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Background:  We previously found that activation of the glial cell line‐derived neurotrophic factor (GDNF) pathway in the ventral tegmental area (VTA) reduces ethanol‐drinking behaviors. In this study, we set out to assess the contribution of endogenous GDNF or its receptor GFRα1 to the regulation of ethanol‐related behaviors.Methods:  GDNF and GFRα1 heterozygote mice (HET) and their wild‐type littermate controls (WT) were used for the studies. Ethanol‐induced hyperlocomotion, sensitization, and conditioned place preference (CPP), as well as ethanol consumption before and after a period of abstinence were evaluated. Blood ethanol concentration (BEC) was also measured.Results:  We observed no differences between the GDNF HET and WT mice in the level of locomotor activity or in sensitization to ethanol‐induced hyperlocomotion after systemic injection of a nonhypnotic dose of ethanol and in BEC. However, GDNF and GFRα1 mice exhibited increased place preference to ethanol as compared with their WT littermates. The levels of voluntary ethanol or quinine consumption were similar in the GDNF HET and WT mice, however, a small but significant increase in saccharin intake was observed in the GDNF HET mice. No changes were detected in voluntary ethanol, saccharin or quinine consumption of GFRα1 HET mice as compared with their WT littermates. Interestingly, however, both the GDNF and GFRα1 HET mice consumed much larger quantities of ethanol after a period of abstinence from ethanol as compared with their WT littermates. Furthermore, the increase in ethanol consumption after abstinence was found to be specific for ethanol as similar levels of saccharin intake were measured in the GDNF and GFRα1 HET and WT mice after abstinence.Conclusions:  Our results suggest that endogenous GDNF negatively regulates the rewarding effect of ethanol and ethanol‐drinking behaviors after a period of abstinence.

Abstract Background Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda. Methods This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence. Results Overall, the median (range) monthly temperature was 30 °C (26–47), rainfall 133.0 mm (3.0–247), NDVI 0.66 (0.24–0.80) and MI was 790 per 1000 person-years (73–3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42–2.83) and the increased cumulative IRR of malaria at month lags 1–4, with the highest cumulative IRR of 8.16 (95% CI: 3.41–20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01–1.52) and the increased cumulative IRR of malaria at month lags 1–4, with the highest cumulative IRR of 1.99(95% CI: 1.22–2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2–4, with the highest cumulative IRR of 1.57(95% CI: 1.09–2.25) at lag-month 4. Conclusions In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

Background and PurposeVarenicline, a neuronal nicotinic acetylcholine receptor (nAChR) modulator, decreases ethanol consumption in rodents and humans. The proposed mechanism of action for varenicline to reduce ethanol consumption has been through modulation of dopamine (DA) release in the nucleus accumbens (NAc) via α4*‐containing nAChRs in the ventral tegmental area (VTA). However, presynaptic nAChRs on dopaminergic terminals in the NAc have been shown to directly modulate dopaminergic signalling independently of neuronal activity from the VTA. In this study, we determined whether nAChRs in the NAc play a role in varenicline's effects on ethanol consumption.Experimental ApproachRats were trained to consume ethanol using the intermittent‐access two‐bottle choice protocol for 10 weeks. Ethanol intake was measured after varenicline or vehicle was microinfused into the NAc (core, shell or core‐shell border) or the VTA (anterior or posterior). The effect of varenicline treatment on DA release in the NAc was measured using both in vivo microdialysis and in vitro fast‐scan cyclic voltammetry (FSCV).Key ResultsMicroinfusion of varenicline into the NAc core and core‐shell border, but not into the NAc shell or VTA, reduced ethanol intake following long‐term ethanol consumption. During microdialysis, a significant enhancement in accumbal DA release occurred following systemic administration of varenicline and FSCV showed that varenicline also altered the evoked release of DA in the NAc.Conclusion and ImplicationsFollowing long‐term ethanol consumption, varenicline in the NAc reduces ethanol intake, suggesting that presynaptic nAChRs in the NAc are important for mediating varenicline's effects on ethanol consumption.

AbstractBackgroundThis study characterized the prevalence, drinking patterns, and sociodemographic characteristics of U.S. adult subpopulations with distinct drinking trajectories during the COVID‐19 pandemic's first 42 weeks.MethodsAdult respondents (n = 8130) in a nationally representative prospective longitudinal study completed 21 biweekly web surveys (March 2020 to January 2021). Past‐week alcohol drinking frequency (drinking days [range: 0 to 7]) and intensity (binge drinking on usual past‐week drinking day [yes/no]) were assessed at each timepoint. Growth mixture models identified multiple subpopulations with homogenous drinking trajectories based on mean drinking days or binge drinking proportional probabilities across time.ResultsFour drinking frequency trajectories were identified: Minimal/stable (72.8% [95% CI = 71.8 to 73.8]) with <1 mean past‐week drinking days throughout; Moderate/late decreasing (6.7% [95% CI = 6.2 to 7.3) with 3.13 mean March drinking days and reductions during summer, reaching 2.12 days by January 2021; Moderate/early increasing (12.9% [95% CI = 12.2 to 13.6) with 2.13 mean March drinking days that increased in April and then plateaued, ending with 3.20 mean days in January 2021; and Near daily/early increasing (7.6% [95% CI = 7.0 to 8.2]) with 5.58 mean March drinking days that continued increasing without returning to baseline. Four drinking intensity trajectories were identified: Minimal/stable (85.8% [95% CI = 85.0% to 86.5%]) with <0.01 binge drinking probabilities throughout; Low‐to‐moderate/fluctuating (7.4% [95% CI = 6.8% to 8%]) with varying binge probabilities across timepoints (range:0.12 to 0.26); Moderate/mid increasing (4.2% [95% CI = 3.7% to 4.6%]) with 0.39 April binge drinking probability rising to 0.65 during August–September without returning to baseline; High/early increasing trajectory (2.7% [95% CI = 2.3% to 3%]) with 0.84 binge drinking probability rising to 0.96 by June without returning to baseline. Males, Whites, middle‐aged/older adults, college degree recipients, those consistently working, and those above the poverty limit were overrepresented in various increasing (vs. minimal/stable) frequency trajectories. Males, Whites, nonmarried, those without college degree, 18 to 39‐year‐olds, and middle aged were overrepresented in increasing (vs. minimal/stable) intensity trajectories.ConclusionsSeveral distinct U.S. adult sociodemographic subpopulations appear to have acquired new drinking patterns during the pandemic's first 42 weeks. Frequent alcohol use assessment in the COVID‐19 era could improve personalized medicine and population health efforts to reduce drinking.

The substrate specificity, pH activity curves, inhibition characteristics and in vitro stabilities of the human ADH isozymes characteristic of the structural loci, ADH1, ADH2 and ADH3, have been investigated using crude tissue extracts and partially purified material. Alcohol substrates: Seventeen different alcohols were tested. The products of the three loci showed differences in their relative activities with the different substrates. Thus ADH1 isozymes were most active with ethanol, allyl alcohol, sec propanol and cyclohexanol; the 'usual' ADH2 were most active with ethanol, butanol, octanol and sec butanol; the 'atypical' ADH2 isozymes were most active with ethanol and octanol, but showed relatively low activity with butanol and Ronicol; the ADH3 isozymes were relatively very active with long straight chain primary alcohols. Aldehyde substrates: Six different aldehydes were tested. No significant differences between the isozyme products of the three loci were detected except in the case of chloral hydrate. The ADH1 and 'usual' ADH2 isozymes showed activity with chloral hydrate but this was a very poor substrate for the ADH3 and 'atypical' ADH2 isozymes. pH activity profiles: With ethanol as substrate the pH optimum for the ADH1, 'usual' ADH2 and the ADH3 isozymes was around pH 11.5 and for the 'atypical' ADH2 isozymes was about pH 8.8. With acetaldehyde as substrate the pH optima for the ADH1, 'usual' ADH2, 'atypical' ADH2 and ADH3 isozymes were about pH 8.8, 6.0, 7.0-7.5 and 6.5, resp. Inhibitors: Trichloroethanol was found to be a potent inhibitor of the ADH1 isozymes; isobutyramide an inhibitor of ADH3; and pyrazole and thiourea were shown to be powerful inhibitors of the 'atypical' ADH2 isozymes. In vitro stability: The ADH1 isozymes appeared to be relatively less stable than the 'usual' ADH2 and ADH3 isozymes. The 'atypical' ADH2 isozymes were found to be relatively very labile and particularly susceptible to freezing and thawing or storage at 10° C. The ADH 1;3 and ADH 2;3 isozymes were not demonstrably different in the properties tested.

AbstractBackground and AimsRecovery management checkups (RMC) have established efficacy for linking patients to substance use disorder (SUD) treatment. This study tested whether using RMC in combination with screening, brief intervention, and referral to treatment (SBIRT), versus SBIRT alone, can improve linkage of primary care patients referred to SUD treatment.DesignA randomized controlled trial of SBIRT as usual (n = 132) versus SBIRT plus recovery management checkups for primary care (RMC‐PC) (n = 134) with follow‐up assessments at 3 months post‐baseline.SettingFour federally qualified health centers in the United States serving low‐income populations.ParticipantsPrimary care patients (n = 266, 64% male, 80% Black, mean age, 48.3 [range, 19–53]) who were referred to SUD treatment after SBIRT.InterventionsSBIRT alone (control condition) compared with SBIRT + RMC‐PC (experimental condition).MeasurementThe primary outcome was any days of SUD treatment in the past 3 months. Key secondary outcomes were days of SUD treatment overall and by level of care, days of alcohol and other drug (AOD) abstinence, and days of using specific substances, all based on self‐report.FindingsAt 3‐month follow‐up, those assigned to SBIRT + RMC‐PC (n = 134) had higher odds of receiving any SUD treatment (46% vs 20%; adjusted odds ratio = 4.50 [2.49, 8.48]) compared with SBIRT only, including higher rates of entering residential and intensive outpatient treatment. They also reported more days of treatment (14.45, vs 7.13; d = +0.26), more days abstinent (41.3 vs 31.9; d = +0.22), and fewer days of using alcohol (27.14, vs 36.31; d = −0.25) and cannabis (19.49, vs 28.6; d = −0.20).ConclusionsRecovery management checkups in combination with screening, brief intervention, and referral to treatment are an effective strategy for improving linkage of primary care patients in need to substance use disorder treatment over 3 months.

Air pollution and climate change are potential drivers for the increasing burden of allergic diseases. The molecular mechanisms by which air pollutants and climate parameters may influence allergic diseases, however, are complex and elusive. This article provides an overview of physical, chemical and biological interactions between air pollution, climate change, allergens, adjuvants and the immune system, addressing how these interactions may promote the development of allergies. We reviewed and synthesized key findings from atmospheric, climate, and biomedical research. The current state of knowledge, open questions, and future research perspectives are outlined and discussed. The Anthropocene, as the present era of globally pervasive anthropogenic influence on planet Earth and, thus, on the human environment, is characterized by a strong increase of carbon dioxide, ozone, nitrogen oxides, and combustion- or traffic-related particulate matter in the atmosphere. These environmental factors can enhance the abundance and induce chemical modifications of allergens, increase oxidative stress in the human body, and skew the immune system toward allergic reactions. In particular, air pollutants can act as adjuvants and alter the immunogenicity of allergenic proteins, while climate change affects the atmospheric abundance and human exposure to bioaerosols and aeroallergens. To fully understand and effectively mitigate the adverse effects of air pollution and climate change on allergic diseases, several challenges remain to be resolved. Among these are the identification and quantification of immunochemical reaction pathways involving allergens and adjuvants under relevant environmental and physiological conditions.