Cannabis use and the development of psychosis or schizophrenia, analysis of current legislation and regional mapping: A systematic review

Introducción: la relación entre el consumo de cannabis y la aparición de síntomas psicóticos está suficientemente fundamentada. La legalización y/o despenalización del cannabis podría aumentar la frecuencia y la cantidad de su consumo entre sus usuarios. Objetivo: esta revisión sistemática pretende resumir los hallazgos de los estudios que investigaron el riesgo, la precocidad y la intensidad de la psicosis en los consumidores de cannabis, teniendo en cuenta el estado de legalización y/o despenalización del consumo de cannabis en diferentes países. Metodología: fueron incluidos artículos publicados hasta mayo de 2018, en lengua inglesa, portuguesa y española, todos extraídos de las bases de datos PubMed y SciELO, respetando los criterios de inclusión y exclusión. Resultados: se incluyeron 19 estudios de 18 países. La relación entre el consumo de cannabis y el inicio de síntomas psicóticos estuvo suficientemente fundamentada. Sin embargo, no hubo datos que respaldaran un aumento en el riesgo, la precocidad o la intensidad de la psicosis en los consumidores de cannabis de países con niveles más altos de legalización/despenalización del uso de cannabis hasta la fecha del presente estudio. Conclusión: el consumo de cannabis está asociado con el desarrollo de psicosis. Hasta el momento, no hay datos que indiquen un aumento en la precocidad, el riesgo o la intensidad de la psicosis en usuarios de cannabis, debido a la legalización o despenalización del uso de cannabis. Sin embargo, la ausencia de datos hasta la fecha no excluye estas posibilidades, ya que ninguno de los estudios analizados en esta revisión evaluó específicamente los efectos de las políticas de legalización/despenalización en esos resultados. Por ello, los estudios prospectivos centrados en los efectos de las políticas de legalización o despenalización deben llevarse a cabo en países como Canadá, España, los Estados Unidos de América (algunos estados), los Países Bajos y Uruguay.Introduction: The relationship between cannabis use and the onset of psychotic symptoms is sufficiently substantiated. The legalization and/or decriminalization of cannabis could increase the frequency and quantity of cannabis use among its users. Objective: This systematic review aims to summarize the findings of studies that investigated the risk, precocity and intensity of psychosis in cannabis users, taking into account the status of legalization and/or decriminalization of cannabis use in different countries. Methodology: Articles published up to May 2018 were included, in English, Portuguese and Spanish, all extracted from the PubMed and SciELO databases, respecting the inclusion and exclusion criteria. Results: 19 studies from 18 countries were included. The relationship of cannabis use and the onset of psychotic symptoms was sufficiently substantiated. However, there was no data that supported an increase in the risk, precocity or intensity of psychosis in cannabis users from countries with higher levels of legalization/decriminalization of cannabis use to the date of the present study. Conclusion: The use of cannabis is associated with the development of psychosis. So far, there is no data pointing to an increase in the precocity, risk or intensity of psychosis in cannabis users, due to the legalization or decriminalization of the use of cannabis. However, the absence of data to date does not exclude these possibilities, since none of the studies analyzed in this review specifically assessed the effects of legalization/decriminalization policies on those outcomes. Therefore, prospective studies focused on the effects of legalization or decriminalization policies should be conducted in countries such as Canada, Spain, the United States of America (some states), the Netherlands, and Uruguay

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost