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Eur J Clin Microbiol Infect Dis ; 32(8): 1083-90, 2013 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-23529345

RESUMO

Peripheral venous catheters (PVCs) are some of the most widely used medical devices in hospitals worldwide. PVC-related infections increase morbidity and treatment costs. The inner surfaces of PVCs are rarely examined for the population structure of bacteria, as it is generally believed that bacteria at this niche are similar to those on the external surface of PVCs. We primarily test this hypothesis and also study the effect of antibiotic treatment on bacterial communities from PVC surfaces. The inner and outer surfaces of PVCs from 15 patients were examined by 454 GS FLX Titanium 16S rRNA sequencing and the culture method. None of the PVCs were colonised according to the culture method and none of the patients had a bacteraemia. From a total of 127,536 high-quality sequence reads, 14 bacterial phyla and 268 diverse bacterial genera were detected. The number of operational taxonomic units for each sample was in the range of 86-157, even though 60 % of patients had received antibiotic treatment. Stenotrophomonas maltophilia was the predominant bacterial species in all the examined PVC samples. There were noticeable but not statistically significant differences between the inner and outer surfaces of PVCs in terms of the distribution of the taxonomic groups. In addition, the bacterial communities on PVCs from antibiotic-treated patients were significantly different from untreated patients. In conclusion, the surfaces of PVCs display complex bacterial communities. Although their significance has yet to be determined, these findings alter our perception of PVC-related infections.


Assuntos
Bactérias/genética , Cateterismo Periférico/instrumentação , Catéteres/microbiologia , Consórcios Microbianos/genética , Tipagem Molecular/métodos , Bactérias/classificação , Bactérias/isolamento & purificação , Análise por Conglomerados , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Método de Monte Carlo , Análise de Componente Principal
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