Tauroursodeoxycholic Acid Improves Motor Symptoms in a Mouse Model of Parkinson's Disease

Parkinson's disease (PD) is characterized by severe motor symptoms, and currently there is no treatment that retards disease progression or reverses damage prior to the time of clinical diagnosis. Tauroursodeoxycholic acid (TUDCA) is neuroprotective in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model of PD; however, its effect in PD motor symptoms has never been addressed. In the present work, an extensive behavior analysis was performed to better characterize the MPTP model of PD and to evaluate the effects of TUDCA in the prevention/improvement of mice phenotype. MPTP induced significant alterations in general motor performance paradigms, including increased latency in the motor swimming, adhesive removal and pole tests, as well as altered gait, foot dragging, and tremors. TUDCA administration, either before or after MPTP, significantly reduced the swimming latency, improved gait quality, and decreased foot dragging. Importantly, TUDCA was also effective in the prevention of typical parkinsonian symptoms such as spontaneous activity, ability to initiate movement and tremors. Accordingly, TUDCA prevented MPTP-induced decrease of dopaminergic fibers and ATP levels, mitochondrial dysfunction and neuroinflammation. Overall, MPTP-injected mice presented motor symptoms that are aggravated throughout time, resembling human parkinsonism, whereas PD motor symptoms were absent or mild in TUDCA-treated animals, and no aggravation was observed in any parameter. The thorough demonstration of improvement of PD symptoms together with the demonstration of the pathways triggered by TUDCA supports a subsequent clinical trial in humans and future validation of the application of this bile acid in PD.National funds, through the Foundation for Science and Technology (Portugal) (FCT), under the scope of the projects PTDC/NEU-NMC/0248/2012, UID/DTP/04138/2013 and POCI-01-0145-FEDER-007038, and post-doctoral grants SFRH/BPD72891/2010 (to A.I.R.), SFRH/BPD/95855/2013 (to M.J.N.), SFRH/BPD/98023/2013 (to A.N.C.), SFRH/BPD/91562/2012 (to A.S.F.) and UMINHO/BI/248/2016 (to S.D.S.). This work has also been developed under the scope of the project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Program (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER), and by FEDER funds, through the Competitiveness Factors Operational Program (COMPETE)info: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 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

Densidade populacional de Ralstonia solanacearum em cultivares de batata a campo Population densities of Ralstonia solanacearum on potato cultivars in the field

A ocorrência de populações latentes de Ralstonia solanacearum em plantas de batata (Solanum tuberosum) pode representar fonte de inóculo de potencial desconhecido. Além disso, também é desconhecido se a população latente da bactéria é menor em cultivares resistentes do que em cultivares suscetíveis. Com a finalidade de estudar estes aspectos, um experimento foi conduzido a campo em dois locais no Estado do Rio Grande do Sul, Brasil. O objetivo foi verificar se havia relação entre a densidade populacional e o grau de resistência de cultivares de batata. O experimento foi conduzido em Eldorado do Sul, durante o período de primavera, e em Caxias do Sul, durante o período de outono. Tubérculos das cultivares Achat, Baronesa, Elvira, Macaca, Monte Bonito e Trapeira foram inoculados com uma estirpe de R. solanacearum, biovar II, e plantados a campo. A densidade populacional da bactéria foi estimada em plantas com e sem sintomas de murcha, através de ELISA e imunofluorescência. Não houve evidência da relação entre densidade populacional da bactéria e cultivar. Além disso, a densidade populacional na cultivar Achat, caracterizada como a mais resistente entre as cultivares testadas, foi igual à registrada nas outras cultivares.<br>The occurrence of latent populations of Ralstonia solanacearum on potato (Solanum tuberosum) plants may represent inoculum source of unknown potential. Besides, it is also unknown if the latent population of the bacterium is lower in resistant than in susceptible cultivars. In order to study these points, an experiment was conducted in the field in two locations in Rio Grande do Sul State, Brazil. The objective was to verify the relationship between R. solanacearum population density and the potato cultivar resistance. The experiment was conducted in Eldorado do Sul, during the Spring, and in Caxias do Sul, during the Fall. Tubers of Achat, Baronesa, Elvira, Macaca, Monte Bonito, and Trapeira cultivars were inoculated with a R. solanacearum strain, biovar II, and planted in the field. Population density of the bacterium was estimated in the assymptomatic and wilted plants, by ELISA and imunofluorescence techniques. No evidence of relationship between population density of R. solanacearum and cultivar was found. Besides, the population density on Achat, known as the most resistant, one was similar to the other cultivars

Correlation of biological serum markers with the degree of hepatic fibrosis and necroinflammatory activity in hepatitis C and schistosomiasis patients

Liver biopsy is the gold-standard method to stage fibrosis; however, it is an invasive procedure and is potentially dangerous. The main objective of this study was to evaluate biological markers, such as cytokines IL-13, IFN-&#947;, TNF-&#945; and TGF-&#946;, platelets, bilirubins (Bil), alanine aminotransferase (ALT) and aspartate aminotransferase (AST), total proteins, &#947;-glutamil transferase (&#947;-GT) and alkaline phosphatase (AP), that could be used to predict the severity of hepatic fibrosis in schistosomiasis and hepatitis C (HC) as isolated diseases or co-infections. The following patient groups were selected: HC (n = 39), HC/hepatosplenic schistosomiasis (HSS) (n = 19), HSS (n = 22) and a control group (n = 13). ANOVA and ROC curves were used for statistical analysis. P < 0.05 was considered significant. With HC patients we showed that TNF-&#945; (p = 0.020) and AP (p = 0.005) could differentiate mild and severe fibrosis. With regard to necroinflammatory activity, AST (p = 0.002), &#947;-GT (p = 0.034) and AP (p = 0.001) were the best markers to differentiate mild and severe activity. In HC + HSS patients, total Bil (p = 0.008) was capable of differentiating between mild and severe fibrosis. In conclusion, our study was able to suggest biological markers that are non-invasive candidates to evaluate fibrosis and necroinflammatory activity in HC and HC + HSS