Dataset Information

Rapid and highly sensitive pathotyping of avian influenza A H5N1 virus by using real-time reverse transcription-PCR.

ABSTRACT: Rapid typing of the pathogenicity of avian influenza A viruses (AIV) of subtypes H5 and H7 is crucial to initiate adequate protective measures preventing the spread of highly pathogenic AIV (HPAIV). Here, a new real-time reverse transcription-PCR assay which enables sensitive and specific detection and cleavage site analysis of HPAIV H5N1 of the Qinghai lineage is described.

SUBMITTER: Hoffmann B

PROVIDER: S-EPMC1829000 | biostudies-literature | 2007 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Rapid and highly sensitive pathotyping of avian influenza A H5N1 virus by using real-time reverse transcription-PCR.

Hoffmann Bernd B Harder Timm T Starick Elke E Depner Klaus K Werner Ortrud O Beer Martin M

Journal of clinical microbiology 20061220 2

Rapid typing of the pathogenicity of avian influenza A viruses (AIV) of subtypes H5 and H7 is crucial to initiate adequate protective measures preventing the spread of highly pathogenic AIV (HPAIV). Here, a new real-time reverse transcription-PCR assay which enables sensitive and specific detection and cleavage site analysis of HPAIV H5N1 of the Qinghai lineage is described. ...[more]

PMID: 17182758

Similar Datasets

Project description:BACKGROUND: The agro-ecology and poultry husbandry of the south Asian and south-east Asian countries share common features, however, with noticeable differences. Hence, the ecological determinants associated with risk of highly pathogenic avian influenza (HPAI-H5N1) outbreaks are expected to differ between Bangladesh and e.g., Thailand and Vietnam. The primary aim of the current study was to establish ecological determinants associated with the risk of HPAI-H5N1 outbreaks at subdistrict level in Bangladesh. The secondary aim was to explore the performance of two different statistical modeling approaches for unmeasured spatially correlated variation. METHODOLOGY/PRINCIPAL FINDINGS: An ecological study at subdistrict level in Bangladesh was performed with 138 subdistricts with HPAI-H5N1 outbreaks during 2007-2008, and 326 subdistricts with no outbreaks. The association between ecological determinants and HPAI-H5N1 outbreaks was examined using a generalized linear mixed model. Spatial clustering of the ecological data was modeled using 1) an intrinsic conditional autoregressive (ICAR) model at subdistrict level considering their first order neighbors, and 2) a multilevel (ML) model with subdistricts nested within districts. Ecological determinants significantly associated with risk of HPAI-H5N1 outbreaks at subdistrict level were migratory birds' staging areas, river network, household density, literacy rate, poultry density, live bird markets, and highway network. Predictive risk maps were derived based on the resulting models. The resulting models indicate that the ML model absorbed some of the covariate effect of the ICAR model because of the neighbor structure implied in the two different models. CONCLUSIONS/SIGNIFICANCE: The study identified a new set of ecological determinants related to river networks, migratory birds' staging areas and literacy rate in addition to already known risk factors, and clarified that the generalized concept of free grazing duck and duck-rice cultivation interacted ecology are not significant determinants for Bangladesh. These findings will refine current understanding of the HPAI-H5N1 epidemiology in Bangladesh.

Dataset Information

Rapid and highly sensitive pathotyping of avian influenza A H5N1 virus by using real-time reverse transcription-PCR.

Publications

Rapid and highly sensitive pathotyping of avian influenza A H5N1 virus by using real-time reverse transcription-PCR.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets