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Start date: 2024Subject: search.filters.subject.Tobacco

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    Potent inhibition of human monoamine oxidase A and B by phenolic compounds and polyunsaturated fatty acids in tobacco smoke
    (Elsevier B V, 2025-05-25) Hong SW; Heydari A; Watson PR; Teesdale-Spittle PH; Page R; Northcote PT; Keyzers RA; Vyssotski M; Truman P
    Smoking is a main cause of premature death and preventable disease in the world. Interestingly, animal studies indicate that inhibition of monoamine oxidase (MAO), key enzymes for the degradation of neurotransmitters, increased self-administration of nicotine. The purpose of this study was to identify and characterize the potential MAO inhibitors in tobacco smoke responsible for MAO inhibition in smokers. A bioassay-guided isolation from an extract of tobacco smoke showed that catechol, 4-methylcatechol, hydroquinone, α-linolenic acid, and linoleic acid all displayed potent human MAO inhibitory activity. Additionally, the tobacco catechols 4-ethylcatechol and 4-vinylcatechol were included to test their inhibitory potencies. Catechol, 4-methylcatechol, 4-ethylcatechol, and hydroquinone are potent and irreversible MAO inhibitors. Among the phenolic compounds tested, 4-methylcatechol and 4-ethylcatechol inhibited MAO A with IC50 values of 10.0 and 12.6 μM, respectively, reducing to 0.27 and 0.43 μM after 1 h preincubation. In addition, α-linolenic acid and linoleic acid competitively inhibited MAO A with Ki values of 10.50 and 6.95 μM, respectively. These results suggest that MAO inhibition by phenolics and polyunsaturated fatty acids in tobacco smoke may be important contributors to the MAO inhibition experienced by smokers and to the enhancement of nicotine dependence this MAO inhibition is believed to cause.
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    Exploring the substitution of cannabis for alcohol and other drugs among a large convenience sample of people who use cannabis.
    (BioMed Central Ltd, 2024-11-05) Wilkins C; Romeo J; Rychert M; Graydon-Guy T
    Background The substitution of cannabis for alcohol and other drugs has been conceptualised in a harm reduction framework as where cannabis is used to reduce the negative side-effects, addiction potential, and social stigma of other drugs. There is currently mixed evidence with recent reviews suggesting cannabis co-use patterns may vary by age and ethnicity. Yet few studies have had large enough samples to examine this demographic variation in detail. Aims To explore the co-use of cannabis with alcohol and other drugs within demographic subgroups of a large sample of people who use cannabis. Specifically: (1) whether cannabis is being substituted for other drugs, and (2), whether cannabis use leads to more, less or the same level of other drug use. Method Online convenience survey promoted via Facebook™ completed by 23,500 New Zealand respondents. Those who had used cannabis and any of eight other substances in the same six-month period were asked if their use of cannabis had any impact on their use of each other substance (“a lot more”, “little more”, “no impact/same”, “little less”, “a lot less”). Frequency and quantity used of each other drug was compared by co-use group. Generalised logistic regression models were developed to predict co-use categories. Results Significant proportions reported cannabis use led to “less” alcohol (60%), synthetic cannabinoid (60%), morphine (44%) and methamphetamine (40%) use. Those who reported using “less” had lower frequency and amount used of other drugs. Approximately seven-out-ten reported cannabis use had “no impact” on LSD, MDMA, and cocaine use. One-in-five reported using cannabis led to “more” tobacco use. Young adults (21–35-years) were more likely to report cannabis use led to “less” drinking and methamphetamine use. Adolescent co-users (16–20 years) reported mixed impacts. Māori were more likely to report cannabis use resulted in “less” alcohol, tobacco, methamphetamine, and LSD use. Students and those living in cities were less likely to report cannabis use lowering use of other substances. Conclusion Cannabis and other drug co-use patterns are moderated by life stages, lifestyles, cultural perspectives, and urbanicity. Harm reduction initiatives and policy reforms should take account of these moderating factors.