Leukocytes and Albumin in Canine Leishmaniasis

Background: Canine Leishmaniasis (CanL) is a multisystemic and chronic inflammatory disease characterized by nonspecific clinical manifestations. In CanL, inflammatory cells and chemical mediators released in response to the parasite play a role in disease development and progression. Alterations on hematological parameters have been documented in CanL. These changes can also be assessed in relation to systemic inflammation caused by this disease. The circulating leukocyte counting, such as neutrophils, as well as the albumin level, are considered direct indicators of an inflammatory host environment. Several studies point to the use of biomarkers on the assistance in diagnosis and prognosis of several canine pathologies. The present study investigated the Neutrophils to Lymphocyte Ratio (NLR), Albumin to Globulin Ratio (AGR), and Neutrophils to Albumin Ratio (NAR) on systemic inflammatory response induced by Canine Leishmaniasis (CanL).Materials, Methods & Results: For this purpose, adult dogs with confirmed diagnosis to CanL were divided into symptomatic (SD, n = 33) and asymptomatic (AD, n = 20) dogs for L. infantum and control dogs (CD, n = 20). Routine hematological and biochemical parameters were determined in blood samples using a veterinary automatic hematology and biochemical analyzers. Asymptomatic dogs (AD) had a higher number of white blood cells and neutrophils (16.48 ± 4.93; 13.41 ± 3.60, respectively) in relation to symptomatic dogs (SD) (13.54 ± 5.13; 10.42± 3.69, respectively) (P = 0.015 and P < 0.0001, respectively). Neutrophils to Lymphocyte Ratio (NLR) was higher in dogs with leishmaniasis (9.45 ± 3.76) than in healthy dogs (3.39 ± 1.19) (P < 0.0001). Serum total proteins (STP) and globulins increased in CanL, while albumin and AGR decreased in CanL, when compared to CD and references values to canine species. Neutrophils to Albumin Ratio (NAR) was higher in AD and SD (5.02 ± 1.14; 4.79 ± 1.07, respectively) when compared to CD (2.36 ± 0.55) (P < 0.0001). Discussion: As reported in scientific researches, dogs with Leishmaniasis present alterations in circulating cell counts. Based on these data, we decided to expand this information using the NLR as a parameter in an attempt to better clarify the changes in these cells in CanL. We observed that NLR was increased on CanL in relation to healthy dogs, which could be a consequence of relative neutrophilia rather than lymphopenia. Neutrophils to Lymphocyte Ratio (NLR) is a biomarker that conveys information about inflammatory conditions. An elevated NLR can reflect an upregulated innate immune response, since neutrophils are effector cells of innate immunity and are involved in several acute and chronic inflammatory processes. Albumin is an acute phase protein that is considered an immune-inflammatory biomarker, which can be found reduced systemically in progressive inflammatory response. Serum total proteins (STP) and globulins were increased in CanL. These data are already well documented in CanL, which serum globulins are mainly associated with the increase of acute phase proteins, cytokines, and increase of specific antibodies to Leishmaniainfantum. Our results showed neutrophilia with hypoalbuminemia in CanL. So, in an attempt to assess the relationship of these two available markers, we used NAR calculation in order to evaluate the changes induced by CanL. In this study NAR was higher in CanL when compared to control dogs. Thus, our data indicate that NLR and NAR could be used as biomarkers in veterinary medical clinics in order to assess inflammatory profile in CanL, mainly in asymptomatic dogs. These parameters obtained from routine blood tests might be useful as cost-effective, easily accessible and helpful markers in order to distinguish the inflammatory response intensity in CanL

Qualidade de ovos caipiras e comerciais submetidos a diferentes períodos e temperaturas de armazenamento

Objetivou-se avaliar a qualidade interna de ovos provenientes de poedeiras comerciais e de galinhas caipiras submetidos a diferentes períodos e temperaturas de armazenamento. Para o experimento, foram utilizados 280 ovos. Os ovos foram distribuídos aleatoriamente nos diferentes tratamentos adotando-se o delineamento experimental inteiramente casualizado, em esquema fatorial 2 x 7, duas temperaturas, sete períodos de armazenamento, totalizando 14 tratamentos com 10 repetições. Os tratamentos consistiram em duas condições de armazenamento: sob refrigeração (6 ± 1,0ºC) e em temperatura ambiente (26,6 ± 1,0ºC). Os ovos foram analisados por um período de 30 dias, com avaliações realizadas em diferentes períodos de armazenamento (0, 5, 10, 15, 20, 25 e 30 dias). Para cada condição de armazenamento, foram separados 140 ovos, sendo 70 ovos comerciais e 70 ovos caipiras. Ocorreu aumento linear na perda de peso dos ovos, peso da gema, pH do albúmen, pH da gema, comprimento e largura do albúmen e da gema dos ovos comerciais e caipiras, à medida que se aumentava o período de armazenamento. Verificou-se redução linear no peso, altura e índice do albúmen e no índice da gema dos ovos comerciais e caipiras conforme se aumentava o período de armazenamento, com respostas mais acentuadas para ovos acondicionados em temperatura ambiente (P<0,05). A porcentagem de albúmen foi reduzida linearmente apenas para ovos comerciais (P<0,05). Ovos comerciais e caipiras armazenados em temperatura ambiente apresentaram flutuabilidade a partir do 20° dia de armazenamento. A qualidade dos ovos comerciais e caipiras é influenciada pela temperatura e períodos de armazenamento. Ovos mantidos sob temperatura ambiente reduzem a sua qualidade a partir dos 15 dias de armazenamento, sendo o armazenamento sob refrigeração durante o período de 30 dias, o recomendado para preservar a vida de prateleira do ovo para consumo. Palavras-chave: Aves; Ovos comerciais; Produtos de origem animal; Tempo de prateleir

Questão agrária e políticas públicas em Minas Gerais : conflitos sociais e alternativas populares

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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