar to that of the entire sample, ranging from about 10% in grade 9 to 16.7% in grade 12. Our study also showed that cigarette, marijuana and any smoking rates were inversely related to SES, where lower SES was associated with higher odds of smoking. Our finding is consistent with the literature that suggests an inverse relationship between individual SES or parental education and cigarette smoking in adolescents. It has been suggested that lower SES households may have a poorer attitude towards health, fewer opportunities or more stressful situations which make them more likely to smoke.This study had many strengths which relate to the size and generalizability of the survey sample and the fact that it examined how all types of smoking related to asthma prevalence. That being said, there are also some limitations. The primary purpose of this survey is to examine health risk behaviours of adolescents in Ontario and not asthma. As such, the number of respondents with asthma was low and this may have contributed to some of the insignificant findings.

Despite the high co-morbidity between marijuana  and nicotine  use, only few studies have directly addressed the mechanisms that lead to their concurrent use. This includes route of administration,cross drug adaptation, response to treatments, environmental effects and genetic factors.Others have also alluded to the “gateway drug” hypothesis whereby the use of one drug may potentiate the effects of the other. For example, in a longitudinal study in 14–15 year olds, marijuana use increased the likelihood of initiating nicotine use up to 8 times and developing nicotine dependence up to 3 times suggesting marijuana’s role as a gateway drug.This was further supported by findings showing that women who used marijuana were at 4.4 odds of later developing nicotine use and dependence.The same group also reported in 43,093 adults that nicotine smoking increased the risk for marijuana use and dependence up to 3 times.This latter finding suggests a bi-directional potentiating effect and indicates that more complex factors may drive combined use. Although the animal literature has characterized the neural mechanisms that may underlie these potentiating effects, it is also possible that personality factors contribute to this phenomenon. Combined marijuana and nicotine use has been associated with differential effects on clinical diagnoses, cognitive and psychosocial problems, and outcomes.For example, Bonn-Miller and colleagues examined associations between negative emotions  that discriminate marijuana-only users from co-morbid marijuana and nicotine users.They found that, in general, nicotine-only using individuals had significantly greater negative emotionality than marijuana users, co-morbid marijuana and nicotine users, and non-using controls. Earlier work by Degenhardt showed that while nicotine and marijuana use were both individually associated with increased rates of negative emotion, this relationship appeared to be driven by neuroticism in marijuana users.

Taken together, these studies argue for different patterns of co-morbidity in nicotine and cannabis grow equipment using populations. To date, however, distinctions in trait markers, such as personality factors, have not yet been addressed in this ubiquitous group of co-morbid users. These differences suggest the need for fine-tuning the ability to discriminate risk-profiles between these groups as they also relate to clinical treatment outcomes. Factors that contribute to risk profiles include personality traits that have been examined as putative markers for treatment outcomes. For example, in a prospective four-year study in 112 adults with chronic alcoholism, Krampe et al.  determined that the presence of any personality disorder was associated with a decrease in four-year abstinence probability. Similarly, using the NEO Personality Inventory-Revised  Betkowska-Korpala  found that following treatment, abstinent patients have higher levels of agreeableness and conscientiousness than patients who relapsed within a year following the therapy. This suggests that personality profiles have high predictive values for SUD outcomes and should be considered during treatment programs. However, to date, only few studies have examined personality factors that distinguish marijuana from nicotine users and even fewer differentiate isolated use from combined use. In terms of isolated use, high openness but lower agreeableness and conscientiousness in marijuana users relative to non-users has been noted,suggesting that marijuana users differ from non-users on dimensions of normal personality traits as measured by the Big Five model of personality. Conversely, greater extraversion is widely reported in nicotine-only users,as well as high neuroticism  and impulsivity.Studies that have performed direct contrasts between isolated marijuana and nicotine users have also shown differences between the two groups. For example, using the wide spectrum Five-Factor Model of personality, Terracciano et al.  showed that nicotine users had lower conscientiousness and higher neuroticism whereas marijuana users had high openness, average neuroticism, and low agreeableness and conscientiousness. However, these studies did not examine personality factors in co-morbid nicotine and marijuana users.

These traits together suggest that co-morbid users would have a personality profile endorsing high openness and neuroticism, but comparatively less of these traits than isolated users. Personality factors are markers that can be used as endophenotypes for substance use disorders  particularly because brain circuits involved in personality traits are also implicated in SUD.For example, emergent literature has classified the Big Five personality model via machine learning techniques from resting state fMRI data.These studies indicate that neuroticism negatively correlated with activity in the middle frontal gyrus and precuneus; extraversion correlated positively with regional activity in the striatum, precuneus, and superior frontal gyrus; openness correlated positively with activity in the thalamus and amygdala, and negatively with the superior frontal gyrus; conscientiousness correlated positively with regional activity of the middle frontal gyrus and correlated negatively with the cerebellum.While these findings have not been consistent across studies, they suggest underlying neurobiological mechanisms/pathways that confer personality factors particularly in similar neural substrates implicated in SUD. Altogether, better understanding of the links between personality and SUD can provide understanding of the brain circuits implicated in SUD that could improve prevention and intervention. Given the paucity in the literature on personality factors that discriminate co-morbid from isolated marijuana and nicotine use, this study examined differential NEO personality profiles in marijuana only, nicotine only, co-morbid marijuana and nicotine use and non-using controls. Because the existing literature has shown that marijuana users and nicotine users differ on openness and neuroticism, we predict that comorbid users would have a personality profile high on these two personality traits, but intermediate to that of the isolated users.Participants were recruited from the general community through flyers and newspaper advertisements to participate in a study to determine behavioral and neural associations of substances at the Mind Research Network in Albuquerque, New Mexico. All participants were between the ages of 1855, without current Axis I disorders, not currently taking any psychotropic medications, and, have no history of brain injury. Because these data were collected as part of a larger fMRI study, participants were further required to be free of MRI contraindications  and be right-handed.

Of the 224 individuals who met study criteria, 80 participants were excluded for having a lifetime substance use disorder other than marijuana and nicotine. Two participants were also excluded due to missing data. Thus, analyses for this study were conducted on a sample size of 142.We then categorized the participants into four groups based on their primary and regularly-used substance: marijuana-only,nicotine-only,co-morbid marijuana and nicotine  and non-using control  groups. For the marijuana only group, regular marijuana use was defined as at least four times a week for the previous six months.For the nicotine-only group, regular nicotine use was defined as smoking at least ten cigarettes per day.The combined marijuana and nicotine group consisted of those who use both marijuana and nicotine regularly, as defined by 60 days out of the past 90 of concurrent use. The non-using control group consisted of participants that were neither regular users of marijuana or nicotine. Table 1 summarizes the substance use characteristics for all of the groups.Because we were interested in patterns of personality traits  that distinguish co-morbid users of both marijuana and nicotine from the marijuana-only or nicotine-only users, we first used a factor analysis with VARIMAX orthogonal rotation method to identify unique relationships  between each NEO personality dimension variable and unobserved latent factors. This method allowed us to combine these five factors into linear models that we could then test in linear regression given our sample size. The logistic regression model was used to estimate how much the two significant personality factors derived from factor analysis discriminate marijuana-only, nicotine-only, co-morbid marijuana and nicotine users, and, controls controlling for covariates of sex, age, race, and education. Odds ratios  and 95% confidence interval  estimates were presented as results. Further, in order to test accuracy in personality factors’ discriminability of groups who use marijuana only from nicotine only and neither marijuana nor nicotine use, 70% of area under the Receiver Operating Characteristic  curves was set as a minimum value for an accuracy of classification.In addition to personality factor patterns as predictors, we also examined the group variance in each NEO personality dimension using Analysis of Variance.ANOVA was run for all five dimensions separately controlling for covariates of sex, race, age, and education. Post-hoc pairwise comparisons between two diagnosis groups were conducted if an overall group effect was statistically significant  and Tukey-Kramer adjusted p values were then reported.

SAS 9.4 version  was used for all statistical analyses and p value less than 0.05 was set as a statistical significance level.A factor analysis was conducted to identify personality profiles that discriminated the groups. The factor analysis identified two personality factors 1 and 2, which were linear associations of all five of neuroticism, extraversion, openness, agreeableness, and conscientiousness NEO Personality Inventory dimension t scores. Each factor-loading pattern is described in Table 3. Two dimensions, neuroticism  and conscientiousness,near equally loaded high to factor 1, while the rest of dimensions, extraversion loaded the highest,followed by openness  and agreeableness,loaded high to factor 2. 3.3. Paired comparisons and logistic regression To investigate the discriminatory effectiveness of personality factors of marijuana use among four groups,we used three paired comparisons: marijuana-only vs. all other groups, marijuana-only vs. nicotine-only, and marijuana only vs. controls.A logistic regression model consisted of two personality factors and covariates of sex, race, age, and education. Logistic regression results showed that as personality factor 2 score increased by 1, the odds of using marijuana increased by 180% compared to non-marijuana using groups, respectively. However, personality factor 1 did not significantly discriminate marijuana group from the other groups.Regarding demographic variables, these comparisons also demonstrated that older participants were less likely to use marijuana, and that females had significantly lower odds than males of using marijuana only compared to neither marijuana nor nicotine use. Additionally, as years of education increased by 1 year, the odds of marijuana use decreased by 74%. The study model was able to discriminate marijuana users from the rest with 83% accuracy, while each personality factor alone discriminated marijuana users from the rest of the sample with less than 60% accuracy.When comparing marijuana-only and nicotine-only groups, neither factor 1 nor factor 2 alone discriminated marijuana-only users from nicotine-only users with greater than 70% accuracy. However, a model with both personality factors and demographic covariates discriminated marijuana only use from nicotine users with 80% accuracy.When comparing the marijuana-only group with the control group, neither factor 1 nor factor 2 alone discriminated marijuana only users from the control group, while a model with both personality factors and demographic covariates discriminated marijuana only use from the control group with 85% accuracy.While no significant overall group effect was found in any of Together, these observations indicate clear personality differences between individuals who use marijuana, nicotine, or both,mobile grow system and implicate differences in treatment between these populations.The aim of this study was to determine personality profiles that distinguish marijuana users, nicotine users, and, co-morbid marijuana-nicotine users. Factor analysis showed that a model with both personality and demographic factors discriminated marijuana users from non-users better than personality factors alone. Logistic regression found strong effects of age, sex and years of education in discriminating marijuana users from non-users whereby the odds of using marijuana increased by being male, younger and less educated.

A model with both personality and demographic factors also discriminated marijuana users from nicotine users with high predictability. ANOVA results showed that the openness dimension discriminated the marijuana users from all other groups and the marijuana-nicotine group from the nicotine users. These findings suggest that the discriminability of the co-morbid group from the nicotine-only and non-using group is primarily due to the contributions of marijuana use. The larger contribution of marijuana in concomitant users in terms of risk is in line with studies that found that the association between comorbid use and negative emotion is largely driven by marijuana users.Our findings are concordant with the literature that showed openness discriminates marijuana users from other groups.Openness identifies the seeking of experiences for their own sake.Relative to marijuana, nicotine use does not have the burden of legal consequences and therefore may not require the same degree of openness as marijuana use. Our findings are also concordant with those suggesting that agreeableness and conscientiousness are lower in drug users.Our findings further add to this literature by showing that agreeableness and conscientiousness in marijuana users are intermediate to that of non-using controls and nicotine users.Interestingly, studies have also reported that extraversion is traditionally lower in drug users compared to non-drug users suggesting that extraversion may confer resilience to the development of addiction disorders in general.

Low CD4 T cell count  was the dominant predictor of infectious pulmonary diagnoses in multi-variable analyses, yet the association between marijuana smoking and infectious pulmonary diagnoses remained significant in adjusted analyses restricted to visits with CD4 ≥ 200 cells/μl, and sensitivity analyses with no CD4 restriction. Tobacco smoking, reported during follow-up for 44% of HIV+ participants, was a stronger risk factor for chronic bronchitis in HIV+ participants compared to marijuana smoking, while for infectious pulmonary diagnoses in HIV+ participants, the risk associated with ≥1/2 pack/day tobacco smoking was comparable to that of daily or weekly marijuana smoking.Strengths of this study included the large sample size, large number of diagnoses reported, and substantial length of follow-up.Men at risk for HIV are recruited by the MACS from four U.S. urban sites, and thus HIV-infected participants share similar demographic and lifestyle characteristics with uninfected participants. HIV+ and HIV− participants reported substantial daily or weekly marijuana smoking,which allowed assessments of both current and average exposures during follow-up. The sample size allowed for stricter control of tobacco smoking with large numbers of participants in stratified analyses,which may in part explain the lack of association between marijuana smoking and pulmonary disease among HIV− individuals found here compared to previous studies. Injection drug use is a possible risk factor for lung disease among HIV-infected persons, and poly drug use is common in HIV-infected individuals, and we therefore excluded heavy cocaine and heroin users from our analysis to reduce the potential for competing risks from multiple inhaled or injected substances. Limitations of this study include those inherent to longitudinal prospective cohort studies, including the potential for findings to be specific to MSM populations recruited by the MACS, and for nonrandom dropout and ascertainment biases.

Measures of cannabis grow tent intake were limited to self-report during follow-up, with limited detail regarding exposure prior to MACS enrollment or marijuana potency, source, or quantity. These concerns are mitigated in part by the use of three separate measures of marijuana smoking,all of which were associated with infectious pulmonary diagnoses and chronic bronchitis in models of HIV-infected participants. Furthermore, the proportion of daily or weekly HIV+ marijuana smokers here  is comparable to reports from other U.S. cohorts of HIV-infected individuals.The use of self-reported pulmonary symptoms and diagnoses is an additional potential source of bias, and possible under-representation of specific non-infectious diagnoses in the MACS, particularly COPD and pulmonary hypertension, has been reported. Most diagnoses were not assessed from both self-report and ICD-coded sources. This limitation was mitigated in part because most diagnoses were obtained from ICD code data, which consists of more specific diagnosis classifications.Potential ambiguities regarding terminology for diagnoses, and discordance between highly prevalent pulmonary symptoms and low rate of pulmonary diagnostic testing among HIV-infected persons has been noted in other clinical settings, and is mitigated here in part by use of composite diagnoses. The number of diagnoses reported for several categories, including tuberculosis, lung cancers, and Pneumocystis pneumonia, was small and lacked statistical power to assess an adjusted association with marijuana or tobacco smoking. In summary, we found a significant association between long-term marijuana smoking and risk of infectious lung disease and chronic bronchitis in HIV-infected men on ART, independent of tobacco smoking and other risk factors.In contrast, we detected no association between marijuana smoking and lung disease among HIV-uninfected men while controlling for tobacco smoking and other demographic characteristics. These findings suggest that marijuana smoking is a modifiable risk factor that healthcare providers should consider when seeking to prevent or treat lung disease in people infected with HIV, particularly those with other known risk factors including heavy tobacco smoking, and low CD4 T cell count or advanced HIV disease.

Given increasing trends of regular marijuana smoking among HIV-infected people and other high-risk populations in the U.S. and other developed and developing countries, more studies are needed to evaluate potential merits of non-smoked rather than smoked forms of marijuana for medicinal and other purposes.Ms. A, a 48-year-old woman with a history of cholecystectomy complicated by bile duct stricture and multiple hepatic abscesses and liver failure was evaluated for an orthotopic liver transplant. She had a psychiatric history of depression and anxiety, with a brief inpatient hospitalization after cutting her wrists 6 months before her transplant. She then had 1 month of outpatient therapy. During a pretransplant psychiatric evaluation, she reported no illicit drug use. Outpatient medications included opioid analgesics and cyclobenzaprine for pain, as well as zolpidem for insomnia secondary to abdominal pain. Ms. A was admitted to the hospital and received a trisegmental orthotopic liver transplant. She was monitored postoperatively in the surgical intensive care unit and started on 2 agents for immunosuppression  as well as broad spectrum antibiotics and anti-fungal agents. The pain management service was consulted for postoperative pain control given her dependence on opioid analgesia as an outpatient. Per their recommendations, she was started on a hydromorphone PCA.Her transaminases and coagulation studies were reassuring and she was stable enough to be transferred out of the intensive care unit on post-transplant day 3. That same day, she was started on tacrolimus at 2 mg q12 hours. A serum tacrolimus level taken the next morning was 7.7 ng/mL which was at goal. On post-transplant day 5, the transplant psychiatry service was consulted for agitation and deliriumspecifically, she was oriented only to herself, had disorganized speech, was observed to be responding to internal stimuli  and was exhibiting psychomotor agitation in the form of purposeless shifting and fidgeting.

Her vital signs were significant for tachycardia and absence of fever. Laboratory testing revealed a significant increase in her tacrolimus level to 17.2 ng/mL despite no changes in dosing. Labs were otherwise stable.Ms. A’s encephalopathy was felt to be secondary to tacrolimus toxicity, though the reason for the change in the tacrolimus level was initially unclear. Her tacrolimus was held and the psychiatry team recommended low-dose quetiapine as needed for agitation to avoid use of restraints and to mitigate the risk of decannulation or removal of other lines. Later that night, Ms. A’s tachycardia, disorientation, and confusion worsened, and she required 2 doses of IV haloperidol. By post-transplant day 7, her encephalopathy began to resolve, with improving orientation and less psychomotor agitation. Her serum tacrolimus level was downtrending to 7.3 ng/mL and she was restarted at a dose of 1 mg q12 hours. At this time, Ms. A’s family mentioned that as her preoperative pain was poorly controlled by various opioids, she had sought pain management from a medical marijuana clinic in a neighboring state where medical marijuana was legalized. A urine drug screen confirmed her use of cannabinoids. Ms. A produced a bottle of medical marijuana lozenges from her purse at the bedside. The bottle indicated that each lozenge contained 10 mg of tetrahydrocannabinol and 1 mg cannabidiol. She had filled the bottle 10 days before admission, and reported taking 24 lozenges per day up until her transplant, though she denied taking any during admission. She denied any other previous marijuana use. On post-transplant day 9, her level was stable at 3.4 ng/mL and her dose was titrated to 2 mg q12 hours. Ms. A was advised to discontinue cannabis usage due to concern for drug-drug interactions. Her postoperative care was uncomplicated by subsequent mental status issues. Her tacrolimus dosage remained stable at 3 mg po q12 and her level remained at goal, most recently 7.4 mg/mL.Marijuana is the most commonly used illicit substance in the US. In the past decade alternative formulations of its active ingredients have been adopted in many states for the treatment of various medical conditions, including chronic pain.In this case, our patient was seen in a medical marijuana clinic by a pain specialist who was registered with the New Jersey Medical Marijuana Treatment Program.

Under this program, patients must be found to have an “approved debilitating medical condition” that has either been demonstrated to be treatable by cannabis or has not responded to conventional treatment. Our patient met criteria for “chronic pain of visceral origin.” She was able to obtain the lozenges at a licensed dispensary affiliated with her doctor’s office. Anecdotal reports of marijuana use leading to decreased reliance on opioid pain medications have led to new investigations into its analgesic effects. One case study from 2016 described the use of marijuana to wean opioid use in a patient who had received an orthotopic liver transplant.Additionally, while many proponents of marijuana’s analgesic properties distinguish between the potential therapeutic effects of its 2 major ingredients, tetrahydrocannabinol and cannabidiol, it is unclear whether their pharmacokinetics differ meaningfully.In vitro studies have demonstrated tetrahydrocannabinol and cannabidiol to be CYP 3A4 substrates and there is some evidence that they act as inhibitors as well.A case report from 2006 described myocardial infarction in an otherwise healthy 41-year-old man after concomitant use of sildenafil and grow lights for marijuana, thought to be due to 3A4 inhibition.Additionally, constituents in marijuana have been shown both to inhibit or induce P-glycoprotein, which could then interfere with the absorption and distribution of medications like tacrolimus.As mentioned above, the resultant variability in tacrolimus levels not only leaves the potential for neurotoxicity, but it is also associated with worse graft survival.The evaluation and treatment of encephalopathy in post-transplant patients are similar those for delirium in the general postoperative patient, with the additional consideration of immunosuppression as a unique factor. While haloperidol and quetiapine are 3A4 substrates, they are not known to interfere with the metabolism of tacrolimus, and their metabolism is not impaired in patients with poor liver function when used conservatively. There is a case report of QT interval prolongation leading to arrhythmia in a patient receiving both tacrolimus and haloperidol, though tacrolimus is less likely to prolong QT independently when compared to other immuno suppressants.

The increased prevalence of both recreational and medical marijuana use represent challenges to safe immunosuppression in post-transplant patients. Since medical marijuana is not consistently monitored between states and is not thought of universally as a legal medication, it can easily be overlooked as part of medication reconciliation. Additionally, as medical marijuana therapy remains a controversial topic among providers, there is significant stigma attached to its use.In one study, 10.4% of transplant candidates used cannabis, and were found to be less likely to receive a transplant despite similar survival rates to those who did not.The perception that disclosing marijuana use  could hurt her chances at receiving a transplant was likely a factor in our patient not disclosing it to her transplant team. Emerging research on cannabinoids suggest that they offer immunosuppressive effects independently which further complicates their use in patients where tightly controlled immunosuppression is necessary for healthy recovery.Marijuana use is widespread and increasing. In the 2015 National Survey on Drug Use and Health, 8.3% of respondents said that they had used marijuana in the past month. As of April 2018, 30 states, the District of Columbia, Guam and Puerto Rico had passed medical marijuana laws permitting programs public medical marijuana. The total number of medical marijuana patients is unknown but estimates place the number at greater than 2 million patients in the United States. Marijuana refers to the dried leaves and flowers of the Cannabis sativa plant, which are rich in phytocannabinoids. The plant is grown either indoors or outdoors before it is harvested, cured, and dried. Molds may be present and can multiply under conditions of high moisture as with inappropriate watering, humidity or ventilation or inadequate drying and curing. Mold spores may survive the drying and curing process even under ideal conditions.Marijuana obtained from medical dispensaries does not differ significantly with regards to microflora when compared to illicit marijuana. One analysis of twenty marijuana samples obtained from dispensaries in northern California showed the presence of 20 fungal genera including Aspergillus, Cryptococcus, and Mucor as well as several bacterial pathogens such as Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa. Previous case reports have documented cases of pulmonary aspergillosis associated with marijuana smoking in immunocompromised patients.

The coding categories used in this study may raise questions about the results in terms of validity and reliability. Thus, future studies should test the same categories that this study used and then develop more correct coding categories. Lastly, the time period for this study was between 1995 and 2014. Although marijuana was initially legalized in 2012, the first recreational sales were in 2014. Thus, news stories during 2014 could be quite different than stories from previous years.Substance use among young adults is a major public health concern and is associated with academic problems. The bulk of research in this area has focused on undergraduate students, as alcohol and marijuana use among this population are fairly common.In addition to academic difficulties, alcohol and marijuana use are associated with other negative consequences during the college years, including risky sexual behaviors, social and interpersonal problems, injury, and impaired driving.Longitudinal research has shown that alcohol and marijuana use during college might have long-term consequences after college graduation. Heavy drinking and marijuana use during college are associated with post-college substance abuse and dependence, unemployment, less prestigious employment, and lower income.Marijuana use during college and the immediate post-college years, particularly heavy use, is associated with several negative health outcomes at ages 24 and 27, including emotional problems, injury, illness, decreased quality of life, and less service utilization for physical and mental health problems.

Degree non-completion as a consequence of substance use has been found in longitudinal studies of high school and college students. Adolescents who use alcohol, tobacco, and cannabis grow tray during ninth grade are less likely to complete high school than nondrug users.One study integrated data from three longitudinal studies and found that daily marijuana use during adolescence was significantly associated with decreased odds of both high school and college completion.In a study of college students, frequent marijuana use during the course of college was associated with increased likelihood of dropping out.Despite evidence of associations between alcohol and marijuana use and high school and undergraduate degree non-completion, the possible impact on graduate degree completion has not been explored. An increasing number of college graduates are enrolling in graduate school, with almost 40% of college graduates pursing a graduate degree within four years of graduation.However, only 50% to about 75% of those who enter graduate school ultimately complete their degree, with differences by degree type and academic discipline.Existing theories of student attrition, centered primarily on the undergraduate student experience, posit that attrition is influenced by individual, institutional, and social factors.Institutional factors include program characteristics, administrative policies, and academic requirements, and social factors include peer culture, faculty/staff interactions, and social integration. Individual pre- and post-matriculation factors include demographic characteristics, skills and abilities, goals and expectations, external commitments, and academic history. Largely missing from theories of student attrition are health status and health behaviors, particularly substance use prior to and after enrollment in an academic degree program. The relationship between alcohol and marijuana use and graduate degree completion is likely influenced by demographic characteristics. Both heavy drinking and marijuana use are more prevalent among college males than females,and substance use disorders are associated with being male, white, and unmarried.Having children is associated with a lower prevalence of substance use among both men and women.

Demographic characteristics are also associated with graduate school completion, with burnout and attrition highest among women.Attrition is also more common among African-American/Black students,domestic students,and students enrolled in master’s degree programs.This study aimed to fill a gap in the literature by assessing the relationships between alcohol and marijuana use before and after graduate school enrollment and graduate degree completion. It is hypothesized that lower levels of alcohol and marijuana use both before and after graduate school enrollment are associated with graduate degree completion after adjustment for potentially confounding variables.Alcohol use was measured annually in Y1-Y12. To assess frequency of alcohol use, participants were asked, “In the past 12 months, on how many days have you drank any drink with alcohol in it?”. To assess quantity of alcohol use, participants were asked the number of drinks they had on a typical drinking day.Data on days used during the past year were used to estimate average alcohol use frequency  for descriptive purposes. Marijuana use frequency was assessed annually in Y1-Y12 with the question “In the past 12 months, on how many days have you used any type of marijuana?”.Data on days used during the past year were used to estimate average marijuana use frequency  for descriptive purposes. Past-month frequency of both alcohol and marijuana use were also assessed, but because of the high degree of correlation with past-year measures,only past-year variables were used in the analyses. For each participant, alcohol use frequency, alcohol use quantity, and marijuana use frequency were averaged separately for each of two time periods: before and after the first year they indicated enrollment in a graduate degree program. The mean for each of the six separate variables  was used to capture variation in substance use during the pre- and post-enrollment periods, particularly because the before enrollment period included the undergraduate college years as well as the interim years after college graduation but before graduate school enrollment.

Descriptive statistics  were used to analyze the distributions of all study variables. Pearson correlation coefficients were used to analyze the relationships between all six alcohol and marijuana use predictor variables. A series of logistic regression models were fit to assess the relationships between alcohol and marijuana use and graduate degree completion. First, in Stage 1, separate logistic regression models were fit to analyze the relationships between each alcohol and marijuana use predictor variable and graduate degree completion while controlling for demographic and program characteristics. Second, in Stage 2, a best fitting model was obtained by entering each of the six alcohol and marijuana use predictor variables into the model one at a time, retaining any predictor variable that was statistically significant and dropping those that were not significant. All demographic and program characteristic variables were retained in the final model regardless of significance. The Nagelkerke R2 value was used to examine the variance in graduate degree completion explained by the Stage 2 variables. A similar method has been used in prior work by the research team.SPSS Version 24.0 was used for all analyses, and the alpha level was set at 0.05.The majority of participants drank alcohol during at least one year before graduate school enrollment  and after graduate school enrollment.Among drinkers, the average alcohol use frequency was about 75 days during the past year before enrollment in graduate school and 88 days during the past year after enrollment.Among drinkers, mean alcohol use quantity decreased from a mean of 3.9 drinks per drinking day before graduate school enrollment to 2.6 drinks per drinking day after enrollment. The typical quantity consumed for male drinkers was greater than female drinkers both before and after graduate school enrollment.Based on past-year data, it was estimated that about 35% of drinkers drank less than weekly and about 24% drank twice a week or more before graduate school enrollment. After graduate school enrollment, 32% of drinkers drank less than weekly and about 31% drank twice a week or more.The prevalence of marijuana use was 72% prior to graduate school enrollment and 49% after graduate school enrollment. As seen in Table 2, marijuana use frequency among users was about the same prior to and after graduate school enrollment with a mean of about 40 days during the past year.

Among those who used marijuana prior to graduate school enrollment, 56% used once a month or less and about a quarter used at least weekly.Among those who used marijuana after graduate school enrollment, 64% used once a month or less and about 18% used at least weekly.The correlations between the six alcohol and marijuana use predictor variables are presented in Table 3. There were moderate to strong correlations between the before enrollment estimates and the after enrollment estimates. Despite this statistical overlap, both before and after enrollment variables were retained due to their importance to the research question of interest. Alcohol use frequency before graduate school enrollment was strongly correlated with alcohol use quantity before graduate school enrollment  and moderately correlated with alcohol use quantity after graduate school enrollment.To avoid the potential for multicollinearity effects on the statistical models, only the alcohol use frequency variables were retained for further analyses. There is prior evidence that frequency of alcohol use increases during the post-college period while quantity of alcohol use decreases,and alcohol use frequency has higher sensitivity and specificity in identifying alcohol-related problems than alcohol use quantity .This study examined whether or not alcohol and marijuana use before and after graduate school enrollment were associated with graduate degree completion. Alcohol and marijuana use were moderate among participants in this sample. Results showed that more frequent marijuana use after graduate school enrollment was associated with decreased odds of graduate degree completion after adjustment for potentially confounding variables. This finding is consistent with prior research that has shown a relationship between frequent marijuana use and degree non-completion among high school and undergraduate college students.Marijuana use was less prevalent after graduate school enrollment as compared with before, vertical grow systems for sale which is consistent with research showing that marijuana use declines as young adults age.However, while past-year marijuana use frequency among marijuana users who completed their graduate degree declined from 40 days before enrollment to 35 days after enrollment, frequency among users who did not complete their graduate degree increased from 45 days before enrollment to 85 days after enrollment.

The first is through decreased academic performance, with underachievement cited as the most well-supported correlate of marijuana use.While little research has been done on the relationship between marijuana use and decreased academic performance among graduate students, existing evidence among high school and college students shows that frequent marijuana use is associated with academic unpreparedness,lower grades,and lower academic achievement.The relationship between marijuana use and degree non-completion might also be explained by the effects of marijuana use on cognition.Verbal learning, memory, executive functioning, IQ, and attention, which are critical for academic success, are impaired by both acute and chronic exposure to marijuana.Arria, Barrall, Allen, Bugbee, and Vincent  suggest that the immediate, rewarding effects of substance use might lead to a re-prioritization of academic pursuits that are associated with longer-term rewards. This study also observed a positive relationship between alcohol use frequency prior to graduate school enrollment and graduate degree completion. There is evidence that alcohol use is associated with dropout from both high school  and college.However, some research has suggested that students who drink more frequently might be more likely to engage in the academic environment and elicit social support.Molnar, Busseri, Perrier, and Sadava  found a prospective relationship between alcohol use and higher levels of subjective well being among college students, and Blank, Connor, Gray, and Tustin  found increased self-efficacy among college students who consumed alcohol. Existing longitudinal studies have found paradoxical effects between alcohol use and education,highlighting the complex relationship between alcohol use and academic success and calling for increased research in this area. The present study findings should not be misinterpreted to mean that excessive drinking is associated with graduate degree completion, as the vast majority of the students in this sample were light to moderate drinkers. A strength of this study was the use of longitudinal cohort data spanning twelve years of young adulthood. However, because the sample was originally enrolled in a single, large publicly-funded university, findings might not be generalizable to young adults starting their college career in different types of educational institutions. The present sample was somewhat homogenous with respect to demographic characteristics. Further research with larger, more diverse samples is needed to explore the associations between demographic variables and developmentally-salient variables, such as having children, on graduate degree completion. Additionally, graduate degree completion among this sample was 82%, which is higher than the national average of around 65%.Completion of a graduate degree was only analyzed through Y12, and students might have completed their graduate degree later on in adulthood. This study also did not account for several factors that might have influenced graduate degree completion, including academic ability, mental health, motivational factors, employment opportunities, personality, academic goals, external commitments, institutional factors, and social and professional support.

Alcohol intoxication is a common comorbidity in traumatic brain injury,with 30%–50% of all TBIs occurring under the influence of alcohol. Preclinical studies have indicated that ethanol pretreatment results in a faster recovery with better outcomes after TBI. However, numerous clinical studies have examined the relationship of alcohol exposure and risk of mortality in patients with TBI with inconsistent results: some studies have found a positive blood alcohol content  had no significant relationship with mortality, while others have found that mortality rate due to TBI with alcohol intoxication is lower compared to those without alcohol intoxication. Additionally, marijuana has been implicated as a major risk factor for all types of trauma. The anti-inflammatory properties of endocannabinoids have been demonstrated to provide neuroprotective effects after TBI. A previous study found a positive tetrahydrocannabinol screen to be independently associated with survival after TBI. While the risk of injury from alcohol,marijuana,and other drugs in combination is increased,the neuroprotective effects of combined marijuana and alcohol have not yet been studied. Few studies have determined the effects of combined drug use on mortality after TBI, and the relationship of combined alcohol and THC on TBI outcomes remains unknown.The aim of this study is to use a data-set of regional data from 26 regional hospitals to evaluate the combined effects of a positive THC and alcohol screen on patient outcomes after sustaining mild, moderate, and severe traumatic brain injury.

Our results demonstrate TBI patients with a positive toxicology for THC and alcohol were found to have significantly lower mortality at discharge when compared to patients with no substances.However,mobile vertical rack in a multiple logistic regression, combined BAC and drug class were not found to be independent predictors of mortality at discharge, while age, GCS, ICU days, ISS, and LOS were found to be independent predictors of mortality. Though somewhat contested, the effect of alcohol intoxication on patients with TBI has been shown in many studies to improve mortality. A meta-analysis of observation studies by Raj et al. included 11 studies with 95,941 patients, and found that positive BAC was significantly associated with lower mortality rates in moderate to severe TBI. Conversely, a meta-analysis examining the impact of day-of-injury alcohol consumption on outcomes after TBI by Mathias et al., found that positive blood alcohol levels were associated with significantly poorer cognitive outcomes and higher levels of disability. Overall, they found that day-of-injury alcohol consumption is not consistently associated with better or worse outcomes, other than subtle cognitive deficits. The effect of marijuana on TBI is far less studied than alcohol, though many preclinical studies have shown THC is associated with neuroprotective effects including alleviation of brain edema, attenuated cell apoptosis, improved neurobehavioral function, and enhanced cerebral blood flow. These effects are partially attributed to the upregulation of NFE-2 factor, which regulates the cellular antioxidant response, following TBI and modulation of the mitochondrial apoptotic pathway. A study by Nguyen et al. found that after adjusting for differences between study cohorts, a positive THC screen was found to be associated with increased survival after TBI. With the individual effects of alcohol and marijuana on TBI still contested, their combined effects on mortality have not been explicitly studied.

DiGiorgio et al. investigated the impact of drug and alcohol intoxication on GCS assessment in patients with TBI, and found that intoxicating substances can confound GCS score with impaired patients having a significantly higher mean change in GCS score compared with patients with a negative screening test. A retrospective review by O’Phelan et al. studied the impact of substance abuse on mortality in patients with TBI by comparing amphetamine, benzodiazepine, narcotic, cannabis, cocaine, alcohol, polydrug, and polydrug, excluding alcohol, and found that methamphetamine use was a significant predictor of mortality. They also demonstrated that patients who tested positive for methamphetamine were also more likely to test positive for cannabis and hypothesized the synergistic effects of methamphetamine and THC may have contributed to overall lower mortality in this cohort. In our study we employed a logistic regression model that controlled for age, gender, GCS, ICU days, LOS days, ventilator days, ISS, and complications and found neither THC nor a positive BAC screen to be independent predictors of mortality, which is consistent with the analysis by O’Phelan et al.Over the last decade, marijuana use and the legalization of marijuana, medically and recreationally, has continued to increase in the United States.1 The internet is rife with claims of the beneficial effects of marijuana on several aspects of sexual function including libido, arousal, and orgasm. However, our scientific research on the effects of marijuana on sexual functioning is limited. Recently Palamar et al2 evaluated self-reported sexual effects of marijuana, ecstasy, and alcohol use in a small cohort of men and women aged 18e25. They found that the majority of marijuana users reported an increase in sexual enjoyment and orgasm intensity, as well as either an increase or no change in desire.2 Endocannabinoids, which are structurally similar to marijuana, are known to help regulate sexual function.3 The cannabinoid receptor, discovered in the 1990s, has been mapped to several areas of the brain that play a role in sexual function.3 Cannabinoids and endocannabinoids interact with the hormones and neurotransmitters that affect sexual behavior. Although these interactions have not been clearly illuminated, some studies in rodents have helped to clarify the relationship between cannabinoids and the hormones and neurotransmitters that affect sexual behavior.Although there is less data on human subjects, some studies have measured patient’s perceptions of the effects of marijuana on sexual function. Studies have reported an increase in desire and improvement in the quality of orgasm.

Most recently, Klein et al6 evaluated the correlation between serum levels of 2 endogenous endocannabinoids and found a significant negative correlation between endocannabinoids and both physiological and subjective arousal in women. Sumnall et al7 reported that drugs such as cannabis and ecstasy were more frequently taken to improve the sexual experience than was alcohol. The primary aim of this study was to determine how women perceive the sexual experience, specifically overall sexual satisfaction, sex drive, orgasm, dyspareunia, and lubrication, when using marijuana before sex. The magnitude of the change was also evaluated. The secondary aim sought to understand the effect of the frequency of marijuana use, regardless of marijuana use before sex, on satisfaction across the different sexual function domains.Women were enrolled prospectively from a single, academic, obstetrics and gynecology practice from March 2016eFebruary 2017, and their data were retrospectively reviewed. The protocol was approved by the Institutional Review Board. Eligibility criteria consisted of being a female, 18 years of age, and presenting for gynecologic care irrespective of the reason. Each participant completed a confidential survey, including demographic data without unique identifiers after their visit, which was placed in a sealed envelope and dropped in a lock box at the clinic. The Sexual Health Survey was developed for the purpose of this study based on the aims of the study. There are several validated tools for evaluation of sexual function. The Female Sexual Function Index 8 assesses several domains of sexual function, but it does not address specifically marijuana or other substance usage. The Golombok Rust Inventory of Sexual Satisfaction 9 specifically relates to vaginal intercourse, but, for purposes of this study, sexual activity was deliberately left open-ended and not restricted to vaginal penetration. In addition, the goal was not to measure whether women had sexual dysfunction, which the FSFI addresses, but to assess basic questions regarding overall sexual activity. To limit bias, the authors embedded the questions about marijuana deeper into the questionnaire.

If these specific questions had been added to the standard FSFI, there was concern that the questionnaire would have been too long and that the patients would get questionnaire fatigue and not finish or answer thoughtfully. Measurement of marijuana use before sex was dichotomized as yes or no. The exact timing of marijuana use in relation to sex was not defined, and the majority of users were smokers of marijuana. For purposes of the study, groups consisted of non-marijuana users, marijuana users before sex, and marijuana users who didn’t use before sex. Patients reported their usage as several times a day or week or year, once a day, week or year and less than once a year. For purpose of analysis, frequency of marijuana use was measured by dichotomizing into frequent  and infrequent .In our study, the majority of women who used marijuana before sex reported positive sexual effects in the domains of overall sexual satisfaction, desire, orgasm, and improvement in sexual pain but not in lubrication. Women who used marijuana before sex and those who used more frequently were more than twice as likely to report satisfactory orgasms as those who did not use marijuana before sex or used infrequently. Our study is consistent with past studies of the effects of marijuana on sexual behavior in women. In the above-mentioned study by Palamar et al,2 38.6% of respondents were women. Participants were asked questions similar to this study’s questions regarding sexual domains, including sexual enjoyment, desire, and orgasm intensity and how these were affected by being under the influence of marijuana. The majority of respondents noted an increase in sexual enjoyment  and orgasm intensity,whereas 31.6% noted an increase in desire, and 51.6% noted no difference.2 Our data showed a higher percentage of participants reporting improvements in each domain across the board. However, vertical grow rack their data included both men’s and women’s responses, and their questions were worded differently. Dawley et al10 evaluated a group of marijuana using students  and found that marijuana smokers reported increased sexual pleasure, increased sensations, and increased intensity of orgasm. Only more-frequent users felt that marijuana was an “aphrodisiac,” a surrogate measure of desire. This study included only 22% women.10 Finally, Koff11 evaluated sexual desire and sexual enjoyment after marijuana use in women via a questionnaire. The majority of the female respondents reported that sexual desire was increased.

Sexual enjoyment increased 42.9% of the time.11 Interestingly, Sun and Eisenberg12 reported a higher frequency of sexual activity in marijuana users, even when controlling for multiple variables.The authors surmise from their data that marijuana use does not seem to impair sexual function. However, it is important to note that marijuana use may be harmful. Our study provides an interesting insight into women’s perceptions of the effect of marijuana on the sexual experience. It differs from other studies in that it is one of the largest series to date and has a wider range of ages. It also differed in that it was a cross-section of healthy women presenting for routine gynecologic care, where most studies target younger patients and include both sexes. For this reason, it is difficult to directly compare the studies, because the sexual activity, frequency, and expectation of these groups may be very different. However, we believe it is important to understand the potential effect in this patient population. The question of how marijuana leads to these positive changes in sexual function is unknown. It has been postulated that it leads to improvement in sexual function simply by lowering stress and anxiety.It may slow the temporal perception of time and prolong the feelings of pleasurable sensations.It may lower sexual inhibitions and increase confidence and a willingness to experiment.7 Marijuana is also known to heighten sensations such as touch, smell, sight, taste, and hearing.Although this was not specifically addressed in this article, according to Halikas et al,the regular female marijuana user reported a heightened sensation of touch and increased physical closeness when using marijuana before sex. It is postulated that marijuana works through a variety of mechanisms. It is recognized that marijuana and the hypothalamic-pituitary-gonadal axis, which controls the sex hormones, interact with each other. There are cannabinoid receptors in the hypothalamus that regulate gonadotrophinreleasing hormone and oxytocin release, both of which play a role in normal sexual functioning.In addition, marijuana has been shown to affect testosterone levels, which play a role in sex drive, but how and in which direction in women is unclear.Female sexual function is not only regulated by hormones, but also by centrally acting neurotransmitters, such as dopamine and serotonin.

In some countries, such as Spain, only cannabinoid-based preparations are approved for select diseases , while other countries, such as Germany or UK, have established a more liberal approach, which allows physicians to prescribe unprocessed herbal cannabis for certain illnesses. Based on a growing body of evidence supporting the therapeutic potential of cannabis, the World Health Organization recommended that cannabis should be rescheduled in order to facilitate medicinal regulations in member states. With some delays, the rescheduling was carried out in December 2020 by the United Nations Commission on Narcotic Drugs. It is now reasonable to expect more European countries to follow this decision and legalise unprocessed herbal cannabis for medical purposes. Moreover, in light of the liberalisation of cannabis policy in North America, the option of recreational legalisation is broadly discussed in many European countries . In fact, Luxembourg announced a decision to legalize the sale of cannabis for recreational use in 2019. In the same year, the Netherlands passed a controlled cannabis supply experiment bill which will evaluate the impact of legalising the supply of cannabis for recreational use in an experimental design. Lastly, legislative changes towards decriminalization have been implemented in 2001 in Portugal and more recently in Czechia. In order to provide a strong empirical framework for assessing the health effects of changes in cannabis policy in Europe,trimming tray weed rigorous public health monitoring of cannabis in Europe is crucial.

Currently, public health monitoring of adult cannabis use is carried out by two international bodies that routinely collect and publish data on several cannabis indicators, including prevalence of use, treatment rates, and potency levels. First, the United Nation Office on Drugs and Crime collects annual data on prevalence of illegal drug use as well as further drug-related indicators from all UN member states . In their annual “World Drug Reports”, these data are summarized at the regional as well as global level. Second, the European Monitoring Centre for Drugs and Drug Addiction the responsible body for monitoring illegal drug use and drug addiction in Europe compiles a number of cannabis related indicators, which also serve as base for the annual European Drug Reports. In addition to these two agencies collecting empirical data on cannabis, the GBD study routinely estimates the prevalence of CUD for all countries. In the current contribution, we extracted and analysed cannabis indicator data from publicly available sources, including prevalence of use, prevalence of cannabis use disorder , treatment rates, and potency of cannabis products in Europe. We aimed to describe the trends of these indicators for the period 2010 to 2019 and the possible public health implications. Further, we aimed to highlight limitations in the available data, in order to identify the steps required to improve current practice in monitoring of cannabis use and harm in Europe.The treatment demand indicator reflects information about the number and the profile of people who enter treatment for drug problems each year. A uniform protocol guides EMCDDA member states to collect the required data in a comparable way across all countries. For the formal TDI definition and further methodological details, see Supplemental Material 1. For interpreting TDI data, variations in coverage of treatment entries between countries and over time need to be taken into consideration. We attempted to consider differences in coverage of TDI data, i.e., the share of all relevant treatment units covered by the indicator, between country and over time.

However, a complete assessment of TDI coverage is not available but the most recent report for the year 2014 marks substantial cross-country differences in the TDI coverage rates from 60% to 100% for out-patient treatment centers and from 30% to 100% for in-patient treatment centers. To further elaborate on TDI coverage variations, we examined how the number of treatment units covered by the TDI differs between countries and developed over time .EMCDDA member states monitor and report cannabis potency according to the total concentration of delta-9-tetrahydrocannabinol in sample weight, to the nearest 0¢1%. For the current analyses, we obtained the median THC levels in herbal cannabis and resin. In contrast to survey and TDI data, the EMCDDA does not disclose any details on the underlying sources of THC data per country and year. While the THC data aim to be representative of the retail level, the agency acknowledges several methodological limitations that might render some data not representative. It can be assumed that the presented data are predominantly obtained from a sample of police seizures of cannabis. For Germany, THC data were corrected and completed by the respective EMCDDA focal point.We reviewed UNODC data but could not identify any cannabis-related indicators relevant for public health monitoring that are not already captured by the EMCDDA data collection. In fact, both agencies collect data on prevalence of use and on treatment rates. However, we chose to refer to EMCDDA data for the following reasons: the UNODC ‘general population’ prevalence database does not include information on the age range of the target population or exact references. Further, it contains several estimates derived from the school survey initiative ESPAD, which should not be reported as general population estimates.

As for TDI, we compared data from UNODC and EMCDDA for 2017 for 16 countries with data available in both data bases. For any drug treatment, data were only consistent in half of the countries. Further, treatment demand for CUD was only reported as percentage of all treatment demand in the UNODC data base, requiring recalculations and additional assumptions to report CUD treatment rates. Based on this assessment, we restricted our analyses to data provided by the EMCDDA.Data on CUD prevalence including uncertainty intervals by 5-year age bands were retrieved from the GBD study for the years 2010 to 2019. In the GBD study, CUD is defined by ICD-10 or DSM-IV criteria for cannabis dependence and prevalence estimates are based on school and adult survey data. In brief, cannabis use prevalence estimates were first converted into regular use estimates and then into CUD estimates. The first conversion ratio was determined using a meta-analytical approach, resulting in a factor of 2¢9 . The second conversion ratio was determined using a Bayesian meta-regression, which accounted for risk differences between youth and adults. For more details on the estimation of CUD, see supplement of .All available data were retrieved from the indicated data sources, however, for prevalence of use, potency and TDI, data were missing for some countries and years. To obtain country- and year-specific CUD estimates for the 15 to 64 year old target population, age-specific data were aggregated using UN population data. Using TDI data, treatment rates, expressed as the number of treatment entries per 100,000 adults were estimated. We calculated the share of daily users among past-month users as an indicator for high-risk consumption patterns for countries with available data. This indicator sheds light on differences in use patterns between countries.

To obtain European averages across all countries with available data, population-weighted means of the indicators were calculated using UN population data. For THC concentrations, weighted averages would have required to account for the respective share of both resin and herbal cannabis in total use per country, however, these data were not available. Thus, we aggregated the country-level estimates by reporting medians and inter-quartile ranges.All data were analysed using R version 4¢0¢5 and are available as Supplementary Material 2, including the corresponding R code. Given the lack of uncertainty intervals for most indicators, meta analytical trend analyses were not feasible. For estimating changes in the indicators at the European level, the oldest and most recent data points were selected and reported. For prevalence of use, at least one of these two points was not available in four countries , which were excluded from estimating changes. The difference in adult and age specific prevalence of use as well as for THC levels did not account for the degree of uncertainty associated with each point estimate, as these data were not available from the EMCDDA data repository.To describe country-level trends in prevalence of use and CUD, statistical models were not applied as too few data points were available for most countries or because the estimates were predicted from statistical models . Thus, for these two indicators, we only compared the first and last available estimate in each country to estimate changes from the oldest to the most recent data point. To describe country-level trends in treatment rates and THC concentrations, linear regression models were conducted, separately for each country with at least 5 observations. In each model, year was entered as a single covariate, describing the annual change score in the outcome.

Results are reported for all models in which the coefficient was significantly different from 0 at alpha = 1%.The most recent estimates of prevalence of use are summarized in the map in Figure 1. Overall, cannabis use appears to be more common in Western than in Eastern countries. Based on data collected between 2013 and 2019, past-month prevalence of use was below 1% in Malta, Hungary, and Turkey. In three countries , between 5 and 6% of adults reported past month cannabis use. Highest use rates were recorded in Spain and France . The country-level cannabis use prevalence rates are further reported in Supplemental Material 1 . Re-examining the available EMCDDA data for all countries since 2010 allowed for a more precise analyses of trends of cannabis use in Europe. At the European level,trimming trays for weed cannabis consumption appeared to have increased in the past decade. Comparing the last and first available estimates, an increasing past-month prevalence was identified for 24 out of 26 countries that had at least two data points available. The country-specific changes in prevalence of use for the adult population are further illustrated in Supplemental Material 1 . Age-specific comparisons of first versus last years suggest that an increase in both past-month and past-year use was observed across all age groups in Europe . Among younger adults, cannabis consumption is overall more prevalent and absolute increases were more pronounced in this age group. Among 35 to 64 year-olds, increases were smaller in absolute terms but greater in relative terms. In this age group, prevalence of use increased by 50% or more between 2010 and 2019. The age-specific trends in prevalence of past-month use at the country level, based on the first and last available estimate, are further displayed in Figure 2. Only in Czechia and Poland, marked decreases in prevalence of use in most if not all age groups were observed.

In France, the Netherlands, and Spain, pronounced increases among middle-aged adults were identified. In contrast, Germany reports increases in total use which were driven mostly by younger adults. Very similar country patterns were present for age specific changes in past-year prevalence . Prevalence estimates for daily cannabis use among 15 to 64 year olds are displayed in Supplemental Figure 4. In 18 countries out of 26 countries with available data, indications for increasing trends in daily cannabis use could be observed.Most pronounced increases were reported in Portugal and Spain . Based on the last available estimates, the share of daily users among past-month users differed largely across all European countries. In countries like Lithuania, Czechia, Bulgaria and Poland, less than one in ten users reported high-risk use patterns. In contrast, 50% and 70% of all past-month users reported daily use in Luxembourg and Portugal, respectively. In half of all countries examined, the share of past-month users engaging in daily use was 20% or higher .In 2019, 115,477 treatment entries were registered by 25 countries and reported to the EMCDDA. At the country level, vast differences in treatment rates are reported. In Bulgaria and Slovenia, less than 2 treatment entries for cannabis problems per 100,000 adults were recorded in 2019. In contrast, more than 100 treatment entries per 100,000 adults were registered in Malta. For an illustration of country-specific trends of treatment rates, see Supplemental Material 1 . Based on the 22 countries with available data in the years 2010 and 2019 , the rate of treatment entries for cannabis as primary problem per 100,000 adults increased from 27¢0 to 35¢1 .

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 microRNAs 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 bioinformatics tools 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 marijuana 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,cheap grow tents the first complete mitochondrial reference genome was also obtained in 2016 from the cannabis hemp variety Carmagnola . Later in July 2016, two complete chloroplast genomes of marijuana African variety Yoruba Nigerian and Korean hemp non-drug variety Cheungsam were sequenced and used to determine the phylogenetic position of C. sativa relative to other members in the order Rosales. Furthermore, in September 2016 released 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, Phylos Bioscience 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 the ancestral 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, the1000 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 bioinformatic 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.The availability of several high-quality cannabis 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 gene networks. In 2011, Bakel et al. sequenced and compared the transcriptomes of marijuana variety Purple Kush and hemp cultivars Finola and USO-31. Gene expression analysis revealed preferential expression of cannabinoid and precursor pathway-associated genes in marijuana . Expression of THCA 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 terpenoid and cannabinoids synthesis were significantly over-expressed in trichomes. In 2021, Grassa et al. used genomic, and expression associated expression of THCAS and CBDAS with THC:CBD ratio by Quantitative trait Loci analysis of Cannabis cultivars . Datasets from similar genomics,grow tent indoor transcriptomics, micro-biome, 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 BioSamples from multiple studies related to Cannabis of which 2871 public BioSamples are exclusively for C. sativa with 2546 DNA and 325 RNASeq datasets 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. The exponential increase in the number of patents shows the future potential for the growing cannabis industry. Here, we analyzed the patents spatiotemporally 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 12 patents 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. However, we have to keep in mind that a certain cannabinoid invention can be referred into more than one patent category.

For instance, cannabinoids are highly hydrophobic by nature and thus they have low bioavailability in the human body. As a result, a new class of cannabinoid-glycosides has been created, whose representatives are produced through enzymatic glycosylation. This novel strategy led to increased aqueous solubility of the target cannabinoids and resulted in four patents . Recently a new method of producing one or more cannabosides by feeding an insect a cannabinoid was patented . These new classes of cannabinoid glycosides generated vast structural diversity and have greatly improved water solubility, enabling new pharmaceutical formulations, and multiple administration routes . The discovery of the genes encoding glycosyltransferases may belong to different categories of the cannabinoid patent family, that is, genes, enzymes, delivery technology, etc. The exponential enhancement of the patent number during recent years in the diverse areas of cannabinoid applications is indicative of the increased commercial interest in this class of natural compounds. The various pharmaceutical applications will continue to shape primarily the the path of the future invention cannabinoids.C. sativa has been well-known for the anti-inflammatory properties reviewed in .