18 research outputs found

    BDNF signaling in the VTA links the drug-dependent state to drug withdrawal aversions

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    Drug administration to avoid unpleasant drug withdrawal symptoms has been hypothesized to be a crucial factor that leads to compulsive drug-taking behavior. However, the neural relationship between the aversive motivational state produced by drug withdrawal and the development of the drug-dependent state still remains elusive. It has been observed that chronic exposure to drugs of abuse increases brain-derived neurotrophic factor (BDNF) levels in ventral tegmental area (VTA) neurons. In particular, BDNF expression is dramatically increased during drug withdrawal, which would suggest a direct connection between the aversive state of withdrawal and BDNF-induced neuronal plasticity. Using lentivirus-mediated gene transfer to locally knock down the expression of the BDNF receptor tropomyosin-receptor-kinase type B in rats and mice, we observed that chronic opiate administration activates BDNF-related neuronal plasticity in the VTA that is necessary for both the establishment of an opiate-dependent state and aversive withdrawal motivation. Our findings highlight the importance of a bivalent, plastic mechanism that drives the negative reinforcement underlying addiction

    Risk-based prioritization of pharmaceuticals in the natural environment in Iraq

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    Numerous studies have demonstrated the occurrence of pharmaceuticals in the natural environment, raising concerns about their impact on non-target organisms or human health. One region where little is known about the exposure and effects of pharmaceuticals in the environment is Iraq. Due to the high number of pharmaceuticals used by the public health sector in Iraq (hospitals and care centres) and distributed over the counter, there is a need for a systematic approach for identifying substances that should be monitored in the environment in Iraq and assessed in terms of environmental risk. In this study, a risk-based prioritization approach was applied to 99 of the most dispensed pharmaceuticals in three Iraqi cities, Baghdad, Mosul and Basrah. Initially, information on the amounts of pharmaceuticals used in Iraq was obtained. The top used medicines were found to be paracetamol, amoxicillin and metformin with total annual consumption exceeding 1000 tonnes per year. Predicted environmental concentrations (PECs) and predicted no-effect concentrations (PNECs), derived from ecotoxicological end-points and effects related to the therapeutic mode of action, were then used to rank the pharmaceuticals in terms of risks to different environmental compartments. Active pharmaceutical ingredients used as antibiotics, antidepressants and analgesics were identified as the highest priority in surface water, sediment and the terrestrial environment. Antibiotics were also prioritized according to their susceptibility to kill or inhibit the growth of bacteria or to accelerate the evolution and dissemination of antibiotic-resistant genes in water. Future work will focus on understanding the occurrence, fate and effects of some of highly prioritized substances in the environment

    COVID‐19 and the UK water sector: Exploring organisational responses through a resilience framework

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    This is the final version. Available on open access from Wiley via the DOI in this record. The unprecedented scale and impact of the COVID-19 pandemic has required organisations to adapt all facets of their operations. The impact on the UK water sector extends beyond engineering and treatment processes, with social, economic and environmental consequences. Semi-structured interviews were conducted with executives from ten UK water companies to investigate the organisational response to the pandemic, and how their response impacted operational delivery. The Safe & SuRe framework was used to structure interview questions and analysis. Emergent themes of: changes to customer behaviour; changes to operational practices; and industry collaboration, were mapped onto the framework and a ripple effect map developed. Lessons learnt highlight: a failure to adequately prepare for the scale of the threat; the success of sector level collaboration; and a need to embrace new ways of working.Engineering and Physical Sciences Research Council (EPSRC)Royal Academy of Engineerin

    Wastewater surveillance monitoring of SARS-CoV-2 variants of concern and dynamics of transmission and community burden of COVID-19

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    ABSTRACTWastewater-based surveillance is a valuable approach for monitoring COVID-19 at community level. Monitoring SARS-CoV-2 variants of concern (VOC) in wastewater has become increasingly relevant when clinical testing capacity and case-based surveillance are limited. In this study, we ascertained the turnover of six VOC in Alberta wastewater from May 2020 to May 2022. Wastewater samples from nine wastewater treatment plants across Alberta were analysed using VOC-specific RT-qPCR assays. The performance of the RT-qPCR assays in identifying VOC in wastewater was evaluated against next generation sequencing. The relative abundance of each VOC in wastewater was compared to positivity rate in COVID-19 testing. VOC-specific RT-qPCR assays performed comparatively well against next generation sequencing; concordance rates ranged from 89% to 98% for detection of Alpha, Beta, Gamma, Omicron BA.1 and Omicron BA.2, with a slightly lower rate of 85% for Delta (p < 0.01). Elevated relative abundance of Alpha, Delta, Omicron BA.1 and BA.2 were each associated with increased COVID-19 positivity rate. Alpha, Delta and Omicron BA.2 reached 90% relative abundance in wastewater within 80, 111 and 62 days after their initial detection, respectively. Omicron BA.1 increased more rapidly, reaching a 90% relative abundance in wastewater after 35 days. Our results from VOC surveillance in wastewater correspond with clinical observations that Omicron is the VOC with highest disease burden over the shortest period in Alberta to date. The findings suggest that changes in relative abundance of a VOC in wastewater can be used as a supplementary indicator to track and perhaps predict COVID-19 burden in a population
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