18 research outputs found
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
Depression rather than liver impairment reduces quality of life in patients with hepatitis C
Peripheral blood fibrocytes: new information to explain the dynamics of Leishmania infection
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
Depression rather than liver impairment reduces quality of life in patients with hepatitis C
Objective: Patients with chronic hepatitis C (CHC) have a poorer quality of life than those with other chronic liver diseases. However, some of the factors that determine health-related quality of life (HRQOL) in these patients, such as the degree of liver fibrosis, are still controversial. Therefore, the aim of the present study was to investigate the impact of CHC on HRQOL by conducting clinical, psychiatric, and sociodemographic evaluations. Methods: One hundred and twenty-four consecutive patients attending a referral center for hepatitis were evaluated using the Mini-International Neuropsychiatry Interview, the Hamilton Depression Rating Scale, the Hospital Anxiety and Depression Scale, and the Medical Outcomes Study 36-Item Short-Form Health Survey. Multiple linear regression analyses were used to quantify independent associations between HRQOL and the clinical, psychiatric, and sociodemographic variables of interest. Results: Reduced HRQOL was independently associated with major depressive disorder (MDD) and with elevated levels of alanine aminotransferase, but was not associated with hepatic cirrhosis. Conclusions: MDD rather than the grade of liver fibrosis was strongly associated with HRQOL impairment in patients with CHC. These findings highlight that, in patients with CHC, the psychological effects of the disease deserve more attention and the implementation of integrated medical, psychiatric, and psychological care may be helpful
Leishmanicidal, antiproteolytic and antioxidant evaluation of natural biflavonoids isolated from Garcinia brasiliensis and their semisynthetic derivatives
The natural biflavonoids morelloflavone-4'-O-beta-D-glycosyl (1), (+/-)-fukugiside (2) and morelloflavone (3) were isolated from the ethyl acetate extract (EAEE) of dried and powdered fruit epicarps of Garcinia brasiliensis and derivatives of morelloflavone were semi-synthesised. Morelloflavone-7,4',7 '',3',4'-penta-O-acetyl (4), morelloflavone-7,4',7 '',3',4'-penta-O-methyl (5) and morelloflavone-7,4',7 '',3',4'-penta-O-butanoyl (6) were prepared by acylation and alkylation reactions. All compounds showed leishmanicidal, antiproteolytic and antioxidant activities in addition to exhibiting low cytotoxicity. Compounds 4, 5 and 6 were highly active against Leishmania amazonensis promastigote forms compared to natural compounds of low lipophilicity, exhibiting IC50 values of 0.0147, 0.0403 and 0.0189 mu M, respectively. Compounds 4, 5 and 6 were also highly active against amastigote forms with IC50 values of 0.042, 0.0603 and 0.059 mu M, respectively. in addition, highly inhibitory activity against r-CPB2.8 and r-CPB3 isoforms was observed with these compounds. Notably, compounds 3 and 4 were the most active against r-CPB2.8 with IC50 values of 0.4200 and 0.6744 mu M, respectively. Compounds 5 and 6 also showed significant inhibitory activities with very similar IC50 values of 1.2663 and 1.0122, respectively. However, compounds 1 and 2 exhibited the lowest inhibitory activity against r-CPB2.8, almost 6 and 11-fold less active than the natural compound 3. in L. (L.) amazonensis lysates, and compounds 3 and 6 were the most active inhibitors of amastigote lysates at pH 5, which is near the pH environment of the parasitophorous vacuole within the macrophage. Finally, compounds 1, 2 and 3 exhibited effective antioxidant activity compared to the reference antioxidant ascorbic acid. However, the activity was lower than that of butylhydroxytoluene (BHT), which may be related to the reduced number of phenolic hydroxyl groups that were replaced by more lipophilic substituents in derivatives 4-6. Compounds 4-6 exhibited reduced antioxidant activity as evidenced by their higher EC50 values. These results provide new perspectives on drug development for the treatment of leishmaniasis and inhibitory enzyme activity on Leishmania (L.) mexicana cysteine proteases and other isoforms. (C) 2012 Published by Elsevier Masson SAS.Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Univ Fed Alfenas, Lab Phytochem & Med Chem, Inst Exact Sci, Belo Horizonte, MG, BrazilMogi das Crazes Univ, Interdisciplinary Ctr Biochem Invest, Mogi Das Cruzes, SP, BrazilUniv Fed Alfenas, Mol Biol Lab, Dept Biol Sci, Belo Horizonte, MG, BrazilUniv Vale Rio Verde, Inst Hlth Sci, Tres Coracoes, MG, BrazilUniversidade Federal de São Paulo, Dept Biophys, São Paulo, SP, BrazilUniversidade Federal de São Paulo, Dept Biophys, São Paulo, SP, BrazilWeb of Scienc