Adult criminal justice systems are incorporating MI techniques through digital health interventions to reduce substance use and in staff trainings to promote overall harm reduction and associated consequences, but studies are with those already using substances.Our data suggests focusing on youth’s internal distress , and cannabis use expectancies, , for those in first-time legal contact and not yet using, could be an important focus for prevention efforts.Depending on resources and time, interventions could be delivered in-person or through digital health technology.A total of 34 states have legalized medical cannabis, and 10 states have legalized cannabis for adult recreational use.Against the backdrop of these changes, there have been increasing reports on health-related motives for cannabis use and adverse events from its use.Examples of motivations for cannabis use include treatment for clinical health conditions—a use supported by the US Food and Drug Administration.Additionally, studies have shown that motivations for cannabis use have been based on the perceived benefits of its use, including its use as a sleep aid and an aid for coping with stress or anxiety.The low perception of harm from cannabis use when compared to that from other psychoactive drugs has also been documented as a motivation for its use.However, cannabis use has been associated with adverse events, such as impaired short-term memory, impaired motor coordination, paranoia, and psychosis; increased levels of depression and anxiety over time; symptoms of chronic bronchitis; addiction; and altered brain development.Although the literature on the motivations for and effects of cannabis use is developing,plant benches medical experts recommend establishing a centralized federal agency for reporting, researching, and regulating cannabis products as a timely public health surveillance strategy.
The FDA’s MedWatch program conducts the surveillance of serious adverse effects from cannabis use, but doubts have been raised over how effective this surveillance system is in identifying reports of cannabis safety signals.The surveillance of health-related behaviors includes the use of digital data sources.Publicly accessible data from individuals who post to social media platforms, such as Twitter, have been used to capture and describe the context of cannabis use.However, health-related conversations surrounding its use have been understudied, and there has been a lack of cannabis-related studies that use social media data.The mining of social media data permits the collection and analysis of qualitative information, is noninvasive , minimizes recall error, and allows for data to be captured in real time.Twitter has been a growing tool in health research, and it has been used for various purposes, including content analysis, surveillance, recruitment, intervention, and network analysis .Twitter in particular reflects the views, attitudes, and behaviors of millions of people and is used by 22% of US adults , with 42% of individuals using the platform daily.This study attempted to determine the extent to which a medical dictionary—the Unified Medical Language System Consumer Health Vocabulary —could accurately identify cannabis-related motivations for use and health consequences of cannabis use based on Twitter posts in 2020.The findings may be useful to state- and federal-level regulatory agencies as they grapple with identifying cannabis safety signals in a comprehensive and scalable way.Twitter posts containing the cannabis-related terms blunt, bong, budder, cannabis, cbd, ganja, hash, hemp, indica, kush, marijuana, marihuana, reefer, sativa, thc, and weed were obtained from January 1 to August 31, 2020.These terms were informed by prior research that focused on comprehensively collecting cannabis-related posts on Twitter.
To treat each observation as independent, retweets were removed, leaving a total of 16,703,751 unique posts that contained these terms during this time.We used the following two dictionaries: the Unified Medical Language System CHV, which comprises 13,479 medical terms that are used by consumers and health care professionals to describe health conditions, and a list of 177 colloquial terms that were generated collaboratively by 2 trained coders and were related to the CHV terms when pertinent.The CHV has been used in prior research for the surveillance of health discussions about e-cigarette use or vaping on Twitter.CHV terms are available at no cost to applicants who have a license, which is assigned upon the completion of a web-based application process.A sample of 609,227 cannabis-related posts referenced at least 1 of these terms.We then identified and removed posts from social bots to reliably describe the public’s health-related motivations for cannabis use or the perceived health effects of its use.In order to distinguish non-bots from social bots, we relied upon Botometer.This program analyzes the features of a Twitter account and provides a score based on how likely the account is to be a social bot.The Botometer threshold was set to ≥4 on an English rating scale of 1 to 5.All Twitter accounts were screened after data were collected.During this process, 127,140 accounts responsible for the tweets in our data were deleted from Twitter.As a result, these accounts could not be processed through Botometer, and their posts were removed from our data.Of the 261,134 available accounts, 15,245 were marked as bots and removed.The final analytic sample contained 353,353 posts from 245,889 unique nonbot accounts.Each post from the final sample was classified into at least 1 of 17 a priori health-related categories by using a rule-based classifier.Each category was defined by the terms in the two dictionaries.The 17 health-related categories included 14 categories from prior research and 3 additional categories that were unique to this study, accounting for the potential psychoactive effects of cannabis use , topical cannabis products , and the intersection of cannabis and food additives.A post could belong to multiple categories.The 17 categories, example keywords, and prevalence of keywords from each category can be found in Table 1.
A stratified random sample of posts was extracted from the corpus based on the original classifications of the posts by using the rule-based classifier.A coding procedure was used to determine if each post pertained to a health-related motivation for cannabis use, a perceived adverse health effect of cannabis use, or neither.Two trained coders double coded each post independently, with κ values ranging from 0.790 to 0.856.Discrepancies were resolved by the two coders and the first author.This analysis served as a validation procedure for the rule-based classifier.This study determined the extent to which a commonly used medical dictionary of health effects could accurately identify cannabis-related motivations for use and health consequences of cannabis use based on Twitter posts in 2020.This is the first study to date to use a high-quality medical dictionary of consumer-oriented health terms to capture the public’s expressions of health concepts and thereby identify health conversations about cannabis use.The findings suggest that a medical dictionary alone is limited in its ability to identify health-related conversations in a cannabis context.The posts discussed the respiratory system, stress to the immune system, and gastrointestinal problems.The posts also discussed mental health, pain, injuries, and poisonings, among other potential health effects.Previous research has identified motivations for cannabis use, including using cannabis to treat chronic conditions, using it as a sleep aid, and using it to help improve mental health.Previous research has also identified adverse reactions associated with cannabis consumption based on search engine queries and found that such queries revealed many of the known adverse effects of cannabis use, such as coughing and psychotic symptoms, as well as plausible reactions that could be attributed to cannabis use, such as pyrexia.A prior content analysis of 5000 tweets about “dabbing” from a 30-day period in 2015 showed that the most common physiologic effects from this form of cannabis use were the loss of consciousness and respiratory effects, such as coughing.Our study compliments prior research by using a professionally used term dictionary.It also indicates that the public made varied health-related references in their conversations about cannabis on Twitter.However, if the mining of social media data is to be proven helpful in the surveillance of cannabis products and their adverse health effects, the use of a standardized medical term dictionary alone will not suffice in the identification of cannabis safety signals.Future research will need to develop a codebook and term dictionary that incorporate a priori categories and data-driven inductive approaches that capture nuanced cannabis and health-related conversations on Twitter.Soil microbes play a major role in plant ecology by providing a variety of benefits such as nitrogen fixation,rolling bench production of growth stimulants, improved water retention, and suppression of root diseases.These vital microbial processes occur predominantly within the rhizosphere and rhizoplane, and are heavily influenced by fungal saprotrophs and plant-mutualists such as endomycorrhizal and ectomycorrhizal fungi.Despite the economic and medicinal importance of Cannabis spp., little is known about its soil-based microbial associations.Microbial composition in soil depends on complex interactions between the soil type, root zone location, and plant species.Rhizosphere microbiota are highly dynamic, and the composition of bacterial communities can fluctuate in response to seasonal and diel temperature changes, water content, pH, CO2 concentration, and O2 levels.
Although evidence has been found for significant effects of plant cultivar on rhizosphere communities and endomycorrhizal fungal communities, some work suggests that these effects are minimal compared to edaphic factors or plant growth stage.Rhizosphere bacteria not only colonize the rhizosphere and/or the rhizoplane soil, but can also colonize plant tissues.Bacteria that have colonized root tissue—more specifically known as the endorhiza—have been reported to support plant growth and suppress plant diseases by providing phytohormones, low molecular weight compounds or enzymes involved in regulating growth and metabolism.In addition, endorhiza bacteria assist their host plants in tolerating the phytotoxic effects of environmental toxicants.Endorhiza communities tend to be more plant-specific, and are often shaped by the compounds or proteins produced by their host.Both endophytes and epiphytes may also play a role in localized ‘flavor’ or terroir for crop plants, as has been shown recently for wines.A growing body of work has united the colonization of both the rhizosphere and plant tissues under the two-tier selection model, where soil type defines the composition of rhizosphere and root inhabiting bacterial communities.Under this model, edaphic factors determine the structure of the local soil microbiota, which become the source for the first bacterial community shift into the nutrient rich environment of the rhizosphere.Following this first shift, migration from the rhizosphere into the plant tissues is based on plant genotype-dependent selection of the endorhiza environment.Along with the prediction that rhizosphere andendorhiza microbiota should be soil-derived, the two-tier selection model predicts several broad changes in phylum-level taxon abundance associated with the shifting microbiota, such as dramatic reduction in Acidobacteria within the endosphere.This study aims to characterize bacterial diversity in the root and soil systems of five strains of Cannabis in order to explore how soil microbiota and plant strain affect the endorhiza microbial community of this commercially important crop.We hypothesize that different cultivars maintain significantly different microbial communities, and that these differences diminish from endorhiza to rhizosphere to bulk soil.he data for this paper were collected in two experiments: First, an experiment to identify variation in the microbial communities, and second, an experiment designed to understand the nature and strength of cultivar-specificity.The first experiment was composed of bulk soil, rhizosphere, and endorhiza samples taken from nine plants of the three different Cannabis spp.tested strains—Burmese, BooKoo Kush, and Sour Diesel.Soil physicochemical data was taken for all bulk soil samples in the first experiment, however there was minimal edaphic variation.The second experiment sought to understand the effect of strain with more significant edaphic variation, and was accomplished using two different strains—White Widow and Maui Wowie—and two different soil types.Four plants of the two strains were grown in the same soil, and then two plants of White Widow were grown in a completely distinct soil type.Triplicate samples were taken from each plant for both the rhizosphere and endorhiza, as well as for each of the two soil types.Endorhiza, rhizosphere soil, and bulk soil samples for the first experiment were taken from 9 organically-grown Cannabis plants of three different strains in Vista, California, in November, 2011, for a total of 27 samples.Therefore, the triplicate DNA extracts were acquired for endorhiza, rhizosphere and bulk-soil for each of the 3 Cannabis spp.strains, resulting in a single endorhiza, rhizosphere, and bulk soil sample for each plant.The plants were grown in locally composted soil.Eight weeks following the harvesting of the Cannabis flowering bud and foliage from each plant, a 50 g bulk soil sample was taken 10 cm from the stem of each of the nine plants at a depth of 20 cm, as well as a larger sample of soil for testing edaphic factors.The bulk soil sample was immediately capped and transported to a 4uC refrigerator.In addition, endorhiza samples were taken from the root ball of each of the six plants.