Virulence genes, antimicrobial resistance profile, phylotyping and pathotyping of diarrheagenic Escherichia coli isolated from children in Southwest Mexico.

Diarrheagenic E. coli (DEC) strains are one of the most important etiology factors causing diarrhea in children worldwide, especially in developing countries. DEC strains have characteristic virulence factors; however, other supplemental virulence genes (SVG) may contribute to the development of diarrhea in children. Therefore, this study aimed to determine the prevalence of DEC in children with diarrhea in southwestern Mexico and to associate childhood symptoms, SVG, and pathotypes with diarrhea-causing DEC strains. DEC strains were isolated from 230 children with diarrhea aged 0-60 months from the state of Oaxaca, southwestern Mexico; clinical data were collected, and PCR was used to identify SVG and pathotypes. Antibiotic resistance profiling was performed on DEC strains. 63% of samples were DEC positive, single or combined infections (two (21%) or three strains (1.3%)) of aEPEC (51%), EAEC (10.2%), tEPEC (5.4%), DAEC (4.8%), ETEC (4.1%), EIEC (1.4%), or EHEC (0.7%) were found. Children aged ≤ 12 and 49-60 months and symptoms (e.g., fever and blood) were associated with DEC strains. SVG related to colonization (nleB-EHEC), cytotoxicity (sat-DAEC and espC-tEPEC), and proteolysis (pic-aEPEC) were associated with DECs strains. E. coli phylogroup A was the most frequent, and some pathotypes (aEPEC-A, DAEC-B), and SVG (espC-B2, and sat-D) were associated with the phylogroups. Over 79% of the DEC strains were resistant to antibiotics, and 40% were MDR and XDR, respectively. In conclusion aEPEC was the most prevalent pathotype in children with diarrhea in this region. SVG related to colonization, cytotoxicity, and proteolysis were associated with diarrhea-producing DEC strains, which may play an essential role in the development of diarrhea in children in southwestern Mexico.

A Comparison between Several Response Surface Methodology Designs and a Neural Network Model to Optimise the Oxidation Conditions of a Lignocellulosic Blend.

In this paper, response surface methodology (RSM) designs and an artificial neural network (ANN) are used to obtain the optimal conditions for the oxy-combustion of a corn-rape blend. The ignition temperature (Te) and burnout index (Df) were selected as the responses to be optimised, while the CO2/O2 molar ratio, the total flow, and the proportion of rape in the blend were chosen as the influencing factors. For the RSM designs, complete, Box-Behnken, and central composite designs were performed to assess the experimental results. By applying the RSM, it was found that the principal effects of the three factors were statistically significant to compute both responses. Only the interactions of the factors on Df were successfully described by the Box-Behnken model, while the complete design model was adequate to describe such interactions on both responses. The central composite design was found to be inadequate to describe the factor interactions. Nevertheless, the three methods predicted the optimal conditions properly, due to the cancellation of net positive and negative errors in the mathematical adjustment. The ANN presented the highest regression coefficient of all methods tested and needed only 20 experiments to reach the best predictions, compared with the 32 experiments needed by the best RSM method. Hence, the ANN was found to be the most efficient model, in terms of good prediction ability and a low resource requirement. Finally, the optimum point was found to be a CO2/O2 molar ratio of 3.3, a total flow of 108 mL/min, and 61% of rape in the biomass blend.