Analysis of mutations in APP1 protein associated with development and protection against Alzheimer's disease - an In silico approach / Análise de mutações na proteína APP1 associadas ao desenvolvimento e proteção contra a doença de Alzheimer - uma abordagem In silico

Introduction: Alzheimer's disease (AD) is the dementia with the highest number of cases worldwide, causing great social and economic impact. The amyloid cascade is the most accepted hypothesis to explain the beginning of AD. According to it, neurodegeneration is caused by the accumulation of Aβ peptides and the formation of amyloid plaques in the brain. In familial cases of Alzheimer's, mutations in the APP1 protein lead to increased production of Aβ plaques. Objectives: Analyze in silico the structural and functional impact of missense mutations in APP1 and construct a complete model for the protein. Methods: We generated and validated a theoretical structure of APP1 protein using structural modeling and quality assessment algorithms. We further analyzed the effects of AD-related mutations on APP1 protein and also the neuroprotective mutation A673T by performing functional predictions, evolutionary conservation analysis, and molecular dynamics (MD). Results: The predictive analysis indicated that most mutations occur in conserved regions of APP1 and also present an elevated rate of deleterious predictions, pointing to their harmful effects. The computational modeling generated an unprecedented, accurate and complete model of human APP1, whose quality was corroborated by validation algorithms and structural alignment. The MD simulations of codon 673 variants pointed to flexibility and essential dynamics alterations at the AICD and Aβ domains, which could have strong and non-intuitive consequences on APP1 interactions, including those involved in β-secretase cleavage and, consequently, aβ peptide formation. Conclusions: Flexibility and essential dynamics alterations upon codon 673 variants may have functional implications for APP1, influencing the generation of Aβ peptide, the main responsible for APP1 toxicity in AD

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

HPMCP-Coated Microcapsules Containing the Ctx(Ile21)-Ha Antimicrobial Peptide Reduce the Mortality Rate Caused by Resistant Salmonella Enteritidis in Laying Hens

The constant use of synthetic antibiotics as growth promoters can cause bacterial resistance in chicks. Consequently, the use of these drugs has been restricted in different countries. In recent years, antimicrobial peptides have gained relevance due to their minimal capacity for bacterial resistance and does not generate toxic residues that harm the environment and human health. In this study, a Ctx(Ile21)-Ha antimicrobial peptide was employed, due to its previously reported great antimicrobial potential, to evaluate its application effects in laying chicks challenged with Salmonella Enteritidis, resistant to nalidixic acid and spectinomycin. For this, Ctx(Ile21)-Ha was synthesized, microencapsulated and coated with hypromellose phthalate (HPMCP) to be released in the intestine. Two different doses (20 and 40 mg of Ctx(Ile21)-Ha per kg of isoproteic and isoenergetic poultry feed) were included in the chick’s food and administered for 28 days. Antimicrobial activity, effect and response as treatment were evaluated. Statistical results were analyzed in detail and indicate that the formulated Ctx(Ile21)-Ha peptide had a positive and significant effect in relation to the reduction of chick mortality in the first days of life. However, there was moderate evidence (p = 0.07), not considered statistically significant, in the differences in laying chick weight between the control and microencapsulation treatment groups as a function of time. Therefore, the microencapsulated Ctx(Ile21)-Ha antimicrobial peptide can be an interesting and promising option in the substitution of conventional antibiotics

Inventário nacional de emissões e remoções antrópicas de gases de efeito estufa.

O Brasil apresenta periodicamente seu inventário nacional de emissões antrópicas por fontes e remoções antrópicas por sumidouros de todos os gases de efeito estufa (GEE) não controlados pelo Protocolo de Montreal (doravante referenciado como Inventário), na medida que permitem as suas capacidades, conforme seu compromisso de atualização dessas estimativas e relato junto à Convenção-Quadro das Nações Unidas sobre Mudança do Clima (UNFCCC, no acrônimo em inglês). Além do Inventário pertinente às Comunicações Nacionais, o Brasil disponibiliza relato atualizado de suas emissões e remoções nos Relatórios de Atualização Bienal (BUR, no acrônimo em inglês). Os GEE estimados no presente Inventário foram o dióxido de carbono (CO2), o metano (CH4), o óxido nitroso (N2O), os hidrofluorcarbonos (HFC), os perfluorcarbonos (PFCs) e o hexafluoreto de enxofre (SF6). Outros gases, como monóxido de carbono (CO), óxidos de nitrogênio (NOx) e outros compostos orgânicos voláteis não metano (NMVOC), são GEE indireto, cujas emissões antrópicas foram incluídas sempre que possível, conforme encorajado pela UNFCCC. Este Inventário apresenta as emissões de 1990 a 2016, com atualização do Terceiro Inventário, que apresentou as emissões de 1990 a 2010 (BRASIL, 2016). A metodologia utilizada no presente Inventário reflete os avanços técnico-científicos consolidados nas "Diretrizes de 2006 do Painel Intergovernamental sobre Mudança do Clima (IPCC, no acrônimo em inglês) para Inventários Nacionais de Emissões de Gases de Efeito Estufa" (2006 IPCC Guidelines for National Greenhouse Gas Inventories - IPCC 2006) (IPCC, 2006). Em virtude das diversas fontes de emissões antrópicas de GEE, o Inventário está organizado segundo as atividades contempladas nos setores: Energia; Processos Industriais e Uso de Produtos (IPPU, no acrônimo em inglês); Agropecuária; Uso da Terra, Mudança do Uso da Terra e Florestas (LULUCF, no acrônimo em inglês); e Resíduos (conforme Figura 2.1). Já as remoções de GEE são contabilizadas apenas no setor LULUCF, como resultado do aumento do estoque de carbono, por meio, por exemplo, do crescimento de vegetação

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background: Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods: The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results: A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P < 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion: Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)