Lectins, Interconnecting Proteins with Biotechnological/Pharmacological and Therapeutic Applications

Lectins are proteins extensively used in biomedical applications with property to recognize carbohydrates through carbohydrate-binding sites, which identify glycans attached to cell surfaces, glycoconjugates, or free sugars, detecting abnormal cells and biomarkers related to diseases. These lectin abilities promoted interesting results in experimental treatments of immunological diseases, wounds, and cancer. Lectins obtained from virus, microorganisms, algae, animals, and plants were reported as modulators and tool markers in vivo and in vitro; these molecules also play a role in the induction of mitosis and immune responses, contributing for resolution of infections and inflammations. Lectins revealed healing effect through induction of reepithelialization and cicatrization of wounds. Some lectins have been efficient agents against virus, fungi, bacteria, and helminths at low concentrations. Lectin-mediated bioadhesion has been an interesting characteristic for development of drug delivery systems. Lectin histochemistry and lectin-based biosensors are useful to detect transformed tissues and biomarkers related to disease occurrence; antitumor lectins reported are promising for cancer therapy. Here, we address lectins from distinct sources with some biological effect and biotechnological potential in the diagnosis and therapeutic of diseases, highlighting many advances in this growing field

I Diretriz brasileira de cardio-oncologia pediátrica da Sociedade Brasileira de Cardiologia

Sociedade Brasileira de Oncologia PediátricaUniversidade Federal de São Paulo (UNIFESP) Instituto de Oncologia Pediátrica GRAACCUniversidade Federal de São Paulo (UNIFESP)Universidade de São Paulo Faculdade de Medicina Instituto do Coração do Hospital das ClínicasUniversidade Federal do Rio Grande do Sul Hospital de Clínicas de Porto AlegreInstituto Materno-Infantil de PernambucoHospital de Base de BrasíliaUniversidade de Pernambuco Hospital Universitário Oswaldo CruzHospital A.C. CamargoHospital do CoraçãoSociedade Brasileira de Cardiologia Departamento de Cardiopatias Congênitas e Cardiologia PediátricaInstituto Nacional de CâncerHospital Pequeno PríncipeSanta Casa de Misericórdia de São PauloInstituto do Câncer do Estado de São PauloUniversidade Federal de São Paulo (UNIFESP) Departamento de PatologiaHospital Infantil Joana de GusmãoUNIFESP, Instituto de Oncologia Pediátrica GRAACCUNIFESP, Depto. de PatologiaSciEL

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

Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics

Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics

Author correction: One sixth of Amazonian tree diversity is dependent on river floodplains

In the version of the article initially published, the affiliation of Edgardo Manuel Latrubesse was incorrect and has now been amended to Environmental Sciences Graduate Program-CIAMB, Federal University of Goiás, Goiânia, Brazil in the HTML and PDF versions of the article

One sixth of Amazonian tree diversity is dependent on river floodplains

Amazonia’s floodplain system is the largest and most biodiverse on Earth. Although forests are crucial to the ecological integrity of floodplains, our understanding of their species composition and how this may differ from surrounding forest types is still far too limited, particularly as changing inundation regimes begin to reshape floodplain tree communities and the critical ecosystem functions they underpin. Here we address this gap by taking a spatially explicit look at Amazonia-wide patterns of tree-species turnover and ecological specialization of the region’s floodplain forests. We show that the majority of Amazonian tree species can inhabit floodplains, and about a sixth of Amazonian tree diversity is ecologically specialized on floodplains. The degree of specialization in floodplain communities is driven by regional flood patterns, with the most compositionally differentiated floodplain forests located centrally within the fluvial network and contingent on the most extraordinary flood magnitudes regionally. Our results provide a spatially explicit view of ecological specialization of floodplain forest communities and expose the need for whole-basin hydrological integrity to protect the Amazon’s tree diversity and its function