ESTUDO DO NÚMERO DE CASOS DE DIFILOBOTRIASE NO BRASIL

A procura por comidas exóticas, principalmente o hábito de comer peixes crus ou mal cozidos, acompanhado pelo crescimento de inúmeros restaurantes que oferecem em seus cardápios pratos como sush, sashimi e salmão consumidos vêm crescendo nos últimos anos, aumentando, portanto o número de casos de difilobotríase. A difilobotríase é uma parasitose intestinal adquirida por ingestão de peixes crus ou mal cozido infectado por larvas plerocercóides de um cestódio de gênero Diphyllobothrium. Diversas espécies de Diphyllobothrium podem parasitar os seres humanos, mas na América do Sul estes casos estão restritos a duas espécies: Diphyllobothrium pacificum e o Diphyllobothrium latum, sendo esta última a mais prevalente. Objetivou-se realizar uma revisão na literatura do número de casos notificados de difilobotríase ocorridos nos estados do Brasil, na última década, pelo consumo de salmão cru. Este trabalho trata-se de uma revisão bibliográfica junto aos bancos de dados da LILACS, MEDLINE e da vigilância sanitária com análise reflexiva do tema Difilobotríase

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

Primeiro encontro de Lutzomyia longipalpis (Lutz & Neiva, 1912) na área urbana de Uberlândia, MG, concomitante com o relato de primeiro caso autóctone de leishmaniose visceral humana First finding of Lutzomyia longipalpis (Lutz & Neiva, 1912) in the urban area of Uberlândia, MG, concomitant with the first reported autochthonous case of human visceral leishmaniasis

Relata-se a primeira ocorrência do vetor da leishmaniose visceral, Lutzomyia longipalpis, na área urbana de Uberlândia, estado de Minas Gerais e o primeiro caso de leishmaniose visceral humana autóctone no município, notificado ao Centro de Controle de Zoonoses, por meio da Vigilância Epidemiológica da Secretaria Municipal de Saúde. Discute-se a importância deste encontro na transmissão da doença nessa área.<br>The first occurrence of the vector for visceral leishmaniasis, Lutzomyia longipalpis, in the urban area of Uberlândia, State of Minas Gerais, and the first autochthonous case of human visceral leishmaniasis recorded in the same locality are reported. These were notified to the Zoonosis Control Center, through the Epidemiological Surveillance sector of the Municipal Health Department. The importance of these findings regarding transmission of the disease in this area is discussed

High-intensity interval training followed by postexercise cold-water immersion does not alter angiogenic circulating cells, but increases circulating endothelial cells

High-intensity interval training (HIIT) induces vascular adaptations that might be attenuated by post-exercise cold-water immersion (CWI). Circulating angiogenic cells (CAC) participate in the vascular adaptations and circulating endothelial cells (CEC) indicate endothelial damage. CAC and CEC are involved in vascular adaptation. Therefore, the aim of the study was to investigate post-exercise CWI during HIIT on CAC and CEC and on muscle angiogenesis-related molecules. Seventeen male subjects performed 13 HIIT sessions followed by 15 min of passive recovery (n=9) or CWI at 10° C (n=8). HIIT consisted of cycling (8-12 bouts, 90-110% peak power). The first and the thirteenth sessions were similar (8 bouts at 90% of peak power). Venous blood was drawn before exercise (baseline) and after the recovery strategy (post-recovery) in the first (pre-training) and in the thirteenth (post-training) sessions. For CAC and CEC identification lymphocyte surface markers (CD133, CD34 and VEGFR2) were used. Vastus lateralis muscle biopsies were performed pre- and post-training for protein (p-eNOSser1177) and gene (VEGF and HIF-1) expression analysis related to angiogenesis. CAC was not affected by HIIT or post-exercise CWI. Post-exercise CWI increased acute and baseline CEC number. Angiogenic protein and genes were not differently modulated by post-CWI. HIIT followed by either recovery strategy does not alter CAC number. Post-exercise CWI increases a marker of endothelial damage both acutely and chronically, suggesting that this post-exercise recovery strategy might cause endothelial damage. ●HIIT followed by CWI did not alter CAC; ●HIIT followed by CWI increased CEC; ●Post-exercise CWI might cause endothelial damage.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Predictors of Arrhythmic Events Detected by Implantable Loop Recorders in Renal Transplant Candidates

AbstractBackground:The recording of arrhythmic events (AE) in renal transplant candidates (RTCs) undergoing dialysis is limited by conventional electrocardiography. However, continuous cardiac rhythm monitoring seems to be more appropriate due to automatic detection of arrhythmia, but this method has not been used.Objective:We aimed to investigate the incidence and predictors of AE in RTCs using an implantable loop recorder (ILR).Methods:A prospective observational study conducted from June 2009 to January 2011 included 100 consecutive ambulatory RTCs who underwent ILR and were followed-up for at least 1 year. Multivariate logistic regression was applied to define predictors of AE.Results:During a mean follow-up of 424 ± 127 days, AE could be detected in 98% of patients, and 92% had more than one type of arrhythmia, with most considered potentially not serious. Sustained atrial tachycardia and atrial fibrillation occurred in 7% and 13% of patients, respectively, and bradyarrhythmia and non-sustained or sustained ventricular tachycardia (VT) occurred in 25% and 57%, respectively. There were 18 deaths, of which 7 were sudden cardiac events: 3 bradyarrhythmias, 1 ventricular fibrillation, 1 myocardial infarction, and 2 undetermined. The presence of a long QTc (odds ratio [OR] = 7.28; 95% confidence interval [CI], 2.01–26.35; p = 0.002), and the duration of the PR interval (OR = 1.05; 95% CI, 1.02–1.08; p < 0.001) were independently associated with bradyarrhythmias. Left ventricular dilatation (LVD) was independently associated with non-sustained VT (OR = 2.83; 95% CI, 1.01–7.96; p = 0.041).Conclusions:In medium-term follow-up of RTCs, ILR helped detect a high incidence of AE, most of which did not have clinical relevance. The PR interval and presence of long QTc were predictive of bradyarrhythmias, whereas LVD was predictive of non-sustained VT