Canine Visceral Leishmaniasis in Rio Grande do Norte State, Northeastern Brazil - Spatial analysis

Background: Human visceral leishmaniasis (HVL) is a potentially fatal disease with a worldwide distribution, being endemic in 12 countries in the Americas. The main reservoir in the urban environment is the dog, whose cases precede the disease in humans. For the control of HVL, the Ministry of Health of Brazil recommends efficiency in the notification of human cases, control of sandflies, elimination of reservoirs and health education, in addition to the interruption in the transmission of the disease by the intensification of surveillance and control of priority areas based on identification by spatial analysis. The objective of the study was to investigate the spatial distribution of canine visceral leishmaniasis (CVL) in the state of Rio Grande do Norte, Brazil, determining areas of risk by identifying spatial clusters, with a view to monitoring and implementing preventive actions.Materials, Methods & Results: Secondary data from sample and/or routine serological surveys for serological diagnosis of LVC in the period from 2011 to 2018 were used. The inclusion of animals in the routine diagnosis per municipality resulted from demands of veterinarians, veterinary clinics, dog tutors, zoonoses control centers and environmental surveillance. The spatial statistical analysis was performed with SatScan software version 9.6 for the detection of spatial clusters, based on using the statistical scan method. Of the total of 231,123 dogs tested in the period, 24,642 (10.6%) were seroreactive for CVL. During the study, the municipalities with the highest number of cases were Natal and Mossoró, with 9,671 and 4,514 cases, respectively. During the years 2011 to 2018, 38 significant clusters (P < 0.05) were identified that included one or more municipalities.Discussion: The state of Rio Grande do Norte has an urban environment susceptible to the occurrence of CVL, with climate and topography that favor the proliferation of the vector and housing in precarious socio-sanitary conditions. The high number of CVL cases in Natal can be explained by the fact that the city is considered endemic for CVL, characterized as an area of intense transmission of the disease, according to the Ministry of Health. In addition, public infrastructure insome locations is deficient and living conditions are unfavorable, so that there is a need to invest in effective protection measures for vector control, as well as a focus on health education, whose HVL control measures in the municipality need to be readjusted. The high rate of cases and the constant presence of clusters in the municipality of Açu can be explained by the increasing degradation of the Caatinga biome, evidenced by the removal of firewood for use in the ceramist pole, whose activity is concentrated on a large scale in the use of raw material and energy, through the production of charcoal, for agricultural and livestock fronts, putting species of fauna and flora at risk. It is also noteworthy that this fact contributes to the destruction of wild ecotopes, resulting in the search for the vector for other sources of human and animal food, allowing an increase in the number of cases of the disease. It’s concluded that canine visceral leishmaniasis is distributed in a large part of the state of Rio Grande do Norte. The underreporting and/or deficiency in the disclosure of data by some municipalities represents a challenge in complying with the actions of the Visceral Leishmaniasis Surveillance and Control Program, and attention should be paid to the monitoring and inspection of the execution actions of municipal managers, as well as how to train professionals who are part of the service. Keywords: georeferencing, public health, zoonoses, leishmaniasis, epidemiology.

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

Canine Visceral Leishmaniasis in Rio Grande do Norte State, Northeastern Brazil - Spatial analysis

Background: Human visceral leishmaniasis (HVL) is a potentially fatal disease with a worldwide distribution, being endemic in 12 countries in the Americas. The main reservoir in the urban environment is the dog, whose cases precede the disease in humans. For the control of HVL, the Ministry of Health of Brazil recommends efficiency in the notification of human cases, control of sandflies, elimination of reservoirs and health education, in addition to the interruption in the transmission of the disease by the intensification of surveillance and control of priority areas based on identification by spatial analysis. The objective of the study was to investigate the spatial distribution of canine visceral leishmaniasis (CVL) in the state of Rio Grande do Norte, Brazil, determining areas of risk by identifying spatial clusters, with a view to monitoring and implementing preventive actions.Materials, Methods & Results: Secondary data from sample and/or routine serological surveys for serological diagnosis of LVC in the period from 2011 to 2018 were used. The inclusion of animals in the routine diagnosis per municipality resulted from demands of veterinarians, veterinary clinics, dog tutors, zoonoses control centers and environmental surveillance. The spatial statistical analysis was performed with SatScan software version 9.6 for the detection of spatial clusters, based on using the statistical scan method. Of the total of 231,123 dogs tested in the period, 24,642 (10.6%) were seroreactive for CVL. During the study, the municipalities with the highest number of cases were Natal and Mossoró, with 9,671 and 4,514 cases, respectively. During the years 2011 to 2018, 38 significant clusters (P < 0.05) were identified that included one or more municipalities.Discussion: The state of Rio Grande do Norte has an urban environment susceptible to the occurrence of CVL, with climate and topography that favor the proliferation of the vector and housing in precarious socio-sanitary conditions. The high number of CVL cases in Natal can be explained by the fact that the city is considered endemic for CVL, characterized as an area of intense transmission of the disease, according to the Ministry of Health. In addition, public infrastructure insome locations is deficient and living conditions are unfavorable, so that there is a need to invest in effective protection measures for vector control, as well as a focus on health education, whose HVL control measures in the municipality need to be readjusted. The high rate of cases and the constant presence of clusters in the municipality of Açu can be explained by the increasing degradation of the Caatinga biome, evidenced by the removal of firewood for use in the ceramist pole, whose activity is concentrated on a large scale in the use of raw material and energy, through the production of charcoal, for agricultural and livestock fronts, putting species of fauna and flora at risk. It is also noteworthy that this fact contributes to the destruction of wild ecotopes, resulting in the search for the vector for other sources of human and animal food, allowing an increase in the number of cases of the disease. It’s concluded that canine visceral leishmaniasis is distributed in a large part of the state of Rio Grande do Norte. The underreporting and/or deficiency in the disclosure of data by some municipalities represents a challenge in complying with the actions of the Visceral Leishmaniasis Surveillance and Control Program, and attention should be paid to the monitoring and inspection of the execution actions of municipal managers, as well as how to train professionals who are part of the service. Keywords: georeferencing, public health, zoonoses, leishmaniasis, epidemiology.

Produção de enzimas por Penicillium chrysogenum em fermentação em estado sólido usando a fibra do coco como substrato

O aproveitamento de resíduos agroindustriais como substrato na produção de enzimas é uma prática atrativa que visa reduzir custos de operação, além de reduzir o impacto ambiental causado pelo acúmulo destes subprodutos. Deste modo, o objetivo deste estudo foi investigar a cinética de produção de enzimas celulolíticas (CMCase, Xilanase, Avicelase e FPase) de Penicillium chrysogenum (807) através da fermentação em estado sólido utilizando como substrato a fibra do coco verde. Os valores máximos das atividades das enzimas foram CMCase (2,58 U/g), FPase (2,24 U/g), Avicelase (2,38 U/g) e Xilanase (6,72 U/g). Baseado nos resultados obtidos o microrganismo e o substrato utilizado apresentam potencial para processos de produção de enzimas em fermentação em estado sólido.The use of agro-industrial waste as a substrate in the production of enzymes is a conventional practice that aims to reduce operating costs, in addition to reducing the environmental impact caused by the accumulation of these by-products. Thus, the objective of this work was to study the production kinetics of cellulolytic enzymes (CMCase, Xylanase, Avicelase and FPase) of Penicillium chrysogenum (807) through solid state fermentation using green coconut fiber as a substrate. The maximum values obtained for enzymes activities were for CMCase (2.58 U/g), FPase (2.24 U/g), Avicelase (2.38 U/g) and Xylanase (6.72 U/g.) Based on the obtained results, the microorganism and the substrate used have the potential for enzyme production processes in solid state fermentation

CagA phosphorylation EPIYA-C motifs and the vacA i genotype in Helicobacter pylori strains of asymptomatic children from a high-risk gastric cancer area in northeastern Brazil

Helicobacter pylori infection is one of the most common infections worldwide and is associated with gastric diseases. Virulence factors such as VacA and CagA have been shown to increase the risk of these diseases. Studies have suggested a causal role of CagA EPIYA-C in gastric carcinogenesis and this factor has been shown to be geographically diverse. We investigated the number of CagA EPIYA motifs and the vacA i genotypes in H. pylori strains from asymptomatic children. We included samples from 40 infected children (18 females and 22 males), extracted DNA directly from the gastric mucus/juice (obtained using the string procedure) and analysed the DNA using polymerase chain reaction and DNA sequencing. The vacA i1 genotype was present in 30 (75%) samples, the i2 allele was present in nine (22.5%) samples and both alleles were present in one (2.5%) sample. The cagA-positive samples showed distinct patterns in the 3’ variable region of cagA and 18 of the 30 (60%) strains contained 1 EPIYA-C motif, whereas 12 (40%) strains contained two EPIYA-C motifs. We confirmed that the studied population was colonised early by the most virulent H. pylori strains, as demonstrated by the high frequency of the vacA i1 allele and the high number of EPIYA-C motifs. Therefore, asymptomatic children from an urban community in Fortaleza in northeastern Brazil are frequently colonised with the most virulent H. pylori strains