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

Biochemical markers as predictors of sepsis and survival in foals born from mares with ascending placentitis

Neonatal sepsis is a common consequence of placentitis and has been considering the leading cause of neonatal death during the first week post-foaling. Assuming clinical and economic importance worldwide due to treatment costs and low survival rate. Considering the roll of early diagnosis of septicemia in neonatal foals and the establishing a prognosis for these foals, it is necessary to investigate markers that presenting high sensitivity that can be easily assessed in clinical practice. Thus, we aimed to evaluate the usefulness of blood markers as early predictors of sepsis and as predictors of non-survival in neonatal foals. Thirty-five foals were used, divided into three groups: seven healthy foals served as a control group, and 28 foals born from mares with experimentally induced ascending placentitis were divided into two groups according to sepsis score in: non-septic foals (n = 19) and septic foals (n = 9), and according to outcome in: survivors (n = 19) and non-survivors (n = 9). In the first study, it was found that foals with neonatal sepsis showed marked changes in the energy metabolism, with hypoglycemia at birth and reduced GGT enzyme activity, and increased lactate and urea levels, besides presenting high concentrations of fibrinogen, AFP and SAA. Cholesterol and lactate have been shown to be good markers for detecting sepsis within the first 48 hours of life. In the second study, we evaluated the relationship among biochemical and inflammatory markers with nonsurviving, and their usefulness as death predictors. Measurement of glucose, triglyceride and GGT levels at birth have been shown to be good markers for predicting non-survival, with high sensitivity and specificity, and can be used to assist in making decisions about the chances of survival of foals in clinical routine. The results showed that septic and non-surviving foals presented several metabolic and inflammatory responses, so peripheral blood markers such as cholesterol and lactate are suitable markers for early detection of sepsis and glucose, triglycerides and GGT are useful in clinical practice for predicting non-survival in foals.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESO desenvolvimento de sepse neonatal é uma consequência comum da placentite, sendo considerada a maior causa de morte em potros com menos de sete dias de vida, assumindo importância clínica e econômica em todo mundo devido aos custos com o tratamento e a baixa taxa de sobrevivência. Considerando a importância e necessidade da detecção precoce da sepse neonatal e do estabelecimento de um prognóstico para esses potros, se faz necessária a investigação de arcadores que apresentem alta sensibilidade e facilidade para mensuração na prática clínica. Desta forma, objetivamos avaliar a utilidade de marcadores sanguíneos como preditores precoces de sepse e de não sobrevivência em potros neonatos. Foram utilizados 35 potros, divididos em três grupos: sete potros saudáveis serviram como grupo controle, e 28 potros nascidos de éguas com placentite ascendente induzida experimentalmente foram divididos em dois grupos de acordo com o escore de sepse, em potros não sépticos (n=19) e potros sépticos (n=9), e de acordo com a sobrevivência, em: sobreviventes (n=19) e não sobreviventes (n=9). Em um primeiro estudo foram comparados os marcadores sanguíneos em potros de acordo com o escore de sepse, verificando-se que potros que apresentam sepse neonatal demonstraram acentuadas alterações no metabolismo energético, com hipoglicemia ao nascimento e redução da atividade da enzima GGT, e aumento do lactato e ureia, além de apresentarem elevadas concentrações de fibrinogênio, AFP e SAA. Colesterol e lactato demonstraram serem bons marcadores para detectar a sepse nas primeiras 48 horas de vida. No segundo estudo, foi avaliada a relação entre os marcadores bioquímicos e inflamatórios com a não sobrevivência, e sua utilidade como preditores de não sobrevivência nos potros durante o primeiro mês de vida. A mensuração dos níveis de glicose, triglicerídeos e GGT ao nascimento demostraram ser bons marcadores para predizer a não sobrevivência, com elevada sensibilidade e especificidade, podendo ser utilizados para auxiliar na tomada de decisões sobre as chances de sobrevivência de potros em rotina clínica. Com os resultados obtidos, ficou demonstrado que potros sépticos e não sobreviventes apresentam metabolismo energético e lipídico alterados, e intensa resposta inflamatória e que marcadores sanguíneos periféricos, como colesterol e lactato, são marcadores adequados para detectar precocemente a sepse e, glicose, triglicerídeos e GGT são úteis na rotina clínica para prever a não sobrevivência em potros

Development of a weight-estimation model to use in pregnant criollo-type mares

<div><p>ABSTRACT: The aims of this study were: 1) to compare the tape weight and associated weight-estimation formula to evaluate weight gain in pregnant mares, and 2) to develop a mathematical model to estimate the weight of pregnant mares using body measurements. Thirty-four criollo-type mares were evaluated every two weeks during the middle and late pregnancy. The mares were weighed on a livestock scale, and we estimated body weight using tape weights and an associated body-weight estimation formula. Also, heart-girth circumference (heartgirth) and abdominal circumference were measured; the latter at the 12th intercostal space (12th ICS) and 18th rib (18th Rib), to use in a mathematical model to estimate the weight of pregnant mares. Observations were divided into three periods of pregnancy: 5th to 7 h month, 7th to 9 h month, and 9th to 11th month. Mares in late pregnancy showed an increase in actual weight and an increase in 12th ICS and 18th Rib measurements. Tape weight and body-weight estimation formula underestimated the weight of pregnant mares. However, the regression model using heart-girth circumference, 12th ICS, and 18th Rib measurements showed high correlation (r2 = 0.87, P<0.001) with actual weight. Finally, the alternative methods usually used in horses are not accurate to estimate body weight in pregnant mares. In conclusion, the regression model Y=-540.143 + (heartgirth x 3.068) + (12th ICS x 1.278) + (18th Rib x 0.944) can be used to estimate body weight in pregnant mares from the 5th to 11th months of pregnancy.</p></div

II Brazilian Society of Rheumatology consensus for lupus nephritis diagnosis and treatment

Abstract Objective To develop the second evidence-based Brazilian Society of Rheumatology consensus for diagnosis and treatment of lupus nephritis (LN). Methods Two methodologists and 20 rheumatologists from Lupus Comittee of Brazilian Society of Rheumatology participate in the development of this guideline. Fourteen PICO questions were defined and a systematic review was performed. Eligible randomized controlled trials were analyzed regarding complete renal remission, partial renal remission, serum creatinine, proteinuria, serum creatinine doubling, progression to end-stage renal disease, renal relapse, and severe adverse events (infections and mortality). The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to develop these recommendations. Recommendations required ≥82% of agreement among the voting members and were classified as strongly in favor, weakly in favor, conditional, weakly against or strongly against a particular intervention. Other aspects of LN management (diagnosis, general principles of treatment, treatment of comorbidities and refractory cases) were evaluated through literature review and expert opinion. Results All SLE patients should undergo creatinine and urinalysis tests to assess renal involvement. Kidney biopsy is considered the gold standard for diagnosing LN but, if it is not available or there is a contraindication to the procedure, therapeutic decisions should be based on clinical and laboratory parameters. Fourteen recommendations were developed. Target Renal response (TRR) was defined as improvement or maintenance of renal function (±10% at baseline of treatment) combined with a decrease in 24-h proteinuria or 24-h UPCR of 25% at 3 months, a decrease of 50% at 6 months, and proteinuria < 0.8 g/24 h at 12 months. Hydroxychloroquine should be prescribed to all SLE patients, except in cases of contraindication. Glucocorticoids should be used at the lowest dose and for the minimal necessary period. In class III or IV (±V), mycophenolate (MMF), cyclophosphamide, MMF plus tacrolimus (TAC), MMF plus belimumab or TAC can be used as induction therapy. For maintenance therapy, MMF or azathioprine (AZA) are the first choice and TAC or cyclosporin or leflunomide can be used in patients who cannot use MMF or AZA. Rituximab can be prescribed in cases of refractory disease. In cases of failure in achieving TRR, it is important to assess adherence, immunosuppressant dosage, adjuvant therapy, comorbidities, and consider biopsy/rebiopsy. Conclusion This consensus provides evidence-based data to guide LN diagnosis and treatment, supporting the development of public and supplementary health policies in Brazil