Surveillance of hemorrhagic fever and/or neuroinvasive disease: challenges of diagnosis

  OBJECTIVE To evaluate the performance of post mortem laboratory analysis in identifying the causes of hemorrhagic fever and/or neuroinvasive disease in deaths by arbovirus infection. METHODS Retrospective cross-sectional study based on the differential analysis and final outcome obtained in patients whose samples underwent laboratory testing for arboviruses at the Pathology Center of the Adolfo Lutz Institute, in São Paulo, Brazil. RESULTS Of the 1355 adults clinically diagnosed with hemorrhagic fever and/or neuroinvasive disease, the most commonly attributed cause of death and the most common final outcome was dengue fever. Almost half of the samples tested negative on all laboratory tests conducted. CONCLUSION The failure to identify the causative agent in a great number of cases highlights a gap in the diagnosis of deaths of unknown etiology. Additional immunohistochemical and molecular assessments need to be added to the post-mortem protocol if all laboratory evaluations performed fail to identify a causative agent. While part of our findings may be due to technical issues related to sample fixation, better information availability when making the initial diagnosis is crucial. Including molecular approaches might lead to a significant advancement in diagnostic accuracy

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

Analysis of differential gene expression between microcorticotrophinomas and macrocorticotrophinomas

Adenomas que se desenvolvem a partir da linhagem corticotrófica (corticotropinomas) secretam ACTH (hormônio adrenocorticotrófico) de modo autônomo. Esta secreção induz a produção crônica e excessiva de cortisol, pelo córtex das glândulas suprarrenais, caracterizando a doença de Cushing (DC). A grande maioria dos adenomas visível à ressonância magnética é microadenoma ( 10 mm), enquanto macroadenomas invasivos são considerados raros. Para investigar os diferentes fenótipos destes tumores, estudamos o padrão de expressão gênica entre microadenomas e macroadenomas, incluindo como critério de classificação sua capacidade de invasão. Utilizando a metodologia de microarray, estudamos 12 amostras de corticotropinomas de indivíduos com diagnóstico clínico, laboratorial e histopatológico de DC (microadenomas não-invasivos n = 4, macroadenomas não-invasivos n = 5 e macroadenomas invasivos n = 3). Além disso, foi investigada a presença de mutações do gene USP8. Observamos que micro e macrocorticotropinomas não-invasivos possuem uma assinatura gênica semelhante, com apenas 48 genes diferencialmente expressos entre si. Por outro lado, macroadenomas invasivos apresentaram um perfil de expressão diferencial mais acentuado, com 168 genes diferencialmente expressos em relação aos não-invasivos (ANOVA p-valor 10 mm) occur in only 10-30 % of individuals with CD, while invasive macroadenomas, although rare, have great clinical relevance. To investigate the different phenotypes of these tumors, we studied the pattern of differential gene expression between microadenomas and macroadenomas, including their invasiveness as classification a criterion. Using DNA microarray methodology, we studied 12 samples of corticotrophinomas of patients with clinical, laboratory and histopathologic diagnosis of CD (non-invasive microadenomas n = 4, non-invasive macroadenomas n = 5 and invasive macroadenomas n=3). In addition, we investigated the presence of USP8 mutations. We observed that non-invasive corticotrophinomas have a similar genic signature with each other, with only 48 genes differentially expressed between them. Moreover, invasive macroadenomas showed a more pronounced differential expression profile, with 168 differentially expressed genes compared to sellar corticotrophinomas (ANOVA p value < 0.05; fold change cut-off = 2; FDR = 0.05). None of them exhibited USP8 variants. Based on expression significance and functionality, we highlighted CCND2, ZNF676 (overexpressed), DAPK1 and TIMP2 (underexpressed). These results were validated through alfaRT-PCR in another cohort of 15 sellar and 3 invasive corticotrophinomas, in which 28% of these tumors harbored USP8 somatic mutations. Among the biological pathways committed with at least two under or overexpressed genes are: Vitamin D receptor pathway, TGF-beta, G-protein signaling, response to DNA damage and control of the cell cycle. Our results can be useful to identify new markers involved in the invasive phenotype of clinically active corticotrophinomas. Although the specific functions of these potential markers still need to be elucidated in corticotropinomas, our results may have a positive impact on choice and therapeutic efficacy, prognosis and prediction of recurrence of these tumor

Technical comparison of MinIon and Illumina technologies for genotyping Chikungunya virus in clinical samples

Abstract New-generation sequencing (NGS) techniques have brought the opportunity for genomic monitoring of several microorganisms potentially relevant to public health. The establishment of different methods with different mechanisms provides a wide choice, taking into account several aspects. With that in mind, the present aim of the study was to compare basic genomic sequencing metrics that could potentially impact genotyping by nanopores from Oxford Nanopore Technologies and by synthesis from Illumina in clinical samples positive for Chikungunya (CHIKV). Among the metrics studied, running time, read production, and Q score were better represented in Illumina sequencing, while the MinIOn platform showed better response time and greater diversity of generated files. That said, it was possible to establish differences between the studied metrics in addition to verifying that the distinctions in the methods did not impact the identification of the CHIKV virus genotype

Núcleos de Ensino da Unesp: artigos 2008

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq