PubMed · 13958496
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C FERNANDEZ CRASSI. [Enterobacteriaceae].. https://pubmed.ncbi.nlm.nih.gov/13958496/
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BACKGROUND: The gut microbiota of living organ donors has been linked to transplant outcomes. However, little is known about the characteristics of the deceased donor gut microbiota or its potential impact on recipient outcomes. METHODS: We analyzed the deep intestinal microbiota from 24 deceased donors. Samples included luminal stool from the right and left colon as well as bile. Microbial composition was characterized using 16S V4 rRNA sequencing. α- and β-diversity analyses were performed to compare microbial communities between donor enteric sites and against stool samples from 28 healthy community controls, 14 critically ill intensive care comparators, and 12 matched liver transplant recipients. Machine learning models and logistic regression analysis were applied to explore whether features of the donor microbiota could predict recipient post-transplant complications. FINDINGS: The deceased donor microbiota showed an absence of the expected compositional variability between sampling sites, with no significant differences in either α- or β-diversity observed between bile, right and left colonic samples (all p > 0.05). Donor samples exhibited distinct microbial profiles compared with stool from both healthy and ICU comparators, including increased abundance of potential pathogens within the Enterobacteriaceae family (all p < 0.001). Features of the donor microbiota, particularly enrichment of Enterobacteriaceae, were associated with an increased risk of early post-transplant infection in recipients (≤ 30 days; p = 0.011). INTERPRETATION: The deceased donor gut microbiota may represent a distinct microbial community with potential clinical relevance. Microbial profiling of donor enteric microbiota may help identify recipients at heightened risk of early post-transplant infectious complications.
BACKGROUND: Community-acquired urinary tract infections (UTIs) are a significant health concern in older adults. However, few studies have investigated the epidemiology of uropathogens across diverse settings where older adults reside. METHODS: In this study, we whole-genome sequenced urinary isolates of Escherichia coli, Klebsiella species, and Enterobacter cloacae complex collected from individuals aged 70 and over living in the community and residential aged care facilities (RACFs) in Queensland, Australia. We investigated the prevalence of antimicrobial resistance (AMR), pathogen population structure, and transmission dynamics across the settings. RESULTS: E. coli was most frequently identified (82.2%, 447/544), followed by K. pneumoniae (10.8%, 59/544) and E. hormaechei (2.6%, 14/544). Intraspecies lineages were diverse, and a total of 177 sequence types were identified. The three predominant lineages were E. coli ST73 (10.7%, 54/504), ST95 (8.1%, 41/504), and ST131 (5.4%, 27/504). Transmission events were minimal, being identified in 13 patients (2.5%), mainly from the community. The resistance rate to antibiotics was low, with only a small proportion (9.7%) of multidrug-resistant (MDR) isolates. The predominant MDR lineage was E. coli ST131, which carried extended-spectrum beta-lactamase bla CTX-M genes. CONCLUSIONS: Community-acquired UTIs in older adults are predominantly caused by diverse E. coli lineages, with limited evidence of transmission within aged care facilities. The low rates of AMR in the community, along with the absence of strains adapted to a particular setting, suggest that current empiric therapy guidelines remain appropriate. Our prospective genomic surveillance offers valuable insights for monitoring UTIs in this population. It demonstrates the importance of an unbiased approach to accurately capture the prevalence and diversity of uropathogen lineages.
Counts of E. coli, Enteroccoci and fecal coliforms in four sites around Lake Kinneret (The Sea of Galilee), collected every 2-4 weeks for about 5 years during 1995-2002 showed irregular fluctuations punctuated by aperiodic outbursts of variable magnitude. Because of the haphazard nature of fecal contamination and large intervals between successive counts, these patterns were described by probabilistic models, based on the truncated Laplace or Extreme Value distribution. Their applicability was tested by comparing the predicted frequencies of counts exceeding different levels calculated from the first half of each record with those actually observed in its second half. Despite the records imperfections and minor violations of the underlying models' assumptions, there was a reasonable agreement between the estimated and actual frequencies. This demonstrated that it is possible to translate the irregular fluctuation pattern into a set of probabilities of future high counts. In principle, such probabilities can be used to quantify the water's fecal contamination pattern and as a tool to assess the efficacy of preventive measures to reduce it.