Phenotypical resistance correlation networks for 10 non-typhoidal Salmonella subpopulations in an active antimicrobial surveillance programme.

Antimicrobials play a critical role in treating cases of invasive non-typhoidal salmonellosis (iNTS) and other diseases, but efficacy is hindered by resistant pathogens. Selection for phenotypical resistance may occur via several mechanisms. The current study aims to identify correlations that would allow indirect selection of increased resistance to ceftriaxone, ciprofloxacin and azithromycin to improve antimicrobial stewardship. These are medically important antibiotics for treating iNTS, but these resistances persist in non-Typhi Salmonella serotypes even though they are not licensed for use in US food animals. A set of 2875 Salmonella enterica isolates collected from animal sources by the National Antimicrobial Resistance Monitoring System were stratified in to 10 subpopulations based on serotype and host species. Collateral resistances in each subpopulation were estimated as network models of minimum inhibitory concentration partial correlations. Ceftriaxone sensitivity was correlated with other ß-lactam resistances, and less commonly resistances to tetracycline, trimethoprim-sulfamethoxazole or kanamycin. Azithromycin resistance was frequently correlated with chloramphenicol resistance. Indirect selection for ciprofloxacin resistance via collateral selection appears unlikely. Density of the ACSSuT subgraph resistance aligned well with the phenotypical frequency. The current study identifies several important resistances in iNTS serotypes and further research is needed to identify the causative genetic correlations.

Indirect carrier detection of canine haemophilia A using factor VIII microsatellite markers.

A panel of factor VIII microsatellite markers was developed for indirect carrier detection of canine haemophilia A (factor VIII deficiency). A total of 78 dogs, representing 14 different breed variants of haemophilia A, were genotyped at six intragenic factor VIII marker loci. The markers spanned approximately 110 kb and were located in the 5' UTR of the factor VIII (F8) gene and within introns 6, 10, 12, 14 and 21. The observed heterozygosity (n = 39 females) for these markers was 0.675, 0.82, 0.868, 0.692, 0.473 and 0.775 respectively. The affected males of each breed variant had unique marker haplotypes. In addition, the marker haplotypes varied for two unrelated haemophilic Jack Russell terriers, compatible with independent mutation events causing haemophilia in different breeds and different families. A three-marker panel (markers within introns 6, 10 and 21) was informative for 37 of the 39 females. The haemophilia-associated haplotype was defined for six breed variants based on the genotypes of an affected male and a clear male sibling, with successful carrier detection of female siblings in each pedigree. Our results demonstrate an apparent allelic heterogeneity in canine haemophilia A; however, an indirect method based on a three-marker panel is feasible to facilitate carrier detection and genetic counselling.

Monte carlo conditional inference for log-linear and logistic models: a survey of current methodology.

Nuisance parameters are parameters that are not of immediate interest to the experimenter. For log-linear and logistic models the null distribution of (most) statistics of interest depends on such parameters. Traditionally, nuisance parameters were eliminated by performing inference with respect to the chi-squared limiting distribution of common test statistics. An alternative solution is to eliminate the nuisance parameters by conditioning on their minimal sufficient statistics. The support of the resulting conditional distribution is often intractable, making null probability calculations challenging. An often feasible way to avoid complete enumeration of this support is to approximate conditional probabilities using Monte Carlo methods. In this article we survey recent developments in Monte Carlo conditional analysis for log-linear and logistic models focusing on the algorithms proposed by Booth and Butler, Diaconis and Sturmfels, Smith et al., and Mehta et al. We illustrate these algorithms with simple motivating examples.