Classification of antimicrobial resistance using artificial neural networks and the relationship of 38 genes associated with the virulence of Escherichia coli isolates from broilers


Autoria(s): Rocha,Daniela T.; Salle,Felipe O.; Perdoncini,Gustavo; Rocha,Silvio L.S.; Fortes,Flávia B.B.; Moraes,Hamilton L.S.; Nascimento,Vladimir P.; Salle,Carlos T.P.
Data(s)

01/02/2015

Resumo

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.

Formato

text/html

Identificador

http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-736X2015000200137

Idioma(s)

en

Publicador

Colégio Brasileiro de Patologia Animal - CBPA. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)

Fonte

Pesquisa Veterinária Brasileira v.35 n.2 2015

Palavras-Chave #Escherichia coli #artificial neural networks #antimicrobials agents #broilers.
Tipo

journal article