Evidências sobre o uso de leite materno no tratamento dermatológico da pele do recém-nascido: Evidence on the use of breast milk in the dermatological treatment of newborn skin

O presente estudo tem como objetivo analisar as evidências sobre o uso de leite materno no tratamento dermatológico da pele do recém-nascido. A pesquisa foi desenvolvida com base em uma Revisão Sistemática da Literatura (RSL). A pesquisa foi realizada na Biblioteca Virtual do Ministério da Saúde (BVS) que indexa artigos de diferentes bases de dados como Scielo, Lilacs e MedLine e na PubMed.  Como critérios de inclusão foi considerado ser disponível em formato completo e publicado nos últimos dez anos (2012-2022). Foram excluídos estudos que não respondessem o tema de pesquisa ou que estivessem duplicados nas bases de dados. O uso do leite materno como tratamento dermatológico de pele é potencial, porém, os estudos ainda são escassos e inconclusivos, fazendo-se importante que estudos sejam realizados para que se possa sanar dúvidas sobre o uso do leite materno, considerando ser um tratamento natural e de baixo custo

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

Impact of LbSapSal Vaccine in Canine Immunological and Parasitological Features before and after Leishmania chagasi-Challenge

Submitted by Nuzia Santos ([email protected]) on 2017-03-02T17:14:04Z No. of bitstreams: 1 ve_Resende_Lucilene_Impact of LbSapSal_CPqRR_2016.pdf: 7982893 bytes, checksum: b3d67b0ad26288c3a2b36cb1f1a24ab5 (MD5)Approved for entry into archive by Nuzia Santos ([email protected]) on 2017-03-02T17:21:02Z (GMT) No. of bitstreams: 1 ve_Resende_Lucilene_Impact of LbSapSal_CPqRR_2016.pdf: 7982893 bytes, checksum: b3d67b0ad26288c3a2b36cb1f1a24ab5 (MD5)Made available in DSpace on 2017-03-02T17:21:03Z (GMT). No. of bitstreams: 1 ve_Resende_Lucilene_Impact of LbSapSal_CPqRR_2016.pdf: 7982893 bytes, checksum: b3d67b0ad26288c3a2b36cb1f1a24ab5 (MD5) Previous issue date: 2016Fundação de Amparo a Pesquisa do Estado de Minas Gerais. Belo Horizonte, MG, BrazilUniversidade Federal de Minas Gerais. Departamento de Morfologia. Laboratório de Biologia das Interações Celulares. Belo Horizonte, MG, Brazil/Universidade Federal de Ouro Preto. Instituto de Ciências Exatas e Biológicas. Núcleo de Pesquisas em Ciências Biológicas/NUPEB Laboratório de Imunopatologia. Ouro Preto, MG, BrazilUniversidade Federal de Ouro Preto. Instituto de Ciências Exatas e Biológicas. Núcleo de Pesquisas em Ciências Biológicas/NUPEB Laboratório de Imunopatologia. Ouro Preto, MG, BrazilUniversidade Federal de Ouro Preto. Instituto de Ciências Exatas e Biológicas. Núcleo de Pesquisas em Ciências Biológicas/NUPEB Laboratório de Imunopatologia. Ouro Preto, MG, BrazilUniversidade Federal de Ouro Preto. Instituto de Ciências Exatas e Biológicas. Núcleo de Pesquisas em Ciências Biológicas/NUPEB Laboratório de Imunopatologia. Ouro Preto, MG, BrazilUniversidade Federal de Minas Gerais. Departamento de Morfologia. Laboratório de Biologia das Interações Celulares. Belo Horizonte, MG, BrazilUniversidade Federal de Minas Gerais. Departamento de Morfologia. Laboratório de Biologia das Interações Celulares. Belo Horizonte, MG, BrazilFundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Laboratório de Biomarcadores de Diagnóstico e Monitoração. Belo Horizonte, MG, BrazilFundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Laboratório de Imunologia Celular e Molecular. Belo Horizonte, MG, BrazilFundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Laboratório de Biomarcadores de Diagnóstico e Monitoração. Belo Horizonte, MG, BrazilFundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Laboratório de Biomarcadores de Diagnóstico e Monitoração. Belo Horizonte, MG, BrazilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Parasitologia. Laboratório de Imunologia e Genômica de Parasitos. Belo Horizonte, MG, BrazilUniversidade Federal de Minas Gerais. Instituto de Ciências Biológicas. Departamento de Parasitologia. Laboratório de Fisiologia de Insetos Hematófagos. Belo Horizonte, MG, BrazilUniversidade Federal de Ouro Preto. Instituto de Ciências Exatas e Biológicas. Núcleo de Pesquisas em Ciências Biológicas/NUPEB Laboratório de Imunopatologia. Ouro Preto, MG, Brazil/Fundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Laboratório de Imunologia Celular e Molecular. Belo Horizonte, MG, BrazilUniversidade Federal de Minas Gerais. Departamento de Morfologia. Laboratório de Biologia das Interações Celulares. Belo Horizonte, MG, Brazil/Fundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Laboratório de Biomarcadores de Diagnóstico e Monitoração. Belo Horizonte, MG, BrazilDogs represent the most important domestic reservoir of L. chagasi (syn. L. infantum). A vaccine against canine visceral leishmaniasis (CVL) would be an important tool for decreasing the anxiety related to possible L. chagasi infection and for controlling human visceral leishmaniasis (VL). Because the sand fly salivary proteins are potent immunogens obligatorily co-deposited during transmission of Leishmania parasites, their inclusion in an anti-Leishmania vaccine has been investigated in past decades. We investigated the immunogenicity of the "LbSapSal" vaccine (L. braziliensis antigens, saponin as adjuvant, and Lutzomyia longipalpis salivary gland extract) in dogs at baseline (T0), during the post-vaccination protocol (T3rd) and after early (T90) and late (T885) times following L. chagasi-challenge. Our major data indicated that immunization with "LbSapSal" is able to induce biomarkers characterized by enhanced amounts of type I (tumor necrosis factor [TNF]-α, interleukin [IL]-12, interferon [IFN]-γ) cytokines and reduction in type II cytokines (IL-4 and TGF-β), even after experimental challenge. The establishment of a prominent pro-inflammatory immune response after "LbSapSal" immunization supported the increased levels of nitric oxide production, favoring a reduction in spleen parasitism (78.9%) and indicating long-lasting protection against L. chagasi infection. In conclusion, these results confirmed the hypothesis that the "LbSapSal" vaccination is a potential tool to control the Leishmania chagasi infection

Impact of distinct immunization protocols on pro-inflammatory cytokine production.

<p>The levels of pro-inflammatory cytokine levels were measured in supernatants from PBMCs cultures maintained upon vaccine-soluble antigen (VSA) or soluble <i>Leishmania chagasi</i> antigen (SLcA) stimuli <i>in vitro</i>. Data were analyzed at baseline before vaccination (T₀), 15 days after third immunization dose (T_3rd) as well as early (90 days—T₉₀) and late (885 days—T₈₈₅) after experimental <i>L</i>. <i>chagasi</i>-challenge. The groups are represented as follows: C (“Control”; white bars); “Sal” (<i>Lutzomyia longipalpis</i> salivary glands; <i>light gray bars</i>); “LbSal” (antigen of <i>L</i>. <i>braziliensis</i> plus <i>Lutzomyia longipalpis</i> salivary glands; <i>dark gray bars</i>); and “LbSapSal” (<i>L</i>. <i>braziliensis</i> antigen plus saponin and <i>Lutzomyia longipalpis</i> salivary glands; black bars). The x-axis displays the different experimental groups (“Control”, “Sal”, “LbSal”, and “LbSapSal”) according to the <i>in vitro</i> stimuli (control culture [CC], VSA or SLcA). The y-axis represents the cytokine levels (pg/mL) for TNF-α (A), IL-12 (B) and IFN-γ (C). Data are presented as mean values ± standard deviations. The connecting lines represent significant difference (<i>P <0</i>.<i>05</i>) between the CC, VSA or SLcA-stimulated cultures. The symbols C, Sal and LbSal indicate significant differences in comparison to the “Control”, “Sal” and “LbSal” groups, respectively.</p

Biomarker networks triggered by distinct immunization protocols.

<p>Network correlation analysis were assembled for pro-inflammatory and regulatory cytokines measured in supernatants from PBMCs cultures maintained upon vaccine-soluble antigen (VSA) or soluble <i>Leishmania chagasi</i> antigen (SLcA) stimuli <i>in vitro</i>. Data were analyzed at baseline before vaccination (T₀), 15 days after third immunization dose (T_3rd) as well as early (90 days—T₉₀) and late (885 days—T₈₈₅) after experimental <i>L</i>. <i>chagasi</i>-challenge. The groups are represented as follows: C (“Control”; white nodes); “Sal” (<i>Lutzomyia longipalpis</i> salivary glands; light gray nodes); “LbSal” (<i>L</i>. <i>braziliensis</i> antigen plus <i>Lutzomyia longipalpis</i> salivary glands; dark gray nodes) and “LbSapSal” (<i>L</i>. <i>braziliensis</i> antigen plus saponin and <i>Lutzomyia longipalpis</i> salivary glands; black nodes). Each connecting line represents a significant correlation between a pair of biomarkers. Dashed linesrepresent negative correlations. Solid lines represent positive correlations, and the degree of significance is represented by the line thickness [moderate correlation (continuous thin lines) for 0.370.67 or strong correlation (continuous thick lines) for r>0.68]. Spearman r indexes are used to classify the connecting edges as negative, moderate, or strong positive correlations, as shown.</p

Parasitological analysis in spleen samples late after (T₈₈₅) <i>L</i>. <i>chagasi</i>-challenge.

<p>Parasitological analysis in spleen samples late after (T₈₈₅) <i>L</i>. <i>chagasi</i>-challenge.</p