Association of diverse bacterial communities in human bile samples with biliary tract disorders: a survey using culture and polymerase chain reaction-denaturing gradient gel electrophoresis methods.

Bacterial infection is considered a predisposing factor for disorders of the biliary tract. This study aimed to determine the diversity of bacterial communities in bile samples and their involvement in the occurrence of biliary tract diseases. A total of 102 bile samples were collected during endoscopic retrograde cholangiopancreatography (ERCP). Characterization of bacteria was done using culture and polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) methods. Antimicrobial susceptibility of the isolates was determined based on the Clinical and Laboratory Standards Institute (CLSI) guidelines and identity of the nucleotide sequences of differentiated bands from the DGGE gels was determined based on GenBank data. In total, 41.2 % (42/102) of the patients showed bacterial infection in their bile samples. This infection was detected in 21 % (4/19), 45.4 % (5/11), 53.5 % (15/28), and 54.5 % (24/44) of patients with common bile duct stone, microlithiasis, malignancy, and gallbladder stone, respectively. Escherichia coli showed a significant association with gallstones. Polymicrobial infection was detected in 48 % of the patients. While results of the culture method established coexistence of biofilm-forming bacteria (Pseudomonas aeruginosa, E. coli, Klebsiella pneumoniae, Enterococcus spp., and Acinetobacter spp.) in different combinations, the presence of Capnocytophaga spp., Lactococcus spp., Bacillus spp., Staphylococcus haemolyticus, Enterobacter or Citrobacter spp., Morganella spp., Salmonella spp., and Helicobacter pylori was also characterized in these samples by the PCR-DGGE method. Multidrug resistance phenotypes (87.5 %) and resistance to third- and fourth-generation cephalosporins and quinolones were common in these strains, which could evolve through their selection by bile components. Ability for biofilm formation seems to be a need for polymicrobial infection in this organ.

Modeling the electrophoretic mobility of analytes in binary solvent electrolyte systems in capillary electrophoresis using an artificial neural network.

An artificial neural network (ANN) methodology was used to model the electrophoretic mobility of basic analytes in binary solvent electrolyte systems. The electrophoretic mobilities in pure solvent electrolytes, and the volume fractions of the solvents in mixtures were used as input. The electrophoretic mobilities in mixed solvent buffers were employed as the output of the network. The optimized topology of the network was 3-3-1. 32 experimental mobility data sets collected from the literature were employed to test the correlation ability and prediction capability of the proposed method. The mean percentage deviation (MPD) between the experimental and calculated values was used as an accuracy criterion. The MPDs obtained for different numerical analyses varied between 0.21% and 13.74%. The results were also compared with similar calculated mobilities which were derived from the best multiple linear model from the literature. From these results it was found that the ANN methodology is superior to the multiple linear model.