Caracterização dos pacientes com diagnóstico de Transtorno do Espectro Autista atendidos no Núcleo de Odontologia Hospitalar do Hospital Universitário Professor Polydoro Ernani de São Thiago – HU - UFSC

TCC (graduação) - Universidade Federal de Santa Catarina. Centro de Ciências da Saúde. Odontologia.A sobrecarga sensorial e o perfil neuropsicológico específico que caracteriza o paciente diagnosticado com Transtorno do Espectro Autista (TEA), pode complicar um simples atendimento odontológico e, como resultado disso, o tratamento sob anestesia geral é amplamente utilizado. O objetivo deste trabalho foi carcterizar os pacientes com diagnóstico de TEA atendidos no Núcleo de Odontologia Hospitalar do HU /UFSC para, em trabalho sequencial, aplicar um roteiro visual pedagógico como estrategia facilitadora para o atendimento odontológico. Foi realizado um levantamento de dados nos prontuários, no período de 2012 à 2017, determinando procedência, sexo, faixa etária, comorbidades, experiência odontológica prévia com ou sem o uso de sedação ou anestesia geral e diagnóstico do TEA, de acordo com a classificação do DSM-5. Do total de pacientes atendidos, 53 eram do sexo masculino (média de 16 anos) e 15 do sexo femino (média de 18,9 anos), a maioria proveniente da micro região da Grande Florianópolis. Foram identificadas vinte diferentes condições associadas ao TEA, das quais as mais prevalentes foram deficiência intelectual, síndrome de Down e epilepsia. Do total 16,2% (n=11) permitiu atendimento odontológico, sem necessidade de técnicas de sedação ou anestesia geral (Grupo 1); 77,9% (n=53) necessitou de sedação leve e/ou moderada (Grupo 2) e apenas 5,9% (n=4) necessitaram de anestesia geral (Grupo 3), com diferença estatisticamente significante entre o grupo 2 em relação aos grupos 1 e 3 (p<0,0001, Q2 de Pearson). De acordo com o DSM-5 apenas 2 (3%) foram diagnosticados com Distúrbio Global do Desenvolvimento e, 66 (97%) foram diagnosticados com Autismo. Não foi encontrado na amostra, pacientes com diagnóstico de Síndrome de Asperger e Transtorno Desintegrativo da Infância. A capacitação do Cirurgião-Dentista, bem como o condicionamento gradual do paciente, podem ser ferramentas valiosas para melhorar a qualidade dos atendimentos odontológicos, tornando a sedação desnecessária.Sensory overload and neuropsychological specific profile that characterizes the patient diagnosed with Autism Spectrum Disorder (ASD) may imply a simple dental consult and, as a result, treatment under general anesthesia is widely used. The goal of this work being to identify, through a data survey in patient charts, characteristics and number of patients diagnosed with ASD attended in the “Núcleo de Odontologia Hospitalar HU-UFSC” to, in a sequential work, apply one visual and pedagogic script to favor dental consultation in ASD patients. Data collection was performed in the medical recordos from 2012 to 2017. It was determining the origin, sex, age, comorbidities, previous dental experience and the diagnosis of ASD, according to the classification of the DSM-5. At the closure of the survey, we have identified 68 ASD patients, of whom 53 were males and 15 females, the majority coming from the micro region of Grande Florianopolis. Twenty different conditions associated with ASD were identified. The most prevelante were intellectual disability, Down syndrome and epilepsy. Of the total 16.2% (n=11) allowed dental care without the need for sedation or general anesthesia (Group 1); 77.9% (n=53) required mild and/or moderate sedation (Group 2) and only 5.9% (n=4) required general anesthesia (Group 3). It was statistically significant difference between the Group 2 with the Groups 1 and 3 (p<0.0001, Q2 Pearson´s Test). According to DSM-5 only 2 (3%) were diagnosed with Developmental Disorder and 66 (97%) were diagnosed with Autism. Patients with a disgnosis of Asperger´s Syndrome and Childhood Desintegrative Disorder were not found in the sample. To capacitate dental surgeon, as gradual condition of patients during treatment may be used as valuables tools to enhance dental consultations quality, turning sedation unnecessary

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

Epidemiological and clinical profile of infective endocarditis at a Brazilian tertiary care center: an eight-year prospective study

INTRODUCTION: Infective endocarditis (IE) is a systemic infectious disease requiring a multidisciplinary team for treatment. This study presents the epidemiological and clinical data of 73 cases of IE in Rio de Janeiro, Brazil. METHODS This observational prospective cohort study of endocarditis patients during an eight-year study period described 73 episodes of IE in 70 patients (three had IE twice). Community-associated (CAIE) and healthcare-acquired infective endocarditis (HAIE) were diagnosed according to the modified Duke criteria. The collected data included demographic, epidemiologic, and clinical characteristics, including results of blood cultures, echocardiographic findings, surgical interventions, and outcome. RESULTS: Analysis of data from the eight-year study period and 73 cases (70 patients) of IE showed a mean age of 46 years (SD=2.5 years; 1-84 years) and that 65.7% were male patients. The prevalence of CAIE and HAIE was 32.9% and 67.1%, respectively. Staphylococcus aureus (30.1%), Enterococcus spp. (19.1%), and Streptococcus spp. (15.0%) were the prevalent microorganisms. The relevant signals and symptoms were fever (97.2%; mean 38.6 + 0.05°C) and heart murmur (87.6%). Vegetations were observed in the mitral (41.1%) and aortic (27.4%) valves. The mortality rate of the cases was 47.9%. CONCLUSIONS: In multivariate analysis, chronic renal failure (relative risk [RR]= 1.60; 95% confidence interval [CI] 1.01-2.55), septic shock (RR= 2.19; 95% CI 1.499-3.22), and age over 60 years (RR= 2.28; 95% CI 1.44-3.59) were indirectly associated with in-hospital mortality. The best prognosis was related to the performance of cardiovascular surgery (hazard ratio [HR]= 0.51; 95% CI 0.26-0.99)

Mortos e mortes da covid-19: saberes, instituições, regulações, v.1, n.9

O objetivo deste Boletim é o de contribuir para a rede de informações, documentos e análises relacionando ciências forenses, direitos humanos e lutas sociais