Mutational profile of TP53 in esophageal squamous cell carcinoma associated with chagasic megaesophagus

Chaga's disease is an important communicable neglected disease that is gaining wider attention due to its increasing incidence worldwide. Achalasia due to chagasic megaesophagus (CM), a complication of this disease, is a known-yet, poorly understood-etiological factor for esophageal squamous cell carcinoma (ESCC) development. In this study, we aimed to perform the analysis of TP53 mutations in a series of Brazilian patients with ESCC that developed in the context CM (ESCC/CM), and to compare with the TP53 mutation profile of patients with benign CM and patients with nonchagasic ESCC. Additionally, we intended to correlate the TP53 mutation results with patient's clinical pathological features. By polymerase chain reaction (PCR) followed by direct sequencing of the hotspot regions of TP53 (exon 5 to 8), we found that TP53 mutations were present in 40.6% (13/32) of the ESCC/CM group, 45% (18/40) of the nonchagasic ESCC group, and in only 3% (1/33) of the benign CM group. Missense mutations were the most common in the three groups, yet, the type and mutated exon mutation varied significantly among the groups. Clinically, the groups exhibited distinct features, with both cancer groups (ESCC and ESCC/CM) been significantly associated higher consumption of alcohol and tobacco, older age, worse Karnofsky performance status, poor outcome than the patients with benign CM. No significant association was found between TP53 mutation profile and clinical-pathological features in any of the three groups. We describe first the time the analysis of TP53 mutations in ESCC that developed in the context of CM, and the observed high frequency of mutations, suggest that TP53 also plays an important role in the tumorigenic process of this unexplored etiological condition.Supported by Barretos Cancer Hospital internal research funds (PAIP). Rui M Reis is recipient of a CNPq Produtividade em Pesquisa grantinfo:eu-repo/semantics/publishedVersio

Differential Regional Immune Response in Chagas Disease

Following infection, lymphocytes expand exponentially and differentiate into effector cells to control infection and coordinate the multiple effector arms of the immune response. Soon after this expansion, the majority of antigen-specific lymphocytes die, thus keeping homeostasis, and a small pool of memory cells develops, providing long-term immunity to subsequent reinfection. The extent of infection and rate of pathogen clearance are thought to determine both the magnitude of cell expansion and the homeostatic contraction to a stable number of memory cells. This straight correlation between the kinetics of T cell response and the dynamics of lymphoid tissue cell numbers is a constant feature in acute infections yielded by pathogens that are cleared during the course of response. However, the regional dynamics of the immune response mounted against pathogens that are able to establish a persistent infection remain poorly understood. Herein we discuss the differential lymphocyte dynamics in distinct central and peripheral lymphoid organs following acute infection by Trypanosoma cruzi, the causative agent of Chagas disease. While the thymus and mesenteric lymph nodes undergo a severe atrophy with massive lymphocyte depletion, the spleen and subcutaneous lymph nodes expand due to T and B cell activation/proliferation. These events are regulated by cytokines, as well as parasite-derived moieties. In this regard, identifying the molecular mechanisms underlying regional lymphocyte dynamics secondary to T. cruzi infection may hopefully contribute to the design of novel immune intervention strategies to control pathology in this 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 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

Biodiversidade.

Neste capítulo, foi adotada uma definição ampla da biodiversidade, incorporando não apenas a complexidade do conceito, mas uma percepção de seu mecanismo, na geração de serviços ecossistêmicos. Essa visão holística da biodiversidade tem implicações importantes para políticas públicas, englobando a conservação de regiões biologicamente relevantes e seus processos ecológicos associados, além da pura proteção das espécies. O Brasil, reconhecido pela sua megabiodiversidade, possui a maior cobertura de florestas tropicais e a flora mais rica do mundo, além de abrigar uma fauna igualmente importante. Abriga diversos biomas terrestres e aquáticos onde se expressa frequentemente, de forma endêmica, o mais vasto e diversificado conjunto de espécies do planeta. Apesar dos avanços nas pesquisas para conhecimento da biodiversidade brasileira, ainda há muitas lacunas sobre a estrutura e composição dos ecossistemas e a maneira adequada de manejá-los, visando sua preservação. Um País megadiverso como o Brasil, com taxas tão altas de biodiversidade e endemismo, também tem grande responsabilidade de preservar sua rica biota e seus ecossistemas. De fato, o patrimônio natural do País inclui dois hotspots de biodiversidade (Mata Atlântica e Cerrado), seis Reservas da Biosfera, reconhecidas pela Organização das Nações Unidas para a Educação, a Ciência e a Cultura (Unesco), 12 ecorregiões prioritárias, definidas pelo Global 200 (WWF, 2001), 11 sítios Ramsar2, para a proteção de zonas úmidas, e quase 1.500 unidades de conservação. Este capítulo traz um panorama das forças motrizes, pressões, estados, impactos e respostas sobre a biodiversidade brasileira. SUMÁRIO: CAPÍTULO 4 – BIODIVERSIDADE; INTRODUÇÃO; ESPÉCIES E ECOSSISTEMAS: Alterações de comunidades e populações; Tráfico de animais silvestres; Espécies exóticas invasoras; Biodiversidade em números; Plantas, algas e fungos; Fauna aquática e terrestre; Avaliação do estado de conservação da biodiversidade brasileira; Espécies ameaçadas; Espécies da flora ameaçadas; Espécies da fauna ameaçadas; Plano de ação para a conservação e regulação do uso de espécies; Flora nativa; Fauna nativa; Espécies exóticas e invasoras; Perda de habitat, fragmentação e deterioração dos ecossistemas; Áreas e ações prioritárias para conservação, utilização sustentável e repartição dos benefícios da biodiversidade brasileira; RECURSOS GENÉTICOS: Biopirataria; Legislação nacional de proteção do patrimônio genético nacional e do conhecimento tradicional associado; Efeitos da política pública de proteção do patrimônio genético nacional, e do conhecimento tradicional associado, na conservação dos recursos genéticos; Proteção, gestão e uso sustentável dos recursos genéticos; Ratificação do Protocolo de Nagoia pelo Brasil; Conservação dos recursos genéticos vegetais e microbianos; GOVERNANÇA: Estratégia e Plano de Ação Nacionais para a Biodiversidade (Epanb); Avaliação das metas nacionais da biodiversidade; BIODIVERSIDADE E SAÚDE: Usos da biodiversidade: dos saberes tradicionais à biotecnologia; Biodiversidade e segurança alimentar; Emergência de zoonoses e biodiversidade; CONSIDERAÇÕES FINAIS; REFERÊNCIAS.ODS 2, ODS 3, ODS 9, ODS 12, ODS 14, ODS 15, ODS 17

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

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

Biological properties of cardiac mesenchymal stem cells in rats with diabetic cardiomyopathy

Cardiomyopathy is a major outcome in patients with diabetes mellitus (DM) and contributes to the high morbidity/mortality observed in this disease. AIMS: To evaluate several biological properties of cardiac mesenchymal stem cells (cMSCs) in a rat model of streptozotocin-induced DM with concomitant diabetic cardiomyopathy. MAIN METHODS: After 10weeks of DM induction, diabetic and control rats were assessed using ECG and ventricular hemodynamics monitoring. Then, the hearts were excised and processed for histology and for extracting non-cardiomyocytic cells. A pool of these cells was plated for a colony forming units-fibroblasts (CFU-F) assay in order to estimate the number of cMSCs. The remaining cells were expanded to assess their proliferation rate as well as their osteogenic and adipogenic differentiation ability. KEY FINDINGS: DM rats presented intense hyperglycemia and changes in ECG, LV hemodynamic, cardiac mass index and fibrosis, indicating presence of DCM. The CFU-F assay revealed a higher number of cardiac CFU-Fs in DM rats (10.4\ub11.1CFU-F/105 total cells versus 7.6\ub10.7CFU-F/105 total cells in control rats, p<0.05), which was associated with a significantly higher proliferative rate of cMSCs in DM rats. In contrast, cMSCs from DM rats presented a lower capacity to differentiate into both osteogenic (20.8\ub14.2% versus 10.1\ub11.0% in control rats, p<0.05) and adipogenic lineages (4.6\ub11.0% versus 1.3\ub10.5% in control rats, p<0.05). SIGNIFICANCE: The findings suggest, for the first time, that in chronic DM rats with overt DCM, cMSCs increase in number and exhibit changes in several functional properties, which could be implicated in the pathogenesis of diabetic cardiomyopathy