Genome scale reconstruction of a Salmonella metabolic model: comparison of similarity and differences with a commensal Escherichia coli strain.

Salmonella are closely related to commensal Escherichia coli but have gained virulence factors enabling them to behave as enteric pathogens. Less well studied are the similarities and differences that exist between the metabolic properties of these organisms that may contribute toward niche adaptation of Salmonella pathogens. To address this, we have constructed a genome scale Salmonella metabolic model (iMA945). The model comprises 945 open reading frames or genes, 1964 reactions, and 1036 metabolites. There was significant overlap with genes present in E. coli MG1655 model iAF1260. In silico growth predictions were simulated using the model on different carbon, nitrogen, phosphorous, and sulfur sources. These were compared with substrate utilization data gathered from high throughput phenotyping microarrays revealing good agreement. Of the compounds tested, the majority were utilizable by both Salmonella and E. coli. Nevertheless a number of differences were identified both between Salmonella and E. coli and also within the Salmonella strains included. These differences provide valuable insight into differences between a commensal and a closely related pathogen and within different pathogenic strains opening new avenues for future explorations.

The Prevalence of Delirium in An Older Acute Surgical Population and Its Effect on Outcome.

BACKGROUND: With an ageing population, an increasing number of older adults are admitted for assessment to acute surgical units. Older adults have specific factors that may influence outcomes, one of which is delirium (acute cognitive impairment). OBJECTIVES: To establish the prevalence of delirium on admission in an older acute surgical population and its effect on mortality. Secondary outcomes investigated include hospital readmission and length of hospital stay. METHOD: This observational multi-centre study investigated consecutive patients, ≥65 years, admitted to the acute surgical units of five UK hospitals during an eight-week period. On admission the Confusion Assessment Method (CAM) score was performed to detect delirium. The effect of delirium on important clinical outcomes was investigated using tests of association and logistic regression models. RESULTS: The cohort consisted of 411 patients with a mean age of 77.3 years (SD 8.1). The prevalence of admission delirium was 8.8% (95% CI 6.2-11.9%) and cognitive impairment was 70.3% (95% CI 65.6-74.7%). The delirious group were not more likely to die at 30 or 90 days (OR 1.1, 95% CI 0.2 to 5.1, p = 0.67; OR 1.4, 95% CI 0.4 to 4.1. p = 0.82) or to be readmitted within 30 days of discharge (OR 0.9, 95% CI 0.4 to 2.2, p = 0.89). Length of hospital stay was significantly longer in the delirious group (median 8 vs. 5 days respectively, p = 0.009). CONCLUSION: Admission delirium occurs in just under 10% of older people admitted to acute surgical units, resulting in significantly longer hospital stays.

Balance algorithm for cluster randomized trials.

BACKGROUND: Within cluster randomized trials no algorithms exist to generate a full enumeration of a block randomization, balancing for covariates across treatment arms. Furthermore, often for practical reasons multiple blocks are required to fully randomize a study, which may not have been well balanced within blocks. RESULTS: We present a convenient and easy to use randomization tool to undertake allocation concealed block randomization. Our algorithm highlights allocations that minimize imbalance between treatment groups across multiple baseline covariates. We demonstrate the algorithm using a cluster randomized trial in primary care (the PRE-EMPT Study) and show that the software incorporates a trade off between independent random allocations that were likely to be imbalanced, and predictable deterministic approaches that would minimise imbalance. We extend the methodology of single block randomization to allocate to multiple blocks conditioning on previous allocations. CONCLUSION: The algorithm is included as Additional file 1 and we advocate its use for robust randomization within cluster randomized trials.