Frequency of viral etiology in symptomatic adult upper respiratory tract infections

AbstractAimsTo determine the frequency of viral pathogens causing upper respiratory tract infections in non-hospitalized, symptomatic adults in the city of Rio de Janeiro.MethodsRespiratory samples (nasal/throat swabs) were collected between August 2010 and November 2012 and real time PCR was used to detect different viral pathogens.ResultsViruses were detected in 32.1% (43/134) of samples from 101 patients. Specifically, 9% (12/134) were positive for HBoV, 8.2% (11/134) were positive for HAdV, 5.2% (7/134) were positive for HRV, and 1.5% (2/134) were positive for FLUBV or HMPV, as single infections. HRSV-A, HPIV-3, and HCoV-HKU1 were detected in one (0.75%) sample each. Co-infections were detected in 4.8% (6/134) of the samples. Peaks of viral infections were observed in March, April, May, August, and October. However, positive samples were detected all year round. Only 23.3% (10/43) of the positive samples were collected from patients with febrile illness.ConclusionResults presented in this report suggest that respiratory viral infections are largely under diagnosed in immunocompetent adults. Although the majority of young adult infections are not life-threatening they may impose a significant burden, especially in developing countries since these individuals represent a large fraction of the working force

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

Glicosilação influenciando a atividade de fusão de estruturas HA e HEF de vírus influenza

Since the most ancient times, influenza viruses have caused lethal respiratory diseases. Their replication process occurs at the epithelial cells of the respiratory tract, where the surface-anchored glycoproteins hemagglutinin (HA) and hemagglutinin-esterase-fusion protein (HEF) of influenza A and C viruses respectively are responsible for the fusion process. The adsorption, sialidase and esterase activities developed by these same structures are inborn, while the fusion process is dependent of previous glycosylation and protein cleavage. Indeed, the glycosylation is strictly related to antigenicity and stability of fusion proteins. This work was designed to analyse the influence of the de-glycosylation for the development of fusion activity, using influenza A and C viruses as study models. The de-glycosylation provoked significative reduction in the fusogenic activity, inducing a reduction equal to 51.0%, 87.5%, 95.5% and 79.3% for A/Memphis/102/72, A/FM/1/47, C/Taylor/1233/47 and C/Paris/1/67 respectively. However, this activity was improved at certain pH values, 10.1% (pH 5.8), 59.4% (pH 5.8), 32.5% (pH 5.8) and 80.7% (pH 5.4) for the 95.7% at pH 5.2 for A/Memphis/102/72, A/FM/1/47, C/Taylor/1233/47 and C/Paris/1/67 of influenza viruses respectively. The fusogenic activity of certain samples was also improved at some pH values. These results permit to conclude that the level of glycosylation is closely related to the protein stability and the de-glycosylation process causes a significative influence on the fusion biological activity.Desde os tempos mais remotos, os vírus influenza têm sido o agente causal de doenças respiratórias letais. O seu processo de replicação ocorre nas células epiteliais do trato respiratório, causando uma síndrome respiratória aguda, na qual as glicoproteínas hemaglutinina (HA) e hemaglutinina-esterase-fusão (HEF), ancoradas na superfície dos vírus influenza A e C, respectivamente, são responsáveis pelos processos de fusão durante o ciclo de replicação viral. Ao contrário dos processos de adsorção, sialidase e esterase, atividades inatas desempenhadas por estas mesmas estruturas, a fusão depende de uma glicosilação e clivagem protéica prévias, para que o vírus se torne infeccioso. A glicosilação das proteínas de superfície está associada principalmente com a antigenicidade e com a estabilidade das proteínas de fusão. O presente trabalho analisa a influência da de-glicosilação sobre a atividade de fusão, usando vírus influenza A e C como modelos de estudo. A de-glicosilação parcial provocou significativa redução da atividade fusogênica, induzindo uma redução de 51,0%, 87,5%, 95,5% e 79,3% em relação às amostras de vírus influenza A/Memphis/102/72, A/FM/1/47, C/Taylor/1233/47 e C/Paris/1/67, respectivamente, acarretando também eventuais aumentos desta atividade biológica para certas amostras: 10,15% (pH 5,8), 59,47% (pH 5,8), 32,55% (pH 5,8) e 80,7% (pH 5,4) com relação às amostras de vírus influenza A/Memphis/102/72, A/FM/1/47, C/Taylor/1233/47 e A/Paris/1/67, respectivamente. Os resultados permitem concluir que o nível de glicosilação mostra-se estreitamente relacionado com a estabilidade das proteínas, causando o processo de-glicosilação uma influência significativa sobre a atividade fusogênica viral

Variantes de receptor de virus influenza H3N2: caracterização de sua atividade sialidásica sobre diferentes substratos

Influenza virus sialidase develops an essential activity on cellular glycoproteins, then permitting the dissemination of the virus infections by preventing virus-virus self aggregation and virus-cell rebinding. Two purified variant samples of influenza A/Memphis/102/72 (H3N2) viruses, which are recognized for their receptor-binding activity to a-2,6 or a- 2,3-sialyllactose structures, were analysed for their sialidase activity on different natural and artificial substrates. The M1/ 5 sample exhibited higher sialidase activity on fetuin (O.D.=0.226), MPN (O.D.=0.110) and human erythrocytes (10,240 HAU/ml), while the activity of the M1/5HS8 sample on these substrates was expressed by O.D.=0.129, O.D.=0.065 and 2,560 HAU/ml when using fetuin, MPN and human erythrocytes as substrates, respectively. However, the M1/5HS8 sample showed more significative sialidase activity on mucin when compared to the M1/5 sample: the enzyme activity of first sample was responsible for liberation of 3.5 nmol of free sialic acids while the last one produced 16.5 nmol of free sialic acids.A atividade sialidásica do vírus influenza tem uma atividade essencial sobre glicoproteínas celulares, permitindo a disseminação de infecções virais por prevenir a auto-agregação entre partículas virais e a re-ligação vírus-célula. Duas amostras variantes purificadas de vírus influenza A/Memphis/102/72 (H3N2), reconhecidas por sua atividade de ligação a receptores apresentando estruturas como a2,6 ou a2,3-sialilactose, foram analisadas por sua atividade sialidásica sobre diferentes substratos naturais e artificiais. A amostra M1/5 mostrou maior atividade sialidásica sobre fetuína (D.O.=0,226), MPN (D.O.=0,110) e eritrócitos humanos (10.240 unidades hemaglutinantes/ml), enquanto a atividade da amostra M1/5HS8 foi expressa por D.O.=0,129, D.O.=0,065 e 2.560 unidades hemaglutinantes/ml quando usados, respectivamente, fetuína, MPN e eritrócitos humanos como substratos. Contudo a amostra M1/5HS8 exibiu uma atividade sialidásica mais significativa sobre mucina quando comparada à amostra M1/5; a atividade enzimática da primeira amostra foi responsável pela liberação de 3,5 nmol de ácidos siálicos livres, enquanto a última produziu 16,5 nmol de ácidos siálicos livres