Enterobacteria associated with houseflies (Musca domestica) as an infection risk indicator in swine production farms.

Houseflies (Musca domestica) spend part of their life development on animal or human manure. Manure is high in pathogenic microbes; thus, houseflies have been known as a mechanical vector for various important zoonotic diseases. Therefore, the present study showcases captured houseflies from intensive swine production regions (which are areas of high manure concentration) in Southern Brazil, and analyses their bodies' to the presence of Escherichia coli and Salmonella sp. and the sensitivity of these bacteria to various antibiotics. Additionally, Quantitative Microbiology Risk Assessment was performed simulating the contamination of lettuce by flies' bacteria and subsequent lettuce consumption by an adult human being. Houseflies were captured in swine buildings and farm houses from five farms. E. coli quantification values ranged from 104 to 106 CFU/20 flies, and all sampling sites had positive results from bacteria presence in the collected houseflies. On the other hand, Salmonella sp. presence was observed in only three farms, where the quantification ranged from 102 to 105 CFU/20 flies. The bacteria showed to be resistant to at least two from the four tested antibiotics (ampicillin, Cefalotin, Ciprofloxacin and Norfloxacin) antibiotics used in human or veterinary medicine. Infection probability analyses showed risk of human infection by E.coli, indicating possible transmission of zoonotic pathogens through flies. In this context, it was possible to conclude that there is a need for flies control, especially in swine farms where zoonotic pathogens can be abundant, to minimize the health impact of the vectorization of enteric bacteria.

Pathway based analysis of genotypes in relation to alcohol dependence.

We introduce a method for detecting variants in several genes of related function with small effect on a phenotype of interest. Our method uses logistic regression to test whether multiple alleles within a functional set have significantly higher than expected predictive value, even though none individually may have strong individual effects. We illustrate this method by testing seven gene sets (including 48 genes), from a study with 1350 single nucleotide polymorphisms in 130 addiction candidate genes studied in a sample of 575 alcohol dependence (AD) cases and 530 controls. We conclude that AD is related to variation in genes participating in Glutamate and γ-amino butyric acid signaling, as has been reported elsewhere, and in stress response pathways, but not with genes in several other systems implicated in other drugs of abuse.

Interactive molecular networks obtained by computer-aided conversion of microarray data from brains of alcohol-drinking rats.

Lists of differentially expressed genes in a disease have become increasingly more comprehensive with improvements on all technical levels. Despite statistical cutoffs of 99% or 95% confidence intervals, the number of genes can rise to several hundreds or even thousands, which is barely amenable to a researcher's understanding. This report describes some ways of processing those data by mathematical algorithms. Gene lists obtained from 53 microarrays (two brain regions (amygdala and caudate putamen), three rat strains drinking alcohol or being abstinent) have been used. They resulted from analyses on Affymetrix chips and encompassed approximately 6 000 genes that passed our quality filters. They have been subjected to four mathematical ways of processing: (a) basic statistics, (b) principal component analysis, (c) hierarchical clustering, and (d) introduction into Bayesian networks. It turns out, by using the p-values or the log-ratios, that they best subdivide into brain areas, followed by a fairly good discrimination into the rat strains and the least good discrimination into alcohol-drinking vs. abstinent. Nevertheless, despite the fact that the relation to alcohol-drinking was the weakest signal, attempts have been made to integrate the genes related to alcohol-drinking into Bayesian networks to learn more about their inter-relationships. The study shows, that the tools employed here are extremely useful for (a) quality control of datasets, (b) for constructing interactive (molecular) networks, but (c) have limitations in integration of larger numbers into the networks. The study also shows that it is often pivotal to balance out the number of experimental conditions with the number of animals.