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Registro Completo |
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Biblioteca(s): |
Embrapa Unidades Centrais. |
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Data corrente: |
18/02/2016 |
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Data da última atualização: |
18/02/2016 |
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Autoria: |
ROCHA, D. T.; SALLE, F. O.; PERDONCINI, G.; ROCHA, S. L. S.; FORTES, B. B.; MORAES, H. L. S.; NASCIMENTO, V. P.; SALLE, C. T. P. |
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Afiliação: |
DANIELA T. ROCHA, UFRGS; FELIPE O. SALLE, UFRGS; GUSTAVO PERDONCINI, UFRGS; SILVIO L. S. ROCHA, UFRGS; FLÁVIA B. B. FORTES, UFRGS; HAMILTON L. S. MORAES, UFRGS; VLADIMIR P. NASCIMENTO, UFRGS; CARLOS T. P. SALLE, UFRGS. |
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Título: |
Classification of antimicrobial resistance using artificial neural networks and the relationship of 38 genes associated with the virulence of Escherichia coli isolates from broilers. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Pesquisa Veterinária Brasileira, Brasília, DF, v. 35, n.2, p. 137-140, fev. 2015. |
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Idioma: |
Português |
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Conteúdo: |
Avian pathogenic Escherichia coli (APEC) is responsible for various pathological processes in birds and is considered as one of the principal causes of morbidity and mortality, associated with economic losses to the poultry industry. The objective of this study was to demonstrate that it is possible to predict antimicrobial resistance of 256 samples (APEC) using 38 different genes responsible for virulence factors, through a computer program of artificial neural networks (ANNs). A second target was to find the relationship between (PI) pathogenicity index and resistance to 14 antibiotics by statistical analysis. The results showed that the RNAs were able to make the correct classification of the behavior of APEC samples with a range from 74.22 to 98.44%, and make it possible to predict antimicrobial resistance. The statistical analysis to assess the relationship between the pathogenic index (PI) and resistance against 14 antibiotics showed that these variables are independent, i.e. peaks in PI can happen without changing the antimicrobial resistance, or the opposite, changing the antimicrobial resistance without a change in PI. |
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Palavras-Chave: |
Agente antimicrobiano; Antimicrobials agents; Rede neural artificial. |
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Thesagro: |
Escherichia Coli; Frango. |
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Thesaurus Nal: |
Neural networks. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/139405/1/Classification-of-antimicrobial.pdf
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Marc: |
LEADER 02057naa a2200277 a 4500
001 2037658
005 2016-02-18
008 2015 bl uuuu u00u1 u #d
100 1 $aROCHA, D. T.
245 $aClassification of antimicrobial resistance using artificial neural networks and the relationship of 38 genes associated with the virulence of Escherichia coli isolates from broilers.
260 $c2015
520 $aAvian pathogenic Escherichia coli (APEC) is responsible for various pathological processes in birds and is considered as one of the principal causes of morbidity and mortality, associated with economic losses to the poultry industry. The objective of this study was to demonstrate that it is possible to predict antimicrobial resistance of 256 samples (APEC) using 38 different genes responsible for virulence factors, through a computer program of artificial neural networks (ANNs). A second target was to find the relationship between (PI) pathogenicity index and resistance to 14 antibiotics by statistical analysis. The results showed that the RNAs were able to make the correct classification of the behavior of APEC samples with a range from 74.22 to 98.44%, and make it possible to predict antimicrobial resistance. The statistical analysis to assess the relationship between the pathogenic index (PI) and resistance against 14 antibiotics showed that these variables are independent, i.e. peaks in PI can happen without changing the antimicrobial resistance, or the opposite, changing the antimicrobial resistance without a change in PI.
650 $aNeural networks
650 $aEscherichia Coli
650 $aFrango
653 $aAgente antimicrobiano
653 $aAntimicrobials agents
653 $aRede neural artificial
700 1 $aSALLE, F. O.
700 1 $aPERDONCINI, G.
700 1 $aROCHA, S. L. S.
700 1 $aFORTES, B. B.
700 1 $aMORAES, H. L. S.
700 1 $aNASCIMENTO, V. P.
700 1 $aSALLE, C. T. P.
773 $tPesquisa Veterinária Brasileira, Brasília, DF$gv. 35, n.2, p. 137-140, fev. 2015.
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