Application of artificial neural networks to prediction of new substances with antimicrobial activity against Escherichia coli

2020 ◽  
Vol 130 (1) ◽  
pp. 40-49
Author(s):  
A. Badura ◽  
J. Krysiński ◽  
A. Nowaczyk ◽  
A. Buciński
Keyword(s):  
Escherichia Coli ◽  
Neural Networks ◽  
Antimicrobial Activity ◽  
Artificial Neural Networks ◽  
Artificial Neural

Predicting Survival of Escherichia coli O157:H7 in Dry Fermented Sausage Using Artificial Neural Networks

2008 ◽  
Vol 71 (1) ◽  
pp. 6-12 ◽  
Author(s):  
A. PALANICHAMY ◽  
D. S. JAYAS ◽  
R. A. HOLLEY
Keyword(s):  
Escherichia Coli ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Model ◽  
Allyl Isothiocyanate ◽  
Escherichia Coli O157 ◽  
Fermented Sausage ◽  
E Coli ◽  
Dry Fermented Sausage ◽  
Artificial Neural

The Canadian Food Inspection Agency required the meat industry to ensure Escherichia coli O157:H7 does not survive (experiences ≥ 5 log CFU/g reduction) in dry fermented sausage (salami) during processing after a series of foodborne illness outbreaks resulting from this pathogenic bacterium occurred. The industry is in need of an effective technique like predictive modeling for estimating bacterial viability, because traditional microbiological enumeration is a time-consuming and laborious method. The accuracy and speed of artificial neural networks (ANNs) for this purpose is an attractive alternative (developed from predictive microbiology), especially for on-line processing in industry. Data from a study of interactive effects of different levels of pH, water activity, and the concentrations of allyl isothiocyanate at various times during sausage manufacture in reducing numbers of E. coli O157:H7 were collected. Data were used to develop predictive models using a general regression neural network (GRNN), a form of ANN, and a statistical linear polynomial regression technique. Both models were compared for their predictive error, using various statistical indices. GRNN predictions for training and test data sets had less serious errors when compared with the statistical model predictions. GRNN models were better and slightly better for training and test sets, respectively, than was the statistical model. Also, GRNN accurately predicted the level of allyl isothiocyanate required, ensuring a 5-log reduction, when an appropriate production set was created by interpolation. Because they are simple to generate, fast, and accurate, ANN models may be of value for industrial use in dry fermented sausage manufacture to reduce the hazard associated with E. coli O157:H7 in fresh beef and permit production of consistently safe products from this raw material.

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

2015 ◽  
Vol 35 (2) ◽  
pp. 137-140 ◽  
Author(s):  
Daniela T. Rocha ◽  
Felipe O. Salle ◽  
Gustavo Perdoncini ◽  
Silvio L.S. Rocha ◽  
Flávia B.B. Fortes ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Antimicrobial Resistance ◽  
Economic Losses ◽  
Artificial Neural ◽  
Relationship Of ◽  
The Relationship

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.

scholarly journals The use of infrared spectroscopy and artificial neural networks for detection of uropathogenic Escherichia coli strains' susceptibility to cephalothin.

1970 ◽  
Vol 60 (4) ◽  
Author(s):  
Lukasz Lechowicz ◽  
Mariusz Urbaniak ◽  
Wioletta Adamus-Białek ◽  
Wiesław Kaca
Keyword(s):  
Escherichia Coli ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Infrared Spectroscopy ◽  
Infrared Spectra ◽  
Bacterial Strains ◽  
Mathematical Methods ◽  
E Coli ◽  
Artificial Neural

Infrared spectroscopy is an increasingly common method for bacterial strains' testing. For the analysis of bacterial IR spectra, advanced mathematical methods such as artificial neural networks must be used. The combination of these two methods has been used previously to analyze taxonomic affiliation of bacteria. The aim of this study was the classification of Escherichia coli strains in terms of susceptibility/resistance to cephalothin on the basis of their infrared spectra. The infrared spectra of 109 uropathogenic E. coli strains were measured. These data are used for classification of E. coli strains by using designed artificial neural networks. The most efficient artificial neural networks classify the E. coli sensitive/resistant strains with an error of 5%. Bacteria can be classified in terms of their antibiotic susceptibility by using infrared spectroscopy and artificial neural networks.

scholarly journals Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Mathieu Daynac ◽  
Alvaro Cortes-Cabrera ◽  
Jose M. Prieto
Keyword(s):  
Neural Networks ◽  
Antimicrobial Activity ◽  
Artificial Neural Networks ◽  
Essential Oils ◽  
Maximum Error ◽  
Antimicrobial Activities ◽  
Output Data ◽  
Chemical Variability ◽  
Artificial Neural ◽  
Disk Susceptibility

Essential oils (EOs) are vastly used as natural antibiotics in Complementary and Alternative Medicine (CAM). Their intrinsic chemical variability and synergisms/antagonisms between its components make difficult to ensure consistent effects through different batches. Our aim is to evaluate the use of artificial neural networks (ANNs) for the prediction of their antimicrobial activity.Methods.The chemical composition and antimicrobial activity of 49 EOs, extracts, and/or fractions was extracted from NCCLS compliant works. The fast artificial neural networks (FANN) software was used and the output data reflected the antimicrobial activity of these EOs against four common pathogens:Staphylococcus aureus, Escherichia coli, Candida albicans, andClostridium perfringensas measured by standardised disk diffusion assays.Results.ANNs were able to predict >70% of the antimicrobial activities within a 10 mm maximum error range. Similarly, ANNs were able to predict 2 or 3 different bioactivities at the same time. The accuracy of the prediction was only limited by the inherent errors of the popular antimicrobial disk susceptibility test and the nature of the pathogens.Conclusions.ANNs can be reliable, fast, and cheap tools for the prediction of the antimicrobial activity of EOs thus improving their use in CAM.

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