QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni.

Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents

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

Aerobic exercise capacity in normal adolescents and those with type 1 diabetes mellitus

Objective: To compare the aerobic exercise capacity between normal adolescents and those with type I diabetes mellitus (T1DM).Methods: An experimental group with 72 individuals diagnosed with T1DM aged 9-20, time from diagnosis 4.9 +/- 3.6 yr, without clinical cardiopulmonary disease or anemia and a control group (C) with 46 healthy individuals aged 10-18, matched by age, weight, height, body mass index, and lean and fat mass (kg), underwent an incremental aerobic exercising test on a motorized treadmill, where gas exchange variables - peak pulmonary ventilation (VE), peak oxygen consumption (VO2), and carbon dioxide production (CO2) - as well as their heart rate (HR) and time to exhaustion were recorded.Results: Body mass composition had no significant difference between experimental and control groups, and male and female subjects had similar exercising performances. the mean of hemoglobin A1c in the control group was 5.2 +/- 0.9% and in the diabetic group 8.1 +/- 2.2%; p = 0.000. the patients with T1DM showed lower levels of aerobic capacity than the control group. Their respective values for each variable were as follows: (i) maximal VO2 (T1DM: 41.57 +/- 7.68 vs. C: 51.12 +/- 9.94 mL/kg/min; p < 0.001) and (ii) maximal VE (T1DM: 76.39 +/- 19.93 vs. C: 96.90 +/- 25.72 mL/kg/min; p < 0.001). Patients with T1DM also had an earlier time to exhaustion (T1DM: 8.75 +/- 1.60 vs. 10.82 +/- 1.44 min).Conclusions: Adolescent patients with T1DM showed a reduced aerobic exercising capacity when compared to healthy peers matched to anthropometric conditions. This potential condition should be taken into consideration by the time of evaluation of the aerobic performance of these patients with glycemic control level.Universidade Federal de São Paulo, Ctr Diabet, São Paulo, BrazilUniversidade Federal de São Paulo, CEMAFE, Phys Act & Sports Med Ctr, São Paulo, BrazilUniversidade Federal de São Paulo, Ctr Diabet, São Paulo, BrazilUniversidade Federal de São Paulo, CEMAFE, Phys Act & Sports Med Ctr, São Paulo, BrazilWeb of Scienc