Cannabis Grow

Attorneys for prosecution, whereas for 97.5 percent, drug trafficking was the most serious offense .In terms of drugs involved for defendants actually convicted of federal drug offenses, 30.6 percent involved marijuana, 22.4 percent involved crack cocaine, 21.5 percent involved cocaine powder, 12.5 percent involved methamphetamine, 7.8 percent involved opiates, 0.5 percent involved hallucinogens, and 4.8 percent other substances . Unsurprisingly, the percentage of incarcerated drug offenders serving time for possession appears to be significantly greater in state as opposed to federal prisons. Analyzing data from the 2004 Survey of Inmates in State and Federal Correctional Facilities, Mumola and Karberg report that in 2004, 5.3 percent of drug offenders in federal prisons and 27.9 percent of drug offenders in state prisons were incarcerated for possession. The authors found that of drug offenders held in state prisons, 61.8 percent reported that cocaine or crack was involved in their offenses, and the analogous figures were 18.6 percent for stimulants, 12.7 percent for marijuana or hashish, 12.2 percent for heroin and other opiates, 2.2 percent for depressants, and 1.7 percent for hallucinogens.One must interpret these data with caution. First, just 20.7 percent of drug offenders in state prisons reported having no prior criminal history . Second, given the pervasiveness of plea bargaining and the evidentiary ease of prosecuting possession relative to other offenses,vertical hydroponic system the percentage of convicts incarcerated in state prisons whose most severe offense truly is possession remains somewhat illusive.

Locking up approximately half-a-million drug offenders has a direct budgetary cost in the billions each year—approximately $6.6 billion for state drug prisoners and perhaps that sum over again for federal prisoners and convicts serving time in jail.In addition to the costs of incarceration borne by government and prisoners, a large toll falls on the families of those incarcerated, partly in terms of lost incomes, many of which were lawful ones . Fifty-nine percent of male state and federal inmates in prison for drug possession or trafficking have minor children, whereas in the general prison population, only fifty-one percent have children, indicating an additional cost stemming from high incarceration rates in the form of children with absent fathers . There is also a startling racial disparity in imprisonment for drug charges. In state prisons, African-Americans account for 38.6 percent of prisoners overall and 45.1 percent of prisoners convicted of drug offenses , though they represent just 13 percent of the U.S. population .There is also evidence that a substantial portion of racial profiling problems result from the targeting of drug sellers through criminal enforcement efforts, which could be greatly reduced under a less punitive drug policy. With the aim of devising rational drug policies based on practical experience rather than predominantly ideological concerns, countries throughout Europe are experimenting with drug policy in a variety of ways. In general, European countries have less punitive—and more harmreduction oriented—approaches to drug policy than the United States. The Action Plan adopted by the German government in 2003 to deal with Germany’s drug problem is representative of this approach, claiming: “The ‘Action Plan on Drugs and Addiction’ advocates a realistic drug policy. It responds more to the concrete reality of life than to any ideological principles. Every addict must have access to appropriate therapy options” .

The plan encompasses both legal and illegal substances, recognizing that far more Germans suffer substance abuse problems related to tobacco and alcohol than illegal drugs . Portugal has become the poster child of European drug reform following its July 1, 2001 decriminalization of formerly illicit substances.Rather than handle drug possession and use as a criminal matter, the police in Portugal give a civil citation to those caught using or possessing a quantity of drugs less than the average amount sufficient for ten-day use by one person. As Greenwald notes, these civil citations instruct recipients to appear before a “dissuasion commission” within seventy-two hours. The dissuasion commission, which is designed to avoid all appearances of a criminal tribunal, is made up of a lawyer and two members of the medical profession, and it may order those caught with drugs to pay a fine or undergo a course of treatment. Greenwald reports, however, that fines are a last resort designed to be suspended except for addicts and repeat offenders, who can have their fines suspended as well, if they agree to treatment .Even European countries that have not followed the extreme depenalization approach of Portugal have experimented with less punitive and more treatment-oriented drug policies. In Switzerland, for example, cannabis use remains a criminal offense . However, Switzerland experimented with a regime of open sales of small quantities of illicit drugs, such as heroin, in Zurich’s Platzspitz . This experiment lasted only five years, from 1987-1992, because the park became unsightly and was viewed as an embarrassment by the city. Instead of resorting to strict punitive measures for drug use, Switzerland then instituted a heroin maintenance program that allowed heroin addicts to receive daily heroin shots supervised by a nurse in a clinical setting. Switzerland has since expanded this program due to evidence that crime rates and unemployment rates among participants drop during participation . Similar programs have been instituted with encouraging results in Vancouver, Canada, and the Netherlands . But the trend toward decriminalization of drugs is not universal: the United Kingdom has gone in the other direction in recent years, at least with respect to marijuana, by increasing the maximum penalties for marijuana use.

Gordon Brown’s government decided to reclassify cannabis from a Class C drug to a more serious Class B drug, resulting in a maximum penalty of fourteen years of imprisonment for marijuana supplying, dealing, producing, and trafficking, and five years for possession . However, while the potential for such penalties exists, the British Home Office describes the “likely” enforcement steps: for a first possession offense police will issue a warning, for a second they will issue a Penalty Notice for Disorder , and for a third, they will arrest the individual . Thus, even in one of Europe’s strictest drug regimes, arrests and criminal punishment are reserved for repeat offenders. While many European countries have more liberal policies toward drug possession, they generally continue to have strict penalties for drug trafficking―though these are appreciably less severe than their counterpart American punishments. As the European Monitoring Centre for Drugs and Drug Addiction puts it, “over the past ten years, most European countries have moved towards an approach that distinguishes between the drug trafficker, who is viewed as a criminal, and the drug user, who is seen more as a sick person who is in need of treatment” . For example, in spite of their relatively liberal policies toward drug users, the maximum drug trafficking penalty in the Netherlands is, nominally at least, 16 years . Even in Portugal,cannabis grow set up drug trafficking remains a criminal offense because it involves possession in excess of the average dose needed for ten days of personal use . Relative to America, Europe has focused more on helping rather than punishing problem users, while still attempting to disrupt large-scale drug networks. Europe is not the only region of the world to have largely eliminated or reduced the penalties associated with possessing and using certain drugs. Latin America has also trended toward decriminalization in recent years. The Argentine Supreme Court decriminalized possession of small amounts of marijuana in August of 2009 . The court based its ruling on the grounds that it is unconstitutional to punish adults for private use of marijuana if that use does not harm anyone else .In declaring unconstitutional a law that provided for sentences of up to two years for drug possession, the court also opened the door for possible decriminalization of other substances, because the specific law overturned was not limited to marijuana.

Lower courts might expand the ruling to other drugs. Following the court ruling, the chief of the Argentine cabinet praised the decision for challenging an American-style war on drugs by ending “the repressive policy that the Nixon administration invented” . A few days prior to the Argentine court ruling, Mexico enacted decriminalization legislation specifying that individuals in possession of small amounts of marijuana, cocaine, heroin, and methamphetamine will not be criminally prosecuted . The new Mexican regime is similar to the Portuguese decriminalization in that those caught by police possessing a small amount of drugs will be encouraged to seek treatment . After being caught three times with drugs, the user will be required to attend treatment. Unlike the prior presidential administration, which sharply criticized earlier attempts by Mexico to decriminalize drugs, President Obama’s drug czar, Gil Kerlikowske, said that the Administration would evaluate the new Mexican law using a “wait and see” approach . In recent years both Brazil and Ecuador have also signaled that they may follow the path of Argentina and Mexico toward decriminalization . Taken together, these developments reflect the dissatisfaction many Latin American governments have with America’s punitive war on drugs: a war that was started in large part to combat drug production and trafficking emanating from Latin America. While it is too soon to tell what effects the Argentine and Mexican reforms will have on use rates in those countries, we will show in subsequent sections that the European experience casts doubt on prohibitionist fears that drug use will inevitably jump sharply. The social costs of recreational drug use in America have been staggering and unabated. According to the ONDCP’s most recent estimate, the economic cost of illegal drug use in the United States in 2002—including lost productivity, health effects, and crime-related costs such as policing expenditures and incarceration—was $180.9 billion, having grown at an average rate of 5.3 percent annually since 1992 .The costs of two legal drugs—alcoholand tobacco—are of a similar order of magnitude. The most recent comprehensive estimate of Harwood puts the annual economic cost of alcohol use at $184.6 billion in 1998.Rice estimates the annual economic cost of smoking in 1995 was $138 billion. Placing these figures in constant 2008 dollars provides a set of crude estimates of current annual social costs of alcohol , tobacco smoking , and illegal drugs .Commentators have rightly pointed out that such cost figures give a misleading impression of precision, ignore the benefits of drug use,and provide scant direction for actual drug policy.We offer these cost estimates for a crude sense of the scale of the problems under the current regime and as a reference point from which to examine the various types of costs associated with drug use—their relative magnitudes, who causes them, and who bears their burdens. It is also worth noting, however, that while such aggregate figures aspire to capture the domestic costs of illegal drugs, the costs imposed on foreign countries by the combination of America’s exceptionally large demand for illegal drugs coupled with its severe attempts at prohibition are also high and growing. Organized criminals from the Taliban in Afghanistan to drug cartels in Colombia and Mexico are enriched by America’s drug consumption and prohibition policy, with many highly unpleasant consequences. The current American administration has shown some signs of appreciating the magnitude of the role played by American drug demand in fostering crime in foreign countries. Following the recent wave of increasingly deadly gang violence near the Mexican-American border, Secretary of State Hillary Clinton surprised the media by candidly admitting that American drug consumers support crime in Mexico fueled by drug profits .Consideration of these foreign costs might bring total social costs of illegal drugs to equal or exceed those of alcohol. While steps toward legalization of currently illegal drugs would likely increase consumption, estimates vary about the extent of this change and how its concomitant costs would compare with gains from decreased law enforcement costs, productivity and other gains from reducing the levels of incarceration, and potentially substantial decreases in the crime and violence stemming from decreased profitability and scope of black markets.Though our best guess is that moving towards legalization would substantially reduce crime, it is possible that aregime shift to depenalization or legalization would increase toxicologically-induced crime and thereby offset expected decreases in black market crimes.

Again we find that individuals living in states that have statutorily removed jail sentences as penalties for possession of up to an ounce of marijuana are statistically less likely to report jail as the maximum penalty and more likely to report fines as the maximum penalty. However, again we see that the actual difference in knowledge across states is small. Interestingly, the “Don’t Know” rates in Table 3 correspond relatively closely to the “Don’t Know” rates in the 1976 and 1980 Monitoring the Future Survey data in Table 2 . There is no indication that citizens perceive themselves to be better informed in one period than in the other. Our study finds significant associations between the maximum penalty specified in state marijuana laws and people’s knowledge of those maximum penalties. But the associations are very small in magnitude. Citizens in decrim states are only about 27 % more likely to believe the maximum penalty for possessing an ounce of marijuana is a fine or probation . About a third of citizens in each type of state believes the maximum penalty is a jail sentence. The modest magnitude of these knowledge effects helps to clarify why decriminalization effects are fairly weak and inconsistent. The answer appears to be that people are not oblivious to their marijuana laws, but their awareness is pretty tenuous. It appears that people were more aware of their state penalties in 1980 than today . Why? One possibility is that it is the publicity surrounding a change in law, drying racks rather than the law’s actual implementation, that produces differences in citizen perceptions by state. In the 1970s, the initial decision to decriminalize marijuana was a matter of active public debate. MacCoun and Reuter report that in the 1970s, 22 out of 22 New York Times op-ed essay on drug legalization or drug decriminalization mentioned marijuana; only 15 out of 37 did so in the 1990s.

We believe that recent changes in these penalties have received far less publicity; only those interested in obtaining this information search it out. Another possibility, again not mutually exclusive, is that there has been erosion over time in knowledge of what may have been, in the 1970s, a real policy change. Indeed, research in other policy areas have shown that the impact of a policy is usually seen within a one to three year period following the policy’s adoption/effectiveness date . Given that many of the depenalization policies examined here occurred well before 2001, time may have decayed people’s knowledge or awareness of the laws. Thus the data we present here create a new puzzle for the decriminalization literature. Early studies failed to find an effect of decriminalization in an era in which citizens clearly recognized a difference in policies across states. But some recent studies have appeared to find a decriminalization effect in a more recent era, when citizens can just barely detect a difference in state laws. This is not how perceptual deterrence is supposed to operate. Either some studies are misestimating decriminalization effects , or we do not adequately understand the psychology of deterrence. South Africa has one of the highest rates of incarceration globally , most commonly among young men under the age of 35 years. Over 97% of SA’s prison population are men. Violence is pervasive with about half of SA men between the ages of 12 and 22 years old admitting to committing at least one criminal offence, and it is almost always a violent offense. Reports include assaults of others at school , in public places and at home , with 92.9% of the victims reporting that they knew the identity of the perpetrator. In addition, 30% of young men report committing physical or sexual violence against their partners. The current study complements previous research focusing on young men’s management of alcohol and drug behaviors as the primary source of criminal acts by considering both individual- and community-level factors associated with ever being arrested in SA.

Substance use is associated with higher rates of township violence and is linked to 75% of homicides, 67% of domestic violence, and 30% of hospital admissions. Gaining access to drugs often involves stealing, selling, or bartering goods and services–each acting as a pathway to engagement in criminal activity. Almost a third of young SA men report symptoms of alcohol dependency; half report recently using cannabis ; and a third recently used methamphetamine. The rates of substance dependence among SA offenders range from 10%–48% for males. In a SA study , 45% of arrestees tested positive for cannabis , methaqualone , opiates, cocaine, amphetamines, and/or benzodiazepines, with cannabis and methaqualone being the most prevalent. Those arrested for housebreaking and drug/alcohol offenses and those arrested at least once before are also more likely to test positive for drugs. Thus, substance use is hypothesized to be associated with ever being arrested among young men living in SA township neighborhoods. The high rates of substance use, violence, and incarceration in SA have been linked to unemployment, lower incomes, fewer years in school, younger age, being unmarried, and having mental health issues. More recently, attention has been focused on the mental health of individuals who come into contact with the criminal justice system. As with many other countries, SA experiences high rates of neuropsychiatric disorders in its prison system. Thus, we expect these individual factors and mental health risks to be associated with arrests. Research often focuses on youth’s background and risk behaviors to understand acts leading to arrests. Yet, globally it has been demonstrated that community disorder, poverty, and infrastructure are linked to rates of crime. Social disorganization theory suggests that where an individual lives has a substantial impact on their propensity to commit crimes or engage in criminal or delinquent behavior. Specifically, in contexts where there are few social bonds, crime and delinquency thrive. However, it is unknown whether these social processes operate in the same way across different disadvantaged neighborhoods. Shaw and McKay’s classic study in Chicago reports an ecological model of crime by mapping thousands of incidents of juvenile delinquency.

The results show that high rates of poverty and ethnic heterogeneity, combined with high levels of residential stability and single-parent households, are indicators of high criminality. Further, neighborhoods characterized by poverty and negative familial factors have been found to contribute to high arrest rates among youth in the U.S. and adolescent anti-social acts in Mexico. However, the applicability of social disorganization theory in SA is unclear. The applicability of Western criminological theories to urban SA has been previously demonstrated, but there are important differences in the ecological dynamics of violent crime across differing cultures. In SA, with the socio-political history of violence and oppression, the socioeconomic conditions of communities are likely to be important contributors to high criminality. However, there are limited data in post-apartheid SA. Research with individuals needs to take into account differences in the properties of the groups in which they belong.In some cases, neighborhood-level differences have been found to be significantly associated with outcomes, even when there is no significant variability between neighborhoods. These data suggest that assessing how individual and neighborhood effects are reciprocal and inherently related would lead to a better understanding of neighborhood effects on arrests. However, the relationship between arrests and individual- or community-level risk factors is far less studied in low and middle-income countries , such as SA. In township neighborhoods, infrastructure , limited opportunities for employment, gang activity, and the presence of small, informal and often illegal bars may function to exacerbate or dampen an individual’s risk of being arrested. The aim of this cross-sectional study is to examine arrest rates across low-resource township neighborhoods in Cape Town, SA. A community sample of young Black-African men was recruited from five areas within two Cape Town townships. Townships are peri-urban settlements that vary significantly in years since establishment, infrastructure,cannabis drying and access to services. In post-apartheid SA, townships experienced a major influx of migrants in search of work, which resulted in the rapid development of new informal township neighborhoods. Accordingly, the older township communities are more formalized with access to municipal roads, a police station, and community clinics.

However, the newly established communities are more informal, where the large majority of the residents are living in informal housing , with limited access to basic services. Thus, community-level factors such as less formal housing, fewer household sources of water , and food security, as well as greater number of bars and violence are hypothesized to be associated with having a history of arrests. This study examines: the individual- and community-level factors associated with a history of arrests among young men living in the townships of Cape Town, SA; and whether these factors are significant predictors of contact with the criminal justice system. The data used in this article are part of the baseline assessment for a larger longitudinal study investigating the impact of a public health intervention to improve the health of young men living in low-resource communities. Understanding the associations between individual factors, the community context, and having a history of arrests is important to plan interventions which seek to reduce the risk of arrests and re-arrests among young men in SA.Community clusters containing approximately 450–600 households, including roughly 50 young men per neighborhood, were identified in two Cape Town townships: Khayelitsha and Mfuleni. In each community, a random spot was selected, and recruitment proceeded house-to-house in concentric circles until a sample of 50 young men aged 18 to 29 was reached. If other household members reported that there was a male that slept there at least four nights a week, the interviewer returned repeatedly to solicit voluntary participation of the young man. Potential participants were only excluded if they were unable to understand the recruiter, appeared to be actively hallucinating, or lacked capacity to consent. Individuals who appeared to be under the influence of alcohol or drugs during the time of recruitment were enrolled in the study, but were only interviewed once they were no longer intoxicated. Assessments were conducted at a local research facility in the township, with transport provided. Of the 994 men approached, 5% declined to participate or were identified but could not be found later to complete the assessment . The final sample included 906 young men aged 18–29 years old .First, individual-level and community-level risk factors were compared based on the history of arrests across all neighborhoods. As a first step, chi-squared analyses were used to identify significant covariates between young men who have been arrested and those who have not. From these identified covariates, the most relevant variables were entered into a multiple predictor logistic regression to identify individual- and community- level risk factors associated with arrests. Odds ratios with 95% confidence intervals are presented. To identify communities with a high versus low number of arrests, we compared the proportion of men who have ever been arrested in each neighborhood. Since less than half of the young men reported ever being arrested, a reasonable split of 25% of the communities with the highest arrest rates was compared with 25% of the communities with the lowest arrest rates . We then compared measures of individual- and community-level risk factors between the communities with a high and low number of arrests using chi-squared analyses . This enabled analysis between neighborhoods by comparing aggregate measures of community-level risk factors. All analyses were conducted using SPSS Statistics 23.Substance use and gang involvement are the strongest predictors of ever being arrested. Substance use is consistently linked with high levels of violence and gang involvement. Most notably, young men using methamphetamine are three times more likely to be arrested than young men who abstained. Alcohol users are almost twice as likely to report ever being arrested compared to non-drinkers. This is consistent with previous studies in both HIC and LMIC –including SA –that link substance use with arrest history and incarcerations. This is one of the first studies to examine both individual and community characteristics linked to contact with the criminal justice systems among SA men. While poverty is widespread in these peri-urban settlements in Cape Town, this classification of resources may not be sufficient to explain the high rates of criminal activity among different SA communities, especially if more than half of young men report engaging in substance use or risky delinquent acts.

Whilst the aim of our study was to examine the relationship between baseline features and subsequent outcome , it is important to note that there are limitations with this approach. First, we did not account psychosocial stressors and other confounding factors/events that may have occurred in the time between baseline and follow-up. Indeed, it is possible that stressor cortisol concordance at follow-up does in fact distinguish between CHR subgroups, but that our measure at baseline was too distal to outcome. Second, CHR individuals are at elevated risk for a wide range of psychiatric disorders, particularly depression and anxiety , and so worsening of prodromal symptoms/transition to psychosis is only one of several potential outcome measures, all of which will inevitably involve more false negatives the shorter the follow-up period. Indeed, a recent study suggested that well-established risk factors are better at predicting poor functioning in CHR populations than transition to psychosis . The extent to which stressor-cortisol concordance at baseline is associated with other non-psychotic disorders and functioning at follow-up is therefore warranted.We assessed HPA axis function using basal salivary cortisol collected in the laboratory, as it is more reliable and, unlike home sampling methods, unlikely to be influenced by confounding factors such as exercise . However,grow cannabis in containers meta-analytic evidence indicates that the effect of chronic stress on cortisol varies across cortisol measures; whilst diurnal cortisol, afternoon/evening cortisol, and the CARi are elevated following chronic stress, basal morning levels are lower and the diurnal rhythm appears to be flatter . Employing alternative cortisol measures might therefore reveal different patterns of stressor-cortisol concordance across CHR individuals and controls.

Indeed, using a home sampling procedure, Cullen and colleagues reported a negative correlation between the CARi and negative life event distress in at-risk children with a family history of schizophrenia but a positive correlation in typically-developing children , whilst a study of adults found that diurnal cortisol was associated negatively with stressful life event exposure in first-episode psychosis patients, most of whom were receiving antipsychotic medication, but positively in controls . Thus, employing multiple measures of cortisol may be more informative than basal cortisol alone and enable the identification of dissociated relationships in at-risk individuals/psychosis patients and healthy controls. Cortisol output is not, however, the only method of assessing HPA axis function. In addition to endocrine measurement neuroimaging can be used to determine pituitary and hippo campal volume and density, distribution and/or affinity of glucocorticoid/mineralocorticoid receptors. The latter is particularly important as these receptors mediate the effects of glucocorticoids on cellular targets. As a related point, future studies are warranted to investigate glucocorticoid sensitisation , in CHR youth, as this may have implications not only for the HPA axis, but also the immune system , and dopamine levels . Thus, studies employing multiple methodological approaches, including genetic profiling, neuroimaging, and endocrine measurement, may be needed to adequately investigate the extent to which individuals at-risk for psychosis are characterised by increased HPA axis sensitivity. Our findings have other implications for future research examining HPA axis responsivity in at-risk individuals. First, we observed that the lapse-of-time between completion of stress measures and cortisol collection moderated stressor-cortisol concordance . Whilst we anticipated this pattern for daily stressors occurring within the past 24 hours, the findings for life events and childhood trauma were not predicted as these events did not occur on the day of measurement. It is plausible that reporting these events in the research environment is itself a stressful experience for some participants, and that it elicits a cortisol elevation and thus a relationship between stressor exposure and cortisol.

These findings highlight the importance of adjusting for the interaction between stressors and timelapse between assessments when examining stressor-cortisol concordance. Second, participant sex was identified as a potential confounder. The updated neural diathesis-stress model noted sex differences to be an important area for future research , whilst it was beyond the scope of the current study to explore whether sex modified the degree of stressor-cortisol concordance, future studies should investigate this possibility. Finally, whilst we defined CHR outcome status on the basis of attenuated positive symptoms and transition to psychosis, as noted above, there has been recent acknowledgement of the need to examine a broader range of outcomes, including, levels of social and role functioning, non-psychotic disorders, and negative symptoms . Future studies might therefore examine whether stressor-cortisol concordance is associated with these outcomes at follow-up.The nation’s health care system faces a mandate to improve quality in multiple dimensions, including those identified by the Institute of Medicine: safety, timeliness, effectiveness, efficiency, equitability and patient centeredness. Expenditures and gaps in health care delivery in general are not evenly distributed throughout the population; only 5% of the population account for half of all health care spending, and quality varies considerably across conditions and settings. An effective response to the quality mandate will require a focus on subgroups of patients who have severe or multiple health conditions associated with significantly higher costs and poorer outcomes.In the National Comorbidity Survey Replication, more than 68% of adults with a behavioral disorder report having at least one general medical disorder, and 29% of those with a medical disorder had a comorbid mental health condition. Research has documented the high rates of psychiatric comorbidity among specific medical conditions, such as HIV, diabetes, asthma and chronic medical illnesses. Conversely, studies have reported high rates of medical comorbidity among patients with psychiatric illness.

The co-occurrence of behavioral and medical conditions leads to elevated symptom burden, functional impairment, decreased length and quality of life, and increased costs. For patients with comorbid behavioral and medical conditions, problems with quality of care occur when they are treated in a primary care and/or specialty mental health setting. Even more concerning, premature mortality is elevated two- to four‐ fold. In response to these findings, care delivery models have been developed for patients with comorbid medical and psychiatric conditions. The most effective have been collaborative care approaches that use a multidisciplinary team to screen and track mental health conditions in primary care. These models build on the Chronic Care Model. Yet, even as these models are promoted, the gaps in our knowledge about cooccurrence may have important implications for how these collaborative models are structured. Research to date on the prevalence of co-occurring medical and psychiatric conditions has focused on national surveys, specific illnesses, disease-focused clinics or claims data for health insurance populations, such as Medicaid or Medicare. To our knowledge, no prior study has focused on a large patient population with a predominately employment-based insurance that receives treatment in an integrated health care system. Examining cooccurrence in this setting addresses several important gaps in the existing literature. First, employed patients in an integrated health care system represent an important and distinct sub-population of patients receiving care in the delivery model promoted by health care reform. Second, this kind of system generates encounter-based rather than claims-based data. The data are generated from all clinical departments within a comprehensive system of care. To address these gaps, pot for cannabis we examine the prevalence of behavioral health conditions in a large integrated health system that primarily serves patients with employment-based insurance. We compare the burden of medical co-morbidity and chronic diseases among those health plan members with a behavioral health condition to matched members without. This provides the opportunity to examine the robustness of the behavioral and medical disease nexus in this sub-population compared to the other sub-populations more commonly studied.Institutional review board approval was obtained from the Kaiser Research Foundation Institute for this retrospective database-only study. Initially, all KPNC patients aged 18+ with any behavioral health diagnoses in 2010 were identified. Automated clinical databases were used to identify all outpatient visits , hospitalizations and emergency department visits at KPNC facilities between January 1, 2010, and December 31, 2010, where a patient had a BHD. The BHDs used for this study included both mental health and substance use disorders: depressive disorders, bipolar spectrum disorders, anxiety disorders, attention deficit hyperactivity disorders , autism spectrum disorders, personality disorders, substance use disorders, dementia, schizophrenia spectrum disorders and other psychoses [see Appendix A for International Classification of Diseases, Ninth Revision codes relevant to this paper]. These categories were selected based on collaborations with NIMH’s Mental Health Research Network and KPNC’s Regional Mental Health leadership.

The first mention for each BHD during the study period was included, so patients in the sample could have multiple BHDs over the 1-year study period . The prevalence rates of BHDs were examined among all adult KPNC patients. Patients insured by Medicare or Medicaid were excluded from the study.Compared to the matched controls, each of the most prevalent BHDs [depression , anxiety , substance use , bipolar spectrum disorder and ADHD ] had significantly more patients with any medical comorbidities based on the ICD-9 categories. This was true across all ICD-9 categories examined, with the exception being Diseases of the Circulatory system and Neoplasms for the ADHD comparison, which did not differ . We had similar findings with respect to the burden of chronic conditions. With the exception of ADHD and bipolar disorder, each specific BHD had a significantly higher prevalence of each chronic condition compared to the matched controls. Those with ADHD diagnoses did not significantly differ in the chronic conditions related to the circulatory system , Parkinson’s disease or osteoporosis. However, there were significant differences between those with ADHD and their matched controls for the other chronic conditions. Patients with bipolar disorder did not significantly differ from their matched controls in prevalence of end-stage renal disease or osteoporosis. We also examined the Charlson Comorbidity Index and found that patients in the SubBHD sample had a significantly higher average Charlson Comorbidity index compared to their controls across all conditions examined .BHDs are highly prevalent in this health system. Fifteen percent of members with a health plan visit in 2010 had at least one BHD. By far, the most common were depression and anxiety. Moreover, among patients with BHD, psychiatric comorbidity is common; of those patients with a BHD, 28% had multiple BHDs. Among the five most prevalent BHDs, the proportion of patients with at least one psychiatric comorbidity ranged from 42% to 60%. Finally, those patients with a BHD carry a disproportionately high medical disease burden.With only a few exceptions, the proportion of BHD patients with a medical illness, including common chronic conditions, was significantly higher than non-BHD patients. Risk of 10-year mortality as calculated by the Charlson Index was found to be significantly higher for those with any of the BHDs examined compared to their matched controls. Thus, similar to studies of other patient sub-populations, BHDs in a largely commercially insured, employment-based health system are common and associated with a higher burden of chronic medical disease. At the same time, these results differ from some of the other large data sets. For example, disabled Medicaid beneficiaries have a prevalence rate of psychiatric illness ranging from nearly 29% to 49%, depending on whether the claims-based data include pharmacy data in addition to diagnostic codes . This higher proportion likely reflects both a more vulnerable patient population and methodology that better captures patients being treated for a psychiatric illness without an associated diagnostic code. In that same data set, the Medicaid aged population’s prevalence of psychiatric illness more closely mirrored the prevalence in this study: 10.4% or 35.9% . Our encounter data were based on ICD-9 coding and therefore are more similar to the diagnosis-only data from the Medicaid data set. The US National Comorbidity Survey Replication, which is based on a face-to-face household survey, reported a 26% any Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, disorder prevalence, and others have reported around 30%. Given that fewer than half of people with a psychiatric illness receive treatment, these estimates are fairly consistent with the 15% prevalence observed in this study, which includes a patient population more representative of the general US population but only captures those who made a health plan visit. This study has several limitations. As with other studies that have used claims-based data, our study only captures patients with behavioral health disorders noted in their visits during the study period. This methodology is vulnerable to under-estimation especially with regards to behavioral health disorders.

Future work could potentially benefit by further extending the present work to other health outcome variables such as ART medication adherence and HIV symptoms among persons with HIV/AIDS. It also may be advisable to explore how distress tolerance and emotion dysregulation relate to a broader array of symptoms and disorders. Finally, the present study was a secondary analysis from a larger study exploring the role of cannabis use among an HIV/AIDS population . Thus, as with any secondary analysis of data, it would be important to replicate and extend the findings in the future with an a priori research investigation. Overall, the results of the current study highlight the importance of perceived distress tolerance and emotion dysregulation in terms of elevated rates of anxiety and depression symptoms among an HIV/AIDS sample. specifically, findings indicated that perceived distress tolerance was significantly related to greater depressive and anxiety symptoms and that emotion dysregulation mediated this association. The present findings therefore suggest that emotion dysregulation may be important in better understanding the link between distress tolerance and certain negative emotional symptoms among people living with HIV. Indeed, it is possible that targeting emotion dysregulation among HIV? persons via strategies aimed at increasing self-efficacy over the ability to regulate affective states and gaining further control over affect-driven behaviors could be an integral step in efforts to promote greater degrees of psychological health among HIV? individuals.

Brain tissue volume losses in the frontal, temporal and select sub-cortical regions of individuals with alcohol use disorders have consistently been reported with volumetric magnetic resonance imaging , cannabis grow equipment and so have deficiencies in executive skills, learning and memory, processing speed, visuo spatial skills and working memory . Today, more than half of individuals with AUD who present for treatment also chronically abuse illicit substances . Substance use disorders have adverse effects on brain biology and function separate from those of AUD . Comorbid alcohol and substance use disorders have also been associated with brain morphological abnormalities. Liu et al. reported smaller normalized gray matter and white matter volumes of the prefrontal lobe in poly substance abusers abstinent from substance use for more than 2 weeks compared to controls. Tanabe et al. also reported smaller GM volumes of the bilateral medial orbito frontal cortex in long term abstinent individuals dependent on two or more substances compared to controls. As in AUD, the neurobiological abnormalities in individuals with PSUD are accompanied by cognitive deficiencies, particularly in visual and verbal memory, attention, psychomotor speed, visuomotor skills, problem solving and abstraction abilities . Thus, brain morphological abnormalities appear to occur in somewhat similar brain regions with similar neurocognitive deficits in AUD with and without comorbid substance abuse. To determine potential unique group differences, there is the need to directly contrast the magnitude and spatial distribution of structural brain abnormalities and their associated neurocognitive abnormalities in AUD with and without comorbid substance abuse. Directly contrasting structural brain abnormalities and their neurocognitive correlates in AUD with and without substance abuse will help design more efficacious treatment strategies tailored to individuals with PSUD or AUD. In this context, we showed recently that one-month-abstinent treatment-seeking PSUD individuals have prefrontal metabolite concentrations that were uniquely different from those of alcohol dependent individuals at similar abstinence duration, reflecting neuronal and glial dysfunction partly related to neurocognition . This quantitative volumetric magnetic resonance imaging study, contrasted differences in total and regional GM, WM and sub-cortical tissue volumes as well as ventricular and sulcal cerebrospinal fluid between abstinent alcohol dependent individuals without current illicit substance dependence and those with current psychostimulant dependence .

The functional relevance of our MRI measures was assessed by correlating them with neurocognitive measures. Since ALC recover brain tissue volume significantly but not completely within their first month of sobriety , while individuals with PSUD show regional GM tissue volume deficits even after many weeks and years of abstinence , we tested the following hypotheses in treatment seeking individuals after one month of abstinence from alcohol and other substances: PSU have smaller lobar GM, WM and sub-cortical tissue volumes as well as larger CSF volumes than light-drinking controls and ALC and in PSU and in ALC, smaller lobar GM and WM volumes correlate with worse measures of working memory, processing speed, visual-spatial learning and memory and auditory-verbal learning and general intelligence.Treatment-seeking PSU and ALC were recruited from the San Francisco VA Medical Center and Kaiser Permanente. For statistical reasons, we reduced our large ALC cohort to 40 individuals by matching them on age, education, smoking status and drinking variables to the smaller PSU group. Both ALC and PSU participants completed the structured clinical interview for the Diagnostic and Statistical Manual of Mental Disorders Axis I Disorder Patient Edition, Version 2.0 . Prior to enrollment, male participants consumed more than 150 alcoholic drinks per month for at least 8 years; females consumed more than 80 drinks per month for at least 6 years. PSU individuals were diagnosed with both alcohol dependence and dependence on at least one psychostimulant, with or without nicotine dependence and cannabis use disorder. All PSU met DSM-IV dependence criteria for at least one illicit substance and 10.5% met criteria for cannabis use disorder. specifically, 12 PSU met criteria for cocaine dependence and 2 of these were either abusing or dependent on cannabis; 2 other PSU met criteria for both cocaine and methamphetamine dependence ; and yet 2 others were dependent on both cocaine and opiates ; 2 other PSU met criteria for methamphetamine dependence only and 1 for opiate dependence only . In the PSU group, 32% were non-smokers, including 2 ex-smokers.

All ALC participants met DSM-IV criteria for alcohol dependence with or without nicotine dependence. 15 ALC participants were non-smokers, including 6 ex-smokers; the proportion of smokers did not differ among ALC and PSU.All ex-smokers among ALC and PSU individuals had stopped smoking for at least 5 years before the study. Within the ALC group, 2 individuals were currently abusing cannabis, while 3 had past cannabis abuse, currently in full remission. In addition, one ALC participant each showed past dependence on cocaine, amphetamines, or opioids, but all were currently in full remission. Thus, while the ALC participants were “clean” alcohol dependent individuals, the PSU participants were all dependent on alcohol and 84% on cocaine; only about 11% in both groups had a current or past cannabis use disorder diagnosis. Other non-substance-related inclusion and exclusion criteria were described previously . All ALC and PSU participants were tested daily with breathalyzers for alcohol consumption and randomly for substance use during outpatient treatment to ensure sobriety during the one-month-abstinence period. Twenty-seven non-substance-using LD, without histories of medical or psychiatric conditions known or suspected to influence brain structural outcome measures were recruited from the local community. Twenty-one of the LD individuals were never-smokers,mobile grow system and the proportion of non-smokers in the LD group was not significantly different from that in ALC or PSU .Within one day of the MR study, participants completed standardized questionnaires for alcohol withdrawal , depression and anxiety symptomatology . Alcohol consumption over lifetime was assessed with the lifetime drinking history . From the LDH, age of onset of heavy drinking [defined as consuming >100 alcoholic drinks per month or >80 drinks per month ] was derived and the average number of alcoholic drinks consumed per month over 1 year, 8 years before enrollment and over lifetime estimated. For PSU, substance use history was assessed with an in-house questionnaire based on the Addiction Severity Index , NIDA Addictive Drug Survey , drinking history, and Axis I disorders Patient Edition, Version 2.0 . The questionnaire probed for information on phases of drug use for each substance that a participant had a current or past disorder diagnosis on. The variables recorded included age of first and last use, number of total lifetime phases, duration of individual and total lifetime phases , frequency and quantity of use during each phase, and route of administration. Another variable recorded was money spent per day on a substance, which was then converted to one metric, using catchment area-specific conversion norms. Thus, monthly averages for grams of the substances over 1 year prior to enrolment and over lifetime were estimated.

To evaluate the nutritional status and alcohol-related or other hepatocellular injury, laboratory tests for serum, pre-albumin, alanine aminotransferase, aspartate aminotransferase, and gamma-glutamyl transferase were obtained within three days of each MR scan. The values of these variables in the liver and the white blood cell counts were not significantly different between the groups. Table 1 shows demographics, alcohol consumption and select blood variables for LD, ALC and PSU.The neurocognitive domains and constituent measures evaluated were as follows : Executive skills: Short Categories Test, color-word portion of the Stroop Test, Trail Making Test part B, Wechsler Adult Intelligence Scale 3rd Edition Similarities, Wisconsin Card Sorting Test- 64: Computer Version 2-Research Edition non-perseverative errors, perseverative errors, and perseverative responses. Fine Motor Skills: Grooved Pegboard Test. General Intelligence: Ward-7 Full Scale IQ; based on WAIS-III Arithmetic, Block Design, Digit Span, Digit Symbol, Information, Picture Completion, and Similarities subtests. Learning and memory: Auditory-verbal: California Verbal Learning Test-II Immediate Recall trials 1–5 , Short and Long Delay Free Recall . Visuospatial: Brief Visuospatial Memory Test-Revised, Total Recall and Delayed Recall . Processing speed: WAIS-III Digit Symbol, Stroop Color & Word, WAIS-III Symbol Search Trail Making Test-A. Visuospatial skills: WAISIII Block Design; Luria-Nebraska Item 99. Working memory: WAIS-III Arithmetic, WAIS-III Digit Span. The raw scores for all neurocognitive measures were converted to age-adjusted or age-and-education-adjusted standardized scores. The standardized scores were then converted to z-scores for all measures. For the Luria-Nebraska Item 99 ratio, raw scores were converted to z-scores based on the performance of 27 non-smoking light drinking controls , since there are no published procedures available for this measure.Whole brain three-dimensional T1-weighted coronal images and two dimensional T2-weighted oblique-axial images were acquired on a 1.5 Tesla magnet , using Magnetization Prepared Rapid Acquisition Gradient Echo imaging sequence and spin-echo imaging sequence , respectively. After re-alignment of each subject’s T1- and T2-weighted images, the expectation maximization segmentation method was applied to the T1-weighted images to segment the brain into WM, GM, and CSF after coregistration and correction for bias field intensity variation of the T1-weighted images. The major lobes and sub-cortical regions were then parcellated by overlaying the tissue maps on a reference atlas containing landmarks of 36 brain regions , including the regions reported here. The volumes of total cortical GM and total lobar WM were calculated by summing the respective GM and WM values from the major lobes. The intracranial volume was estimated by summing all regional tissue and CSF volumes.Multivariate analysis of variance assessed group differences on age and education, differences between ALC and PSU on drinking and smoking severities, days of abstinence, anxiety and depression symptoms, as well as basic clinical laboratory measures . Multivariate analyses of covariance examined group differences on ICV-normalized volumes of 4 GM regions , 4 WM regions , 5 sub-cortical regions and 5 CSF regions separately . For total and regional cortical GM volumes and CSF, only age was a significant predictor of group variances and was therefore used as a covariate. For total and regional lobar WM volumes, only the body mass index contributed significantly to the variances and therefore was the only used covariate. Neither age nor BMI was a significant predictor of sub-cortical tissue volume variance. Participants’ cigarette smoking status was not a significant predictor of any tissue volume; however, this exploration has to be treated with caution, as the proportion of smokers in the LD sample tended to be smaller than in the patient groups. Given our a priori hypotheses, all MANCOVAs of tissue volumes were followed-up with post hoc analyses as well as pairwise and univariate t-tests . Also because of our a priori hypothesis, we did not correct for multiple comparisons. The reported p-values for GM are 1-tail, but those for WM and CSF volumes are 2-tail, because PSU unexpectedly had greater WM and smaller CSF volumes than LD and ALC. Although ALC and PSU did not differ significantly on the frequency of medical and psychiatric co-morbidities, these comorbidities were controlled for in all group comparisons. Effect sizes for pairwise comparisons were calculated using Cohen’s d .

However, most programs allowed tobacco use in designated outdoor areas, such as front or back porches, program parking lots, or other specified areas.Data collection was conducted by research staff during site visits in 2019. All adult clients enrolled in each program on site visit days were eligible to complete the survey.Research staff reviewed study information with clients in small groups. Those interested to participate were given an iPad with a pre-populated research ID number, reviewed the study information sheet on the iPad, and clicked “Agree” to complete an online Qualtrics™ survey. Informed consent was obtained from all individual participants included in the study. The self administered survey took approximately 30 minutes and participation was anonymous. Participants received a $20 gift card for study participation. Study procedures were approved by the institutional review board of the University of X. There were 682 clients enrolled in the participating programs at the time of the site visits and 562 completed the survey, giving an 82% participation rate. Current use of tobacco products was self-reported. Current cigarette smokers were those who reported having smoked ≥100 cigarettes during their lifetime and currently smoked at the time of survey. Current users of each of the other tobacco products were those who had reported using [e-cigarette, cigar/cigarillo,cannabis grow tent and smokeless tobacco during the past 30 days.There were 5 participants selecting “Don’t know” for current use of alternative tobacco products and their responses were coded as missing.

Based on their reports on use of four tobacco products, participants were categorized into one of three categories: poly tobacco users , dual tobacco users , and single tobacco users . Nicotine dependence was measured using the Heaviness of Smoking Index ,a 6- point scale based on two self-report items . The HSI has demonstrated reliability and validity as a measure of nicotine dependence severity, with internal consistency of 0.63,and the correlation with the Fagerstrom Test for Nicotine Dependence score of 0.94.Quitting experience was measured by two items. Having past-year quit attempts was assessed by the item “In the past year, did you quit smoking voluntarily for at least 24 hours?” Participants also reported whether they had ever used e-cigarettes to try to quit smoking. Intention to quit smoking was assessed by the item “Are you seriously thinking of quitting smoking?” with response options including “Yes, in the next 30 days”, “Yes, within the next six months but not in the next 30 days;” and “No”.Intention to quit smoking was defined as seriously thinking of quitting in the next 30 days . Past 30-day use of blunt or spliff was assessed by the item “Have you mixed tobacco and marijuana and smoked them together in the past 30 days?” Perceived physical and mental health assessments were based on the Centers for Disease Control and Prevention’s Healthy Days Measures.As defined by the CDC, the term “physically unhealthy days” was used to describe a number of days when physical health was not good, and was assessed by the item “Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?” Likewise, the term “mentally unhealthy days” was used to describe a number of days when mental health was not good, and was assessed by the item “.

Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?” Demographic characteristics included age, gender, race/ethnicity, and educational attainment. Age was self-reported. Gender was self-reported as male, female, transgender, gender queer, and other. Since there were 10 participants identifying as transgender and gender queer, these individuals were combined into the “Other” group. Race/ethnicity was measured by combining two items: race and ethnicity . Due to small numbers of Non-Hispanic Asian/Pacific Islander and American Indian/Alaskan Native participants , these subgroups were collapsed into “Other/Multi-race”, resulting in only four groups in the analysis . Educational attainment was measured as less than a high school education, high school or GED equivalent, and greater than a high school education. Participants were asked about reasons for entering treatment programs with answer options including treatment for SUD, for both SUD and a mental health condition, or for other reasons. Those with a SUD problem, or SUD and mental health problem, were asked about primary drug for treatment and/or mental health diagnosis, respectively. Persons reporting treatment for other reasons were most often referred by criminal justice sources. If they did not disclose a SUD or mental health reason for treatment, they were not asked for primary drug or mental health diagnoses. In addition, participants reported their time in treatment . Participant characteristics were summarized for the total sample and by tobacco use pattern . Since the outcome was a three category variable, we employed multi-nomial regression modeling with single tobacco use as the referent outcome and cluster–robust standard errors to account for clients nested within each treatment program.A multivariate model included all independent variables and controlled for demographic variables and time in treatment. The analysis used complete cases. Multi-collinearity among the independent variables was examined by Variance Inflation Factor and the results showed no presence of multi-collinearity. All tests were two-tailed with a significance level of α less than 0.05. Statistical analyses were performed using STATA version 15 .Table 2 describes tobacco use patterns among the total tobacco users and among each tobacco use subgroup. Of the total sample, 32.6% reported single tobacco use, 18.9% reported dual tobacco use, and 14.0% reported poly tobacco use. Most tobacco users were using cigarettes . Among the alternative tobacco products, e-cigarettes were the most commonly used in the past 30 days , followed by cigars/cigarillos , and smokeless tobacco .

Among tobacco users, the top three common patterns were exclusive cigarette use , dual use of cigarettes and e-cigarettes , and dual use of cigarettes and cigars/cigarillos . Compared to single tobacco users, dual and poly users had greater HIS scores and higher proportions of ever using e-cigarettes for quitting smoking and past 30-day use of blunts/spliffs. Using cross-sectional data from 562 clients in residential SUD treatment programs in California, we found that 65.5% of the sample were using any tobacco, with multiple tobacco use being equally prevalent as single tobacco use . Our estimate of multiple tobacco use is much higher than that in the general population 6,24 and also higher than the previous estimate from a national sample of people in SUD treatment.This study adds to current evidence identifying multiple tobacco use as an emerging public health issue among people in SUD treatment. Notably, our observation that proportion of dual users was higher than that of poly tobacco users has not been reported in previous research in SUD populations. In addition, consistent with previous studies among SUD samples,we found that e-cigarette was the most common alternative tobacco product used and dual use of cigarettes and e-cigarettes was the most common pattern of multiple tobacco use. However, our study is among the first bringing up the popularity of cigars/cigarillos and its dual use with cigarettes, calling for greater attention to this dual use pattern among people with SUD. This study extends existing literature by indicating factors associated with dual and poly tobacco use among people in SUD treatment. Current tobacco users who had ever used ecigarettes for quitting smoking were more likely to be both dual and poly tobacco users. In addition, among those who reported ever using e-cigarettes to quit smoking, 54% were still using e-cigarettes at the time of our survey . Although the efficacy of e-cigarettes as cessation aids is unclear,25 current evidence indicated that e-cigarette use was related to greatersmoking26 and the escalation of poly tobacco use over time,and thus,grow lights for cannabis may not be an effective aid for long-term cessation.Aligned with this evidence, our study suggested that current tobacco users in SUD treatment who are unsuccessful in quitting by using e-cigarettes may continue using multiple tobacco products. In our study, past 30-day use of blunt/spliff was strongly associated with poly tobacco use. Since use of cigars/cigarillos was prevalent in our sample, it is possible that participants were using cigars/cigarillos for blunt use. This finding may partly reflect our recruitment in California – a state having legalized recreational cannabis use since 2018 and considered the largest cannabis market in the US.Co-use of tobacco and cannabis is even higher than tobacco use alone among California’s young people.In the general population, cannabis use is associated with persistent cigarette smoking, high nicotine dependence, and low cessation among cigarette smokers.Likewise, a study among the SUD population found that ever users of blunt/spliff were less likely to plan to quit in the next 30 days.Given the increasing legalization of cannabis use in the US, our finding and other emerging evidence points to a need to address co-use of tobacco and cannabis and its potential impacts on tobacco use and cessation among people in SUD treatment.

Consistent with previous research among the general population and the SUD treatment population, we found that poly tobacco users had greater nicotine dependence and dual users had fewer days of good mental health as compared to single tobacco users. Mental health is frequently comorbid with SUD, and mental health problems is also linked to multiple tobacco use.This highlights a need to address the intersection of multiple tobacco use and mental health in SUD treatment programs. While prior studies in the SUD population found that multiple tobacco users were more likely to have past-year quit attempts,we did not find differences in having quit attempts and intention to quit smoking between dual or poly tobacco users and single tobacco users. More research is needed to better understand quitting intention, quit attempts, and cessation outcomes among dual and poly tobacco users in SUD treatment. Our study has implications for efforts to address tobacco use in the SUD population. As dual and poly tobacco use is highly prevalent among clients in SUD treatment and may confer additive risk compared to cigarette smoking alone, SUD treatment programs need to screen and assess the use of alternative tobacco products to better provide cessation supports for quitting cigarette smoking as well as quitting other types of tobacco use. Dual and poly tobacco users may comprise distinct groups, given that their use of multiple tobacco products is associated with greater nicotine dependence, co-use with cannabis in the form of blunt/spliff, and mental health problems, and thus, tailored interventions or multi-component interventions may be needed to address multiple health risks simultaneously. Particularly, interventions targeting dual use of cigarettes with e-cigarettes or cigars/cigarillos should be provided since they may place people in SUD treatment at risk for increased negative health effects and continued tobacco use rather than quitting.Although in-door smoking is prohibited in residential SUD treatment programs in California, clients may still smoke cigarettes and use other tobacco products outdoors. Tobacco free grounds policies, which ban use of tobacco products on treatment program grounds, should be adopted to reduce tobacco use among clients in SUD treatment.16 Study limitations include reliance on cross-sectional data, which precludes causal inference. Second, self-reported data in this study may have been susceptible to some degree of recall and social desirability bias. Third, the generalization of study findings is limited by the convenience sampling strategy, the inclusion of residential SUD programs only, and the fact that all programs were located in California. Finally, we could not explore reasons for dual and poly tobacco use patterns as well as context of using alternative tobacco products due to the small sample size and original measures. Future research should investigate mechanisms underlying tobacco use patterns among people in SUD treatment to find the best ways to treat tobacco use in this population. In conclusion, this study revealed high prevalence of dual and poly tobacco use among people in SUD treatment, and suggested that SUD treatment programs should address use of other tobacco products as well as cigarette smoking among their clients. In addition, interventions for dual and poly tobacco users should address use of e-cigarettes, cigars/cigarillos, and blunt/spliff as well as mental health to improve cessation outcomes and reduce tobacco related health disparities among this population.Cigarette smoking is strongly associated with alcohol use , and smoking is especially common among heavy drinkers and binge drinkers .

In our multivariate analysis that included RASS and CAM-ICU findings, age did not continue to be a predictor of WS . However, the RASS and delirium findings, when added to the model, significantly increased the model fit. That is, we found that they are both related to WS. From a conceptual and clinical perspective, it could be important for providers to recognized agitation/restlessness and delirium when caring for ICU patients being weaned from opioids and/or benzodiazepines.The final model also showed that cumulative opioid dose amounts prior to weaning were associated with development of WS, although the number of days that patients received opioids was protective. In our study, as expected, days on opioids and cumulative opioid dose were strongly correlated . The nature and the strength of the relationship between these two variables could be the reason behind our findings: in the multivariate regression analysis, while holding the cumulative opioid dose constant, the “days on opioid” variable showed a slightly protective odds ratio for WS . That is, given the same cumulative dose of opioid, patients with longer duration on opioid had lower odds of developing WS.Another explanation for this finding is that there are several factors that can cause differences in opioid tolerance, the precursor to WS, at the opioid receptor level.In addition, genetic differences in opioid receptor synthesis and variable opioid receptor affinity, the difference in type of opioid administered, and the use of continuous versus intermittent administration may be influential factors . Use of multi-modal analgesia may help to counteract development of WS through reduction of opioid amounts administered to the patient . However,cannabis grow tray further research is warranted on time versus amount differences in opioids and their risk for WS. This study has several strengths. The assessment of WS was done using a prospective approach two times a day for 72 hours or more. Furthermore, in the absence of an instrument validated to measure opioid and benzodiazepine WS in ICU adults, we developed a checklist using several reliable sources: the DSM-5 criteria, International Classification of Diseases, 10th Edition criteria WS, and symptoms identified in previous adult WS studies.

Therefore, the checklist had content validity. Furthermore, given that our study was exploratory in nature, we were able to conduct several analyses by constructing various models between patient- and clinical-related factors and the probable presence of WS. Our study has notable limitations. Consistent with other WS studies in ICU , our sample was small. In addition, the TICU did not have a protocol for daily sedation interruption or a pain management protocol. Therefore, there was a large degree of variability in the opioid and/or benzodiazepine weaning process between patients; this could have influenced differences in WS development. In addition, we were unable to evaluate some symptoms on the checklist in patients with RASS –3 to –5 such as hallucinations, delusions, illusions, dysphoria, nausea, insomnia, and delirium. Also, the intensity of the probable WS sign and symptoms was not evaluated. Our checklist has not yet undergone a formal validation process and reliability testing. Interrater or intrarater reliability was not possible because only one person performed all measures and the occurrence of WS was not constant between measurements. Finally, the signs and symptoms on our checklist are not specific for WS; thus, we could not rule out other conditions associated with these signs or symptoms. Further research on the psychometric characteristics of our checklist is warranted.Social media is now a common source of health-related information. This includes user-generated conversations about a variety of topics, with an emerging field focused on better understanding tobacco and alternative and emerging tobacco and electronic nicotine delivery system related knowledge, attitudes, and behaviors. User generated social media conversations can be assessed to better understand how health behaviors are changing closer to real-time.This approach introduces certain advantages over traditional survey methodology including faster identification of emerging trends. However, methods to appropriately code social media content for specific health-related topics remain underdeveloped, particularly in the context of characterizing transitions in behaviors that change over time. Twitter is a micro blogging social networking platform that allows users to tweet 280-character messages, which can then be retweeted, favorited, and shared across a network of online users. Users can form online communities by interacting with other users who share similar beliefs, interests, and opinions about topics.This includes users who initiate, use, and transition between different tobacco and ATP and ENDS products.

In fact, Twitter has specifically become a platform for sharing information about electronic cigarettes a nicotine delivery device commercially available only in the past decade.Evidencing growing popularity of vaping behavior, studies have shown that online searches for electronic cigarettes have increased. However, increased uptake of different types of e-cigarettes , particularly among youth and young adults, has not been without controversy. Ongoing concerns about the long-term health impact of nicotine consumption, e-cigarette-related adverse events and mixed evidence about the efficacy of ATPs as cessation devices, continues to generate public health and patient safety concerns. These concerns are accentuated when trying to assess the interaction of use behavior between traditional combustible tobacco products and ENDS. Understanding the pathways of transition of tobacco and ATP use—including what products users initiate on, why they switch between products, and unique health harms related to dual-use —is still a relatively underdeveloped area of study. Hence, the objective of this study was to examine Twitter user conversations to characterize users’ conversations in relation to transition of use associated with ENDS, with a focus on developing an inductive coding approach specific to characterizing transition of use knowledge, attitudes, and behaviors.To identify themes in our full corpus of tweets, we used an unsupervised machine learning approach called the Biterm Topic Model designed to detect patterns in data and summarize the entire corpus of tweets into distinct highly correlated categories. BTM is used to sort short text into highly prevalent themes without the need for predetermined coding or training and has been previously used for exploration of key public health topics. For each topic, BTM generates the top 20 words that represent the topic cluster. These topics were then reviewed and selected to identify clusters of Twitter conversations relevant to vaping and transition of use. Using BTM, we are able to identify “signal” topics based on the BTM output and eliminate irrelevant topics. BTM topics were first generated after applying keyword filters and were included for further analysis if they were pertinent to vape and vaping behavior, topics were excluded if they contained irrelevant topics or appeared to correlate with non-user generated conversations.We then extracted all the posts from the select vaping BTM topics and manually coded the content of tweets in these topics to ensure relevance to user-generated tobacco and ENDS use behavior. Posts were excluded as signals if they were: news related and not organically user-generated content; not written in English; and retweets, the tweets that were retweeted counted as only one tweet. However, all tweets, replies, and tweets containing photos or videos were included to assess additional contextual information in addition to content analysis of text of tweet. Transition of use was classified as switching from one tobacco or ATP/ENDS product to another.Tweets and any associated URLs/hyperlinks were aggregated into a table and imported into Atlas.ti qualitative software for content analysis. A first iterative, inductive analysis of the data was conducted to identify thematic areas and classify tweets into codes with code descriptions. Tweets were read for identification of thematic areas in the dataset,vertical grow system for sale then coded based on thematic areas of interest. Codes and coding descriptions were developed and modified iteratively throughout the coding process. A second analysis of the dataset was undertaken to expand the codebook to include subcodes. Subcodes and subcode descriptions were created and modified iteratively during a second round of data coding. Once a coding scheme was developed, the data were coded, extracted, and reviewed to assess the validity of the coding scheme by a second coder . Te final coding scheme and distribution of codes is presented in Fig. 1 and Table 1.Data was collected from the Twitter public API stream and included publicly available tweets that were filtered for posts with geolocation/geotagged information.

As the study did not involve human subjects, involved no interactions with online users, and only used publicly available data that was further de-identified for research purposes, ethics, and IRB approval was not required and twitter users were not consented into this study. Any user identifiable information was removed from the study results. This study explored user-generated conversations occurring on Twitter in relation to tobacco and ATP/ ENDS use, with a specific focus on transition of use between these highly addictive products. We observed that this subset of Twitter users actively tweeted about their experience using tobacco and ATPs/ENDS, representing powerful information about this behavior that is influenced by a changing landscape of new and emerging nicotine products. Te majority of tweets reviewed related to tobacco and ATP/ENDS use and behavior characteristics, including users asking about tobacco/ATP/ENDS products, how to quit, observations of tobacco/ATP/ENDS use behavior, opinions about products and vaping , sharing knowledge about tobacco/ ATP/ENDS products, and specific characteristics of use Close to half of all conversations discussed transition of use behavior, including users actively discussed the types of tobacco/ATP/ENDS products used and switched between, as well as provided reasons for product use change. A wide variety of tobacco/ATP/ ENDS products were mentioned, including combustible tobacco products , chewing tobacco, different types of e-cigarettes and cannabis smoking products. Transition was observed between different products and within specific product classes , with some users self-reporting poly tobacco and poly-substance behavior . Users expressed various sentiment about different products including how products could act as substitutes for others, what products made them feel better, attempts to quit use of one product by switching to another, and issues related to cost and access. Some users stated that cannabis vaping products helped them with cessation of nicotine addiction. Based on these preliminary results, Twitter appears to enable robust conversation and sharing of information related to tobacco and ATP/ENDS use and can act as a digital forum for smokers and vapers to accumulate knowledge, share experiences, and actually lead to potential behavior change associated with nicotine use and addiction.Since early March of 2020, the COVID-19 pandemic and the mitigation strategies put in place have had dramatic impacts on mental health and well being around the world . The public health response to COVID-19, such as social isolation and quarantines, resulted in several unintended consequences that increased the risk of anxiety and depression, as well as substance misuse and overdoses . The rise in substance misuse is particularly alarming, given that prior to the pandemic, in 2019, 60% of the United States population aged 12 and older used substances, including tobacco, alcohol, and illicit drugs, and an estimated 20 million had a substance use disorder . In addition to SUDs, one in five U.S. adults had a mental illness, which often co-occurs with SUDs . The prevalence of mental health symptoms continued to increase throughout the pandemic while disruptions in health care services resulted in unmet mental health care needs for many . At the same time, many individuals may have been reluctant to seek treatment for SUDs due to stay-at-home orders and worries about contracting COVID-19 . The increased rate of substance misuse raises several concerns regarding its impact on people who use drugs and overwhelmed health care systems. A rapid and drastic rise in drug overdoses and overdose-related deaths led to an emergency health advisory by the Centers for Disease Control and Prevention . Moreover, people with SUDs have been at increased risk of COVID-19 infection and poorer health outcomes . In turn, COVID-19 may increase the risk of overdose in PWUD . Furthermore, the pandemic exacerbated long-standing social and health disparities among under served and vulnerable populations. Not surprisingly, pre-existing disparities in mental health conditions and substance misuse have only widened during the pandemic . Among those who may be disproportionately affected by the pandemic are people living with HIV , a socially vulnerable population over represented in U.S. minorities . Indeed, COVID-19 infections correlate with social vulnerability and with county-level HIV prevalence .

Fourty-four percent of twelfth-graders have used marijuana in their lifetime, 20% used in the past month, and 5% use daily , representing a large increase from the 16% of 8th graders who have tried marijuana. Furthermore, 40% of high school students who used marijuana in the past year met criteria for marijuana abuse or dependence . Marijuana use in adolescence causes significant concern since marijuana use may impact the brain, which is still developing throughout adolescence. Though overall brain size stabilizes around age five , important progressive and regressive developmental processes continue throughout adolescence, including myelination , synaptic refinement , reductions of grey matter volumes and improved cognitive and functional efficiency . It is unclear how heavy marijuana use at this time could influence neural development. The long-term effects of marijuana have not yet been determined, but could potentially have major implications on social, academic, and occupational functioning. Although a good deal of research has been done on the effects of marijuana in chronic adult users, very little is known about adolescent users. Studies have shown that chronic marijuana has an influence on the neuropsychological performance of adults within a week of use. Specifically differences have been found in attention and executive functioning , memory , psychomotor speed and manual dexterity . One study demonstrated verbal learning deficits among cannabis flood table users compared to controls 0, 1, and 7 days following use, but that these impairments subsided after a 28-day abstinence period .

However, others have identified impairments in memory, executive functioning, psychomotor speed, and manual dexterity after 28 days of verified abstinence compared to published norms . Furthermore, adults who began use early in adolescence demonstrated greater decrements on verbal IQ after a 28-day abstinence period those who began late in adolescence and non-using controls, suggesting an adolescent vulnerability . Due to its high safety profile and good spatial resolution, functional magnetic resonance imaging has become a powerful method for visualizing neural activation. Research on adult marijuana users has shown alterations in brain response via fMRI scanning. More specifically, these studies have demonstrated an increase in spatial working memory brain response in marijuana users compared to normal age-matched controls in the pre-frontal cortex, anterior cingulate, and the basal ganglia . This suggests a compensatory neural response as well as recruitment of additional brain areas to achieve necessary task requirements, as seen in a recent study of task performance and brain functioning in marijuana users . However, because this study was done on adults who were abstinent for only 6-36 hours prior to the scan, it may be that these effects reflect recent use and not persisting effects . Others have characterized visuospatial attention among 12 recent marijuana users who had used 2 – 24 hours earlier, 12 abstinent users who not used for an average of 38 months, and 19 non-using controls . Both active and abstinent users showed decreased brain response in prefrontal, parietal, and cerebellar regions that normally subserve visual attention, and increased activation in alternate regions, suggesting brain response alterations even after extended abstinence. These adult fMRI studies point to altered neural functioning among marijuana using adults during visuospatial tasks, particularly in frontal and parietal regions. Less is known about neurocognitive functioning in adolescent marijuana users. A longitudinal study of ten adolescent marijuana users showed incomplete recovery of learning and memory impairments even after six weeks of abstinence . Recent fMRI studies of SWM involving alcohol-abusing adolescents and marijuana and alcohol-abusing adolescents have found that marijuana and alcohol were associated with greater changes than alcohol alone. Specifically, after an average of 8 days of abstinence, adolescent marijuana users showed an increase in dorsolateral prefrontal activation and reduced inferior frontal response compared to alcohol users and non-using controls, suggesting compensatory working memory and attention activity associated with heavy marijuana use during youth.

Adolescent marijuana users demonstrated increased right hippo campal activity and poorer attention and verbal working memory performance compared to demographically similar tobacco smokers and non-using controls, suggesting compensatory neural recruitment, even after a month of abstinence . In a follow-up study, marijuanausing youths who were abstinent at least two weeks performed similarly as non-users on verbal working memory during ad libitum smoking and again during nicotine withdrawal, but exhibited increased parietal activation and poorer verbal delayed recall during nicotine withdrawal compared to non-marijuana users . Together, these studies suggest altered working memory functioning among adolescent marijuana users that may persist after a month of abstinence. Yet it is unclear how variability in task performance might contribute to brain activation patterns. Among normal adolescents, spatial working memory task performance is associated with activation in bilateral prefrontal and posterior parietal brain regions . Adult studies have suggested increased frontal and parietal activity associated with greater spatial working memory task difficulty . FMRI studies of adolescent and adult marijuana users have suggested that increased neural responding associated with marijuana use may be evidence of compensatory neural recruitment to maintain task performance . Therefore, the relationship between task performance and neural response may differ between marijuana users and controls, with a stronger positive relationship among marijuana users. The interaction between task performance and fMRI response to SWM has not yet been studied in adolescent marijuana users. The goal of the present study was to understand how task performance patterns contribute to neural activation in abstinent adolescent marijuana users. We studied blood oxygen level dependent fMRI neural activation during a SWM task which typically activates bilateral prefrontal and posterior parietal networks in adolescents and adults .

This SWM task has been shown to be sensitive to brain response abnormalities in adolescent alcohol and marijuana users. In this study, both adolescent users and controls were required to abstain from all drugs and alcohol for 28 days prior to their fMRI scan, and all were free from psychiatric disorders and learning disabilities. Based on our previous work we predicted that after 28 days of abstinence, marijuana users as a group would perform as well as controls; however, the task performance would vary within each group resulting in a group by task performance interaction that would be associated with brain response. Specifically, we hypothesized that there would be interactions between task performance and fMRI response in the bilateral dorsolateral prefrontal and posterior parietal cortices, such that marijuana users show a stronger positive association between performance and brain response than controls in these regions. This study examined the association between behavioral performance and brain response during a SWM task among 16- to 18-year-old marijuana users and controls after 28 days of abstinence. Results suggest that, in general, marijuana-using teens performed similarly on SWM than controls, perhaps due to the low difficulty level of the task , which approached ceiling effects. This has been observed in fMRI studies of SWM in adult cannabis grow supplier users . However, specific localization and intensity of response varied between the MJ users and controls, with MJ users showing more performance-related activation in certain regions and less in others. These differential patterns emerged despite similar overall task performance across groups, suggesting an alternate relationship between task performance and brain activity among marijuana users. MJ users showed significantly more activation than controls in the right basal ganglia, an area associated with skill learning . Since the subjects were only allowed to practice the task once before entering the scanner, it is possible that the MJ users were still in the skill learning process during imaging. The other two clusters, which were significantly more activated in marijuana users than controls, were the right and left parietal lobes. Bilateral parietal regions have been implicated in attention, spatial perception, imagery, working memory, special encoding, episodic retrieval, skill learning monitoring, organization, and planning during working memory . It is possible that there is compensatory neural effort in these areas, as observed in SWM studies of adult marijuana users . The performance data positively related to activation in several areas, and did not negatively associate with brain response in any region. Performance was positively associated with activation in the left and right temporal regions, which are associated with verbal mechanisms and episodic, nonverbal working memory and retrieval, respectively . This suggests that good task performance may be related to using multiple memory modalities. High-scorers showed more activation in the bilateral prefrontal and bilateral parietal regions that have been shown to activate during SWM tasks in youths . The performance by group interactions were the focus of this study and yielded the most interesting results. In particular, an interaction in the left superior temporal gyrus suggested a positive association in the users and a negative association in the controls. This may imply that the MJ users used more of a verbal strategy to achieve high task performance scores than the controls. This is interesting when considering the previous findings of deficits in verbal learning and IQ in marijuana using adolescents compared to controls . Furthermore, the right superior temporal gyrus showed an interaction where users had a negative association and controls had a positive association. Previous studies have shown this area to be involved in poorer recognition of previously seen words . This would support the notion that users are relying on a verbal strategy so that better performance linked to a decrease in activation in the right superior temporal gyrus. Moreover, an interaction in the right thalamus and pulvinar showed a negative association in the users and a positive association in the controls. These subcortical structures have shown an association with spatial neglect when damaged.

It is interesting that these areas have a negative association in users and a positive association in controls, and may suggest that marijuana users utilize less spatial strategies than controls. The nature of the interaction revealed a positive association in marijuana users and a negative association in controls in the left anterior cingulate. This region has been linked to attention, decision-making, cue response, and response monitoring . It may be that good performing marijuana users are making a more conscious decision to react to task cues than controls, who may be reacting more automatically. The left parahippocampal gyrus demonstrated an interaction of negative association in marijuana users and positive association for controls. This region is involved in working memory and is recruited when the temporal lobe is not in use . Since marijuana users are using more energy in the left temporal lobe as their performance increases, higher scoring subjects may rely less on the parahippocampal gyrus. The distinct interactions viewed in these different areas of the brain can mean that different systems are at work, and as one part of a system decreases in action, the other area of the system increases in activation. Previous studies suggest that subjects who do not use traditional strategies for specific tasks showed an increased extent of activation and recruitment of additional areas, specifically verbal areas, to accomplish the task . More specifically, the pattern of results suggests that marijuana users may apply a verbal strategy to the task when achieving higher scores. It is possible that this alternative way of using the brain may be less efficient; this would explain the greater overall activation in users versus controls and recruitment of other brain regions as a compensation method. A recent review also found that multiple neuroimaging studies of marijuana users pointed toward recruitment of compensatory regions as well as task-related regions to achieve task demands . A possible limitation to this study is the interpretation of a difference in fMRI activation between experimental groups. It is possible that alternative neural pathway use is more dynamic and versatile. It is unclear whether the results are an adverse effect of the marijuana use or merely a benign difference. Further studies that more carefully describe the relationships between task performance and brain response will clarify this question. Another limitation of thecurrent study is that most marijuana users were also moderately heavy alcohol drinkers. While these participants are representative of the population of adolescent marijuana users, most of whom also consume alcohol , it is nonetheless difficult to disentangle the effects that may be related to alcohol use.

While multiple complex processes are involved in the pathogenesis of ischemic AKI, tubular cell injury, increased oxidant stress, and inflammation are common denominators.IR-induced renal tubular injury results in increased expression of adhesion molecules, such as vascular and intercellular adhesion molecule1 , and selectins, such as Pselectin and E-selectin.This is followed by the attachment, activation, and transmigration of immune cells into renal tissue, which results in inflammation. Subsequent production of reactive oxygen species and disruption of the nitric oxide pathways lead to further tubular damage, inflammation, and oxidative stress.7 These abnormalities trigger a pathologic cascade of events that ultimately lead to propagation of renal injury and manifest clinically as kidney failure. The endocannabinoids are endogenous, bio-active lipid mediators that exert their effects mainly through specific G protein-coupled receptors: type-1 cannabinoid receptor and type-2 cannabinoid receptor . The most extensively studied ECs are arachidonoyl ethanolamide and 2-arachidonoylglycerol . They are synthesized on demand through distinct cellular pathways and are released in the local micro-environment, leading to autocrine or paracrine downstream effects. Given the abundance of CB1 receptors found in the central nervous system and CB2 receptors on immune cells, the ECs were initially thought to be active only in these systems. However, CB1 and CB2 receptors have been discovered in a multitude of peripheral tissue, including the kidneys.Although not fully understood,heavy duty propagation trays the activation of CB1 receptors in the periphery has been shown to be associated with increased oxidative stress and inflammation, whereas activation of CB2 receptor has been known to have the opposite effect.Furthermore, the ECs and their metabolites can also exert hemodynamic and other effects through CB receptor dependent and -independent pathways.

Given the major role that inflammation and oxidative stress play in IRI and the known involvement of the EC system in these pathways, there has been extensive evaluation of the EC system in pathophysiology of IRI of several organ systems, including the brain, heart, and the liver. Several reports indicate that both the blockade of CB1 receptors and the activation of CB2 receptors protect against IRI in the tissues mentioned.Interestingly, these findings have also been confirmed in nephrotoxic AKI using a murine cisplatin renal tubular injury model.In addition, there are now numerous reports that highlight the involvement of the EC system in renal injury and fibrosis in a variety of settings, including diabetic nephropathy.Despite the preponderance of evidence implicating the EC system and ECs in nonrenal IRI, data on the role of ECs in renal IRI remain sparse.In addition, most of the studies examining the role of the EC system in renal injury focus on the effects of the activation or inhibition of the CB receptors and do not provide data on the tissue level of the endogenous ligands for these receptors, 2-AG and AEA. In this study, we show for the first time that renal IRI leads to a significant increase in renal level of 2-AG, one of the major activators of the EC system. Furthermore, enhancement of renal 2-AG levels using pharmacologic tools improved indices of renal function without changing markers of inflammation or oxidative stress. These results indicate that renal ECs are involved in the pathogenesis of IR-induced AKI, and how modulation of the EC system may impact renal injury and function will need to be studied in further detail.All animals were handled and procedures were performed in adherence to the National Institutes of Health Guide for the Care and Use of Laboratory Animals, and all protocols were approved by the University of California, Irvine Institutional Animal Care and Use Committee. Male C57BL/6 mice 8–12 weeks old were obtained from Jackson Laboratories . They were housed in the UC Irvine facility under specific pathogen-free conditions, were allowed free access to standard chow and water, and were kept in a 12-h light:12-h dark cycle. In the first set of experiments, 20 mice were divided into two groups: one was sham operated and the other underwent 30 min of bilateral ischemia . For the second set of experiments, 20 animals were divided into two groups: one received the monoacylglycerol lipase inhibitor JZL184 and the other received the correspondent vehicle 2 h before I/R .We used an established mouse model of ‘‘warm’’ renal I/R injury.27 Briefly, mice were anesthetized with ketamine/xylazine and kept on a homoeothermic station to maintain body temperature at 37 C. A warming blanket was used throughout the procedure and for 30 min post procedure . The body temperature of each animal was monitored closely throughout the procedure and afterward using a noninvasive infrared digital temperature device. A midline incision was made and bilateral renal pedicles were exposed.

Using atraumatic Micro Serrefne straight clamps , both renal pedicles were cross-clamped. To maintain fluid balance, mice received 0.7 mL of sterile 0.9% saline by intraperitoneal injection. After 30 min of warm ischemia, clamps were removed initiating renal reperfusion. Sham control animals were subjected to identical operation without clamping. Mice were sacri- fificed at 24 h after reperfusion for serum/kidney sampling under terminal general anesthesia using isoflurane. Blood was taken by intracardiac puncture after accessing the chest cavity from underneath the diaphragm. Briefly, before the opening of the chest for blood collection through cardiac puncture, anesthesia was induced in a chamber with 4–5% isoflurane in 100% O2 and then maintained by continuous administration of 1– 2% isoflurane through nose cone. We made certain that all animals were fully anesthetized prior and during surgery. This method is approved by the University of California Irvine Institutional Animal Care and Use Committee and consistent with the AVMA Guidelines for Euthanasia. Approximately 100 lL of serum was isolated and stored at 80 C. The kidneys were harvested after euthanasia/cardiac puncture. Both kidneys were cut in half along the renal pelvis. One and half kidney was immediately snap-frozen in liquid nitrogen while half of the kidney was fixed immediately. To avoid inter tissue variability and avoid comparing different regions of the kidney, we fixed the same half of the kidney for all animals and compared the same region of the fixed kidney for histopathology evaluation.This is the first study that examines the effect of kidney IRI on renal EC levels and their potential impact on pathophysiology of AKI. We have found that kidney IRI is associated with a significant increase in renal2-AG content. Kidney 2-AG levels correlated positively and significantly with serum BUN and creatinine. Furthermore, enhancement of renal 2-AG levels using the selective MGL inhibitor, JZL184, caused a modest but significant improvement in renal function. Interestingly, the latter findings were not associated with improvement in renal markers of inflammation and oxidative stress, indicating that the improvement in renal function induced by 2-AG may be independent of proinflammatory and oxidative processes.

The findings of our study are unique in several respects. First, while the role of the EC system in AKI has been examined through modulation of CB receptors in animal models of nephrotoxic tubular injury, this is the first investigation examining EC levels and the role of the EC system in ischemic AKI and renal IRI.Therefore, the current findings shed light on how the EC system as a whole may be involved in the changes observed in renal IRI. Furthermore, our study demonstrates that renal IRI is associated with increased kidney 2-AG content. The latter findings are significant because understanding how ECs are mobilized in renal IRI is crucial to their potential exploitation as a therapeutic strategy. It is also intriguing to note that increased tissue 2-AG content has also been reported in IRI in other organ systems such as the liver,vertical cannabis and, just as in the kidney, enhancement of tissue 2-AG levels was associated with improvement of IRI.Therefore, it is noteworthy that the findings described in this study are not exclusive to the kidney, thus reflecting a potential physiopathological role of 2-AG and the EC system in IRI, which needs to be further explored. While increased 2-AG levels have mostly been reported to be associated with reduced tissue injury in IR, the mechanisms through which 2-AG affords protection in IRI remain unclear. Several possibilities have been examined, one of them being a potential anti-inflammatory action of 2-AG mediated by CB2 receptors. However, available data on the impact of 2-AG on inflammation are contradictory, with some studies reporting anti-inflammatory properties, while others noting proinflammatory effects.In our study, we did not find significant changes in the expression of proinflammatory cytokines or adhesion molecules following pharmacological enhancement of renal 2-AG levels. In fact, we observed a trend toward an increased expression of these markers. Indeed, there are recent studies that link increased 2-AG levels with worsening of inflammation.It is possible that increased tissue levels of 2-AG may lead to activation of CB1 receptor, hence causing a trend toward worsening inflammation; however, this mechanism has not been established in renal IRI. Given that indices of renal inflammation and oxidative stress remain unchanged, our findings support the notion that the modest improvement in renal function observed with enhanced 2-AG levels may be related to effects independent of its role in in- flammation and oxidative stress. In this regard, there are several studies indicating that 2-AG has vasodilatory properties through CB1-dependent and -independent mechanisms.For instance, Awumey et al. have demonstrated that 2-AG can cause relaxation of arterial smooth muscle through its metabolite glycerated epoxyeicosatrienoic acid, which can activate potassiumgated calcium channels on vascular smooth muscle cells resulting in hyperpolarization of these cells and vascular relaxation.One possibility is that increased kidney 2- AG levels in renal IRI could cause arterial vasodilatation, which would lead to improved renal perfusion and enhanced glomerular filtration rate, thereby explaining the improvement observed in renal function.

It should also be noted that since we administered JZL184 systemically and most likely increased 2-AG levels in other parts of the body, it is possible that the renoprotective effect observed in our study may be emanating from outside of the kidneys . These possibilities will need to be examined in future studies.First, there is emerging evidence that dysregulation of this system may be involved in diabetic nephropathy, proteinuria, renal fibrosis, and AKI.Second, pharmacological agents are available, which can modulate EC levels and CB receptor activity, thereby providing potential therapeutic strategies. Finally, considering recent reports pointing at synthetic cannabinoid use as a cause of AKI,investigation of the EC system in renal disease may shed light on the mechanism by which these recreational drugs can potentially cause renal injury and help formulate preventive plans in risk population. Several limitations of our study need to be mentioned. The potential role of 2-AG as a mediator of vasodilation and its impact on renal blood flow rate will need to be thoroughly explored in future studies. Furthermore, in our mouse model of AKI, we had a limited supply of plasma, and therefore, systemic levels of ECs in IR AKI remain to be determined. Moreover, given that JZL184 was administered systemically, we cannot rule out inhibition of MGL in other organs that could have had a downstream effect on the kidneys. In addition, despite its specificity for MGL, it is possible that JZL184 may have an impact on other serine hydrolase enzymes that may explain some of the results we are observing in our studies. Furthermore, our evaluation of renal markers of inflammation and oxidative stress pathways was limited to mRNA analysis, and therefore, renal abundance of each protein will need to be determined to complement the mRNA findings. Finally, our study does not address the mechanism/s responsible for increased renal 2-AG levels. We have recently reported that oxidative stress can cause the reversible sulfenylation of MGL and inhibition of its activity, hence leading to decreased 2-AG breakdown and increased 2-AG levels.42 Given that kidney injury is associated with significant oxidative stress, it is possible that MGL inhibition is the mechanism responsible for increased 2-AG levels in renal IRI, however, this possibility will need to be confirmed in future studies. In conclusion, renal IRI is associated with a significant increase in kidney 2-AG content. Further enhancement of renal 2-AG levels using a pharmacologic tool, which inhibits its breakdown, improves indices of renal function and kidney injury, without affecting expression of markers of inflammation and oxidative stress.

Beyond these three limitations, Lehner and others point out that smaller vessels may not have AIS and may also be more difficult to differentiate from false-alarms, like breaking waves. We posit that few ships intent on criminal activity would have AIS either. Finally,Paes and others note that the Earth’s curvature and meteorological influences on data transmission leads to instances where vessels far from the coast are not present in the AIS databases. To get around some of these issues, some scholars used maritime patrol aircraft to survey blank areas, had analysts do manual inspection of images or did on-the-ground validations of ships. All of these techniques are difficult, time consuming and expensive to enact, thus making it likely that validation of actively identified marine crimes will follow similar trends as terrestrial drug production or smuggling. Aside from the more refined remote sensing techniques we mention above, law enforcement and government officials have leveraged the power of freely available remotely sensed products, like Google Earth, to detect crime. Although, to date, there is a limited discussion of the use of Google Earth to detect crime in the academic literature, it is widely discussed in the popular press . These discussions note that Google Earth is being deployed by law enforcement officers, government employees, scientists and even private citizens to actively detect crimes in progress around the world. For example, a Swiss police department “stumbled across a large marijuana plantation while using Google Earth”. Aside from international agencies and law enforcement departments, researchers, like Anthony Silvaggio, an environmental sociologist at Humboldt State University,cannabis equipment have sought to point out where large-scale, unregulated industrial marijuana grow sites are occurring in Humboldt county, California, including in national forests. Amateur searchers have also started seeking out and identifying marijuana growing using Google Earth .

Google Earth’s use for crime investigation does not stop at drug production, however. In Greece, Italy, Argentina, India and the United States, Google Earth has been used by government officials to identify homes that have violated building codes, built swimming pools without permits and to compare declared home values with actual existing structures. Though in North Carolina, U.S. government officials only used Google Earth to verify code violation complaints, in places like India, New York, Argentina and Greece, Google Earth was used in the active reconnaissance of committed crimes. Marine researchers have also used analyses of Google Earth to evaluate the veracity of fish-catch reports made to the UN. Spain’s Green Party has reported illegal bottom trawling of beaches for fish using Google Earth images, as well. Google Earth has also been used to detect illegal dumping. For example, in Florida, a sheriff’s deputy used Google Earth to apprehend an individual who dumped a large boat; in Mississippi, a landowner identified a stolen and illegally dumped truck on his property using Google Earth; while in Bangalore, Google Earth was used to identify unauthorized and illegal waste dumping sites. Illegal logging is also actively identified using Google Earth by such groups as local police departments in the Philippines, the Finnish Association for Nature Conservation and their associated NGOs in Russia, the Amazon Conservation Team and associated indigenous groups. Amateur Google Earth users have reported potential body-dumping based on the imagery available, as well. Some of the issues associated with Google Earth arise from the fact that its images are made available by a privately-owned corporation and are technology driven. Thus, as Sheppard and Cizek note, the visualizations of the Earth made available by this interface are more geared towards “efficiency, convenience…entertainment value, popular demand, and profit” than they are towards “truth, deeper understanding, improved civil discourse, safer and more informed decisions, and other ethical considerations”. As these and other authors point out, realism in landscape visualization is not the same as accuracy or validity.

Virtual globes, like Google Earth, may suffer from low data resolution, interfering with image clarity and accuracy, missing data or inaccurately displayed data. Further, it is often impossible to know the exact date of the imagery available on Google Earth and whether all images in a scene are from the same date . Thus, a potential crime sighted on Google Earth may be months or even years old or may be exaggerated by differing image dates. Finally, these data may be manipulated by the producers of these virtual globes for various privacy reasons; some areas are intentionally blurred or objects are not displayed. More significant than spatial and temporal accuracy is the consumption and use of these images by untrained or informal interpreters. These informal interpreters may not understand the temporal or spatial inaccuracies inherent in these data. Goodchild points out that users of Google Earth may be misled to think it is more accurate than it is in reality. Despite the fact that Google Earth images’ absolute positional accuracy is sufficient for assessing remote sensing products of moderate resolution, errors and positional inaccuracies are still a problem. Trained remote sensing analysts understand these limitations and may be able to account for them, whereas casual users may not. Untrained remote sensing analysts may also misinterpret the images available to them. For example, in the case mentioned above, where amateur Google Earth users reported a dumped body, their interpretation of the image was flawed. In this case, the “dumped body” turned out to be a swimming dog. The dog’s watery trail on the cedar wood dock and the dog lying on that dock appeared to be a bloodied body rather than a picture of a sunny day at a lake.Un-validated identifications of “crimes” using Google Earth images by amateur analysts unfamiliar with the inaccuracies of these images or the nuances of image interpretation may be problematic for several reasons. First, they may cause law enforcement officers to seek places or things that are not where they are purported to be, are no longer present or never existed in the first place. This may result in a waste of funds, resources and personnel hours. Second, the mis identification of a site as a place where a crime is or has occurred opens that place and its residents up to potentially needless intrusion, intimidation, surveillance or violence.

Despite the increasing ease with which satellite images and other spatially explicit data flow to us, ethical and scientific rigor should not be laid aside. Finally, as Purdy and Leung note, Earth Observation data like those used in products like Google Earth may have their evidential weight in a court of law seriously reduced if un-validated, because the medium by which it was taken, the data management systems used or even the date the image was taken may be unknown. Given the potential for amateur misinterpretation or overconfidence in Google Earth images, it is obvious that crimes detected in this manner must be validated to ensure appropriate, timely and safe responses by government of law enforcement officers. While there have been a few cases where crimes detected using Google Earth were validated, either by fly-overs or personal ground validation missions , in the majority of cases, there is no discussion of accuracy assessment or validation. This dangerous trend toward trained and untrained analysts taking Google Earth images as “truth” with no validation may have broad reaching potential impacts on law enforcement efforts and personal security. Despite the fact that cutting-edge technologies are being used to remotely detect crime, the accuracy assessments of those analyses lag well behind current remote sensing standards. Indeed, as we have shown above, some studies that attempt to remotely sense crime do not perform accuracy assessments at all, depend on the opinions of “experts” or “surrogate ground truth data”, all of which are deemed to be substandard by today’s remote sensing community. Many of the studies noted above performed no accuracy assessment at all; they did not even use Google Earth or Digital Globes to validate their data. Particularly, in situations that may have life-and-death implications or serious environmental effects ,vertical grow shelf law enforcement officers must strive to be as accurate as possible in their targeting of crimes and criminals. Although drones or unmanned aerial vehicles/systems may present excellent options for accuracy assessment, offering up quiet, real-time, high resolution imagery of remote or distant areas without threat to human life, they are not ideal solutions in every situation. The equipment, licensing, training and maintenance required to acquire and safely maintain a UAV may be well beyond the means of many local police departments or underfunded government agencies. In the United States, the Federal Aviation Administration has seriously restricted the use of unmanned aircraft in national airspace . Further, there are serious questions about the constitutionality of using UAVs for law enforcement. Critics of UAV use by law enforcement argue that these vehicles impede an individual’s reasonable expectation of privacy as protected by the fourth amendment Despite these concerns, law enforcement is increasingly using UAVs to detect crimes and facilitate law enforcement . In the following section, we propose some alternate or additional means of validating remotely sensed crime. We hope that this initial thought experiment may help spark a conversation about the methods and ethics of remote sensing in law enforcement. We define “first order” accuracy assessments as those described in the accepted remote sensing protocol , which include ground-based validation or the use of imagery of higher resolutions than the imagery to be validated. Since these first order assessments can be limited by security, funding and terrain issues and drone use presents funding and legal issues, we propose a “second order” level of accuracy assessment. This second order accuracy assessment analyzes the larger geographical and social context in which remotely sensed crimes are detected by remote sensors. Such assessments could utilize crowd sourcing, big data mining, landscape-scale ecological data and anonymous surveys to determine whether and how crimes are occurring and where remote sensing analysts think they are. Second order accuracy assessments may allow remote sensors and law enforcement officers to confirm that crimes are taking place where analysts say they are without facing rugged terrain, insecure conditions or using costly overflight methods. Further, second order validation may enable analysts to gain better contextual understandings of those crimes, allowing for more ethical and proportionate responses by law enforcement. While these second order validation techniques may not be as reliable as first order techniques, they are better than no validation at all.

Alternatively, these second order techniques could be incorporated into interdisciplinary crime detection techniques that may increase detection accuracy. Urban areas are well suited to second order accuracy assessments because of the amount of available social data produced and available at any given moment. For example, Oakland’s Domain Awareness Center plans to link public and private cameras and sensors within the city limits into a single hub for law enforcement use . While highly controversial, these centers present numerous opportunities to validate remotely sensed crimes with closed-circuit television , as well as readily available on-the-ground policing. Rural or more remote areas present more of a challenge, however. These places typically lack surveillance cameras and mounted sensors. It is also in these places that large-scale drug production, human and drug smuggling frequently occur. Thus, here, we use illicit cannabis production as a case study to think through three potential second order accuracy assessment techniques in non-urban zones. Though we acknowledge that each of these methods would require further development and thought and that methods may exist beyond those we propose here, it is our hope that this will be the first effort in a larger conversation as to second order validation techniques in the remote sensing of crime. Social media: Location-based social network analysis may be helpful in validating crimes remotely sensed in other ways through geolocated self-reporting or observations by others. LBSN has been shown to provide reliable spatio-temporal information about incidents occurring in a broad landscape. For example, researchers from the Institute of Environment and Sustainability in Italy used a Twitter application programming interface to retrieve tweets and related metadata for a specific topic, the 2009 Marseille forest fire. These tweets were then organized into meaningful summary statistics using data mining and web crawling scripts. These researchers found that the LBSN data collected were temporally synchronized with actual events and provided some geographically accurate reporting. They note that Twitter “could offer promising seeds for crawlers to collect event-related data where time and location matter”.

Methods exist for extraction of cannabinoids from oral fluid using the Quantisal device. However, the goal of this manuscript is to combine previous methods into a single extraction procedure without a hydrolysis step that would allow for the quantification of the following compounds by one method: THC, cannabidiol , cannabinol , cannabigerol , Δ9 -tetrahydrocannabinolic acid , tetrahydrocannabiverin , 11-hydroxy-Δ9 -THC , 11- nor-9-carboxy-Δ9 -THC , 11-nor-9-carboxy-Δ9 -THC-glucuronide , and Δ9 -THC-glucuronide . This assay will be useful for OF cannabinoid analysis in the establishment of a cannabinoid concentration associated with driving impairment.Oasis prime HLB 96-well extraction plates were purchased from Waters . Mass spectrometry grade methanol , acetonitrile , and formic acid were purchased from Fisher scientific . Blank synthetic OF matrix used to prepare calibrators and quality control specimens was purchased from Immunalysis . OF was collected with the Quantisal™ device also from Immunalysis. Participants refrained from food or drink for 10 min, then the absorptive cellulose pad was placed under their tongue until the indicator turned blue or 5 min had passed. The collection pad was then placed into the plastic collection device containing 3 mL of extraction/stabilization buffer. The extraction buffer is supplied with the Quantisal™ device. The capped tube was placed at room temperature for at least 4 h but not > 24 h. The pad was then removed from the stem using fisher brand standard serum filters and decanted into nunc 3 mL cryovials from Wheaton.The samples were then stored at 4 °C. Each sample was weighed in attempt to derive a short sample correction factor before being analyzed within 2 months of collection.

Positive QC standard solutions of 300, 60, and 12 ng/mL were prepared by parallel dilutions from a 1000 ng/mL stock in methanol made the same as described for the calibrator solution except using stock solutions from a different lot than the calibrators. Each solution was aliquoted into amber glass auto sampler vials,garden racks wholesale sealed with parafilm and stored at −20 °C. The solutions correspond to three levels of QC at 60, 12, and 2.4 ng/mL when processed in synthetic OF. The lower level of QC was chosen to reflect a low concentration that was closer to per se cut off values adapted by several states. QC results were reviewed according to an absolute criteria of ± 20% of target values.All calibrators and QCs were prepared by adding 50 μL of calibrator, 50 μL of working IS, followed by one mL of blank matrix to corresponding borosilicate tubes. Subject specimens were treated in a similar manner except methanol was substituted for the calibrator. Samples were then acidified with 400 μL of 4% phosphoric acid. Samples were vortexed briefly then contents were transferred to a well of a 96 well Oasis Prime HLB C18 SPE plate. Samples were forced through the wells using a positive pressure manifold on low pressure until all liquid was pushed through. This took approximately five minutes to drip through. Each well was washed with 500 μL of SPE wash buffer twice under low pressure. The pressure was switched to max flow for one minute following the second wash to push any excess liquid through the well. Compounds of interest were eluted into 750 μL glass inserts with three successive 100 μL aliquots of 98% ACN with 2% formic acid for a total of 300 μL eluant. Extracts were evaporated under nitrogen at 40 °C with gas flow set to 70 psi for 30 min. Dried extracts were reconstituted with 200 μL of 50% ACN containing 0.1% formic acid. Plates were covered with a silicone/PTFE treated, pre-slit cap mat and vortexed using the Fisher scientific Ana Multi-tube vortexer on speed setting of 4 for 5 min.

Plates were centrifuged at 1962 x g for 10 min in Sorvall legend XFR centrifuge and then transferred to the sample organizer for LC-MS/MS analysis.Chromatography was performed using a Waters Aquity i-class UPLC system equipped with sample organizer, binary solvent manager, auto sampler, and a column oven . Separation of analytes was achieved using a Waters 2.1 × 50 mm Acquity UPLC BEH C18 column packed with 1.7 μm sized particles. The analytical column was attached to a 2.1 mm × 5 mm ACQUITY UPLC BEH C18 VanGuard Pre-column packed with 1.7 μm particle size to prevent column degradation due to sample buildup. Guard columns were replaced after every 1000 injections. The auto sampler was set to 10 °C. The column heater was set to 40 °C. A full-loop 10 μL injection was made for each sample. Gradient elution was performed using a mobile phase A of 5 mM ammonium formate buffer with 0.1% formic acid and a mobile phase B of acetonitrile with 0.1% formic acid at a constant flow rate of 400 μL/min. The initial gradient conditions were 50% MPB, held for 30 s, and then linearly increased to 90% MPB over 3.5 min. The final MPB concentration was maintained for 15 s, before returning to initial conditions and holding for 45 s. The maximum pressure was set to 15,000 psi.The LC system was coupled to a Waters TQ-S-micro triple quadrupole mass spectrometer interfaced with an electrospray ionization probe. Negative ionization was used for THC-COOH-gluc. All other compounds used positive ionization. The mass spectrometry transition ions were collected using a scheduled multiple reaction monitoring mode with four separate time windows. The first time window was collected in negative ion mode from 1.00 to 1.50 min. The subsequent windows were collected in positive ion mode from 1.51 to 2.59 min for TW-2, 2.60 to 3.22 min for TW-3, and 3.23 to 4.20 min for TW-4. The selected precursor and product ions, collision energy, retention times and associated windows are displayed in Table 1. The source temperature was set to 550 °C for both modes.

The instrument was controlled with Masslynx V4.1 SCN945 SCN960 software and peaks were processed using TargetLynxs XS. A representative reconstructed chromatogram of all quantifier ions from a 20 ng/mL calibrator is displayed in Fig. 1.To demonstrate that the synthetic OF was a valid substitute for OF specimens from humans, OF from 10 drug free volunteers was collected and processed such that a one mL aliquot was fortified with low QC and IS, while another one mL aliquot was processed unaltered . The percent bias was calculated by dividing the difference between the averaged concentration of the low QC in human OF samples from the average concentration of low QC in blank matrix by the averaged concentration of low QC in blank matrix. A qualitative assessment of matrix interference was also performed by injecting each of the unspiked OF samples while simultaneously infusing a calibrator solution containing 10 ng/mL of each analyte. See Fig. 2 for total ion chromatograms of the blank OF samples. Potential drug interferences were assessed by generating 5 pools of 10 different drugs belonging to opiates, benzodiazepines, and other common drugs of abuse that could be present in a suspected DUI subject . Superphysiological concentrations of the pools of drugs were added to blank OF samples fortified with low QC. Recovery of the QC within ± 20% of expected concentration in the presence of super physiological concentrations of drug pools was required to demonstrate lack of interference.Auto-sampler stability was assessed by comparing the average area counts from the low QC to the area counts of an injection at 24 and 48 h post-extraction. Acceptable stability was set at ± 20% CV in area of a 1.25 ng/mL stock compared to the initial injection. Lack of carryover was established by injecting a blank matrix fortified with IS immediately after the highest calibrator and then comparing the area counts to the same blank matrix with IS injected prior to the calibration curve. The acceptable level of carryover was a set to < 20% increase in area counts of the blank matrix following reinjection after the highest calibrator.Proof of applicability is demonstrated by evaluating the concentrations of cannabinoids in three participants enrolled in an Institutional Review Board-approved study evaluating the effects of inhaled cannabis containing either placebo , 5.9% or 13.4% THC by weight. Inclusion criteria for participation were individuals had to be at a minimum an occasional user ,hydroponic racks abstain from marijuana use 48 h prior to testing, and have a valid drivers license. Oral fluid samples were collected upon arrival to the laboratory which was tested to demonstrate THC < 5 ng/mL using the Alere OF point of care instrument. Individuals whose OF screened negative on the Alere device then smoked a joint containing either placebo, 5.9%, or 13.4% THC. Oral fluid was then collected 15, 90, 210, and 280 min after smoking and processed as described above.

The complete study design and detailed methods will be published after the target enrollment of 180 subjects is complete.A qualitative matrix effect study was performed by infusing a 10 ng/ mL calibrator solution during an injection of an extracted oral fluid specimen from drug-free volunteers . There was no observable ion suppression or enhancement across any of the analytes peaks in the human oral fluid specimens . Quantitative assessment of matrix effects was performed by fortifying the human drug-free oral fluid specimens with low QC and calculating the percent recovery to expected values established in the inter-day validation . Acceptable critera were set as a percent difference < 20% from expected. No matrix effect exceeding this criteria was observed in the human oral fluid specimens when compared with the synthetic oral fluid used for calibrators and controls. Extraction efficiency was determined by comparing average peak areas of extracted blank matrix samples fortified with low, mid, or high QC divided by peak areas of blank matrix samples fortified post-extraction with QC. All analytes had less than a 9% difference in extraction efficiency between any level of QC with a range of efficiencies from 26.0–98.8% . Percent matrix bias were determined by comparing average peak areas of blank matrix samples fortified post-extraction with low, mid, or high QC divided by neat solutions of QC. The range of percent matrix bias was −37.6–23.7%. THCA-A observed the worst ion suppression followed by THC-V . THC-COOH observed the greatest ion enhancement due to matrix effects observed to be > 20% in the mid QC, whereas all other analytes had percent differences < 20%. The THC-COOH internal standard compensated for the matrix enhancement providing results within ± 20% of target values. Interferences were assessed from five pools of ten drugs in blank OF fortified with low QC. The percent bias for all cannabinoids ranged from −17.4–12.7% . Thus, no drugs that were tested caused any interference in calculating the low QC concentration.The areas for all the compounds were within ± 20% upon reinjection at 24 h in the auto-sampler. The 48 h injection of samples had a percent difference within 20% for CBN, CBD, THC, 11-OH-THC, THCCOOH, and CBG, but a percent difference < 28% for THC-gluc, THCCOOH-gluc, THC-V, and THCA-A. In the 48 h reinjections, the internal standards compensated for changes in area counts so quantitative results were within 20% of the initial values. Dilution integrity was acceptable within ± 20% of target concentrations for THC after diluting 1:10 with blank matrix. THC quantified within 3.1% of expected concentrations. There was no evidence of carry-over for any of the cannabinoids following injection of a sample containing 2000 ng/mL.Quantification of THC and related metabolites is part of a research project that aims to establish the concentration of cannabinoids associated with driving impairment following consumption of a low does , high dose , or placebo . Participants have their OF samples collected prior to and immediately after smoking one of the randomly assigned joints. The study is a double-blinded approach, thus the laboratory is blinded to which participants have smoked which kind of joint until the conclusion of the study. To demonstrate proof of applicability, the laboratory was unblinded to identify the first three participants in this study that smoked either the placebo, low or high dose joint. The purpose of including data from three subjects who smoked marijuana is to demonstrate the analytical method is capable of measuring these compounds in specimens obtained from human volunteers, and not to draw any conclusions regarding pharmacokinetics or determining severity of impairment. One participant from each group had their oral fluid samples assessed by this method with concentrations of each cannabinoid listed in Table 7.